Exercise Coordinates Neural Plasticity from the Mesencephalic Locomotor Region to the Spinal Cord in Mice

Exercise Coordinates Neural Plasticity from the Mesencephalic Locomotor Region to the Spinal Cord in Mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exercise Coordinates Neural Plasticity from the Mesencephalic Locomotor Region to the Spinal Cord in Mice Yue Dai, Liming Yang, Yi Cheng, Xinyi Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8363166/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Locomotion involves circuits connecting mesencephalic locomotor region (MLR) and spinal cord (SC). Although chronic exercise improves neuronal adaptability, its impact on functional and structural plasticity along the MLR–SC pathway remains unclear. Here, we examined exercise-induced neuroplasticity in MLR and lumbar SC neurons using whole-cell patch-clamp recordings from P42-P45 mice after three-week treadmill exercise. Key findings include: (1) Exercise increased excitability, shown by lowering rheobase and voltage threshold, with ventral SC neurons more affected than dorsal ones; (2) Exercise enhanced persistent inward currents (PICs) in terms of hyperpolarizing onset voltage and increasing amplitude, with effects stronger in SC than MLR neurons. Pharmacological data indicated calcium‑mediated PICs modulated firing duration/frequency, while sodium‑mediated PICs influenced threshold/capacity; (3) Exercise increased dendritic complexity (total length, branch points, and terminals), more markedly in SC versus MLR neurons; (4) Ventral spinal neurons displayed greater dendritic complexity than dorsal neurons, and were more modulated by exercise; (5) Correlation suggested exercise-driven dendritic plasticity potentiated PICs and excitability, collectively promoting locomotor adaptation. These results revealed an exercise-induced, coordinated plasticity throughout locomotor system, wherein spinal circuits, particularly ventral components, exhibited greater functional and structural adaptability than MLR. This study provided electrophysiological, ionic, and morphological insights into activity-dependent neural adaptation. Biological sciences/Physiology/Neurophysiology Biological sciences/Neuroscience/Motor control/Spinal cord Biological sciences/Neuroscience/Neuronal physiology/Excitability Exercise Plasticity Motor Control Locomotion Neuromodulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Locomotion is one of the most fundamental limb movements in vertebrates. It is initiated within the mesencephalic locomotor region (MLR) and controlled by spinal cord neural networks known as central pattern generators (CPGs) (Brownstone & Chopek, 2018 ; S. Grillner & A. El Manira, 2020; Kiehn, 2011 ; P. Lacroix-Ouellette & R. Dubuc, 2023 ; Leiras et al., 2022 ).This system not only generates rhythmic locomotor output but also integrates proprioceptive and sensory feedback to adapt to dynamic environments. Adaptive plasticity in locomotor circuits occurs in response to acute and/or chronic exercise, manifesting as alterations in neuronal excitability, dendritic plasticity, channel modulation, as well as gene expression and protein synthesis (Button & Kalmar, 2019 ; Chen et al., 2019 ; Dai et al., 2024 ; Gardiner et al., 2006 ; Krawitz et al., 2001 ; Lockyer et al., 2023 ; MacDonell et al., 2015 ; Power et al., 2022 ) Recent studies on mouse midbrain neurons have demonstrated that chronic exercise enhances persistent inward currents (PICs), promotes dendritic plasticity, and upregulates the excitability of serotonin neurons in the dorsal raphe nucleus(Ge & Dai, 2020 ). Consistent findings have been observed in mouse spinal cord interneurons, where three-week treadmill exercise increases neuronal excitability, strengthens PICs, and facilitates dendritic plasticity in lamina X interneurons(Chen & Dai, 2022 ). These studies indicate that exercise modulates the locomotor system at both the midbrain, a region involved in locomotor pattern generation, and the spinal cord, the segment controlling locomotion. Specifically, alterations in the mechanisms of voltage-gated channels and modifications in neuronal dendritic plasticity directly induce changes in intrinsic membrane properties, enhance neuronal excitability, and ultimately drive adaptations in neurons and the locomotor system (Chen et al., 2019 ; Cormery et al., 2005 ; Ge & Dai, 2020 ). Based on the above research findings, several critical issues remain to be addressed: 1) What are the differences in the regulatory effects of exercise on neuronal excitability between MLR neurons and spinal interneurons? In which region does this regulatory strength predominate? 2) Can the enhancing effect of exercise on PICs be observed simultaneously in the MLR and the spinal cord? What are the differences in this regulatory effect in terms of the kinetic properties of ion channels? 3) Does exercise-induced promotion of neuronal dendritic plasticity occur synchronously in MLR neurons and spinal interneurons? In which region is this promotional effect more pronounced? 4) Which region, the MLR or the spinal cord, plays a dominant role in the regulatory effect of exercise intervention on the adaptability of the locomotor system? Answering these questions constitutes the primary objectives of this study. To address these issues, we subjected C57BL/6J mice to a 3-week treadmill exercise regimen and examined the concurrent effects of chronic exercise on the electrophysiological, morphological, and ionic properties of MLR neurons and spinal interneurons using whole-cell patch-clamp recording. Our data demonstrated that exercise concurrently enhanced neuronal excitability, promoted dendritic plasticity, and increased persistent inward currents in both MLR and spinal neurons. Furthermore, exercise induced a more pronounced effect on the spinal motor system compared to the midbrain system. This study provides insights into the cellular and ionic mechanisms underlying exercise-induced adaptation and coordination within the midbrain-spinal motor system. Results Spinal cord neurons exhibit higher excitability than MLR neurons. In this study we systematically investigated the concurrent regulatory effects of a moderate-intensity three-week treadmill exercise on cellular excitability regulation, ion channel modulation, and morphological plasticity of neurons in the MLR and SC interneurons in mice. Figure 1 E and 1 F are representative neuronal distribution map from this study, showing the distribution of neurons within the MLR (Fig. 1 E) and SC (Fig. 1 F) in control and exercise intervention. For clarity, neurons from the control group were plotted on the left side of the anatomical transvers sections for the MLR (n = 48) and SC (n = 36), indicated in black. Neurons from the exercise group were plotted on the right side of the anatomical cross-sections for the MLR (n = 70) and SC (n = 65), indicated in green. Neurons of control group located on the right side of the sections were mirror-reflected to the left side of the plots. Similarly, neurons of exercise group located on the left side were mirror-reflected to the right side. To compare the intrinsic excitability of SC and MLR neurons, we systematically compared electrophysiological parameters recorded from SC and MLR neurons within the same mouse. Figures 2 A and 2 B show a representative example of current-clamp recordings. Here, the SC neuron had a more hyperpolarized Vth (-39.4 mV) compared to the MLR neuron (-35.2 mV), indicating that this specific SC neuron was more excitable than its corresponding MLR neuron (Fig. 2 A&B). Statistical data from 30 neurons (SC: n = 15; MLR: n = 15) revealed significant differences between SC and MLR neurons in Vth, AHP amplitude, and AHP half-width (Fig. 2 C). Specifically, the Vth of SC neurons was 4.01 ± 4.8 mV more hyperpolarized than that of MLR neurons (Fig. 2 C3, p = 0.006). The AHP amplitude of SC neurons was 3.66 ± 4.9 mV smaller than that of MLR neurons (Fig. 2 C6, p = 0.012). The AHP half-width of SC neurons was 91.85 ± 80.3 ms shorter than that of MLR neurons (Fig. 2 C7, p 0.05). These results indicate that SC neurons possess higher intrinsic excitability compared to MLR neurons. Exercise enhanced the excitability of both SC and MLR neurons. Our previous studies have confirmed that chronic exercise can significantly enhance the excitability of SC neurons, particularly those in lamina X (Chen & Dai, 2022 ; Chen et al., 2019 ). Based on this result, the present study further investigated the effects of exercise on neuronal excitability across the lumbar spinal cord region. Figures 3 A and 3 B show representative recordings of membrane potential and related electrophysiological properties of SC neurons in control and exercise groups. These recordings indicate that treadmill exercise reduced both the rheobase (Fig. 3 A) and the Vth (Fig. 3 A&B) of SC neurons. Statistical analysis revealed that chronic exercise significantly decreased the rheobase by 4.76 ± 1.4 pA (Fig. 3C2, Control: n = 20, Exercise: n = 34, p = 0.001) and the Vth by 6.96 ± 1.9 mV (Fig. 3C3, p 0.05). These results indicate that exercise intervention significantly lowers the current and voltage thresholds required for activating SC neurons, thereby enhancing their excitability. Similarly, our prior research demonstrated that chronic exercise enhanced the excitability of 5-hydroxytryptamine (5-HT) neurons in the dorsal raphe nucleus (DRN) of the midbrain (Ge & Dai, 2020 ). However, whether exercise exerts a similar effect on MLR neurons in the midbrain remained unclear. Therefore, in this study we further examined the regulatory effect of exercise training on the excitability of neurons within the MLR region. Figures 3 D and 3 E show representative recordings of changes in electrophysiological parameters of MLR neurons between control and exercise groups, indicating that exercise intervention reduced both the rheobase (Fig. 3 D) and the Vth (Fig. 3 E) of MLR neurons. Statistical analysis showed that exercise significantly decreased the rheobase by 2.73 ± 1.3 pA (Fig. 3F2, Control: n = 27; Exercise: n = 36, p = 0.045) and the Vth by 3.82 ± 1.55 mV (Fig. 3F3, p = 0.014). However, no significant change was found in the Em, AP amplitude, AP half-width, AHP amplitude, and Rin (Fig. 3F1, F4-F8; Control: n = 27; Exercise: n = 36; p > 0.05). These results indicate that exercise training significantly lowers the current threshold (rheobase) and voltage threshold (Vth) of MLR neurons, thereby enhancing their excitability. This conclusion is consistent with the findings for SC neurons, demonstrating that exercise intervention can modulate both the active and passive membrane properties of neurons, which are governed by ion channels and cellular morphology, respectively. Exercise affected SC neuron excitability more than MLR neurons A key feature of this study is that we recorded the regulatory effects of exercise on the electrophysiological parameters of both MLR and SC neurons within the same mouse. This experimental design allowed for a quantitative analysis and comparative study of the effects induced by exercise in these two regions. In the simultaneous recording experiments, we systematically analyzed the relative change magnitude (percentage) of electrophysiological indices in SC and MLR neurons following exercise. Figure 4 A shows representative recordings of firing frequencies from both neuron types under the same experimental conditions in the same mouse. Figure 4 B illustrates their frequency-current (F-I) relationships. Statistical results from 22 pairs of neurons revealed that exercise led to a decrease in rheobase by 44.85% in SC neurons and 22.76% in MLR neurons (Fig. 4C2, SC Exercise: n = 22, MLR Exercise: n = 22, p = 0.029), and a hyperpolarization of Vth by 21.5% and 11.4%, respectively (Fig. 4C3, p = 0.033). In contrast, no significant difference was observed between the two neuron groups in other electrophysiological parameters (Em, AP amplitude, AP half-width, AHP amplitude, Rin; Fig. 4C1, C4–C8, p > 0.05). The magnitudes of the decreases in rheobase and Vth were greater in SC neurons than in MLR neurons by 22.1% and 10.1%, respectively, indicating that exercise exerted a stronger regulatory effect on the excitability of SC neurons compared to MLR neurons. Exercise-induced modulation of PICs in SC and MLR neurons Our previous studies found that treadmill exercise enhances the excitability of DRN 5-HT neurons and spinal interneurons, an enhancement closely associated with persistent inward currents (PICs) (Chen & Dai, 2022 ; Ge & Dai, 2020 ). Therefore, in this study we further investigated whether exercise influences neuronal excitability by modulating the PIC properties of SC and MLR neurons (Fig. 5 ). Using voltage ramps, we first recorded the parameters of PICs in both regions under resting conditions (control). Figure 5A1 shows a representative example, indicating that the PIC onset in MLR neurons was more hyperpolarized and the PIC amplitude was larger compared to SC neurons. Statistical results from 10 pairs of synchronously recorded neurons showed: the PIC onset in MLR neurons was 2.43 ± 2.68 mV more hyperpolarized than that in SC neurons (Fig. 5A2, SC: -49.17 ± 2.1 mV; MLR: -51.6 ± 2.5 mV, p = 0.017), and the PIC amplitude of MLR neurons was significantly larger than that of SC neurons by 27.95 ± 32.5 pA (Fig. 5A3, SC: 124.33 ± 19.47 pA; MLR: 152.28 ± 26.8 pA, p = 0.017). Using step voltages (Fig. 5B1), we measured and calculated the kinetic parameters of PICs in SC and MLR neurons under control conditions, constructing corresponding activation curves (Fig. 5B2) and statistical data for the half-activation voltage (Vmid) (Fig. 5B3). A representative example was shown in Fig. 5B1. Data from 5 pairs of SC and MLR neurons indicated that the Vmid of MLR neurons was significantly more hyperpolarized by 3.07 ± 1.7 mV than that of SC neurons (Fig. 5B3, Control SC: -20.25 ± 1.4 mV; Control MLR: -23.31 ± 1.49 mV, p = 0.01). The above experiments revealed inherent differences in PIC properties between SC and MLR neurons. MLR neurons exhibited a lower (more hyperpolarized) PIC activation voltage and a larger PIC amplitude compared to SC neurons. Subsequently, we explored the modulatory effects of exercise intervention on the PICs of these two neuron types (Fig. 5 C-D). Statistical results demonstrated that treadmill exercise significantly enhanced the PIC properties of SC neurons. Exercise significantly hyperpolarized the PIC onset in SC neurons by 8.58 ± 1.2 mV (Fig. 5C1, Control SC: -49.13 ± 4.3 mV; Exercise SC: -56.88 ± 3.9 mV, p < 0.0001) and increased the PIC amplitude by 40.29 ± 11.3 pA (Fig. 5C2, Control SC: 120.58 ± 23.4 pA; Exercise SC: 160.87 ± 41.9 pA, p < 0.001). Furthermore, exercise caused a leftward shift of the activation curve in SC neurons by 2.82 ± 1.5 mV (Fig. 5C3 & C4; Control SC: Vmid=-21.57 ± 1.09 mV; Exercise SC: Vmid=-24.39 ± 0.99 mV; n = 5, p = 0.002). Similar effects were observed in MLR neurons. Exercise significantly enhanced PICs in MLR neurons in terms of hyperpolarizing the PIC onset by 3.80 ± 1.2 mV (Fig. 5D1, Control MLR: -52.44 ± 2.52 mV; Exercise MLR: -56.23 ± 4.91mV, p = 0.0015), increasing the PIC amplitude by 32.80 ± 10.6 pA (Fig. 5D2; Control MLR: 139.19 ± 39.7 pA; Exercise MLR: 171.99 ± 32.9 pA, p = 0.004), and shifting the PIC activation curve leftward by 3.47 ± 2.0 mV (Fig. 5D3 & D4, Control MLR: Vmid=-22.24 ± 1.94 mV; Exercise MLR: Vmid=-25.71 ± 1.3 mV; n = 5, p = 0.017). These results indicate that exercise significantly enhances PICs in both SC and MLR neurons, in terms of both onset voltage and amplitude, thereby highlighting the important role of PICs in the regulation of neuronal excitability. Exercise exerted greater effects on PICs in SC neurons than MLR neurons To further compare the strength of exercise-induced modulation on PICs between the two neuron types, we recorded the exercise effects on PICs in both SC and MLR neurons within the same mouse preparation (Fig. 5 E). Statistical analysis of PICs from 20 pairs of SC and MLR neurons revealed that exercise hyperpolarized the PIC onset by 14.04% in SC neurons, compared to only 8.16% in MLR neurons, a statistically significant difference (Fig. 5 E1, SC: n = 20, MLR: n = 20, p = 0.005). Similarly, exercise increased the PIC amplitude by 40.5% in SC neurons, significantly more than the 18.22% increase in MLR neurons (Fig. 5 E2, p = 0.023). Finally, we compared the activation curves of the two neuron types. As shown in Fig. 5 E3, exercise induced a leftward shift in the PIC activation curves for both SC and MLR neurons. However, the shift magnitude was greater in SC neurons than in MLR. Specifically, the Vmid hyperpolarized by 25.53% in SC neurons, compared to only 9.22% in MLR neurons, a significant difference (Fig. 5 E4, SC: n = 4, MLR: n = 4, p = 0.014). These results indicate that, compared to MLR neurons, PICs in SC neurons exhibit a greater degree of responsiveness to exercise intervention, suggesting their potentially more significant role in regulating neuronal excitability. Modulatory effects of exercise on different PIC components Our previous research indicated that PICs in midbrain and spinal cord neurons are composed of multiple ionic components, primarily including sodium (Na-PIC) and calcium (Ca-PIC) currents (Cheng et al., 2021 ; Ge & Dai, 2020 ). To further investigate the modulation of these distinct components by exercise intervention, we specifically blocked Ca-PIC and Na-PIC using nimodipine and riluzole, respectively, in SC and MLR neurons to study the regulatory effects of exercise (Fig. 6 A). Figure 6 A shows representative examples, where bath application of 25 µM nimodipine (Fig. 6A1&A2) and 3 µM riluzole (Fig. 6A3&A4) to the recording solution obviously reduced the PIC amplitude and depolarized the PIC onset, in SC neurons in both control (Fig. 6A1&A3) and exercise (Fig. 6A2&A4) groups, respectively. Similar results were collected in MLR neurons, where the same amount of nimodipine (Fig. 6B1&B2) and riluzole (Fig. 6B3&B4) decreased PIC amplitude and depolarized the PIC onset in both control (Fig. 6B1&B3) and exercise (Fig. 6B2&B4) groups, respectively. Statistical results from SC neurons showed that exercise hyperpolarized PIC onset from − 41.63 ± 8.46 to -57.55 ± 2.76 mV (Fig. 6C1: difference: 15.92 ± 3.37 mV, P = 0.02, Control: n = 6, Exercise: n = 7) and increased PIC amplitude from 131.72 ± 18.85 to 163 ± 9.34 pA (Fig. 6C2: difference: 31.28 ± 8.05 pA, P = 0.003), respectively in SC neurons. Nimodipine depolarized the PIC onset from − 41.63 ± 8.46 to -39.65 ± 8.91 mV (Fig. 6C1: difference: 1.98 ± 1.43 mV, P = 0.019, n = 6) and reduced PIC amplitude from 131.72 ± 18.85 to 103.18 ± 21.63 pA (Fig. 6C2: difference: 28.53 ± 4.11 pA, P < 0.001), respectively, in control group. Nimodipine also depolarized the PIC onset from − 57.55 ± 2.76 to -53.31 ± 3.44 mV (Fig. 6C1: difference: 4.24 ± 1.34 mV, P < 0.001, n = 7) and reduced PIC amplitude from 163 ± 9.34 to 112.29 ± 16.35 pA (Fig. 6C2: difference: 50.71 ± 12.14 pA, n = 7; P < 0.001), respectively, in exercise group. Similarly, statistical results from SC neurons indicated that exercise lowered PIC onset from − 51.99 ± 3.88 to -57.54 ± 3.54 mV (Fig. 6C3: difference: 5.55 ± 2.08 mV, P = 0.02, Control: n = 6, Exercise: n = 9) and increased PIC amplitude from 126.63 ± 20.9 to 165.51 ± 33.91 pA (Fig. 6C4 left: difference: 38.87 ± 16.65 pA, P = 0.036) in SC neurons, respectively. Riluzole raised the PIC onset from − 51.99 ± 3.88 to -45.14 ± 47.12 mV (Fig. 6C3: difference: 6.86 ± 2.28 mV, P < 0.001, n = 6) and decreased PIC amplitude from 126.63 ± 20.9 to 89.02 ± 10.66 pA (Fig. 6C4: difference: 37.62 ± 22.17 pA, P = 0.009), respectively, in control group. Riluzole also depolarized the PIC onset from − 57.54 ± 3.54 to -41.87 ± 3.9 mV (Fig. 6C3: difference: 15.67 ± 2.57 mV, P < 0.001, n = 9) and reduced PIC amplitude from 165.51 ± 33.91 to 120.1 ± 25.78 pA (Fig. 6C4: difference: 45.4 ± 13.25 pA, P < 0.001), respectively, in exercise group. The above results indicated that exercise enhanced the PICs in terms of hyperpolarizing PIC onset and increasing PIC amplitude in SC neurons. Both Ca-PIC and Na-PIC contributed to this enhancement of PICs in SC neurons during the exercise intervention. Similar results were obtained from MLR neurons in both control and exercise groups. Statistical results indicated that exercise hyperpolarized PIC onset by 5.35 ± 1.96 mV (Fig. 6D1, Control: -49.56 ± 3.85 mV, n = 6; Exercise: -54.91 ± 3.24 mV, n = 7; P = 0.02) and increased PIC amplitude by 39.87 ± 14.7 pA (Fig. 6D2, Control: 138.03 ± 25.69 pA; Exercise: 177.9 ± 27.01 pA; P = 0.02), respectively, in MLR neurons. Nimodipine depolarized the PIC onset by 1.98 ± 1.58 mV (Fig. 6D1, Control: -49.56 ± 3.85 mV; Nimodipine: -47.59 ± 3.14 mV, n = 6; P = 0.028) and reduced PIC amplitude by 31.52 ± 7.28 pA (Fig. 6D2, Control: 138.03 ± 25.69 pA; Nimodipine: 106.52 ± 26.53 pA; P < 0.001) in control group. Nimodipine also depolarized the PIC onset by 3.32 ± 0.81 mV (Fig. 6D1, Exercise: -51.59 ± 3.17 mV; Nimodipine: -52.24 ± 2.2 mV, n = 7; P < 0.001) and decreased PIC amplitude by 41.9 ± 9.18 pA (Fig. 6D2, Exercise: 177.9 ± 27.01 pA; Nimodipine: 136 ± 21.1 pA; P < 0.001) in exercise group. Similar results were also collected with riluzole in MLR neurons. Exercise lowered PIC onset by 6.48 ± 2.67mV (Fig. 6D3, Control: -50.54 ± 4.25 mV, n = 6; Exercise: -57.01 ± 5.52 mV, n = 7; P = 0.03) and increased PIC amplitude by 41.03 ± 12.71 pA (Fig. 6D4, Control: 132.18 ± 31.9 pA; Exercise: 173.21 ± 17.57 pA; P = 0.007), respectively, in MLR neurons. Riluzole depolarized the PIC onset by 8.03 ± 2.68 mV (Fig. 6D3, Control: -50.34 ± 4.25 mV, n = 6; Riluzole: -42.51 ± 5.73 mV, n = 7; P < 0.001) and reduced PIC amplitude by 31.37 ± 5.9 pA (Fig. 6D2, Control: 132.18 ± 31.9 pA; Riluzole: 100.82 ± 30.97 pA; P < 0.001) in control group. Riluzole also raised the PIC onset by 12.48 ± 2.06 mV (Fig. 6D1, Exercise: -57.01 ± 5.52mV, n = 6; Riluzole: -44.53 ± 5.38 mV, n = 7; P < 0.001) and decreased PIC amplitude by 39.72 ± 7.28 pA (Fig. 6D2, Exercise: 173.21 ± 17.57mV; Riluzole: 133.49 ± 13.82pA; P < 0.001), respectively, in exercise group. These results demonstrated that exercise potentiated the PICs with lowering PIC onset and increasing PIC amplitude in MLR neurons. Both Ca-PIC and Na-PIC contributed to this potentiation of PICs in MLR neurons during the chronic exercise. To further explore the mechanism underlying the exercise-induced PIC changes in SC versus MLR neurons, we calculated the percentage contributions of Na-PIC and Ca-PIC to the total changes in PICs in each neuron type (Fig. 6 E). Statistical results showed that the contribution of Ca-PIC to the exercise-induced changes in PIC onset was 1.13% in SC neurons and 0.69% in MLR neurons, respectively, with no significant difference (Fig. 6 E1, n = 7, p > 0.05). However, regarding changes in PIC amplitude, the Ca-PIC contribution was significantly higher in SC neurons (0.78%) compared to MLR neurons (0.33%) (Fig. 6 E2, n = 7, P = 0.043). Similarly, the Na-PIC contribution to the exercise-induced alterations in PIC onset was higher in SC neurons (1.28%) than in MLR neurons (0.56%) (Fig. 6 E3, n = 9, p = P < 0.001). The Na-PIC contribution to changes in PIC amplitude also appeared to be greater in SC neurons (1.69%) than in MLR neurons (1.23%). However, this contribution was not significant (Fig. 6 E4, n = 9, P = 0.092). The above results demonstrated that exercise intervention enhanced both Na-PIC and Ca-PIC components in SC and MLR neurons, affecting both onset and amplitude. However, the functional contribution of these components to the overall PICs differed between neuron types. The modulatory effect of Ca-PIC on the PIC onset was minimal and similar between SC and MLR neurons (Fig. 6E1), whereas its effect on the PIC amplitude was stronger in SC neurons than in MLR neurons (Fig. 6E2). On the other hand, the modulatory effect of Na-PIC on PIC onset was stronger in SC neurons than in MLR neurons (Fig. 6E3), while its effect on PIC amplitude appeared to be similar in SC and MLR neurons (Fig. 6E4). These findings revealed the mechanism underlying the greater modulatory weight of exercise intervention on PICs in SC neurons compared to MLR neurons. Exercise-induced modulation of PICs and neuronal excitability PICs play a key role in regulating the excitability of spinal and midbrain neurons (Deutsch & Elbasiouny, 2024 ; ElBasiouny et al., 2010 ; Hassan et al., 2021 ; Heckman, Johnson, et al., 2008 ; Orssatto et al., 2022 ). Among them, Ca-PIC is the primary mechanism underlying the onset/offset hysteresis of PICs in spinal and midbrain neurons (Binder et al., 2020 ; Cheng et al., 2025 ; Dai & Jordan, 2010 ; Moritz et al., 2007 ; Svirskis & Hounsgaard, 1997 ), while Na-PIC plays a dominant role in maintaining repetitive firing (Cheng et al., 2025 ; Cheng et al., 2020 ; Harvey et al., 2006 ; Kuo et al., 2006 ; Lee & Heckman, 1999 ). In the above experiments, we found that exercise had a greater modulatory effect on PICs in SC neurons compared to those in MLR neurons. What is the significance of this difference for regulating the excitability of SC and MLR neurons? We addressed this issue in the following experiments. We used a bi-ramp current (duration 10 s, peak amplitude 60 pA, starting from 0) to measure the electrophysiological parameters (see Methods for details), including Vth), recruitment current (Irec), and de-recruitment current (Idec). We calculated the difference between Irec and Idec (ΔI = Irec - Idec) and used this to assess the role of PICs in regulating neuronal excitability (Cheng et al., 2021 ; Cheng et al., 2025 ). Representative recordings of firing properties and instantaneous firing frequencies of SC and MLR neurons from control and exercise are shown in Figs. 7A1 and 7A2, respectively. The results indicate that exercise significantly increased the instantaneous firing frequency of both SC and MLR neurons and caused a leftward shift in the frequency-current (F-I) curve. Exercise significantly enhanced the sustained firing capacity of SC neurons (Fig. 7A1, left), reducing ΔI from 8.8 pA in the control (black) to 3.2 pA in exercise (Fig. 7A1, right, green). Similarly, exercise enhanced the sustained firing capacity of MLR neurons (Fig. 7A2, left), reducing ΔI from 18.1 pA in the control (gray) to 2.4 pA in exercise (Fig. 7A2, right, orange). Statistical results showed that exercise significantly decreased the Vth of SC neurons by 6.52 ± 2.1 mV (Fig. 7B1, left; Control: -34.76 ± 6.8 mV, n = 19; Exercise: -41.28 ± 6.7 mV, n = 24; p = 0.003) and the Vth of MLR neurons by 4.18 ± 1.74 mV (Fig. 7B1, right; Control: -31.42 ± 4.78 mV, n = 20; Exercise: -35.13 ± 6.18 mV, n = 23; p = 0.021). These results indicate that exercise significantly enhanced the excitability of both SC and MLR neurons. Next, we investigated the modulatory effect of exercise on neuronal recruitment current. Experimental data showed that exercise significantly decreased Irec in SC neurons by 4.05 ± 1.6 pA (Fig. 7B2, left; Control: 13.52 ± 6.4 pA; Exercise: 9.46 ± 3.9 pA, p = 0.015). Although exercise decreased Irec in MLR neurons by 1.57 ± 1.4 pA, this reduction was not statistically significant (Fig. 7B2, right; Control: 12.49 ± 4.13 pA; Exercise: 10.92 ± 5.23 pA, p = 0.287). This result suggests that exercise lowers the current threshold for firing in SC neurons, significantly enhancing their excitability. Further analysis revealed that exercise significantly decreased Idec in SC neurons by 8.73 ± 1.7 pA (Fig. 7B3, left; Control: 22.36 ± 7.31 pA; Exercise: 13.3 ± 4.04 pA, p < 0.001) and in MLR neurons by 5.10 ± 1.6 pA (Fig. 7B3, right; Control: 22.29 ± 4.89 pA; exercise: 17.19 ± 5.59 pA, p = 0.003), indicating that exercise prolonged the firing duration in both neuron types. Finally, we analyzed the regulatory effect of exercise on ΔI. Statistical data showed that exercise significantly reduced ΔI to 4.77 ± 1.6 pA in SC neurons (Fig. 7B4, left; Control: 8.85 ± 7.0 pA, n = 19; Exercise: 4.08 ± 4.7 pA, n = 24; p = 0.011) and 3.38 ± 2.1 pA in MLR neurons (Fig. 7B4, right; Control: 9.81 ± 5.02 pA, n = 20; Exercise: 6.43 ± 5.27 pA, n = 23; p = 0.038). The reduced ΔI indicates that exercise prolonged neuronal firing duration and increased the probability of firing hysteresis (ΔI < 0). The above results demonstrate that exercise significantly enhances the ability of PICs to regulate the excitability of SC and MLR neurons, particularly evidenced by the reduction in action potential firing threshold and the prolongation of sustained firing time. Modulation of exercise on Ca-PIC, Na-PIC, and neuronal excitability The above results indicated that exercise intervention modulated neuronal excitability by enhancing PIC. Next, we further investigated how exercise intervention regulates neuronal excitability through Ca-PIC and Na-PIC. We first examined the modulation of Ca-PIC by exercise on the excitability of SC and MLR neurons (Fig. 7 C). Experimental results showed that bath application of 25 µM Nimodipine (Nim) to the slice recording solution after exercise caused minor changes in the Vth of SC and MLR neurons. The Vth difference was only 1.03 ± 1.1 mV in SC neurons (Fig. 7 C1 left, Exercise, SC: Vth = -50.54 ± 4.27 mV; Nim: Vth = -49.5 ± 4.47 mV; n = 7, p = 0.058) and 0.93 ± 1.35 mV in MLR neurons (Fig. 7 C1 right, Exercise, MLR: Vth=-43.62 ± 4.19 mV; Nim: Vth =-42.69 ± 3.76 mV; n = 7, p = 0.119), indicating that the exercise-induced hyperpolarization of Vth in SC and MLR neurons was independent of Ca-PIC. However, the same concentration of nimodipine caused significant changes in Irec, Idec, and ΔI of SC and MLR neurons (Fig. 7 C, n = 7). Specifically, nimodipine increased Irec, Idec, and ΔI in SC neurons by 10.09 ± 3.3 pA (Fig. 7 C2 left, SC: 8.67 ± 3.4 pA; Nim: 18.76 ± 3.2 pA, p = 0.0002), 21.84 ± 7.2 pA (Fig. 7 C3 left, SC: 12.14 ± 4.2 pA; Nim: 33.99 ± 4.4 pA, p = 0.0001), and 11.76 ± 5.6 pA (Fig. 7 C4 left, SC: 3.47 ± 4.87 pA; Nim: 15.23 ± 5.18 pA, p = 0.034), respectively. Similar results were observed in MLR neurons. Nimodipine increased Irec, Idec, and ΔI in MLR neurons by 4.09 ± 1.7 pA (Fig. 7 C2 right, MLR: 10.8 ± 5.6 pA; Nim: 14.89 ± 5.8 pA, p = 0.0008), 8.77 ± 5.2 pA (Fig. 7 C3 right, MLR: 16.01 ± 4.1 pA; Nim: 24.79 ± 7.4 pA, p = 0.0043), and 4.69 ± 4.3 pA (Fig. 7 C4 right, MLR: 5.21 ± 3.52 pA; Nim: 9.9 ± 3.22 pA, p = 0.034), respectively. These results indicated that Ca-PIC played an important role in regulating the excitability of SC and MLR neurons during exercise, particularly in modulating properties such as the current threshold for sustained firing, firing duration, and the probability of delayed firing. Specifically, exercise enhanced Ca-PIC, leading to a decreased current threshold for sustained firing, prolonged firing duration, and an increased probability of delayed firing in SC and MLR neurons. Next, we investigated the role of Na-PIC in regulating the excitability of SC and MLR neurons during exercise intervention. Applying 3 µM Riluzole (Ril) to the recording solution following exercise intervention, we observed a significant depolarization of Vth in SC and MLR neurons. The Vth of SC neurons increased from − 49.7 ± 4.2 mV to -38.1 ± 4.2 mV with an increase of 11.6 ± 4.7 mV (Fig. 7D1 left, n = 9, p < 0.0001). The Vth of MLR neurons increased from − 41.8 ± 4.7 mV to -36.1 ± 4.1 mV with an increase of 5.77 ± 4.1 mV (Fig. 7D1 right, n = 9, p = 0.003). This result indicates that the hyperpolarization of Vth in SC and MLR neurons induced by exercise intervention is determined by Na-PIC. The same concentration of riluzole caused significant changes in Irec and Idec of SC and MLR neurons (Fig. 7D2-3, n = 9). Specifically, Irec and Idec in SC neurons increased by 9.31 ± 4.1 pA (from 9.63 ± 6.1 pA to 18.94 ± 5.9 pA; Fig. 7D2 left, p < 0.001) and 9.21 ± 5.4 pA (from 14.87 ± 4.5 pA to 24.08 ± 5.9 pA; Fig. 7D3 left, p = 0.001) respectively. And Irec and Idec in MLR neurons increased by 6.39 ± 4.7 pA (from 8.32 ± 4.3 pA to 14.71 ± 5.3 pA; Fig. 7D2 right, p = 0.004) and 5.84 ± 3.7 pA (from 14.3 ± 5.1 pA to 20.14 ± 4.8 pA; Fig. 7D3 right, p = 0.002), respectively. However, riluzole did not cause significant changes in ΔI. ΔI in SC neurons changed from 4.99 ± 7.49 pA to 5.13 ± 5.07 pA with a change of 0.14 ± 4.15 pA (Fig. 7D4 left, p = 0.919), and ΔI in MLR neurons changed from 5.98 ± 2.55 pA to 5.43 ± 5.82 pA with a change of 0.54 ± 4.51 pA (Fig. 7D4 right, p = 0.726). These results indicate that Na-PIC plays an important role in regulating the excitability of SC and MLR neurons during exercise intervention, particularly in modulating the voltage threshold for sustained firing, the current threshold for sustained firing, and the firing duration, but has minimal effect on neuronal delayed firing. Specifically, exercise enhances Na-PIC, leading to a decreased voltage threshold and current threshold for sustained firing, and prolonged firing duration in SC and MLR neurons. Finally, we analyzed the relative (percentage) differences in the regulatory strength of Ca-PIC and Na-PIC on Vth, Irec, and Idec in SC and MLR neurons (Fig. 7 E). Statistical results showed that the contribution of Ca-PIC to Vth was 2.08% in SC neurons and 2.04% in MLR neurons, both minimal and not significantly different (Fig. 7 E1, p = 0.979, n = 7). However, the regulatory strength of Ca-PIC on Irec was 141.32% in SC neurons, significantly higher than the 52.78% in MLR neurons (Fig. 7 E2, p = 0.037, n = 7). The regulatory strength on Idec was 211.65% in SC neurons, also significantly higher than the 70.39% in MLR neurons (Fig. 7 E3, p = 0.018, n = 7). Similarly, for Na-PIC, its regulatory strength on Vth was 23% in SC neurons, significantly higher than the 13.38% in MLR neurons (Fig. 7F1, p = 0.025, n = 9). Its regulatory strength on Irec was 150.13% in SC neurons, also significantly higher than the 43.25% in MLR neurons (Fig. 7F2, p = 0.039, n = 9). However, the regulatory strength of Na-PIC on Idec was 56.49% in SC neurons and 28.41% in MLR neurons, showing no significant difference (Fig. 7F3, p = 0.209, n = 9). The above results demonstrated that Ca-PIC and Na-PIC played important roles in regulating the excitability of SC and MLR neurons during exercise intervention. Both Ca-PIC and Na-PIC modulated the neuronal current threshold and firing duration. Ca-PIC primarily governed the delayed firing characteristics of neurons, while Na-PIC determined the voltage threshold for action potential generation. This combined modulation resulted in a significant enhancement of SC and MLR neuronal excitability by exercise. Specifically, the modulatory strength of Ca-PIC and Na-PIC in SC neurons was higher than that in MLR neurons. Dendritic morphology analysis of SC and MLR neurons Our previous studies have shown that treadmill training promote dendritic plasticity in neurons of the mouse spinal cord and midbrain (Chen & Dai, 2022 ; Ge & Dai, 2020 ). However, can this exercise-induced dendritic plasticity be observed simultaneously in both SC and MLR neurons within the same mouse? What are the morphological features of dendritic plasticity in these different regions? What potential effects does dendritic plasticity have on the intrinsic membrane properties of these neurons? We employed Sholl analysis to investigate these questions. Figure 8 shows the morphological distribution of the stained SC and MLR neurons in this study, including data from two groups of SC neurons: Control (Fig. 8A1, black, n = 27) and Exercise (Fig. 8A2, gree n , n = 42). In the Control group, ventral neurons accounted for 70%, and dorsal neurons for 30% (Fig. 8A1); in the Exercise group, ventral neurons accounted for 74%, and dorsal neurons for 26% (Fig. 8A2). Similarly, Fig. 8 B displays the morphological distribution of MLR neurons, including data from Control (Fig. 8 B1, black, n = 29) and Exercise (Fig. 8 B2, orange, n = 70) groups. To further quantify the dendritic morphological characteristics and differences between SC and MLR neurons, we selected 22 pairs of neurons that were simultaneously recorded from the SC and MLR regions of the same mice for morphological analysis (Fig. 9 ). Figure 9 A and 9 B show the typical morphological features of SC and MLR neurons from the control groups, respectively. The results of Sholl analysis revealed that within the range of 50–125 µm from the soma, SC neurons had a significantly greater number of dendritic intersections than MLR neurons (Fig. 9 C1, p < 0.05, n = 22). However, no significant differences were found in total dendritic length (Fig. 9 C2, p = 0.165, n = 22), the number of primary dendrites (Fig. 9 C3, p = 0.126, n = 22), or somatic surface area (Fig. 9 C6, p = 0.391, n = 22). Regarding the number of dendritic branch points, SC neurons had 3 ± 2.3, while MLR neurons had 1.83 ± 1.3. The difference was 1.17 ± 2.7 with statistically significant (Fig. 9 C4, p = 0.047, n = 22). Similarly, for the number of dendritic terminals, SC neurons had 7 ± 2.9 and MLR neurons had 5.29 ± 2.26. The difference was 1.71 ± 3.36 with statistically significant (Fig. 9 C5, p = 0.027, n = 22). These results indicate that SC neurons exhibit higher dendritic complexity than MLR neurons in terms of the number of dendritic branch points and terminals, suggesting that SC neurons may possess greater structural-level plasticity compared to MLR neurons. Exercise promotes dendritic plasticity in SC and MLR neurons In order to evaluate the effect of exercise intervention on the dendritic structure of SC and MLR neurons, we measured and analyzed morphological metrics of SC and MLR neurons following exercise training. Figure 10 A shows representative morphological images of SC neurons, where the exercise group (Fig. 10A1) exhibited longer dendritic distributions compared to the control group (Fig. 10A2). Sholl analysis revealed that exercise significantly increased the number of dendritic intersections in SC neurons within the range of 50–225 µm from the soma (Fig. 10 B, P < 0.05). Further quantitative analysis indicated that exercise induced a significant increase in the total dendritic length of SC neurons from 528.82 ± 200.3 µm in the control group (n = 27) to 664.4 ± 264.9 µm in the exercise group (n = 42) with an increase of 135.58 ± 59.6 µm (Fig. 10C1, P = 0.026). The number of primary dendrites also increased from 4.07 ± 1.1 to 4.79 ± 1.55 with an increase of 0.71 ± 0.3 (Fig. 10C2, P = 0.041). Regarding the number of dendritic branch points and terminals, the exercise group showed significant increases of 2.66 ± 0.7 (Fig. 10C3; Control: 1.63 ± 1.21; Exercise: 4.29 ± 3.47, P < 0.001) and 3.46 ± 0.9 (Fig. 10C4; Control: 5.78 ± 2.12; Exercise: 9.24 ± 4.73, P < 0.001), respectively. In contrast, no significant difference was observed in somatic surface area between the two groups (Fig. 10C5; Control: 314.74 ± 84.39 µm²; Exercise: 304.74 ± 121.58 µm², P = 0.688). These results demonstrated that exercise intervention significantly enhanced dendritic plasticity in SC neurons, particularly in parameters such as dendritic length, number of branches, and number of terminals. Similar to SC neurons, as shown in Fig. 10 D, the dendritic arborization of MLR neurons in the exercise group (Fig. 10D2) appeared more complex than that in the control group (Fig. 10D1), characterized by broader dendritic extension and more terminals. Sholl analysis results indicated that exercise intervention significantly increased the number of dendritic intersections in MLR neurons (Control n = 29; Exercise n = 70) within the range of 150–325 µm from the soma (Fig. 10 E, P < 0.05). Total dendritic length increased from 583.37 ± 217.4 µm to 761.69 ± 313.3 µm with an increase of 178.32 ± 63.81 µm (Fig. 10F1, P = 0.006). The number of branch points increased from 1.76 ± 1.37 to 2.79 ± 1.74 with an increase of 1.03 ± 0.36 (Fig. 10F3, P = 0.006). The number of terminals increased from 5.17 ± 2.19 to 6.41 ± 1.99 with an increase of 1.24 ± 0.45 (Fig. 10F4, P = 0.007). However, no significant change was observed in somatic surface area (Fig. 10F5; Control: 332.04 ± 173.71 µm²; Exercise: 360.13 ± 172.02 µm², P > 0.05) or number of primary dendrites (Fig. 10F2; Control: 3.41 ± 1.18; Exercise: 3.54 ± 1.05, P > 0.05) in MLR neurons after exercise. These results indicate that exercise significantly promotes dendritic plasticity in MLR neurons, particularly in total dendritic length, number of dendritic branch points, and number of dendritic terminals. Analysis of exercise effects on dendritic plasticity in SC and MLR neurons To further analyze the differential effects of exercise intervention on dendritic plasticity between SC and MLR neurons, we examined the relative percentage changes in morphological metrics from 28 pairs of SC and MLR neurons that were simultaneously recorded within the same mouse preparations from the exercise group. Figure 11 A displays the structure and morphology of two representative pairs of SC and MLR neurons following exercise. Statistical analysis revealed (Fig. 11 B) that exercise induced a significantly greater increase in total dendritic length in SC neurons (56.3%) compared to MLR neurons (25.5%), with a mean difference of 30.8% (Fig. 11 B1, P = 0.006, n = 28). Regarding the number of dendritic branch points, SC neurons showed an increase of 107.14%, which was significantly larger than the 39.05% increase observed in MLR neurons (Fig. 11 B3, P = 0.025, n = 28). A similar result was observed for the number of dendritic terminals, where SC neurons exhibited a 61.2% increase, significantly greater than the 27.6% increase in MLR neurons, resulting in a difference of 33.6% (Fig. 11 B4, P = 0.025, n = 28). In contrast, no significant differences were found between the two neuron types in the number of primary dendrites (Fig. 11 B2, P > 0.05, n = 28) and somatic surface area parameters (Fig. 11 B5, P > 0.05, n = 28). These results indicate that while exercise significantly enhanced dendritic plasticity in both SC and MLR neurons, the magnitude of increase was markedly greater in SC neurons than in MLR neurons. This enhanced plasticity in SC neurons was primarily evident in parameters such as total dendritic length, number of dendritic branch points, and number of dendritic terminals, suggesting that SC neurons exhibit a higher degree of structural remodeling capacity in response to exercise intervention compared to MLR neurons. Modulation of neuronal excitability in dorsal vs. ventral SC neurons by exercise The SC neurons recorded in this study were primarily distributed in the dorsal and ventral regions of the spinal cord. Dorsal neurons play a major role in proprioceptive signal transmission (Leiras et al., 2022 ), whereas ventral neurons are primarily involved in the control of locomotor activity (Hsu et al., 2023 ; Kiehn, 2016 ). Given the functional differences between these two neuronal populations, we sought to investigate whether they also exhibit differences in neuronal plasticity in response to exercise intervention. In the subsequent experiments, we selected SC neurons with clear laminar locations, classifying those in laminae I-IV as dorsal interneurons and laminae V-X the as ventral interneurons (except for lamina IX), and analyzed their membrane properties. Figures 12A1-2 show representative examples of a dorsal and a ventral neuron, respectively, with their action potentials (APs) overlapped. Compared to the dorsal neuron, the ventral neuron exhibited a more hyperpolarized threshold (Vth), a longer afterhyperpolarization (AHP) half-width, and a lower input resistance (Rin). Statistical analysis (Figs. 12A3-10) confirmed that the Vth of ventral SC neurons was significantly more hyperpolarized than that of dorsal neurons by 6.1 ± 5.83 mV (Fig. 12A5; dorsal: -33.44 ± 5.75 mV; ventral: -39.10 ± 4.30 mV; P = 0.032, n = 8). The AHP half-width was significantly longer in ventral neurons by 78.29 ± 63.16 ms (Fig. 12A9; dorsal: 77.76 ± 53.01 ms; ventral:133.31 ± 66.23 ms; P = 0.01, n = 8). The Rin of ventral neurons was significantly lower than that of dorsal neurons by 443.4 ± 403.8 MΩ (Fig. 12A10; dorsal: 1193.5 ± 376.94 MΩ; ventral: 731.22 ± 308.16 MΩ; P = 0.02; n = 8). No significant difference was observed in other parameters, including resting membrane potential (Em), rheobase, AP amplitude, AP half-width, or AHP amplitude (Figs. 12A3-4 & A6-8, P > 0.05, n = 8). These results indicated that ventral SC neurons possessed higher excitability than dorsal neurons, primarily characterized by a lower Vth, suggesting they were more easily activated and may exhibit greater plasticity during exercise. We next examined the modulatory effects of exercise on the electrophysiological properties of dorsal and ventral SC neurons (Fig. 12 B). Figures 12B1-2 (black for Control; green for Exercise) showed a representative example where exercise intervention significantly hyperpolarized the Vth in a dorsal neuron without altering its rheobase. Statistical analysis revealed that exercise intervention hyperpolarized the Vth of dorsal neurons by 12.5 ± 3.37 mV (Fig. 12 B5; Control: -33.01 ± 5.75 mV, n = 8; Exercise: -45.51 ± 8.49 mV, P = 0.026, n = 10). No significant change was detected in Em, rheobase, AP amplitude, AP half-width, AHP amplitude, AHP half-width, and Rin (Figs. 12B3-4 & B6-10, total n = 18). In contrast, exercise induced more pronounced changes in ventral SC neurons (Fig. 12 C). Figures 12C1-2 (black for Control; orange for Exercise) showed a representative example where exercise resulted in hyperpolarization of Vth and a decrease in rheobase in a ventral neuron. Statistical analysis confirmed that exercise significantly decreased the rheobase of ventral neurons by 7.43 ± 1.69 pA (Fig. 12 C4; Control: 14.38 ± 3.2 pA, n = 8; Exercise: 6.94 ± 4.49 pA, n = 18, P < 0.001) and hyperpolarized their Vth by 8.75 ± 2.18 mV (Fig. 12 C5; Control: -39.1 ± 5.82 mV; Exercise: -47.85 ± 7.27 mV, P < 0.001). No significant change was observed in Em, AP amplitude, AP half-width, AHP amplitude, AHP half-width, or Rin (Figs. 12C3 & C6-10). Further comparative analysis revealed that, compared to dorsal neurons, exercise caused significantly greater reductions in ventral neurons for rheobase (Fig. 12D2; ventral: -51.69%, n = 18; dorsal: -5.88%, n = 10; P = 0.029), AP half-width (Fig. 12D5; ventral: -36.89%; dorsal: 16.75%; P = 0.019), and AHP half-width (Fig. 12D7; ventral: -33.19%; dorsal: -28.57%; P 0.05). These results demonstrated that exercise enhanced the excitability of both dorsal and ventral SC neurons. Dorsal neurons primarily exhibited a lower Vth, whereas ventral neurons showed concurrent reductions in both Vth and rheobase. Furthermore, the magnitude of excitability modulation by exercise was greater in ventral neurons than in dorsal neurons, particularly regarding the relative decreases in rheobase, AP half-width, and AHP half-width. These results suggested that ventral neurons played a dominant role in the adaptation of the motor system during exercise, indicating that the locomotor system exhibited greater plasticity than the proprioceptive system. Analysis of exercise on dendritic plasticity in dorsal vs. ventral SC neurons In the above experiments, we examined the differential effects of exercise on the membrane properties of dorsal and ventral spinal cord neurons. We now extend our analysis to compare the influence of exercise on dendritic plasticity between these two neuronal populations. Figure 13 A illustrated the morphological characteristics of both neuron types in control and exercise groups. Qualitatively, dorsal neurons in the control group (Fig. 13A1) appeared slightly smaller in dendritic extent than ventral neurons (Fig. 13A3), though this difference was not significant. Exercise markedly promoted dendritic growth in both dorsal (Fig. 13A2, green) and ventral (Fig. 13A4, orange) neurons. Statistical analysis indicated that ventral neurons exhibited a trend towards larger values than dorsal neurons in total dendritic length, number of primary dendrites, dendritic branch points, dendritic terminals, and somatic area (Figs. 13B1-5). However, a statistically significant difference was only observed in the number of primary dendrites: ventral neurons had 1.03 ± 0.34 more primary dendrites than dorsal neurons (Fig. 13B2; ventral: 4.28 ± 0.83, n = 18; dorsal: 3.25 ± 0.71, n = 8; P = 0.005). No significant difference was found in the other metrics. Despite the minimal baseline morphological differences between the two populations, exercise profoundly influenced dendritic plasticity. In dorsal neurons, exercise significantly increased the number of primary dendrites from 3.25 ± 0.70 to 4.27 ± 1.10 with an increase of 1.02 ± 1.07 (Fig. 13C2; Control: n = 8; Exercise: n = 11; P = 0.025), the number of dendritic branch points from 1.38 ± 0.92 to 3.55 ± 2.03 with an increase of 2.17 ± 0.82 (Fig. 13C3; P = 0.009), and the number of dendritic terminals from 4.63 ± 1.3 to 7.91 ± 2.7 with an increase of 3.28 ± 1.01 (Fig. 13C4; P = 0.003). In contrast, no significant change was observed in total dendritic length or somatic area following exercise (Figs. 13C1 & C5; P > 0.05). Similarly, exercise significantly enhanced dendritic morphology in ventral neurons. Total dendritic length increased from 545.31 ± 238.11 µm to 762.7 ± 269.38 µm with an increase of 217.4 ± 82.07 µm (Fig. 13D1; Control: n = 18; Exercise: n = 21; P = 0.011). The number of dendritic branch points from 1.56 ± 1.1 to 4.0 ± 3.32 with an increase of 2.44 ± 0.82 (Fig. 13D3; P = 0.004), and the number of dendritic terminals from 5.89 ± 1.57 to 9.0 ± 4.06 with an increase of 3.11 ± 1.02 (Fig. 13D4; P = 0.003). However, no significant change was detected in the number of primary dendrites or somatic area (Figs. 13D2 & D5; p > 0.05). Finally, we compared the relative magnitude of exercise-induced dendritic plasticity between dorsal and ventral neurons (Fig. 13 E). The results showed that the percent increase in total dendritic length was significantly greater in ventral neurons (46.5%, n = 18) than in dorsal neurons (7.7%, n = 21; Fig. 13 E1; P = 0.009). In contrast, the relative changes in the number of primary dendrites, dendritic branch points, dendritic terminals, and somatic area were not significantly different between the two populations (Figs. 13E2-5; p > 0.05). These findings indicated that exercise intervention robustly enhanced dendritic plasticity in both dorsal and ventral SC neurons, which likely had implications for the regulation of passive membrane properties and neuronal excitability. The greater increase in dendritic length observed in ventral neurons suggested that exercise may exert a stronger modulatory effect on the excitability of ventral neurons compared to dorsal neurons, consistent with the electrophysiological findings presented in Fig. 12 B, C & D. Analysis of functional significance of exercise-induced plasticity The findings above indicated that exercise intervention simultaneously promoted neuronal dendritic plasticity, enhanced the persistent inward current (PIC), and increased neuronal excitability, a decrease in Vth and rheobase in particular, in both the MLR and SC neurons. However, how dendritic growth and PIC modulation collectively altered neuronal excitability remains unknown. In the following analysis, we conducted a correlational study to address this issue. Based on the physiological significance of each electrophysiological parameter and the underlying neural mechanisms they represent, we paired these parameters and performed correlation analyses on the data from the control and exercise groups. Figure 14 (A, B & C) presented the results of the correlation analysis for neuronal parameters in SC neurons from the control and exercise groups. The results showed that among the parameter pairs in the control group, only a weak correlation (R² < 0.4) was observed for Vth vs. PIC onset (Fig. 14A1) and Vth vs. Na-PIC onset (Fig. 14A2). However, exercise intervention significantly enhanced the correlation between these two parameter pairs, elevating it from weak to medium (R² > 0.4) or strong (R² > 0.7). Similar results were observed for the parameter pairs PIC amplitude vs. total dendritic length (Fig. 14A3) and Ca-PIC amplitude vs. total dendritic length (Fig. 14A4), where exercise increased R² from 0.2 to 0.7. These results suggest that the hyperpolarization of Vth induced by exercise is primarily due to the hyperpolarization of PIC onset, specifically driven by the hyperpolarization of Na-PIC. Furthermore, the exercise-induced increase in PIC amplitude, particularly in Ca-PIC amplitude, may result from the growth in dendritic length. To further validate this conclusion, we analyzed experimental data from bath application of riluzole (3 µM) and nimodipine (25 µM) in the SC neurons of the exercise group. We examined four parameter pairs directly related to neuronal excitability: Vth vs. PIC onset (Fig. 14B1), Vth vs. PIC amplitude (Fig. 14B2), rheobase vs. PIC onset (Fig. 14B3), and rheobase vs. PIC amplitude (Fig. 14B4). Riluzole reduced their correlation coefficients from strong to weak (R²<0.4), indicating that exercise-induced hyperpolarization of Na-PIC onset and an increase in Na-PIC amplitude constituted a potential mechanism underlying the enhanced excitability of SC neurons, manifested as Vth hyperpolarization and rheobase reduction. We further analyzed the experimental data from the bath application of nimodipine (25 µM) in the exercise group. In contrast to the results with riluzole, nimodipine had only a minor impact on the correlations for Vth vs. PIC onset (Fig. 14C1), Vth vs. PIC amplitude (Fig. 14C2), rheobase vs. PIC onset (Fig. 14C3), and rheobase vs. PIC amplitude (Fig. 14C4), with a decrease in R² of less than 0.2. Among these, Vth vs. PIC onset, Vth vs. PIC amplitude, and rheobase vs. PIC onset retained medium-to-strong correlations (R² > 0.6). Only the correlation for rheobase vs. PIC amplitude showed a noticeable decline, with R² decreasing from 0.668 to 0.475. These results suggested that while exercise-induced enhancement of Ca-PIC amplitude contributed to the reduction of rheobase in SC neurons, it had little effect on the hyperpolarization of Vth. In the subsequent study, we performed the same correlation analysis on MLR neurons, with the results presented in Fig. 14 (D, E & F). We first conducted a correlation analysis on four parameter pairs in the MLR, including Vth vs. PIC onset (Fig. 14 D1), Vth vs. Na-PIC onset (Fig. 14 D2), PIC amplitude vs. total dendritic length (Fig. 14 D3), and Ca-PIC amplitude vs. total dendritic length (Fig. 14 D4). Similar to the findings in SC neurons, exercise enhanced the correlations for these four parameter pairs from weak (R²0.6). This indicated that the exercise-induced hyperpolarization of PIC onset, particularly Na-PIC onset, was the primary cause of Vth hyperpolarization; whereas the increase in dendritic length resulting from exercise served as a potential mechanism underlying the increase in PIC amplitude, especially Ca-PIC amplitude. We conducted analysis on the MLR neurons with riluzole (3 µM) in exercise group for the four parameter pairs: Vth vs. PIC onset (Fig. 14E1), Vth vs. PIC amplitude (Fig. 14E2), rheobase vs. PIC onset (Fig. 14E3), and rheobase vs. PIC amplitude (Fig. 14E4). The results showed that the correlations decreased from strong (R²>0.7) to weak (R²<0.4). This finding demonstrated that exercise-induced hyperpolarization of Na-PIC onset and the increase in Na-PIC amplitude were the main contributors to the enhanced excitability of MLR neurons, manifested as hyperpolarized Vth and reduced rheobase. Further analysis revealed that nimodipine had little effect on the correlations for three parameter pairs: Vth vs. PIC onset (Fig. 14F1), Vth vs. PIC amplitude (Fig. 14F2), and rheobase vs. PIC onset (Fig. 14F3), with a decrease in R² of less than 0.1. These correlations remained at a medium-to-strong level (R²>0.6). Only the correlation for rheobase vs. PIC amplitude (Fig. 14F4) showed a decline, with R² decreasing from 0.510 to 0.387. These results implicated that the exercise-induced enhancement of Ca-PIC amplitude contributed to the reduction of rheobase in MLR neurons, but had a little effect on the hyperpolarization of Vth. The above results of the correlation analysis indicate that the exercise-induced enhancement of excitability in SC and MLR neurons can be directly explained by the increased dendritic length and enhanced regulatory function of the ion channel PICs. Furthermore, this increased neuronal excitability was the potential mechanism underlying the plasticity of the motor system in response to environmental changes. Summary of exercise-induced plasticity in SC vs. MLR neurons This study elucidated the mechanisms underlying adaptation of the locomotor system in response to exercise intervention from three perspectives: intrinsic membrane properties, ionic channel kinetics, and neuronal morphology. We demonstrated that the locomotor system exhibited differential plasticity in the MLR and SC neurons following exercise, with the SC neurons playing a dominant role in this process. Figure 15 summarizes the percentage changes in 19 key neurophysiological parameters induced by exercise intervention in the MLR and SC neurons, along with comparative analysis of synchronized changes between these two regions. The results indicated that exercise significantly enhanced plasticity in both MLR and SC neurons, as evidenced by improved neuronal excitability (rheobase, Vth), PIC channel kinetics (onset and amplitude), and dendritic plasticity (dendritic length, branch points & terminals) in both regions (longitudinal data in Fig. 15 ). More importantly, comparative analysis of simultaneously measured parameters revealed that SC neurons exhibited significantly greater plasticity than MLR neurons, showing more pronounced changes in the following parameters: rheobase (SC: -0.45; MLR: -0.23), Vth (SC: -0.22; MLR: -0.11), PIC onset (SC: -0.14; MLR: -0.08) and amplitude (SC: 0.40; MLR: 0.18), Na-PIC onset (SC: -0.27; MLR: -0.22) and amplitude (SC: 0.29; MLR: 0.23), Ca-PIC amplitude (SC: 0.34; MLR: 0.25), dendritic length (SC: 0.56; MLR: 0.26), branch points (SC: 1.07; MLR: 0.39) and ends (SC: 0.61; MLR: 0.28). These findings collectively indicated that while exercise enhanced plasticity in both SC and MLR neurons, the SC motor system demonstrated significantly greater plasticity than the MLR motor system, suggesting that the SC motor system played a dominant role in the adaptive remodeling of the locomotor system. Discussion This study systematically elucidated the mechanisms by which moderate-intensity treadmill exercise modulated neuronal plasticity in both the MLR and SC regions. The results demonstrated that exercise intervention concurrently enhanced neuronal excitability, potentiated persistent inward currents (PICs), and promoted dendritic plasticity in these two key locomotor areas. The principal innovation of this research lies in our approach of simultaneously measuring and analyzing exercise-induced plasticity changes in both regions within the same animal preparations. Our findings revealed that the plastic changes induced by exercise intervention were more pronounced in SC neurons compared to MLR neurons. Furthermore, we demonstrated that within the spinal cord, exercise induced stronger plasticity in ventral SC neurons than in dorsal SC neurons. These two key findings indicated that during exercise-induced adaptation of the locomotor system: (1) the spinal cord motor system played a more crucial role than the MLR motor system, and (2) the ventral spinal circuitry controlling locomotor output contributed more significantly than the dorsal spinal pathways integrating proprioceptive information. Exercise enhanced neuronal excitability, PICs, and dendritic plasticity Chronic exercise potentiates rodent spinal motoneuron excitability by altering key electrophysiological properties (Em, Vth, rheobase, firing frequency, AHP), which heightens synaptic sensitivity and optimizes motor output (Dai et al. 2025; Gardiner et al. 2006 ; Zhang & Dai 2020). This plasticity extends to spinal interneurons, where treadmill training hyperpolarizes Vth, reduces rheobase in ventromedial/laminar X populations, and increases AP amplitude in dorsal horn interneurons. Exercise also enhances PICs and facilities dendritic plasticity in the spinal neurons (Chen & Dai 2022 ; Chen et al. 2019 ). Similarly, chronic exercise enhances excitability in midbrain DRN 5-HT neurons through similar changes in membrane properties, PICs and dendritic developments (Ge & Dai 2020 ), demonstrating a broad effect across spinal cord and midbrain regions. Nevertheless, how exercise simultaneously affects SC and MLR neurons is still unknown. Results from the present study demonstrated that exercise intervention concurrently enhanced excitability in both SC and MLR neurons, primarily evidenced by reduced action potential voltage threshold and rheobase current (Fig. 3 ). Comparative analysis of simultaneously recorded SC and MLR neurons revealed a greater magnitude of excitability enhancement in SC neurons than in MLR neurons (Fig. 4 ), although the underlying mechanisms require further investigation. Similar to threshold modifications, exercise potentiated persistent inward currents (PICs) in both SC and MLR neurons, characterized by increased PIC amplitude and a hyperpolarizing shift in PIC activation voltage (onset) (Fig. 5 ). Pharmacological experiments indicated that the enhanced PIC amplitude was primarily mediated by Ca-PIC, while the hyperpolarization of PIC onset was predominantly determined by Na-PIC (Fig. 6 ). These findings suggest that exercise intervention systematically modulates distinct PIC components to enhance neuronal excitability, thereby promoting adaptive plasticity within the motor system. Further analysis revealed that the exercise-induced increase in PIC amplitude was significantly larger in SC neurons than in MLR neurons, potentially explaining the greater enhancement of excitability in spinal neurons compared to MLR neurons (Fig. 4 ). Additionally, we observed that exercise significantly hyperpolarized Vth in both SC and MLR neurons (Fig. 7B1). However, this effect was abolished following Ca-PIC blockade (Figs. 7C1, 7E1), indicating that Ca-PIC had minimal influence on spike threshold. In contrast, exercise reduced both recruitment current (Irec) and de-recruitment current (Idec) in SC and MLR neurons (Figs. 7B2-4). Ca-PIC blockade significantly increased both Irec and Idec, with a more pronounced effect in SC neurons than in MLR neurons (Figs. 7C2-4, 7E2-3), suggesting that exercise enhanced sustained firing capacity in these neurons via upregulation of Ca-PIC. Unlike Ca-PIC, Na-PIC blockade significantly increased Vth, Irec, and Idec in both neuron types (Figs. 7D1-3), with greater changes observed in SC neurons (Figs. 7F1-3). This indicates that Na-PIC primarily governed neuronal excitability thresholds, and exercise-induced upregulation of Na-PIC enhanced both excitability and sustained firing capacity in SC and MLR neurons. These results further demonstrated that the greater exercise-induced enhancement of excitability in SC neurons compared to MLR neurons was mediated by complementary mechanisms involving both Na-PIC and Ca-PIC. This study also showed that treadmill exercise significantly enhanced dendritic plasticity in SC and MLR neurons. Sholl analysis revealed that exercise intervention increased total dendritic length, number of branch points, and number of dendritic terminals, with significantly greater enhancements in SC neurons than in MLR neurons (Fig. 11 ). Enhanced dendritic plasticity may strengthen the weight and efficacy of neural network connections, thereby improving motor system adaptability and learning capacity (Edgerton et al., 2004 ). Additionally, it may provide more potential sites for Cav1.3 channel expression, potentiating Ca-PIC-mediated modulation of neuronal excitability (Carlin et al., 2000 ; Jiang et al., 1999 ). Exercise coordinately enhanced plasticity in SC and MLR neurons Locomotor function is initiated and controlled by neural circuits located in the MLR and executed and coordinated by networks within the spinal cord. Motor commands originating from the MLR descend via brainstem pathways to spinal neural networks, where they are executed and regulated by central pattern generators (CPGs) distributing in the ventral spinal cord (Bouvier et al., 2015 ; Caggiano et al., 2018 ; S. Grillner & A. El Manira, 2020; Leiras et al., 2022 ). Simultaneously, somatosensory signals are integrated by dorsal spinal circuits to enable precise coordination and control of locomotion (Hsu et al., 2023 ; Kiehn, 2016 ). Exercise intervention essentially involves periodic acute and chronic modulation and adaptive remodeling of this entire neural pathway, from the MLR to the spinal cord, with the regulation of neuronal excitability being a core mechanism in this process (Dai et al., 2024 ; Gardiner et al., 2006 ; Pearcey et al., 2021 ; K. E. Power et al., 2022 ). Our previous research demonstrated that exercise enhanced excitability in spinal neurons and midbrain 5-HT neurons (Chen & Dai, 2022 ; Ge & Dai, 2020 ). However, those studies specifically targeted lamina X neurons in the spinal cord and 5-HT neurons in the dorsal raphe nucleus (DRN), excluding other spinal interneurons and MLR neurons. Compared to these earlier studies, the present work introduces two major innovations: (1) The recorded SC neurons herein encompassed interneurons from both dorsal and ventral spinal regions, and the midbrain neurons cover the entire MLR area, constituting a systematic investigation of plasticity within the locomotor system. (2) We performed simultaneous measurements and analyses of MLR and SC neurons from the same mouse preparations. This approach allowed us to comprehensively and systematically study the effects of exercise on the locomotor initiation region (MLR), the execution region (ventral SC), and the proprioceptive integration region (dorsal SC). It also enabled us to investigate the mechanisms and differences in plasticity between the SC and MLR motor systems from three perspectives: intrinsic membrane properties, PIC channel characteristics, and dendritic plasticity, leading to the key discovery of the dominant role played by the SC motor system during exercise. Considering the results from SC and MLR independently, the observed neuronal plasticity closely mirrors our previous findings in lamina X neurons (Chen & Dai, 2022 ) and DRN 5-HT neurons (Ge & Dai, 2020 ). Specifically, exercise enhanced excitability (Fig. 3 ), improved PIC regulatory properties (Figs. 5 , 6 , 7 ), and promoted dendritic plasticity (Fig. 10 ) in both SC and MLR neurons. However, the results from simultaneous SC-MLR measurements revealed a distinctive and important finding: exercise induced greater plasticity in SC neurons than in MLR neurons. This conclusion was primarily supported by the larger magnitude of change induced by exercise in key neurophysiological parameters in SC neurons compared to MLR neurons (Fig. 15 ). These parameter changes included the decrease in rheobase and Vth, the hyperpolarizing shift in the onset and the increase in amplitude of composite PIC, Na-PIC, and Ca-PIC, and the increases in dendritic length, branch points, and terminals. Decreases in rheobase and Vth altered intrinsic membrane properties, lowered the current and voltage thresholds for action potential initiation, and thereby enhanced neuronal excitability. The decrease in PIC onset and increase in amplitude enhanced sustained firing excitability through modulation of ion channel function (Cheng et al., 2025 ). The enhancement of dendritic plasticity further strengthened neural network connectivity, improving the precision and efficiency of neurotransmitter release, modulation, and motor control (Adkins et al., 2006 ; Wolpaw & Tennissen, 2001 ). Consequently, we draw a significant conclusion: compared to the MLR motor system, the SC motor system plays a dominant role in the adaptation of the locomotor system induced by exercise. Numerous factors may contribute to the greater magnitude of excitability changes in SC neurons compared to MLR neurons. These included inherent functional differences between the SC and MLR in motor control and, more importantly, their distinct positions within the neural circuitry. Spinal interneurons participate in mono- or polysynaptic spinal reflexes. Their excitability regulation involves shorter pathways with more direct responses, allowing for rapid adaptation to functional remodeling induced by motor exercise (Côté et al., 2018 ; Hultborn et al., 1971 ). In contrast, the MLR, as a higher-order motor center, influences motor output indirectly through multiple relays involving brainstem reticular formation neurons and spinal interneurons (Philippe Lacroix-Ouellette & Réjean Dubuc, 2023; Noga & Whelan, 2022 ). Consequently, excitability changes in the MLR require integration and modulation through complex neural circuits, potentially resulting in a relatively slower and more moderated response to exercise intervention. The fundamental architectural differences between the SC and MLR networks likely constitute the primary reason for the more pronounced excitability changes observed in spinal neurons following exercise. This issue requires further investigation. Exercise induced adaptation of dorsal vs. ventral interneurons in the spinal cord This study focuses on the plasticity of spinal interneurons. Functionally, the central pattern generator (CPG) networks located in the ventral spinal cord are primarily responsible for the rhythm generation and control of locomotion (Hsu et al., 2023 ; Kiehn, 2016 ; Rossignol et al., 2006 ). In contrast, neurons locating in the dorsal spinal cord are involved in coordinating various systemic functions, including sensory signal feedback and integration (Bourane et al., 2015 ; Foster et al., 2015 ), precise regulation of CPG-mediated motor control (Goulding, 2009 ; Hägglund et al., 2010 ), as well as motor learning and adaptive remodeling (Edgerton et al., 2004 ; Takeoka et al., 2014 ). In general, SC neurons in the dorsal and ventral spinal cord of rodents exhibit physiological properties highly aligned with their respective functions. Dorsal neurons are more specialized in the diverse and specific transmission as well as preliminary integration of sensory information (Usoskin et al. 2015 ), whereas ventral neurons—particularly V2a interneurons—play a pivotal role in generating motor rhythm and enabling precise control of movement intensity through their specific firing patterns and finely organized modular circuit structures (Song et al. 2018 ). Dorsal neurons utilize diverse firing patterns and a higher action potential threshold to precisely encode complex sensory information, whereas ventral neurons ensure stable and reliable motor output through a relatively depolarized resting membrane potential, a lower action potential threshold, and regular tonic firing (Crozat et. al., 2025 ). Our results demonstrated that exercise enhanced the excitability of spinal neurons and promoted dendritic growth. However, the modulatory effects of exercise on dorsal versus ventral spinal interneurons remain poorly understood. To address this, we classified interneurons in laminae I-IV as dorsal and those in laminae V-X as ventral. The results revealed distinct differences between dorsal and ventral spinal interneurons in four key parameters: Vth, AHP half-width, Rin (Fig. 12 A), and the number of primary dendrites (Fig. 13 B). These differences indicated that ventral neurons possessed higher intrinsic excitability than dorsal neurons. Exercise induced a hyperpolarization of Vth in both dorsal and ventral neurons (Figs. 12 B & C), while also causing a significant decrease in rheobase specifically in ventral neurons. Furthermore, exercise intervention enhanced dendritic plasticity in both populations (Figs. 13 C & D), with a significant increase in total dendritic length observed particularly in ventral neurons (Fig. 13D1). These findings indicated that exercise intervention concurrently enhanced excitability in both dorsal and ventral neurons, but the magnitude of this enhancement was greater in ventral neurons. A comparative analysis of the relative changes further showed that the decrease in rheobase, AP half-width, and AHP half-width, as well as the increase in dendritic length, were more pronounced in ventral neurons than in dorsal neurons after exercise (Figs. 12 D & 13 E). Collectively, these results suggested that ventral interneurons played a dominant role in the adaptive remodeling of the motor system during exercise, indicating that the plasticity of the locomotor execution system surpassed that of the proprioceptive integration system. Exercise induced adaptation of SC and MLR neurons vs. DRN 5-HT neurons The MLR neurons and DRN 5-HT neurons are both located in the midbrain but reside in distinct functional regions. The MLR primarily governs the initiation and control of locomotion (Caggiano et al., 2018 ; Kiehn, 2016 ), whereas DRN 5-HT neurons are implicated in depression and anxiety-related disorders (Albert et al., 2014 ; Gross & Hen, 2004 ). Notably, the MLR encompasses two key subregions – the cuneiform nucleus (CnF) and the pedunculopontine nucleus (PPN) – with the PPN known to contain 5-HT neurons (Ge et al., 2019 ). As our study did not differentiate between neurons from the CnF and PPN subregions, it was likely that our recorded MLR neuron sample included a subset of 5-HT neurons. Despite the distinct physiological functions of MLR neurons and DRN 5-HT neurons, the adaptive remodeling induced by exercise yielded remarkably similar outcomes in both populations. These shared enhancements included increased neuronal excitability, potentiated PIC regulation, and promoted dendritic plasticity (Ge & Dai, 2020 ). Strikingly similar results were also observed when comparing MLR and SC neurons. This consistent pattern suggests that the modulatory effects of exercise intervention selectively target fundamental neuronal membrane properties. The regulation of these core properties appears to be independent of the specific functional roles of the neural systems involved. Nevertheless, once exercise successfully remodels these intrinsic membrane properties, the functional output and adaptive capacity of the respective neural systems are consequently reshaped. The MLR, a central focus of this study, is a critical brain region for locomotor control. It integrates commands from higher brain centers and projects descending signals to the spinal cord to activate CPG networks, thereby generating rhythmic motor outputs for walking, running, and stopping (Bouvier et al., 2015 ; Caggiano et al., 2018 ; Grillner & Manira, 2020 ; Harris-Warrick, 2010 ; Leiras et al., 2022 ; Opris et al., 2019 ). Our findings demonstrated that exercise enhanced plasticity within both the MLR and SC motor systems. Specifically, the observed increase in neuronal excitability likely improved the speed and precision of motor control (Heckman, Hyngstrom, & Johnson, 2008 ). The enhancement of PIC regulation probably augmented the duration and intensity of sustained neuronal firing (Cheng et al., 2025 ), thereby refining the accuracy and efficiency of motor control (Josset et al., 2018 ; Rybak et al., 2006 ). Furthermore, the strengthening of dendritic plasticity was poised to reinforce synaptic connectivity within MLR and SC neural networks, potentially enhancing the system's adaptive capability, learning, and memory (Edgerton et al., 2004 ). Our results further indicated that the SC nervous system exhibited greater exercise-induced plasticity than the MLR system. This suggests that spinal cord-mediated locomotion plays a dominant role in the adaptive responses to motor training. However, the specific impact of this spinal dominance on the control of movement patterns (e.g., running, jumping, walking) and locomotion speed remains unclear and warrants further investigation. Functional significance of exercise-induced plasticity in the MLR and SC neurons This study found that exercise coordinately promoted neuronal plasticity in both the MLR and SC neurons, including increased neuronal excitability, enhanced persistent inward currents (PICs), and facilitated dendritic growth. Correlation analyses revealed that dendritic growth was a potential mechanism underlying the enhanced modulatory function of PICs, whereas the increase in PICs could directly increase neuronal excitability in terms of hyperpolarization of the voltage threshold and reduction in rheobase, thereby promoting adaptability of the locomotor system. Enhanced excitability of MLR neurons implicated that motor commands originating from the MLR could be initiated more rapidly and transmitted more reliably to spinal CPG neural networks, improving motor control and coordination (Caggiano et al. 2018 ; Rossignol et al. 2006 ). Increased excitability of SC neurons could improve the stability of rhythmic generation, the adaptability of motor patterns, and the efficiency of neuromuscular coupling, ultimately recruiting more spinal motoneurons to generate stronger and more coordinated skeletal muscular force (Zhang et al. 2022; Grillner 2003 ). This study demonstrated that chronic exercise synchronously enhanced neuronal excitability in both SC and MLR neurons, promoting neuronal plasticity. Such changes in physiological function may enhance the adaptability of the locomotor system to environmental changes through multiple neurophysiological pathways including ion channel modulation, dendritic plasticity modifications, neurotransmitter release, and neurotrophic factor expression, operating at both acute and chronic states (Dai et al. 2024 ). In the perspective of neurorehabilitation, locomotor training improves post spinal cord injury (SCI) ambulation globally (Iddings et al., 2021 ), and repetitive intensive training can activate sub-injury spinal circuits via afferent feedback (Rossignol et al., 2011 ). Our data showed that exercise training enhanced Na-PIC and Ca-PIC in MLR and SC neurons, which could facilitate post-SCI locomotor recovery. Results from this study provided a potential neurophysiological framework and foundation for optimizing exercise training strategies and developing rehabilitation paradigms for spinal cord injury. Conclusions Exercise concurrently enhanced neuronal excitability, PIC regulation, and dendritic plasticity in both MLR and SC regions. Dendritic plasticity increased PICs, and the potentiation of PICs directly elevated neuronal excitability, with Ca-PIC primarily determining the duration and frequency of sustained firing, while Na-PIC governed the threshold and capacity for action potential initiation. The enhancement of dendritic plasticity strengthened neural network connectivity and promoted the adaptive capacity of the motor system. The spinal motor system exhibited greater plasticity than the MLR motor system, whereas the spinal ventral locomotor system demonstrated stronger plasticity than the spinal dorsal proprioceptive system. This research elucidated the mechanisms underlying exercise-induced adaptation of the locomotor system from three integrated perspectives: cellular physiology, ion channel properties, and dendritic plasticity. Methods and Materials Animals and ethical approval In this study, we utilized a total of 50 healthy adult C57BL/6J mice (IMSR_JAX:000664), aged 42–45 days. The mice were randomly assigned to different experimental groups at 21 days of age, prior to the start of the experiment, to minimize inter-group differences. The experimental protocol was approved by the Animal Ethics Committee of East China Normal University and strictly adhered to international guidelines for the care and use of laboratory animals (ARXM2023116). All animal procedures followed the ARRIVE guidelines. The animals were housed under controlled conditions: 3–6 mice per cage, with ad libitum access to food and water, in a barrier environment maintained at 22 ± 1°C and 50 ± 10% humidity, under an artificially controlled light-dark cycle (light period: 07:00–19:00). The inclusion criteria for the study required healthy, adult mice with no prior medical conditions, while the exclusion criteria included any mice exhibiting abnormal behavior, infection, or weight loss exceeding 20% during the acclimation period. The mice were selected with balanced gender distribution to account for sex as a biological variable. No attrition occurred during the study, as all enrolled animals completed the experimental procedures. Treadmill exercise protocol This study employed a moderate-intensity treadmill running protocol, adapted from a previously established design for mouse treadmill training (Fernando et al., 1993 ). The experimental mice first underwent a 3-day acclimation period, running for 30 minutes daily at a speed of 5 m/min on a flat treadmill to adapt to the experimental setup. The formal training lasted for 3 weeks, conducted 6 days per week for 60 minutes each day. Each training session consisted of a 40-minute core phase followed by a 20-minute incremental phase. The core phase included a 5-minute warm-up (5 m/min) and 35 minutes of moderate-intensity training (10 m/min), aimed at enhancing endurance. The incremental phase was divided into three stages, each lasting 5 minutes at speeds of 12 m/min, 15 m/min, and 18 m/min, respectively, gradually increasing the exercise intensity, and concluded with a 5-minute cool-down at 5 m/min. All training sessions were scheduled between 08:00 and 09:30 daily to minimize circadian rhythm interference. Aside from the exercise intervention, both the control and exercise groups were housed under identical husbandry and environmental conditions (Fig. 1 A). Preparation of slices Mice of postnatal days 42–45 were deeply anesthetized with 3% isoflurane, and decapitation for tissue harvesting was performed after the absence of reflex responses was confirmed. The brainstem and spinal cord were rapidly dissected in ice-cold artificial cerebrospinal fluid (aCSF), during which the aCSF was continuously bubbled with 95% O₂ and 5% CO₂ (carbogen) to maintain oxygenation. Transverse slices (130 µm thickness) of the prepared brainstem and spinal cord were obtained using a vibrating microtome (VT1000E, Leica Microsystems, Wetzlar, Germany) under ice-cold conditions. The slices were then transferred to recording aCSF and incubated at room temperature for at least 1 hour before subsequent patch-clamp recordings (Fig. 1 B). Whole-cell patch-clamp recordings The whole-cell patch-clamp recording methodology was adapted from our previous studies (Chen & Dai, 2022 ; Ge & Dai, 2020 ) with appropriate modifications. During experiments, brainstem and spinal cord slices were transferred to a recording chamber mounted on an upright microscope (BX50, Olympus, Tokyo, Japan) equipped with differential interference contrast (DIC) optics. The slices were continuously perfused at a rate of 2 ml/min with recording aCSF saturated with 95% O₂ and 5% CO₂ using a gravity-fed perfusion system. Neurons in the spinal cord and MLR were identified and selected for whole-cell patch-clamp recordings. Recording electrodes were fabricated from borosilicate glass capillaries (1B150F-4, World Precision Instruments, USA) using a P-1000 puller (Sutter Instrument Co., USA). When filled with the intracellular solution, the electrodes had a resistance of 5–8 MΩ. The recording setup consisted of a MultiClamp 700B patch-clamp amplifier, an Axon Digidata 1550B data acquisition system, and pClamp 10.7 software (RRID:SCR_011323). Bridge balance compensation was applied in current-clamp mode, and 80–85% series resistance compensation (initial series resistance 10–30 MΩ) was applied in voltage-clamp mode. The series resistance (Rs) was carefully monitored and kept stable throughout control and drug application periods. Signals were low-pass filtered at 3 kHz and sampled at 10 kHz. All electrophysiological data were analyzed using Clampfit 10.7 software (Molecular Devices). All recordings were performed at room temperature (20–22°C). Measurement of membrane parameters This study utilized the current-clamp recording technique to evaluate the effects of exercise intervention on the excitability of spinal cord (SC) and MLR neurons. To assess changes in membrane properties, we measured the following parameters: resting membrane potential (Em), rheobase, action potential voltage threshold (Vth), action potential (AP) amplitude, AP half-width, afterhyperpolarization (AHP) amplitude, AHP half-width, and input resistance (Rin). The specific calculation methods followed a previous study (Ge & Dai, 2020 ). Briefly, the rheobase was determined by injecting a series of 1.5-second step currents with 5 pA increments; the minimum current step that elicited neuronal firing was defined as the rheobase. For some spontaneously active neurons, the rheobase was defined as 0 pA. The action potential voltage threshold (Vth) was defined as the membrane potential at which the rate of membrane potential change (dV/dt) was ≥ 10 mV/ms. The Vth value used was the average measured from the first three action potentials elicited by the rheobase current stimulus. The resting membrane potential (Em) was continuously monitored during recordings; the Em value reported in this study is the average membrane potential measured over the 100 ms period preceding the rheobase current injection. AP and AHP parameters were calculated based on the average of three action potentials elicited by the rheobase current. When calculating AP amplitude and AHP depth, the Vth was used as the reference point. To evaluate the contribution of persistent inward currents (PICs) to neuronal excitability, we injected a triangular ramp current into the neurons (10-second duration, peak amplitude 50–80 pA, baseline current 0 pA, Fig. 1 C) and measured the recruitment current (Irec), the decruitment current (Idec), and their difference (ΔI = Idec-Irec). This stimulus protocol induces a slow ramp depolarization and subsequent slow ramp hyperpolarization in the neuron, allowing for analysis of the role of PICs in sustained neuronal firing. Additionally, in this study, we also measured the Vth of action potentials evoked by the triangular ramp current to analyze the modulatory effect of PICs on neuronal excitability (Fig. 1 C). We recorded PICs in voltage-clamp mode. PICs were evoked using a protocol of five slow ramp voltage commands, starting from an initial holding potential of -70 mV, increasing in 30 mV steps to a peak of + 40 mV, with each ramp lasting 10 seconds (Fig. 1 D). To ensure data consistency, PICs evoked by the fourth or fifth voltage ramp were selected for parameter analysis. For PIC measurement, the leak current was first subtracted from the total current. A baseline was drawn along the rising phase of the current trace; the current value at the last point of tangency between this baseline and the current trace was defined as the PIC onset current (Io), and the corresponding voltage was defined as the PIC onset potential. The peak PIC current (Ip) was defined as the lowest point (maximum inward current) on the current curve. The PIC amplitude was calculated as the difference between Ip and Io (PICs = Ip - Io). The measurement process was illustrated in Fig. 1 D. This study only analyzed PIC data evoked during the rising phase of the voltage ramp, including the PIC onset potential and amplitude. For the concurrently measured neuronal excitability and PIC parameters, the percentage change after the exercise intervention was calculated (Δ% = [(post-intervention value - control mean)/control mean]×100%) to assess the contribution of the exercise intervention to changes in neuronal excitability and PICs. A paired t-test was used to determine statistical significance. Imaging and Sholl analysis Spinal cord (SC) neurons and MLR neurons were identified based on their anatomical locations and were recorded and labeled using the whole-cell patch-clamp technique. During the measurement of neuronal electrophysiological parameters, 3% tetramethylrhodamine was added to the intracellular pipette solution to simultaneously label the recorded cells. After the fluorescent dye was injected into the cells for 5–10 minutes, we immediately capture images of neuronal morphology in the slices, using a Nikon Eclipse Ni fluorescence microscope (equipped with a Nikon DS-Ri2 color digital camera). Sholl analysis was employed to analyze neuronal morphology. Concentric circles were drawn at 25 µm intervals centered on the soma to quantitatively assess dendritic complexity. Quantification parameters included the number of intersections between dendrites and concentric circles, dendritic segment length, soma diameter, number of dendritic terminals, and primary dendrite length. Image analysis was performed using ImageJ software (version 1.52g, RRID:SCR_003070) in combination with the Sholl Analysis and NeuronJ plugins (RRID:SCR_002074) (Meijering et al., 2004 ). Correlation analysis Correlation analysis of neurophysiological parameters in SC and MLR neurons were performed in this study. A linear regression was applied to a group of selected paraments, and the correlation coefficient R 2 was used to divide the correlation into three categories: strong (R 2 > 0.7), medium (0.4≤R 2 ≤0.7) and weak correlation (R 2 < 0.4). Solutions and chemicals Dissection artificial cerebrospinal fluid (aCSF, in mM): 25 NaCl, 188 sucrose, 1.9 KCl, 1.2 NaH₂PO₄, 10 MgSO₄, 26 NaHCO₃, 1.5 kynurenic acid, 25 glucose, and 1.0 CaCl₂. Recording aCSF (in mM): 125 NaCl, 2.5 KCl, 26 NaHCO₃, 1.25 NaH₂PO₄, 25 glucose, 1.0 MgCl₂, and 2.0 CaCl₂. For the voltage-clamp experiments of measuring PICs, 10 mM TEA-Cl was added to the Recording aCSF. The intracellular recording solution (in mM): 130 K-gluconate, 10 NaCl, 10 HEPES, 2 MgCl₂, 5 Mg-ATP, and 0.5 GTP. For the voltage-clamp experiments measuring PICs, 20 mM TEA-Cl was added to the intracellular recording solution. The aCSF used during tissue dissection and recording was pH-adjusted to 7.3 using HCl, and the osmolarity was maintained at approximately 305 mOsm by supplementing with sucrose. All drug stock solutions were dissolved in DMSO and stored at -20°C. The persistent sodium current (Na-PIC) was blocked by bath applying 3 µM riluzole (HY-B0211, MCE) to the recording solution. The persistent calcium current (Ca-PIC), specifically the L-type calcium channel current (Cav1.3), was blocked by bath administrating 25 µM nimodipine (HY-B0265, MCE). Statistical analysis In this study, Microsoft Excel (Office 2020) (RRID:SCR_016137) was used for data formatting, and Prism (RRID:SCR_002798) was employed for statistical analysis. Data are presented as Mean ± SD. Based on the data characteristics, both unpaired (two-tailed) and paired t-tests were performed for statistical analysis. Statistical significance was determined using a P-value threshold of < 0.05. The results section includes the t-statistics, p-values, degrees of freedom, and effect size with confidence intervals. All graphs and figures were created using GraphPad Prism 9.0 software (RRID:SCR_002798) and the Python (RRID:SCR_024202) environment with Seaborn V.0.10.0, DABEST, and Matplotlib v.3.1.3 (RRID:SCR_008624) (Ho et al., 2019 ). Declarations Competing interests The authors declare no competing interests. Author contribution Conceptualization: YD; Methodology: YC, LY; Investigation: LY, YC, XW; Analysis: LY, YC; Visualization: LY, YC; Writing – Original Draft: YD, LY; Writing – Review & Editing: YD, LY; Supervision: YD, YC; Funding acquisition: YD. Acknowledgements This study is supported by National Nature Science Foundation of China to YD (Grant No. 32171129; No. 32471187). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Adkins, D. L., Boychuk, J., Remple, M. S., & Kleim, J. A. (2006). Motor training induces experience-specific patterns of plasticity across motor cortex and spinal cord. JOURNAL OF APPLIED PHYSIOLOGY , 101 (6), 1776-1782. https://doi.org/10.1152/japplphysiol.00515.2006 Albert, P. R., Vahid-Ansari, F., & Luckhart, C. (2014). Serotonin-prefrontal cortical circuitry in anxiety and depression phenotypes: pivotal role of pre- and post-synaptic 5-HT1A receptor expression. Frontiers in Behavioral Neuroscience , 8 . https://doi.org/10.3389/fnbeh.2014.00199 Binder, M. D., Powers, R. K., & Heckman, C. J. (2020). Nonlinear Input-Output Functions of Motoneurons. Physiology (Bethesda) , 35 (1), 31-39. https://doi.org/10.1152/physiol.00026.2019 Bourane, S., Grossmann, Katja S., Britz, O., Dalet, A., Del Barrio, Marta G., Stam, Floor J., Garcia-Campmany, L., Koch, S., & Goulding, M. (2015). Identification of a Spinal Circuit for Light Touch and Fine Motor Control. Cell , 160 (3), 503-515. https://doi.org/10.1016/j.cell.2015.01.011 Bouvier, J., Caggiano, V., Leiras, R., Caldeira, V., Bellardita, C., Balueva, K., Fuchs, A., & Kiehn, O. (2015). Descending Command Neurons in the Brainstem that Halt Locomotion. Cell , 163 (5), 1191-1203. https://doi.org/10.1016/j.cell.2015.10.074 Brownstone, R. M., & Chopek, J. W. (2018). Reticulospinal Systems for Tuning Motor Commands. Front Neural Circuits , 12 , 30. https://doi.org/10.3389/fncir.2018.00030 Button, D. C., & Kalmar, J. M. (2019). Understanding exercise-dependent plasticity of motoneurons using intracellular and intramuscular approaches. Applied Physiology, Nutrition, and Metabolism , 44 (11), 1125-1133. https://doi.org/10.1139/apnm-2018-0862 Caggiano, V., Leiras, R., Goñi-Erro, H., Masini, D., Bellardita, C., Bouvier, J., Caldeira, V., Fisone, G., & Kiehn, O. (2018). Midbrain circuits that set locomotor speed and gait selection. Nature , 553 (7689), 455-460. https://doi.org/10.1038/nature25448 Carlin, K. P., Jiang, Z., & Brownstone, R. M. (2000). Characterization of calcium currents in functionally mature mouse spinal motoneurons. European Journal of Neuroscience , 12 (5), 1624-1634. https://doi.org/10.1046/j.1460-9568.2000.00050.x Chen, K., & Dai, Y. (2022). Chronic exercise increases excitability of lamina X neurons through enhancement of persistent inward currents and dendritic development in mice. J Physiol , 600 (16), 3775-3793. https://doi.org/10.1113/JP283037 Chen, K., Ge, R., Cheng, Y., & Dai, Y. (2019). Three-week treadmill training changes the electrophysiological properties of spinal interneurons in the mice. Exp Brain Res , 237 (11), 2925-2938. https://doi.org/10.1007/s00221-019-05647-3 Cheng, Y., Song, N., Ge, R., & Dai, Y. (2021). Serotonergic Modulation of Persistent Inward Currents in Serotonergic Neurons of Medulla in ePet-EYFP Mice. Front Neural Circuits , 15 , 657445. https://doi.org/10.3389/fncir.2021.657445 Cheng, Y., Wang, X., Zhang, Q., Ge, R., Zhou, M., & Dai, Y. (2025). Multiple patterns of persistent inward currents with multiple types of repetitive firings in medullary serotonergic neurons of mice: An experimental and modeling study. PLoS Comput Biol , 21 (4), e1012918. https://doi.org/10.1371/journal.pcbi.1012918 Cheng, Y., Zhang, Q., & Dai, Y. (2020). Sequential activation of multiple persistent inward currents induces staircase currents in serotonergic neurons of medulla in ePet-EYFP mice. J Neurophysiol , 123 (1), 277-288. https://doi.org/10.1152/jn.00623.2019 Cormery, B., Beaumont, E., Csukly, K., & Gardiner, P. (2005). Hindlimb unweighting for 2 weeks alters physiological properties of rat hindlimb motoneurones. The Journal of Physiology , 568 (3), 841-850. https://doi.org/10.1113/jphysiol.2005.091835 Côté, M. P., Murray, L. M., & Knikou, M. (2018). Spinal Control of Locomotion: Individual Neurons, Their Circuits and Functions. Front Physiol , 9 , 784. https://doi.org/10.3389/fphys.2018.00784 Crozat, E., Edith Blasco, Jorge Ramirez-Franco, Priscille Riondel, Nina Jurcic, Riad Seddik, Caroline Michelle, Jérôme Trouslard & Nicolas Wanaverbecq (2025) Cerebrospinal fluid-contacting neurons are sensory neurons with uniform morphological and region-specific electrophysiological properties in the mouse spinal cord. communications biology 8, 1233. https://doi.org/10.1038/s42003-025-08559-x Dai, Y., Cheng, Y., Ge, R., Chen, K., & Yang, L. (2024). Exercise-induced adaptation of neurons in the vertebrate locomotor system. J Sport Health Sci , 13 (2), 160-171. https://doi.org/10.1016/j.jshs.2023.10.006 Dai, Y., & Jordan, L. M. (2010). Multiple patterns and components of persistent inward current with serotonergic modulation in locomotor activity-related neurons in Cfos-EGFP mice. J Neurophysiol , 103 (4), 1712-1727. https://doi.org/10.1152/jn.01111.2009 Deutsch, A. J., & Elbasiouny, S. M. (2024). Dysregulation of persistent inward and outward currents in spinal motoneurons of symptomatic SOD1-G93A mice. J Physiol , 602 (15), 3715-3736. https://doi.org/10.1113/jp286032 Edgerton, V. R., Tillakaratne, N. J., Bigbee, A. J., de Leon, R. D., & Roy, R. R. (2004). Plasticity of the spinal neural circuitry after injury. Annu Rev Neurosci , 27 , 145-167. https://doi.org/10.1146/annurev.neuro.27.070203.144308 ElBasiouny, S. M., Schuster, J. E., & Heckman, C. J. (2010). Persistent inward currents in spinal motoneurons: important for normal function but potentially harmful after spinal cord injury and in amyotrophic lateral sclerosis. Clin Neurophysiol , 121 (10), 1669-1679. https://doi.org/10.1016/j.clinph.2009.12.041 Fernando, P., Bonen, A., & Hoffman-Goetz, L. (1993). Predicting submaximal oxygen consumption during treadmill running in mice. Can J Physiol Pharmacol , 71 (10-11), 854-857. https://doi.org/10.1139/y93-128 Foster, E., Wildner, H., Tudeau, L., Haueter, S., Ralvenius, William T., Jegen, M., Johannssen, H., Hösli, L., Haenraets, K., Ghanem, A., Conzelmann, K.-K., Bösl, M., & Zeilhofer, Hanns U. (2015). Targeted Ablation, Silencing, and Activation Establish Glycinergic Dorsal Horn Neurons as Key Components of a Spinal Gate for Pain and Itch. Neuron , 85 (6), 1289-1304. https://doi.org/10.1016/j.neuron.2015.02.028 Gardiner, P., Dai, Y., & Heckman, C. J. (2006). Effects of exercise training on alpha-motoneurons. J Appl Physiol (1985) , 101 (4), 1228-1236. https://doi.org/10.1152/japplphysiol.00482.2006 Ge, R., Chen, K., Cheng, Y., & Dai, Y. (2019). Morphological and electrophysiological properties of serotonin neurons with NMDA modulation in the mesencephalic locomotor region of neonatal ePet-EYFP mice. Exp Brain Res , 237 (12), 3333-3350. https://doi.org/10.1007/s00221-019-05675-z Ge, R., & Dai, Y. (2020). Three-Week Treadmill Exercise Enhances Persistent Inward Currents, Facilitates Dendritic Plasticity, and Upregulates the Excitability of Dorsal Raphe Serotonin Neurons in ePet-EYFP Mice. Front Cell Neurosci , 14 , 575626. https://doi.org/10.3389/fncel.2020.575626 Goulding, M. (2009). Circuits controlling vertebrate locomotion: moving in a new direction. Nature Reviews Neuroscience , 10 (7), 507-518. https://doi.org/10.1038/nrn2608 Grillner, S., & El Manira, A. (2020). Current Principles of Motor Control with Special Reference to Vertebrate Locomotion. Physiol Rev , 100 (1), 271-320. https://doi.org/10.1152/physrev.00015.2019 Grillner, S. (2003) The motor infrastructure: from ion channels to neuronal networks. Nature Reviews Neuroscience 4(7):573-86. https://doi.org/10.1038/nrn1137 Gross, C., & Hen, R. (2004). The developmental origins of anxiety. Nature Reviews Neuroscience , 5 (7), 545-552. https://doi.org/10.1038/nrn1429 Hägglund, M., Borgius, L., Dougherty, K. J., & Kiehn, O. (2010). Activation of groups of excitatory neurons in the mammalian spinal cord or hindbrain evokes locomotion. Nature Neuroscience , 13 (2), 246-252. https://doi.org/10.1038/nn.2482 Harris-Warrick, R. M. (2010). General Principles of Rhythmogenesis in Central Pattern Generator Networks. In Progress in Brain Research (pp. 213-222): Elsevier. Harvey, P. J., Li, Y., Li, X., & Bennett, D. J. (2006). Persistent sodium currents and repetitive firing in motoneurons of the sacrocaudal spinal cord of adult rats. J Neurophysiol , 96 (3), 1141-1157. https://doi.org/10.1152/jn.00335.2005 Hassan, A. S., Fajardo, M. E., Cummings, M., McPherson, L. M., Negro, F., Dewald, J. P. A., Heckman, C. J., & Pearcey, G. E. P. (2021). Estimates of persistent inward currents are reduced in upper limb motor units of older adults. J Physiol , 599 (21), 4865-4882. https://doi.org/10.1113/jp282063 Heckman, C. J., Hyngstrom, A. S., & Johnson, M. D. (2008). Active properties of motoneurone dendrites: diffuse descending neuromodulation, focused local inhibition. The Journal of Physiology , 586 (5), 1225-1231. https://doi.org/10.1113/jphysiol.2007.145078 Heckman, C. J., Johnson, M., Mottram, C., & Schuster, J. (2008). Persistent inward currents in spinal motoneurons and their influence on human motoneuron firing patterns. Neuroscientist , 14 (3), 264-275. https://doi.org/10.1177/1073858408314986 Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: data analysis with estimation graphics. Nature Methods , 16 (7), 565-566. https://doi.org/10.1038/s41592-019-0470-3 Hsu, L. J., Bertho, M., & Kiehn, O. (2023). Deconstructing the modular organization and real-time dynamics of mammalian spinal locomotor networks. Nat Commun , 14 (1), 873. https://doi.org/10.1038/s41467-023-36587-w Hultborn, H., Jankowska, E., & Lindström, S. (1971). Recurrent inhibition of interneurones monosynaptically activated from group Ia afferents. J Physiol , 215 (3), 613-636. https://doi.org/10.1113/jphysiol.1971.sp009488 Iddings JA, Zarkou A & Field-Fote EC. (2021). Noninvasive neuromodulation and rehabilitation to promote functional restoration in persons with spinal cord injury. CURR OPIN NEUROL 34, 812-818.https://doi.org/10.1097/WCO.0000000000000997 Jiang, Z., Rempel, J., Li, J., Sawchuk, M. A., Carlin, K. P., & Brownstone, R. M. (1999). Development of L-type calcium channels and a nifedipine-sensitive motor activity in the postnatal mouse spinal cord. European Journal of Neuroscience , 11 (10), 3481-3487. https://doi.org/10.1046/j.1460-9568.1999.00765.x Josset, N., Roussel, M., Lemieux, M., Lafrance-Zoubga, D., Rastqar, A., & Bretzner, F. (2018). Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse. Current Biology , 28 (6), 884-901.e883. https://doi.org/10.1016/j.cub.2018.02.007 Kiehn, O. (2011). Development and functional organization of spinal locomotor circuits. Current Opinion in Neurobiology , 21 (1), 100-109. https://doi.org/https://doi.org/10.1016/j.conb.2010.09.004 Kiehn, O. (2016). Decoding the organization of spinal circuits that control locomotion. Nature Reviews Neuroscience , 17 (4), 224-238. https://doi.org/10.1038/nrn.2016.9 Krawitz, S., Fedirchuk, B., Dai, Y., Jordan, L. M., & McCrea, D. A. (2001). State-dependent hyperpolarization of voltage threshold enhances motoneurone excitability during fictive locomotion in the cat. J Physiol , 532 (Pt 1), 271-281. https://doi.org/10.1111/j.1469-7793.2001.0271g.x Kuo, J. J., Lee, R. H., Zhang, L., & Heckman, C. J. (2006). Essential role of the persistent sodium current in spike initiation during slowly rising inputs in mouse spinal neurones. J Physiol , 574 (Pt 3), 819-834. https://doi.org/10.1113/jphysiol.2006.107094 Lacroix-Ouellette, P., & Dubuc, R. (2023). Brainstem neural mechanisms controlling locomotion with special reference to basal vertebrates [Mini Review]. Frontiers in Neural Circuits , Volume 17 - 2023 https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2023.910207 Lacroix-Ouellette, P., & Dubuc, R. (2023). Brainstem neural mechanisms controlling locomotion with special reference to basal vertebrates. Front Neural Circuits , 17 , 910207. https://doi.org/10.3389/fncir.2023.910207 Lee, R. H., & Heckman, C. J. (1999). Paradoxical effect of QX-314 on persistent inward currents and bistable behavior in spinal motoneurons in vivo. J Neurophysiol , 82 (5), 2518-2527. https://doi.org/10.1152/jn.1999.82.5.2518 Leiras, R., Cregg, J. M., & Kiehn, O. (2022). Brainstem Circuits for Locomotion. Annual Review of Neuroscience , 45 (1), 63-85. https://doi.org/10.1146/annurev-neuro-082321-025137 Lockyer, E. J., Alizadeh, S., Compton, C. T., Button, D. C., & Power, K. E. (2023). Two weeks of arm cycling sprint interval training enhances spinal excitability to the biceps brachii. J Appl Physiol (1985) , 134 (6), 1422-1437. https://doi.org/10.1152/japplphysiol.00367.2022 MacDonell, C. W., Power, K. E., Chopek, J. W., Gardiner, K. R., & Gardiner, P. F. (2015). Extensor motoneurone properties are altered immediately before and during fictive locomotion in the adult decerebrate rat. The Journal of Physiology , 593 (10), 2327-2342. https://doi.org/10.1113/jp270239 Meijering, E., Jacob, M., Sarria, J. C. F., Steiner, P., Hirling, H., & Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry Part A , 58A (2), 167-176. https://doi.org/10.1002/cyto.a.20022 Moritz, A. T., Newkirk, G., Powers, R. K., & Binder, M. D. (2007). Facilitation of somatic calcium channels can evoke prolonged tail currents in rat hypoglossal motoneurons. J Neurophysiol , 98 (2), 1042-1047. https://doi.org/10.1152/jn.01294.2006 Noga, B. R., & Whelan, P. J. (2022). The Mesencephalic Locomotor Region: Beyond Locomotor Control [Review]. Frontiers in Neural Circuits , Volume 16 - 2022 . https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2022.884785 Opris, I., Dai, X., Johnson, D. M. G., Sanchez, F. J., Villamil, L. M., Xie, S., Lee-Hauser, C. R., Chang, S., Jordan, L. M., & Noga, B. R. (2019). Activation of Brainstem Neurons During Mesencephalic Locomotor Region-Evoked Locomotion in the Cat. Frontiers in Systems Neuroscience , 13 . https://doi.org/10.3389/fnsys.2019.00069 Orssatto, L. B. R., Fernandes, G. L., Blazevich, A. J., & Trajano, G. S. (2022). Facilitation-inhibition control of motor neuronal persistent inward currents in young and older adults. J Physiol , 600 (23), 5101-5117. https://doi.org/10.1113/jp283708 Pearcey, G. E. P., Alizedah, S., Power, K. E., & Button, D. C. (2021). Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain? Eur J Appl Physiol , 121 (9), 2413-2422. https://doi.org/10.1007/s00421-021-04730-4 Power, K. E., Lockyer, E. J., Botter, A., Vieira, T., & Button, D. C. (2022). Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans. Eur J Appl Physiol , 122 (6), 1367-1381. https://doi.org/10.1007/s00421-022-04918-2 Rossignol S, Frigon A, Barrière G, Martinez M, Barthélemy D, Bouyer L, Bélanger M, Provencher J, Chau C & Brustein E. (2011). Spinal plasticity in the recovery of locomotion. PROG BRAIN RES 188, 229-241.https://doi.org/10.1016/B978-0-444-53825-3.00021-8 Rossignol, S., Dubuc, R., & Gossard, J.-P. (2006). Dynamic Sensorimotor Interactions in Locomotion. Physiological Reviews , 86 (1), 89-154. https://doi.org/10.1152/physrev.00028.2005 Rybak, I. A., Shevtsova, N. A., Lafreniere-Roula, M., & McCrea, D. A. (2006). Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. The Journal of Physiology , 577 (2), 617-639. https://doi.org/10.1113/jphysiol.2006.118703 Song, J., Ampatzis, K., Björnfors, E. R., & El Manira, A. (2018). Motor neurons control locomotor circuit function retrogradely via gap junctions. Nature Communications , 9(1), 5016. https://doi.org/10.1038/nature16497 Svirskis, G., & Hounsgaard, J. (1997). Depolarization-induced facilitation of a plateau-generating current in ventral horn neurons in the turtle spinal cord. J Neurophysiol , 78 (3), 1740-1742. https://doi.org/10.1152/jn.1997.78.3.1740 Takeoka, A., Vollenweider, I., Courtine, G., & Arber, S. (2014). Muscle Spindle Feedback Directs Locomotor Recovery and Circuit Reorganization after Spinal Cord Injury. Cell , 159 (7), 1626-1639. https://doi.org/10.1016/j.cell.2014.11.019 Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., Linnarsson, S., & Zhang, X . (2015). Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Cell Research , 25(7), 801–813. https://doi.org/10.1038/nn.3881 Wolpaw, J. R., & Tennissen, A. M. (2001). Activity-Dependent Spinal Cord Plasticity in Health and Disease. Annual Review of Neuroscience , 24 (1), 807-843. https://doi.org/10.1146/annurev.neuro.24.1.807 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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12:49:37","extension":"html","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":277227,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/65d5e3211808d77f5d3df9b1.html"},{"id":99789192,"identity":"b0af18a2-c874-4022-8f81-87accc9020b4","added_by":"auto","created_at":"2026-01-08 12:49:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371187,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental protocol and neurons recorded. \u003cstrong\u003eA. \u003c/strong\u003eB6 mice were randomly assigned to a control group (Control) and an exercise group (Exercise). The Control group remained sedentary in their cages. Mice in the Exercise group first underwent a 3-day acclimation period, running on a flat treadmill at a speed of 5 m/min for 30 minutes each day. After three days, the formal training began, lasting for 3 weeks, 6 days per week, and 60 minutes per day. Each training session consisted of a 40-minute core stage and a 20-minute incremental stage. The core stage included a 5-minute warm-up (5 m/min) and 35 minutes of moderate-intensity training (10 m/min). The incremental stage was divided into three phases: running at 12 m/min, 15 m/min, and 18 m/min for 5 minutes each, gradually increasing the exercise intensity. The session concluded with a 5-minute cool-down at 5 m/min. After three weeks of training, the mice were used for electrophysiological experiments. \u003cstrong\u003eB.\u003c/strong\u003e Experimental anatomical diagram. Slices were taken from the midbrain locomotor region (MLR) and the lumbar spinal cord (T13-L5). \u003cstrong\u003eC.\u003c/strong\u003e Recording protocol. Current clamp recording: A bi-ramp current (10s duration, starting from 0 pA, peak 50–80 pA) was injected into the cells to induce tonic firing in neurons. Measured parameters include: recruitment current (Irec), decruitment current (Idec), recruitment current difference (ΔI), and voltage threshold (Vth). \u003cstrong\u003eD. \u003c/strong\u003eVoltage clamp recording: A bi-ramp voltage (10s duration, starting from -70 mV, peak 20-50 mV) was injected into the cells to induce persistent inward currents (PICs). Measured parameters include: PIC onset voltage, PIC amplitude, PIC peak current (Ip), and PIC onset current (Io). \u003cstrong\u003eE.\u003c/strong\u003e Distribution of MLR neurons. MLR neurons recorded in this study include those from the control group (black dots, n=48) and the exercise group (green dots, n=70). \u003cstrong\u003eF.\u003c/strong\u003eDistribution of spinal cord (SC) neurons. SC neurons recorded in this study include those from the control group (black dots, n=36) and the exercise group (green dots, n=65). Note: For better visualization, control group neurons distributed on the right side were mirrored to the left side using the midline of the section as a reference line; similarly, exercise group neurons distributed on the left side were mirrored to the right side.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/d71b7b4aafaf16aae66b894c.png"},{"id":99373720,"identity":"e350cc4b-2865-40c2-b2b5-de50e38f16f0","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148798,"visible":true,"origin":"","legend":"\u003cp\u003eSimultaneously recorded spinal cord (SC) and MLR neurons from the same mouse preparation. \u003cstrong\u003eA\u003c/strong\u003e. Tonic firing in SC and MLR neurons was induced by step current injection. In this example, the voltage threshold of the spinal cord neuron (-39.4 mV) was more hyperpolarized than that of the MLR neuron (-35.2 mV). \u003cstrong\u003eB\u003c/strong\u003e. Overlay of action potentials from SC and MLR neurons, showing that the SC neuron had a lower Vth and a smaller AHP compared to the MLR neuron. \u003cstrong\u003eC\u003c/strong\u003e. Statistical analysis of comparisons in electrophysiological parameters between SC and MLR neurons. These parameters include: resting membrane potential Em (C1), rheobase (C2), voltage threshold Vth (C3), action potential (AP) amplitude (C4) , AP half-width (C5)，Afterhyperpolarization (AHP) amplitude (C6), AHP half-width (C7), input resistance Rin (C8). Data represented as Mean ±SD, paired t-tests performed, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. The inset of the nervous system with the MLR and SC marked by red dots showed data recorded from MLR and SC paired slices of the same mice.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/2c9da309272d6157eb6348dd.jpeg"},{"id":99373695,"identity":"8405ba4b-d3c3-4e97-b835-f2ffee96eedc","added_by":"auto","created_at":"2026-01-02 07:06:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208257,"visible":true,"origin":"","legend":"\u003cp\u003eTreadmill exercise increased excitability of SC and MLR neurons. \u003cstrong\u003eA\u003c/strong\u003e. An example of two SC neurons recorded in control (A1) and after exercise (A2), respectively. Exercise hyperpolarized Vth and reduced rheobase in SC neurons. Control: Vth=-37.2 mV, rheobase=20 pA; Exercise: Vth=-48.7mV, rheobase=5pA. \u003cstrong\u003eB.\u003c/strong\u003e Vth of SC neurons from control (black) and exercise (green) groups, respectively. Vth was defined as membrane potential at which dV/dt = 10 mV/ms. \u003cstrong\u003eC.\u003c/strong\u003e Parameters of SC neurons recorded in control (black) and exercise (green), respectively. These parameters included Em (C1), rheobase (C2), Vth (C3), AP amplitude (C4), AP half-width (C5), AHP amplitude (C6), AHP half-width (C7), and Rin (C8) recorded in control and exercise, respectively. \u003cstrong\u003eD.\u003c/strong\u003e An example of two MLR neurons recorded in control (D1) and after exercise (D2), respectively. Exercise hyperpolarized Vth and reduced rheobase in MLR neurons. Control: Vth=-38.5 mV, rheobase=15pA; Exercise: Vth=-43.4 mV, rheobase=5pA. \u003cstrong\u003eE\u003c/strong\u003e. Vth of MLR neurons recorded in control (gray) and exercise (orange) groups, respectively. \u003cstrong\u003eF.\u003c/strong\u003eParameters of MLR neurons recorded in control (gray) and exercise (orange) groups, respectively. Similarly, these parameters included Em (F1), rheobase (F2), Vth (F3), AP amplitude (F4), AP half-width (F5), AHP amplitude (F6), AHP half-width (F7), and Rin (F8) recorded in control and exercise, respectively. Δ=exercise-control, unpaired t-tests performed, *P \u0026lt;0.05, **P \u0026lt;0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/1252ea0e7ef268f6fa036b7d.jpeg"},{"id":99789208,"identity":"92b2e4e2-1558-4e9f-9bf5-385cb7887c7b","added_by":"auto","created_at":"2026-01-08 12:49:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136836,"visible":true,"origin":"","legend":"\u003cp\u003eSynchronizing effects of treadmill exercise on SC and MLR neurons. \u003cstrong\u003eA\u003c/strong\u003e. A typical example of two neurons recorded from exercised SC (green) and MLR (orange) groups in the same mouse preparation. Repetitive firings were evoked by the same step currents injected into the two neurons. The SC neuron exhibited more excitable than MLR neuron. \u003cstrong\u003eB\u003c/strong\u003e. Frequency and current relationships (F-I curves) of the two neurons demonstrated that the SC neuron was more excitable than the MLR neuron. \u003cstrong\u003eC\u003c/strong\u003e. Statistical results from 22 pairs of SC and MLR neurons recorded from the same mice. Exercise-induced changes (Δ) in membrane properties of SC and MLR neurons included Em (C1), rheobase (C2), Vth (C3), AP amplitude (C4), AP half-width (C5), AHP amplitude (C6), AHP half-width (C7) and Rin (C8). Δ= (exercise-control) /control mean. Paired t-tests performed, *P \u0026lt; 0.05，**P \u0026lt; 0.01，***P \u0026lt; 0.001. The inset of the nervous system with the MLR and SC marked by red dots showed data recorded from MLR and SC paired slices of the same mice.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/89f5a04f7e9696fb6ee0daad.png"},{"id":99373732,"identity":"54386510-c5ca-4c7a-849d-ea4c55f85d70","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":239821,"visible":true,"origin":"","legend":"\u003cp\u003eExercise effects on PIC kinetics of SC and MLR neurons. A. PICs were induced by a bi-ramp voltage in SC (black) and MLR (gray) neurons (A1). Statistical results from 20 neurons (SC: n=10, MLR: n=10) showed significant difference in PIC onset (A2) and amplitude (A3) between the SC and MLR neurons. B. PICs were induced by step voltages in SC (black) and MLR (gray) neurons (B1). Statistical results from 5 neurons (SC: n=5, MLR: n=5) displayed the difference in PIC activation curves (B2) and half-activation voltage (Vmid, B3) between the SC and MLR neurons. Vmid were -20.25±1.4 and -23.31±1.49 mV for PICs of SC and MLR neurons, respectively. C. Exercise hyperpolarized PIC onset (C1), increased amplitude (C2), left shifted PIC activation curves (C3) and lowered Vmid (C4) in SC neurons (Control: n=5, Exercise: n=5). Vmid were -21.57±1.09 and -24.39±0.99 mV for PICs of control and exercise neurons, respectively, Δ= Ex-Ctrl. D. Exercise hyperpolarized PIC onset (D1), increased amplitude (D2), left shifted PIC activation curves (D3) and lowered Vmid (D4) in MLR neurons (Control: n=5, Exercise: n=5). Vmid were -22.24±1.94 and -25.71±1.3 mV for PICs of control and exercise neurons, respectively, Δ=Ex-Ctrl. E. Comparison of exercise-induced changes in PIC kinetics of SC and MLR neurons, including significant changes in PIC onset (E1), amplitude (E2), activation curve (E3) and Vmid (E4). Vmid changes were -25.53% (SC) and -9.22% (MLR) relative to control. Δ= (exercise-control) /control mean, paired t-tests were performed for panels A, B and E and unpaired t-tests for panels C and D. * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001. The inset of the nervous system with the MLR and SC marked by red dots showed data recorded from MLR and SC paired slices of the same mice.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/c738a6af7d2fd6369c0b1e0e.jpeg"},{"id":99373730,"identity":"5b4a3879-02bc-432c-85d1-5540ebd90c61","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":209072,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of Ca-PIC and Na-PIC to exercise-induced changes in composite PICs in SC and MLR neurons. \u003cstrong\u003eA\u003c/strong\u003e. 25μM nimodipine (A1\u0026amp;A2, blue) and 3μM riluzole (A3\u0026amp;A4, green) reduced PIC amplitudes and depolarized PIC onsets, respectively, in SC neurons in control (A1\u0026amp;A3) and exercise (A2\u0026amp;A4) groups, respectively. \u003cstrong\u003eB\u003c/strong\u003e. The same amount of nimodipine (B1\u0026amp;B2, blue) and riluzole (B3\u0026amp;B4, green) reduced PIC amplitudes and depolarized PIC onsets, respectively, in MLR neurons in control (B1\u0026amp;B3) and exercise (B2\u0026amp;B4) groups, respectively. \u003cstrong\u003eC\u003c/strong\u003e. Contributions of Ca-PIC (C1\u0026amp;C2) and Na-PIC (C3\u0026amp;C4) to composite PICs in SC neurons in control and exercise groups. Nimodipine (C1\u0026amp;C2) and riluzole (C3\u0026amp;C4) depolarized PIC onsets and reduced PIC amplitudes, respectively, in control and exercise groups. \u003cstrong\u003eD\u003c/strong\u003e. Contributions of Ca-PIC (D1\u0026amp;D2) and Na-PIC (D3\u0026amp;D4) to composite PICs in MLR neurons in control and exercise groups. Nimodipine (D1\u0026amp;D2) and riluzole (D3\u0026amp;D4) depolarized PIC onsets and reduced PIC amplitudes, respectively, in control and exercise groups. \u003cstrong\u003eE\u003c/strong\u003e. Contributions of Ca-PIC and Na-PIC to exercise-induced changes in PICs in SC and MLR neurons. Ca-PIC contributed to changes in PIC onset (E1) and amplitude (E2) in SC and MLR neurons (n=7). Na-PIC contributed to changes in PIC onset (E3) and amplitude (E4) in SC and MLR neurons (n=9). Δ=(ΔEx-ΔCtrl mean)/ΔCtrl mean; Paired t-tests performed, * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/924c969bbfae9125c3f19f64.jpeg"},{"id":99789534,"identity":"99806f60-1ea2-46a6-b577-03cf982afcd6","added_by":"auto","created_at":"2026-01-08 12:49:56","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":220564,"visible":true,"origin":"","legend":"\u003cp\u003eExcitability of SC and MLR neurons regulated by Ca-PIC and Na-PIC after treadmill exercise. \u003cstrong\u003eA\u003c/strong\u003e. Repetitive firings were evoked in SC (A1, left) and MLR (A2, left) neurons collected from control (black for SC, gray for MLR) and exercise (green for SC, orange for MLR) groups. Relationships of instantaneous firing frequencies vs ramp currents (F-I curves) were established for the SC (A1, right) and MLR (A2, right) neurons. \u003cstrong\u003eB\u003c/strong\u003e. Exercise-induced changes in SC and MLR neuronal properties were calculated. Sampling neurons were collected from control (SC: n=19; MLR: n=20) and exercise (SC: n=24; MLR: n=23) groups. Statistical results were shown for Vth (B1), Irec (B2), Idec (B3), and ΔI (B4, ΔI=exercise-control). \u003cstrong\u003eC\u003c/strong\u003e. Excitability of SC and MLR neurons (n=7) regulated by Ca-PIC after exercise. 25μM nimodipine altered the excitability of the SC and MLR neurons with statistical analysis performed for Vth (C1), Irec (C2), Idec (C3) and ΔI (C4, ΔI=nimodipine-exercise). \u003cstrong\u003eD\u003c/strong\u003e. Excitability of SC and MLR neurons (n=9) regulated by Na-PIC after exercise. 3μM riluzole altered the excitability of the SC and MLR neurons with statistical analysis performed for Vth (D1), Irec (D2), Idec (D3) and ΔI (D4, ΔI=riluzole-exercise). \u003cstrong\u003eE\u003c/strong\u003e. Contribution of Ca-PIC to exercise-induced changes (Δ) in Vth (E1), Irec (E2) and Idec (E3) were calculated for SC and MLR neurons (n=7). Δ=(post-pre) nimodipine/pre nimodipine. \u003cstrong\u003eF\u003c/strong\u003e. Contribution of Na-PIC to exercise-induced changes (Δ) in Vth (F1), Irec (F2) and Idec (F3) were calculated for SC and MLR neurons (n=9). Δ=(post-pre)riluzole/pre riluzole; Paired t-tests performed, * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/48fcb433fe4fef206c1afb48.jpeg"},{"id":99373717,"identity":"66f8f873-fddc-47d2-a261-c4456bd2f995","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":169250,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SC and MLR neurons in spinal cord and mesencephalic locomotor region. \u003cstrong\u003eA.\u003c/strong\u003e Morphology of SC neurons in control (A1, black, n=27) and exercise (A2, green, n=42) groups. Super dorsal horn and ventral neurons accounted for 30% (8/27) and 70% (19/27), respectively in control group and 26% (11/42) and 74% (31/42), respectively in exercise group. \u003cstrong\u003eB.\u003c/strong\u003e Morphology of MLR neurons in control (B1, black, n=29) and exercise (B2, orange, n=70) groups.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/e3320b08d13be808c79969f1.jpeg"},{"id":99373741,"identity":"25791eac-8176-4d42-97be-4b68969d2805","added_by":"auto","created_at":"2026-01-02 07:06:08","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":169323,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological analysis of SC and MLR neurons recorded synchronously from the same mouse preparations.\u003cstrong\u003eA.\u003c/strong\u003e Three neurons were recorded in SC with cell morphology labeled by 3% of intracellular tetramethylrhodamine. \u003cstrong\u003eB. \u003c/strong\u003eThe corresponding neurons were recorded in MLR. \u003cstrong\u003eC.\u003c/strong\u003e Morphology of\u003cstrong\u003e \u003c/strong\u003e22 pairs of SC and MLR neurons were analyzed by Sholl analysis for dendritic intersection number (C1), total dendritic length (C2), number of primary dendrites (C3), number of branch points (C4), branch ends (C5) and soma surface area (C6). Paired t-tests performed, * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001. The inset of the nervous system with the MLR and SC marked by red dots showed data recorded from MLR and SC paired slices of the same mice.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/d3d766c40ee55851a6b3e51c.jpeg"},{"id":99373698,"identity":"38ab8891-0c37-40a8-b9fd-13b9406a6a19","added_by":"auto","created_at":"2026-01-02 07:06:05","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":279359,"visible":true,"origin":"","legend":"\u003cp\u003eExercise enhanced dendritic plasticity in both SC and MLR neurons. \u003cstrong\u003eA.\u003c/strong\u003e Typical morphology of six SC neurons recorded in control (A1) and after exercise (A2). \u003cstrong\u003eB. \u003c/strong\u003eStatistic results showed that exercise significantly increased intersection number of dendrites (control, black, n=27; exercise, green, n=42). \u003cstrong\u003eC\u003c/strong\u003e. Exercise also induced changes (Δ) in the total dendritic length (C1), number of primary dendrites (C2), number of branch points (C3), branch ends (C4) and soma surface area (C5). \u003cstrong\u003eD.\u003c/strong\u003e Similarly, typical morphology of six MLR neurons recorded in control (D1) and after exercise. (D2). \u003cstrong\u003eE.\u003c/strong\u003e Statistic results indicated that exercise significantly increased intersection number of dendrites (control, gray, n=29; exercise, orange, n=70). \u003cstrong\u003eF.\u003c/strong\u003e Exercise also induced changes (Δ) in the total dendritic length (F1), number of primary dendrites (F2), number of branch points (F3), branch ends (F4) and soma surface area (F5). Δ=exercise-control, unpaired t-tests performed, * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/5da525628ccaa0ca645be340.jpeg"},{"id":99373722,"identity":"77704a30-76fb-4522-84ee-691b6783caa2","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":145421,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of exercise-induced dendritic plasticity between SC and MLR neurons. \u003cstrong\u003eA.\u003c/strong\u003e Typic morphology of 2 pairs of neurons recorded synchronously in SC and MLR neurons from the same mouse preparations. \u003cstrong\u003eB. \u003c/strong\u003eDendritic plasticity of 28 pairs of SC and MLR neurons were analyzed (SC, green; MLR, orange, n=28). Exercise-induced changes (Δ) were calculated for SC and MLR neurons in total dendritic length (B1), number of primary dendrites (B2), number of branch points (B3), branch ends (B4) and soma surface area (B5). In general, SC neurons demonstrated higher dendritic plasticity than MLR neurons. Δ=(exercise-control)/control mean; paired t-tests performed; * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001. The inset of the nervous system with the MLR and SC marked by red dots showed data recorded from MLR and SC paired slices of the same mice.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/9025676803824336afaba518.jpeg"},{"id":99373729,"identity":"f88dd659-e20e-4819-bea3-ebcf52ff04be","added_by":"auto","created_at":"2026-01-02 07:06:07","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":543111,"visible":true,"origin":"","legend":"\u003cp\u003eExercise increased neuronal excitability of dorsal and ventral SC interneurons. SC interneurons were classified as dorsal (lamina I-IV) and ventral (lamina V-X) neurons (motoneurons in lamina IX excluded). \u003cstrong\u003eA.\u003c/strong\u003e As a typical example, two neurons were recorded from dorsal and ventral neurons with action potential overlapped (A1). A step current of -10pA was injected into these two neurons and led to a hyperpolarization of membrane potential (A2). Statistical analysis showed the differences between dorsal (n=8) and ventral (n=8) neurons in Em (A3), rheobase (A4), Vth (A5), AP amplitude (A6), AP half-width (A7), AHP amplitude (A8), AHP half-width (A9) and Rin (A10). \u003cstrong\u003eB.\u003c/strong\u003e Two dorsal neurons from control (black) and exercise (green) groups were recorded in dorsal area with action potential overlapped (B1) and repetitive firings evoked by step currents (B2). Statistical analysis (control n=8; exercise n=10) showed the exercise-induced changes in Em (B3), rheobase (B4), Vth (B5), AP amplitude (B6), AP half-width (B7), AHP amplitude (B8), AHP half-width (B9) and Rin (B10). \u003cstrong\u003eC.\u003c/strong\u003e Similarly, two ventral neurons from control (black) and exercise (orange) groups were recorded in ventral area with action potential overlapped (C1) and repetitive firings evoked by step currents (C2). Statistical analysis (control n=8; exercise n=18) demonstrated exercise-induced changes in Em (C3), rheobase (C4), Vth (C5), AP amplitude (C6), AP half-width (C7), AHP amplitude (C8), AHP half-width (C9) and Rin (C10). \u003cstrong\u003eD.\u003c/strong\u003eExercise-induced changes (Δ) in membrane properties of dorsal (n=10) and ventral (n=18) neurons were analyzed. Changes in the membra properties included Em (D1), rheobase (D2), Vth (D3), AP amplitude (D4), AP half-width (D5), AHP amplitude (D6), AHP half-width (D7) and Rin (D8). Δ=(exercise-control)/control mean. The violin diagram and box plot showed the distribution of data with median numbers; unpaired t-tests performed; * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/9d71e5f2f8d7e262c95a298f.png"},{"id":99373716,"identity":"f9a46640-4cbf-4c37-945b-f32aeb628d3b","added_by":"auto","created_at":"2026-01-02 07:06:06","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":503023,"visible":true,"origin":"","legend":"\u003cp\u003eExercise enhanced dendritic plasticity of dorsal and ventral SC interneurons. \u003cstrong\u003eA.\u003c/strong\u003e Typical examples of morphology of dorsal (A1, A2) and ventral (A3, A4) neurons in control (A1, A3; black) and exercise (A2, A4; green for dorsal, orange for ventral) groups. \u003cstrong\u003eB\u003c/strong\u003e. Except for number of primary dendrites (B2), there was no significant difference between dorsal (n=8) and ventral (n=18) neurons in the total dendritic length (B1), number of branch points (B3), branch ends (B4) and soma surface area (B5). \u003cstrong\u003eC.\u003c/strong\u003eExercise enhanced dendritic plasticity of dorsal neurons (control, n=8; exercise, n=11) in terms of changes in total dendritic length (C1), number of primary dendrites (C2), number of branch points (C3), branch ends (C4) and soma surface area (C5).\u003cstrong\u003e D.\u003c/strong\u003e Exercise increased dendritic plasticity of ventral interneurons (control, n=18; exercise, n=21) with changes in total dendritic length (D1), number of primary dendrites (D2), number of branch points (D3), branch ends (D4) and soma surface area (D5). \u003cstrong\u003eE.\u003c/strong\u003e Comparison of dendritic plasticity between dorsal (green, n=21) and ventral (orange, n=18) neurons with exercise-induced changes (Δ) in total dendritic length (E1), number of primary dendrites (E2), number of branch points (E3), branch ends (E4) and soma surface area (E5). Δ=(exercise-control)/control mean. The violin diagram and box plot showed the distribution of data with median numbers; unpaired t-tests performed; * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/8daf87f2076ea66d6b8e3b2c.png"},{"id":99789648,"identity":"9e460d7b-253b-41ac-94b2-5f804427e6fe","added_by":"auto","created_at":"2026-01-08 12:50:13","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":442310,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis.\u003cstrong\u003e A\u003c/strong\u003e. Correlation analysis of neurophysiological parameters between control and exercise groups in SC neurons were performed for the following parameter pairs: Vth vs. PIC onset (A1), Vth vs. Na-PIC onset (A2), PIC amplitude vs. total dendritic length PIC (A3), and Ca-PIC amplitude vs. total dendritic length PIC (A4). \u003cstrong\u003eB.\u003c/strong\u003e Furthermore, correlation analyses between the groups of exercise and exercise with riluzole in SC neurons were conducted for the parameter pairs: Vth vs. PIC onset (B1), Vth vs. PIC amplitude (B2), rheobase vs. PIC onset (B3), and rheobase vs. PIC amplitude (B4). \u003cstrong\u003eC.\u003c/strong\u003e Similarly, correlation analyses between the groups of exercise and exercise with nimodipine in SC neurons were performed for the parameter pairs: Vth vs. PIC onset (C1), Vth vs. PIC amplitude (C2), rheobase vs. PIC onset (C3), and rheobase vs. PIC amplitude (C4). \u003cstrong\u003eD\u003c/strong\u003e. Correlation analysis of neurophysiological parameters between control and exercise groups in MLR neurons were conducted for the following parameter pairs: Vth vs. PIC onset (D1), Vth vs. Na-PIC onset (D2), PIC amplitude vs. total dendritic length PIC (D3), and Ca-PIC amplitude vs. total dendritic length PIC (D4). \u003cstrong\u003eE.\u003c/strong\u003eMoreover, correlation analyses between the groups of exercise and exercise with riluzole in MLR neurons were performed for the parameter pairs: Vth vs. PIC onset (E1), Vth vs. PIC amplitude (E2), rheobase vs. PIC onset (E3), and rheobase vs. PIC amplitude (E4). \u003cstrong\u003eF.\u003c/strong\u003e Similarly, correlation analyses between the groups of exercise and exercise with nimodipine in MLR neurons were conducted for the parameter pairs: Vth vs. PIC onset (F1), Vth vs. PIC amplitude (F2), rheobase vs. PIC onset (F3), and rheobase vs. PIC amplitude (F4).\u003c/p\u003e","description":"","filename":"image14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/12abb84fdbc2b162e9e57065.jpeg"},{"id":99373734,"identity":"88b69657-56ca-452b-9abc-5fab1a82194e","added_by":"auto","created_at":"2026-01-02 07:06:08","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":177960,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of exercise-induced plasticity of MLR and SC neurons. This figure summarizes the changes in the magnitude of 19 key neurophysiological parameters measured in the MLR and SC regions following exercise intervention, as well as the relative comparison of synchronous changes between the MLR and SC parameters. The horizontal figures represent the percentage changes in the MLR and SC during simultaneous measurement of the parameters, and are calculated by the following formula：(SM Exercise Mean - SM Control Mean)/SM Control Mean, where, SM Exercise Mean and SM Control Mean are defined as exercise and control mean values of the parameters measured simultaneously in SC and MLR neurons. The vertical figures represent the percentage changes after exercise intervention in the MLR and SC individually, and are calculated by the formula: (IM Exercise Mean - IM Control Mean)/IM Control Mean, where, IM Exercise Mean and IM Control Mean are defined as exercise and control mean values of the parameters measured individually in SC and MLR neurons. Red color indicates an increase, blue indicates a decrease, and the shade of the color reflects the magnitude of the change. Unpaired t-tests performed; * P \u0026lt; 0.05，** P \u0026lt; 0.01，*** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/638c12e805da8a054a33c579.png"},{"id":99801917,"identity":"98fd8763-0e3c-4d49-9fa5-a5bfb6c2981d","added_by":"auto","created_at":"2026-01-08 14:07:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5126617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8363166/v1/b0818195-77f1-4aa1-8dd1-880e3b6f428f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Exercise Coordinates Neural Plasticity from the Mesencephalic Locomotor Region to the Spinal Cord in Mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLocomotion is one of the most fundamental limb movements in vertebrates. It is initiated within the mesencephalic locomotor region (MLR) and controlled by spinal cord neural networks known as central pattern generators (CPGs) (Brownstone \u0026amp; Chopek, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; S. Grillner \u0026amp; A. El Manira, 2020; Kiehn, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; P. Lacroix-Ouellette \u0026amp; R. Dubuc, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leiras et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).This system not only generates rhythmic locomotor output but also integrates proprioceptive and sensory feedback to adapt to dynamic environments. Adaptive plasticity in locomotor circuits occurs in response to acute and/or chronic exercise, manifesting as alterations in neuronal excitability, dendritic plasticity, channel modulation, as well as gene expression and protein synthesis (Button \u0026amp; Kalmar, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gardiner et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krawitz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lockyer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; MacDonell et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Power et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Recent studies on mouse midbrain neurons have demonstrated that chronic exercise enhances persistent inward currents (PICs), promotes dendritic plasticity, and upregulates the excitability of serotonin neurons in the dorsal raphe nucleus(Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consistent findings have been observed in mouse spinal cord interneurons, where three-week treadmill exercise increases neuronal excitability, strengthens PICs, and facilitates dendritic plasticity in lamina X interneurons(Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies indicate that exercise modulates the locomotor system at both the midbrain, a region involved in locomotor pattern generation, and the spinal cord, the segment controlling locomotion. Specifically, alterations in the mechanisms of voltage-gated channels and modifications in neuronal dendritic plasticity directly induce changes in intrinsic membrane properties, enhance neuronal excitability, and ultimately drive adaptations in neurons and the locomotor system (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cormery et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the above research findings, several critical issues remain to be addressed: 1) What are the differences in the regulatory effects of exercise on neuronal excitability between MLR neurons and spinal interneurons? In which region does this regulatory strength predominate? 2) Can the enhancing effect of exercise on PICs be observed simultaneously in the MLR and the spinal cord? What are the differences in this regulatory effect in terms of the kinetic properties of ion channels? 3) Does exercise-induced promotion of neuronal dendritic plasticity occur synchronously in MLR neurons and spinal interneurons? In which region is this promotional effect more pronounced? 4) Which region, the MLR or the spinal cord, plays a dominant role in the regulatory effect of exercise intervention on the adaptability of the locomotor system? Answering these questions constitutes the primary objectives of this study.\u003c/p\u003e \u003cp\u003eTo address these issues, we subjected C57BL/6J mice to a 3-week treadmill exercise regimen and examined the concurrent effects of chronic exercise on the electrophysiological, morphological, and ionic properties of MLR neurons and spinal interneurons using whole-cell patch-clamp recording. Our data demonstrated that exercise concurrently enhanced neuronal excitability, promoted dendritic plasticity, and increased persistent inward currents in both MLR and spinal neurons. Furthermore, exercise induced a more pronounced effect on the spinal motor system compared to the midbrain system. This study provides insights into the cellular and ionic mechanisms underlying exercise-induced adaptation and coordination within the midbrain-spinal motor system.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSpinal cord neurons exhibit higher excitability than MLR neurons.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study we systematically investigated the concurrent regulatory effects of a moderate-intensity three-week treadmill exercise on cellular excitability regulation, ion channel modulation, and morphological plasticity of neurons in the MLR and SC interneurons in mice. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF are representative neuronal distribution map from this study, showing the distribution of neurons within the MLR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) and SC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF) in control and exercise intervention. For clarity, neurons from the control group were plotted on the left side of the anatomical transvers sections for the MLR (n\u0026thinsp;=\u0026thinsp;48) and SC (n\u0026thinsp;=\u0026thinsp;36), indicated in black. Neurons from the exercise group were plotted on the right side of the anatomical cross-sections for the MLR (n\u0026thinsp;=\u0026thinsp;70) and SC (n\u0026thinsp;=\u0026thinsp;65), indicated in green. Neurons of control group located on the right side of the sections were mirror-reflected to the left side of the plots. Similarly, neurons of exercise group located on the left side were mirror-reflected to the right side.\u003c/p\u003e \u003cp\u003eTo compare the intrinsic excitability of SC and MLR neurons, we systematically compared electrophysiological parameters recorded from SC and MLR neurons within the same mouse. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB show a representative example of current-clamp recordings. Here, the SC neuron had a more hyperpolarized Vth (-39.4 mV) compared to the MLR neuron (-35.2 mV), indicating that this specific SC neuron was more excitable than its corresponding MLR neuron (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026amp;B). Statistical data from 30 neurons (SC: n\u0026thinsp;=\u0026thinsp;15; MLR: n\u0026thinsp;=\u0026thinsp;15) revealed significant differences between SC and MLR neurons in Vth, AHP amplitude, and AHP half-width (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Specifically, the Vth of SC neurons was 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 mV more hyperpolarized than that of MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC3, p\u0026thinsp;=\u0026thinsp;0.006). The AHP amplitude of SC neurons was 3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 mV smaller than that of MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC6, p\u0026thinsp;=\u0026thinsp;0.012). The AHP half-width of SC neurons was 91.85\u0026thinsp;\u0026plusmn;\u0026thinsp;80.3 ms shorter than that of MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant differences were found between the two groups in Em, rheobase, AP amplitude, AP half-width, and Rin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC1, C2, C4, C5, C8, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results indicate that SC neurons possess higher intrinsic excitability compared to MLR neurons.\u003c/p\u003e\u003cp\u003e \u003cb\u003eExercise enhanced the excitability of both SC and MLR neurons.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur previous studies have confirmed that chronic exercise can significantly enhance the excitability of SC neurons, particularly those in lamina X (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Based on this result, the present study further investigated the effects of exercise on neuronal excitability across the lumbar spinal cord region. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB show representative recordings of membrane potential and related electrophysiological properties of SC neurons in control and exercise groups. These recordings indicate that treadmill exercise reduced both the rheobase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and the Vth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026amp;B) of SC neurons. Statistical analysis revealed that chronic exercise significantly decreased the rheobase by 4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 pA (Fig.\u0026nbsp;3C2, Control: n\u0026thinsp;=\u0026thinsp;20, Exercise: n\u0026thinsp;=\u0026thinsp;34, p\u0026thinsp;=\u0026thinsp;0.001) and the Vth by 6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 mV (Fig.\u0026nbsp;3C3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant change was found in other electrophysiological parameters (Em, AP amplitude, AP half-width, AHP amplitude, Rin, Fig.\u0026nbsp;3C1, C4, C5, C6, C7, C8, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results indicate that exercise intervention significantly lowers the current and voltage thresholds required for activating SC neurons, thereby enhancing their excitability.\u003c/p\u003e \u003cp\u003eSimilarly, our prior research demonstrated that chronic exercise enhanced the excitability of 5-hydroxytryptamine (5-HT) neurons in the dorsal raphe nucleus (DRN) of the midbrain (Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, whether exercise exerts a similar effect on MLR neurons in the midbrain remained unclear. Therefore, in this study we further examined the regulatory effect of exercise training on the excitability of neurons within the MLR region. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE show representative recordings of changes in electrophysiological parameters of MLR neurons between control and exercise groups, indicating that exercise intervention reduced both the rheobase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and the Vth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) of MLR neurons. Statistical analysis showed that exercise significantly decreased the rheobase by 2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 pA (Fig.\u0026nbsp;3F2, Control: n\u0026thinsp;=\u0026thinsp;27; Exercise: n\u0026thinsp;=\u0026thinsp;36, p\u0026thinsp;=\u0026thinsp;0.045) and the Vth by 3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55 mV (Fig.\u0026nbsp;3F3, p\u0026thinsp;=\u0026thinsp;0.014). However, no significant change was found in the Em, AP amplitude, AP half-width, AHP amplitude, and Rin (Fig.\u0026nbsp;3F1, F4-F8; Control: n\u0026thinsp;=\u0026thinsp;27; Exercise: n\u0026thinsp;=\u0026thinsp;36; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results indicate that exercise training significantly lowers the current threshold (rheobase) and voltage threshold (Vth) of MLR neurons, thereby enhancing their excitability. This conclusion is consistent with the findings for SC neurons, demonstrating that exercise intervention can modulate both the active and passive membrane properties of neurons, which are governed by ion channels and cellular morphology, respectively.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExercise affected SC neuron excitability more than MLR neurons\u003c/h2\u003e \u003cp\u003eA key feature of this study is that we recorded the regulatory effects of exercise on the electrophysiological parameters of both MLR and SC neurons within the same mouse. This experimental design allowed for a quantitative analysis and comparative study of the effects induced by exercise in these two regions. In the simultaneous recording experiments, we systematically analyzed the relative change magnitude (percentage) of electrophysiological indices in SC and MLR neurons following exercise. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA shows representative recordings of firing frequencies from both neuron types under the same experimental conditions in the same mouse. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB illustrates their frequency-current (F-I) relationships. Statistical results from 22 pairs of neurons revealed that exercise led to a decrease in rheobase by 44.85% in SC neurons and 22.76% in MLR neurons (Fig.\u0026nbsp;4C2, SC Exercise: n\u0026thinsp;=\u0026thinsp;22, MLR Exercise: n\u0026thinsp;=\u0026thinsp;22, p\u0026thinsp;=\u0026thinsp;0.029), and a hyperpolarization of Vth by 21.5% and 11.4%, respectively (Fig.\u0026nbsp;4C3, p\u0026thinsp;=\u0026thinsp;0.033). In contrast, no significant difference was observed between the two neuron groups in other electrophysiological parameters (Em, AP amplitude, AP half-width, AHP amplitude, Rin; Fig.\u0026nbsp;4C1, C4\u0026ndash;C8, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The magnitudes of the decreases in rheobase and Vth were greater in SC neurons than in MLR neurons by 22.1% and 10.1%, respectively, indicating that exercise exerted a stronger regulatory effect on the excitability of SC neurons compared to MLR neurons.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExercise-induced modulation of PICs in SC and MLR neurons\u003c/h3\u003e\n\u003cp\u003eOur previous studies found that treadmill exercise enhances the excitability of DRN 5-HT neurons and spinal interneurons, an enhancement closely associated with persistent inward currents (PICs) (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, in this study we further investigated whether exercise influences neuronal excitability by modulating the PIC properties of SC and MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing voltage ramps, we first recorded the parameters of PICs in both regions under resting conditions (control). Figure\u0026nbsp;5A1 shows a representative example, indicating that the PIC onset in MLR neurons was more hyperpolarized and the PIC amplitude was larger compared to SC neurons. Statistical results from 10 pairs of synchronously recorded neurons showed: the PIC onset in MLR neurons was 2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68 mV more hyperpolarized than that in SC neurons (Fig.\u0026nbsp;5A2, SC: -49.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 mV; MLR: -51.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 mV, p\u0026thinsp;=\u0026thinsp;0.017), and the PIC amplitude of MLR neurons was significantly larger than that of SC neurons by 27.95\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5 pA (Fig.\u0026nbsp;5A3, SC: 124.33\u0026thinsp;\u0026plusmn;\u0026thinsp;19.47 pA; MLR: 152.28\u0026thinsp;\u0026plusmn;\u0026thinsp;26.8 pA, p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003cp\u003eUsing step voltages (Fig.\u0026nbsp;5B1), we measured and calculated the kinetic parameters of PICs in SC and MLR neurons under control conditions, constructing corresponding activation curves (Fig.\u0026nbsp;5B2) and statistical data for the half-activation voltage (Vmid) (Fig.\u0026nbsp;5B3). A representative example was shown in Fig.\u0026nbsp;5B1. Data from 5 pairs of SC and MLR neurons indicated that the Vmid of MLR neurons was significantly more hyperpolarized by 3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 mV than that of SC neurons (Fig.\u0026nbsp;5B3, Control SC: -20.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 mV; Control MLR: -23.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 mV, p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThe above experiments revealed inherent differences in PIC properties between SC and MLR neurons. MLR neurons exhibited a lower (more hyperpolarized) PIC activation voltage and a larger PIC amplitude compared to SC neurons. Subsequently, we explored the modulatory effects of exercise intervention on the PICs of these two neuron types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Statistical results demonstrated that treadmill exercise significantly enhanced the PIC properties of SC neurons. Exercise significantly hyperpolarized the PIC onset in SC neurons by 8.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 mV (Fig.\u0026nbsp;5C1, Control SC: -49.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 mV; Exercise SC: -56.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 mV, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and increased the PIC amplitude by 40.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 pA (Fig.\u0026nbsp;5C2, Control SC: 120.58\u0026thinsp;\u0026plusmn;\u0026thinsp;23.4 pA; Exercise SC: 160.87\u0026thinsp;\u0026plusmn;\u0026thinsp;41.9 pA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, exercise caused a leftward shift of the activation curve in SC neurons by 2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mV (Fig.\u0026nbsp;5C3 \u0026amp; C4; Control SC: Vmid=-21.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 mV; Exercise SC: Vmid=-24.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99 mV; n\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eSimilar effects were observed in MLR neurons. Exercise significantly enhanced PICs in MLR neurons in terms of hyperpolarizing the PIC onset by 3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 mV (Fig.\u0026nbsp;5D1, Control MLR: -52.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 mV; Exercise MLR: -56.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91mV, p\u0026thinsp;=\u0026thinsp;0.0015), increasing the PIC amplitude by 32.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 pA (Fig.\u0026nbsp;5D2; Control MLR: 139.19\u0026thinsp;\u0026plusmn;\u0026thinsp;39.7 pA; Exercise MLR: 171.99\u0026thinsp;\u0026plusmn;\u0026thinsp;32.9 pA, p\u0026thinsp;=\u0026thinsp;0.004), and shifting the PIC activation curve leftward by 3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 mV (Fig.\u0026nbsp;5D3 \u0026amp; D4, Control MLR: Vmid=-22.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94 mV; Exercise MLR: Vmid=-25.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 mV; n\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003cp\u003eThese results indicate that exercise significantly enhances PICs in both SC and MLR neurons, in terms of both onset voltage and amplitude, thereby highlighting the important role of PICs in the regulation of neuronal excitability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExercise exerted greater effects on PICs in SC neurons than MLR neurons\u003c/h3\u003e\n\u003cp\u003eTo further compare the strength of exercise-induced modulation on PICs between the two neuron types, we recorded the exercise effects on PICs in both SC and MLR neurons within the same mouse preparation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Statistical analysis of PICs from 20 pairs of SC and MLR neurons revealed that exercise hyperpolarized the PIC onset by 14.04% in SC neurons, compared to only 8.16% in MLR neurons, a statistically significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE1, SC: n\u0026thinsp;=\u0026thinsp;20, MLR: n\u0026thinsp;=\u0026thinsp;20, p\u0026thinsp;=\u0026thinsp;0.005). Similarly, exercise increased the PIC amplitude by 40.5% in SC neurons, significantly more than the 18.22% increase in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE2, p\u0026thinsp;=\u0026thinsp;0.023). Finally, we compared the activation curves of the two neuron types. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE3, exercise induced a leftward shift in the PIC activation curves for both SC and MLR neurons. However, the shift magnitude was greater in SC neurons than in MLR. Specifically, the Vmid hyperpolarized by 25.53% in SC neurons, compared to only 9.22% in MLR neurons, a significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE4, SC: n\u0026thinsp;=\u0026thinsp;4, MLR: n\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.014). These results indicate that, compared to MLR neurons, PICs in SC neurons exhibit a greater degree of responsiveness to exercise intervention, suggesting their potentially more significant role in regulating neuronal excitability.\u003c/p\u003e\n\u003ch3\u003eModulatory effects of exercise on different PIC components\u003c/h3\u003e\n\u003cp\u003eOur previous research indicated that PICs in midbrain and spinal cord neurons are composed of multiple ionic components, primarily including sodium (Na-PIC) and calcium (Ca-PIC) currents (Cheng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To further investigate the modulation of these distinct components by exercise intervention, we specifically blocked Ca-PIC and Na-PIC using nimodipine and riluzole, respectively, in SC and MLR neurons to study the regulatory effects of exercise (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows representative examples, where bath application of 25 \u0026micro;M nimodipine (Fig.\u0026nbsp;6A1\u0026amp;A2) and 3 \u0026micro;M riluzole (Fig.\u0026nbsp;6A3\u0026amp;A4) to the recording solution obviously reduced the PIC amplitude and depolarized the PIC onset, in SC neurons in both control (Fig.\u0026nbsp;6A1\u0026amp;A3) and exercise (Fig.\u0026nbsp;6A2\u0026amp;A4) groups, respectively. Similar results were collected in MLR neurons, where the same amount of nimodipine (Fig.\u0026nbsp;6B1\u0026amp;B2) and riluzole (Fig.\u0026nbsp;6B3\u0026amp;B4) decreased PIC amplitude and depolarized the PIC onset in both control (Fig.\u0026nbsp;6B1\u0026amp;B3) and exercise (Fig.\u0026nbsp;6B2\u0026amp;B4) groups, respectively.\u003c/p\u003e \u003cp\u003eStatistical results from SC neurons showed that exercise hyperpolarized PIC onset from \u0026minus;\u0026thinsp;41.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46 to -57.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76 mV (Fig.\u0026nbsp;6C1: difference: 15.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37 mV, P\u0026thinsp;=\u0026thinsp;0.02, Control: n\u0026thinsp;=\u0026thinsp;6, Exercise: n\u0026thinsp;=\u0026thinsp;7) and increased PIC amplitude from 131.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85 to 163\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34 pA (Fig.\u0026nbsp;6C2: difference: 31.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05 pA, P\u0026thinsp;=\u0026thinsp;0.003), respectively in SC neurons. Nimodipine depolarized the PIC onset from \u0026minus;\u0026thinsp;41.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46 to -39.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91 mV (Fig.\u0026nbsp;6C1: difference: 1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 mV, P\u0026thinsp;=\u0026thinsp;0.019, n\u0026thinsp;=\u0026thinsp;6) and reduced PIC amplitude from 131.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85 to 103.18\u0026thinsp;\u0026plusmn;\u0026thinsp;21.63 pA (Fig.\u0026nbsp;6C2: difference: 28.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11 pA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, in control group. Nimodipine also depolarized the PIC onset from \u0026minus;\u0026thinsp;57.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76 to -53.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44 mV (Fig.\u0026nbsp;6C1: difference: 4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34 mV, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;7) and reduced PIC amplitude from 163\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34 to 112.29\u0026thinsp;\u0026plusmn;\u0026thinsp;16.35 pA (Fig.\u0026nbsp;6C2: difference: 50.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.14 pA, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, in exercise group. Similarly, statistical results from SC neurons indicated that exercise lowered PIC onset from \u0026minus;\u0026thinsp;51.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88 to -57.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54 mV (Fig.\u0026nbsp;6C3: difference: 5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08 mV, P\u0026thinsp;=\u0026thinsp;0.02, Control: n\u0026thinsp;=\u0026thinsp;6, Exercise: n\u0026thinsp;=\u0026thinsp;9) and increased PIC amplitude from 126.63\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9 to 165.51\u0026thinsp;\u0026plusmn;\u0026thinsp;33.91 pA (Fig.\u0026nbsp;6C4 left: difference: 38.87\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65 pA, P\u0026thinsp;=\u0026thinsp;0.036) in SC neurons, respectively. Riluzole raised the PIC onset from \u0026minus;\u0026thinsp;51.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88 to -45.14\u0026thinsp;\u0026plusmn;\u0026thinsp;47.12 mV (Fig.\u0026nbsp;6C3: difference: 6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28 mV, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;6) and decreased PIC amplitude from 126.63\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9 to 89.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.66 pA (Fig.\u0026nbsp;6C4: difference: 37.62\u0026thinsp;\u0026plusmn;\u0026thinsp;22.17 pA, P\u0026thinsp;=\u0026thinsp;0.009), respectively, in control group. Riluzole also depolarized the PIC onset from \u0026minus;\u0026thinsp;57.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54 to -41.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 mV (Fig.\u0026nbsp;6C3: difference: 15.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57 mV, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;9) and reduced PIC amplitude from 165.51\u0026thinsp;\u0026plusmn;\u0026thinsp;33.91 to 120.1\u0026thinsp;\u0026plusmn;\u0026thinsp;25.78 pA (Fig.\u0026nbsp;6C4: difference: 45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25 pA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, in exercise group. The above results indicated that exercise enhanced the PICs in terms of hyperpolarizing PIC onset and increasing PIC amplitude in SC neurons. Both Ca-PIC and Na-PIC contributed to this enhancement of PICs in SC neurons during the exercise intervention.\u003c/p\u003e \u003cp\u003eSimilar results were obtained from MLR neurons in both control and exercise groups. Statistical results indicated that exercise hyperpolarized PIC onset by 5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 mV (Fig.\u0026nbsp;6D1, Control: -49.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85 mV, n\u0026thinsp;=\u0026thinsp;6; Exercise: -54.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24 mV, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;=\u0026thinsp;0.02) and increased PIC amplitude by 39.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7 pA (Fig.\u0026nbsp;6D2, Control: 138.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.69 pA; Exercise: 177.9\u0026thinsp;\u0026plusmn;\u0026thinsp;27.01 pA; P\u0026thinsp;=\u0026thinsp;0.02), respectively, in MLR neurons. Nimodipine depolarized the PIC onset by 1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58 mV (Fig.\u0026nbsp;6D1, Control: -49.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85 mV; Nimodipine: -47.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14 mV, n\u0026thinsp;=\u0026thinsp;6; P\u0026thinsp;=\u0026thinsp;0.028) and reduced PIC amplitude by 31.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28 pA (Fig.\u0026nbsp;6D2, Control: 138.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.69 pA; Nimodipine: 106.52\u0026thinsp;\u0026plusmn;\u0026thinsp;26.53 pA; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in control group. Nimodipine also depolarized the PIC onset by 3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mV (Fig.\u0026nbsp;6D1, Exercise: -51.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17 mV; Nimodipine: -52.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mV, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decreased PIC amplitude by 41.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18 pA (Fig.\u0026nbsp;6D2, Exercise: 177.9\u0026thinsp;\u0026plusmn;\u0026thinsp;27.01 pA; Nimodipine: 136\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1 pA; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in exercise group. Similar results were also collected with riluzole in MLR neurons. Exercise lowered PIC onset by 6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67mV (Fig.\u0026nbsp;6D3, Control: -50.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25 mV, n\u0026thinsp;=\u0026thinsp;6; Exercise: -57.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52 mV, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;=\u0026thinsp;0.03) and increased PIC amplitude by 41.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.71 pA (Fig.\u0026nbsp;6D4, Control: 132.18\u0026thinsp;\u0026plusmn;\u0026thinsp;31.9 pA; Exercise: 173.21\u0026thinsp;\u0026plusmn;\u0026thinsp;17.57 pA; P\u0026thinsp;=\u0026thinsp;0.007), respectively, in MLR neurons. Riluzole depolarized the PIC onset by 8.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68 mV (Fig.\u0026nbsp;6D3, Control: -50.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25 mV, n\u0026thinsp;=\u0026thinsp;6; Riluzole: -42.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73 mV, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced PIC amplitude by 31.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 pA (Fig.\u0026nbsp;6D2, Control: 132.18\u0026thinsp;\u0026plusmn;\u0026thinsp;31.9 pA; Riluzole: 100.82\u0026thinsp;\u0026plusmn;\u0026thinsp;30.97 pA; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in control group. Riluzole also raised the PIC onset by 12.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06 mV (Fig.\u0026nbsp;6D1, Exercise: -57.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52mV, n\u0026thinsp;=\u0026thinsp;6; Riluzole: -44.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38 mV, n\u0026thinsp;=\u0026thinsp;7; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decreased PIC amplitude by 39.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28 pA (Fig.\u0026nbsp;6D2, Exercise: 173.21\u0026thinsp;\u0026plusmn;\u0026thinsp;17.57mV; Riluzole: 133.49\u0026thinsp;\u0026plusmn;\u0026thinsp;13.82pA; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, in exercise group. These results demonstrated that exercise potentiated the PICs with lowering PIC onset and increasing PIC amplitude in MLR neurons. Both Ca-PIC and Na-PIC contributed to this potentiation of PICs in MLR neurons during the chronic exercise.\u003c/p\u003e \u003cp\u003eTo further explore the mechanism underlying the exercise-induced PIC changes in SC versus MLR neurons, we calculated the percentage contributions of Na-PIC and Ca-PIC to the total changes in PICs in each neuron type (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Statistical results showed that the contribution of Ca-PIC to the exercise-induced changes in PIC onset was 1.13% in SC neurons and 0.69% in MLR neurons, respectively, with no significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE1, n\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, regarding changes in PIC amplitude, the Ca-PIC contribution was significantly higher in SC neurons (0.78%) compared to MLR neurons (0.33%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE2, n\u0026thinsp;=\u0026thinsp;7, P\u0026thinsp;=\u0026thinsp;0.043). Similarly, the Na-PIC contribution to the exercise-induced alterations in PIC onset was higher in SC neurons (1.28%) than in MLR neurons (0.56%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE3, n\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;=\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Na-PIC contribution to changes in PIC amplitude also appeared to be greater in SC neurons (1.69%) than in MLR neurons (1.23%). However, this contribution was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE4, n\u0026thinsp;=\u0026thinsp;9, P\u0026thinsp;=\u0026thinsp;0.092).\u003c/p\u003e \u003cp\u003eThe above results demonstrated that exercise intervention enhanced both Na-PIC and Ca-PIC components in SC and MLR neurons, affecting both onset and amplitude. However, the functional contribution of these components to the overall PICs differed between neuron types. The modulatory effect of Ca-PIC on the PIC onset was minimal and similar between SC and MLR neurons (Fig.\u0026nbsp;6E1), whereas its effect on the PIC amplitude was stronger in SC neurons than in MLR neurons (Fig.\u0026nbsp;6E2). On the other hand, the modulatory effect of Na-PIC on PIC onset was stronger in SC neurons than in MLR neurons (Fig.\u0026nbsp;6E3), while its effect on PIC amplitude appeared to be similar in SC and MLR neurons (Fig.\u0026nbsp;6E4). These findings revealed the mechanism underlying the greater modulatory weight of exercise intervention on PICs in SC neurons compared to MLR neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExercise-induced modulation of PICs and neuronal excitability\u003c/h3\u003e\n\u003cp\u003ePICs play a key role in regulating the excitability of spinal and midbrain neurons (Deutsch \u0026amp; Elbasiouny, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; ElBasiouny et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Heckman, Johnson, et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Orssatto et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among them, Ca-PIC is the primary mechanism underlying the onset/offset hysteresis of PICs in spinal and midbrain neurons (Binder et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dai \u0026amp; Jordan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Moritz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Svirskis \u0026amp; Hounsgaard, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), while Na-PIC plays a dominant role in maintaining repetitive firing (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Harvey et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kuo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lee \u0026amp; Heckman, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In the above experiments, we found that exercise had a greater modulatory effect on PICs in SC neurons compared to those in MLR neurons. What is the significance of this difference for regulating the excitability of SC and MLR neurons? We addressed this issue in the following experiments.\u003c/p\u003e \u003cp\u003eWe used a bi-ramp current (duration 10 s, peak amplitude 60 pA, starting from 0) to measure the electrophysiological parameters (see Methods for details), including Vth), recruitment current (Irec), and de-recruitment current (Idec). We calculated the difference between Irec and Idec (ΔI\u0026thinsp;=\u0026thinsp;Irec - Idec) and used this to assess the role of PICs in regulating neuronal excitability (Cheng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Representative recordings of firing properties and instantaneous firing frequencies of SC and MLR neurons from control and exercise are shown in Figs.\u0026nbsp;7A1 and 7A2, respectively. The results indicate that exercise significantly increased the instantaneous firing frequency of both SC and MLR neurons and caused a leftward shift in the frequency-current (F-I) curve. Exercise significantly enhanced the sustained firing capacity of SC neurons (Fig.\u0026nbsp;7A1, left), reducing ΔI from 8.8 pA in the control (black) to 3.2 pA in exercise (Fig.\u0026nbsp;7A1, right, green). Similarly, exercise enhanced the sustained firing capacity of MLR neurons (Fig.\u0026nbsp;7A2, left), reducing ΔI from 18.1 pA in the control (gray) to 2.4 pA in exercise (Fig.\u0026nbsp;7A2, right, orange). Statistical results showed that exercise significantly decreased the Vth of SC neurons by 6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 mV (Fig.\u0026nbsp;7B1, left; Control: -34.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 mV, n\u0026thinsp;=\u0026thinsp;19; Exercise: -41.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 mV, n\u0026thinsp;=\u0026thinsp;24; p\u0026thinsp;=\u0026thinsp;0.003) and the Vth of MLR neurons by 4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 mV (Fig.\u0026nbsp;7B1, right; Control: -31.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78 mV, n\u0026thinsp;=\u0026thinsp;20; Exercise: -35.13\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18 mV, n\u0026thinsp;=\u0026thinsp;23; p\u0026thinsp;=\u0026thinsp;0.021). These results indicate that exercise significantly enhanced the excitability of both SC and MLR neurons.\u003c/p\u003e \u003cp\u003eNext, we investigated the modulatory effect of exercise on neuronal recruitment current. Experimental data showed that exercise significantly decreased Irec in SC neurons by 4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 pA (Fig.\u0026nbsp;7B2, left; Control: 13.52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 pA; Exercise: 9.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 pA, p\u0026thinsp;=\u0026thinsp;0.015). Although exercise decreased Irec in MLR neurons by 1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 pA, this reduction was not statistically significant (Fig.\u0026nbsp;7B2, right; Control: 12.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13 pA; Exercise: 10.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23 pA, p\u0026thinsp;=\u0026thinsp;0.287). This result suggests that exercise lowers the current threshold for firing in SC neurons, significantly enhancing their excitability. Further analysis revealed that exercise significantly decreased Idec in SC neurons by 8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 pA (Fig.\u0026nbsp;7B3, left; Control: 22.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31 pA; Exercise: 13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04 pA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in MLR neurons by 5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 pA (Fig.\u0026nbsp;7B3, right; Control: 22.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89 pA; exercise: 17.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59 pA, p\u0026thinsp;=\u0026thinsp;0.003), indicating that exercise prolonged the firing duration in both neuron types.\u003c/p\u003e \u003cp\u003eFinally, we analyzed the regulatory effect of exercise on ΔI. Statistical data showed that exercise significantly reduced ΔI to 4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 pA in SC neurons (Fig.\u0026nbsp;7B4, left; Control: 8.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 pA, n\u0026thinsp;=\u0026thinsp;19; Exercise: 4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 pA, n\u0026thinsp;=\u0026thinsp;24; p\u0026thinsp;=\u0026thinsp;0.011) and 3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 pA in MLR neurons (Fig.\u0026nbsp;7B4, right; Control: 9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02 pA, n\u0026thinsp;=\u0026thinsp;20; Exercise: 6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27 pA, n\u0026thinsp;=\u0026thinsp;23; p\u0026thinsp;=\u0026thinsp;0.038). The reduced ΔI indicates that exercise prolonged neuronal firing duration and increased the probability of firing hysteresis (ΔI\u0026thinsp;\u0026lt;\u0026thinsp;0). The above results demonstrate that exercise significantly enhances the ability of PICs to regulate the excitability of SC and MLR neurons, particularly evidenced by the reduction in action potential firing threshold and the prolongation of sustained firing time.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModulation of exercise on Ca-PIC, Na-PIC, and neuronal excitability\u003c/h2\u003e \u003cp\u003eThe above results indicated that exercise intervention modulated neuronal excitability by enhancing PIC. Next, we further investigated how exercise intervention regulates neuronal excitability through Ca-PIC and Na-PIC. We first examined the modulation of Ca-PIC by exercise on the excitability of SC and MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Experimental results showed that bath application of 25 \u0026micro;M Nimodipine (Nim) to the slice recording solution after exercise caused minor changes in the Vth of SC and MLR neurons. The Vth difference was only 1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 mV in SC neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC1 left, Exercise, SC: Vth = -50.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27 mV; Nim: Vth = -49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47 mV; n\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;=\u0026thinsp;0.058) and 0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35 mV in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC1 right, Exercise, MLR: Vth=-43.62\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19 mV; Nim: Vth =-42.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76 mV; n\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;=\u0026thinsp;0.119), indicating that the exercise-induced hyperpolarization of Vth in SC and MLR neurons was independent of Ca-PIC. However, the same concentration of nimodipine caused significant changes in Irec, Idec, and ΔI of SC and MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, n\u0026thinsp;=\u0026thinsp;7). Specifically, nimodipine increased Irec, Idec, and ΔI in SC neurons by 10.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC2 left, SC: 8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 pA; Nim: 18.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 pA, p\u0026thinsp;=\u0026thinsp;0.0002), 21.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC3 left, SC: 12.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 pA; Nim: 33.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 pA, p\u0026thinsp;=\u0026thinsp;0.0001), and 11.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC4 left, SC: 3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87 pA; Nim: 15.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18 pA, p\u0026thinsp;=\u0026thinsp;0.034), respectively. Similar results were observed in MLR neurons. Nimodipine increased Irec, Idec, and ΔI in MLR neurons by 4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC2 right, MLR: 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 pA; Nim: 14.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 pA, p\u0026thinsp;=\u0026thinsp;0.0008), 8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC3 right, MLR: 16.01\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 pA; Nim: 24.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 pA, p\u0026thinsp;=\u0026thinsp;0.0043), and 4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC4 right, MLR: 5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52 pA; Nim: 9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22 pA, p\u0026thinsp;=\u0026thinsp;0.034), respectively. These results indicated that Ca-PIC played an important role in regulating the excitability of SC and MLR neurons during exercise, particularly in modulating properties such as the current threshold for sustained firing, firing duration, and the probability of delayed firing. Specifically, exercise enhanced Ca-PIC, leading to a decreased current threshold for sustained firing, prolonged firing duration, and an increased probability of delayed firing in SC and MLR neurons.\u003c/p\u003e \u003cp\u003eNext, we investigated the role of Na-PIC in regulating the excitability of SC and MLR neurons during exercise intervention. Applying 3 \u0026micro;M Riluzole (Ril) to the recording solution following exercise intervention, we observed a significant depolarization of Vth in SC and MLR neurons. The Vth of SC neurons increased from \u0026minus;\u0026thinsp;49.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 mV to -38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 mV with an increase of 11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 mV (Fig.\u0026nbsp;7D1 left, n\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The Vth of MLR neurons increased from \u0026minus;\u0026thinsp;41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 mV to -36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 mV with an increase of 5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 mV (Fig.\u0026nbsp;7D1 right, n\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;=\u0026thinsp;0.003). This result indicates that the hyperpolarization of Vth in SC and MLR neurons induced by exercise intervention is determined by Na-PIC.\u003c/p\u003e \u003cp\u003eThe same concentration of riluzole caused significant changes in Irec and Idec of SC and MLR neurons (Fig.\u0026nbsp;7D2-3, n\u0026thinsp;=\u0026thinsp;9). Specifically, Irec and Idec in SC neurons increased by 9.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 pA (from 9.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1 pA to 18.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 pA; Fig.\u0026nbsp;7D2 left, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 pA (from 14.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 pA to 24.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 pA; Fig.\u0026nbsp;7D3 left, p\u0026thinsp;=\u0026thinsp;0.001) respectively. And Irec and Idec in MLR neurons increased by 6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 pA (from 8.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 pA to 14.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 pA; Fig.\u0026nbsp;7D2 right, p\u0026thinsp;=\u0026thinsp;0.004) and 5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 pA (from 14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 pA to 20.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 pA; Fig.\u0026nbsp;7D3 right, p\u0026thinsp;=\u0026thinsp;0.002), respectively. However, riluzole did not cause significant changes in ΔI. ΔI in SC neurons changed from 4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49 pA to 5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07 pA with a change of 0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15 pA (Fig.\u0026nbsp;7D4 left, p\u0026thinsp;=\u0026thinsp;0.919), and ΔI in MLR neurons changed from 5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55 pA to 5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82 pA with a change of 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51 pA (Fig.\u0026nbsp;7D4 right, p\u0026thinsp;=\u0026thinsp;0.726). These results indicate that Na-PIC plays an important role in regulating the excitability of SC and MLR neurons during exercise intervention, particularly in modulating the voltage threshold for sustained firing, the current threshold for sustained firing, and the firing duration, but has minimal effect on neuronal delayed firing. Specifically, exercise enhances Na-PIC, leading to a decreased voltage threshold and current threshold for sustained firing, and prolonged firing duration in SC and MLR neurons.\u003c/p\u003e \u003cp\u003eFinally, we analyzed the relative (percentage) differences in the regulatory strength of Ca-PIC and Na-PIC on Vth, Irec, and Idec in SC and MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Statistical results showed that the contribution of Ca-PIC to Vth was 2.08% in SC neurons and 2.04% in MLR neurons, both minimal and not significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE1, p\u0026thinsp;=\u0026thinsp;0.979, n\u0026thinsp;=\u0026thinsp;7). However, the regulatory strength of Ca-PIC on Irec was 141.32% in SC neurons, significantly higher than the 52.78% in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE2, p\u0026thinsp;=\u0026thinsp;0.037, n\u0026thinsp;=\u0026thinsp;7). The regulatory strength on Idec was 211.65% in SC neurons, also significantly higher than the 70.39% in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE3, p\u0026thinsp;=\u0026thinsp;0.018, n\u0026thinsp;=\u0026thinsp;7). Similarly, for Na-PIC, its regulatory strength on Vth was 23% in SC neurons, significantly higher than the 13.38% in MLR neurons (Fig.\u0026nbsp;7F1, p\u0026thinsp;=\u0026thinsp;0.025, n\u0026thinsp;=\u0026thinsp;9). Its regulatory strength on Irec was 150.13% in SC neurons, also significantly higher than the 43.25% in MLR neurons (Fig.\u0026nbsp;7F2, p\u0026thinsp;=\u0026thinsp;0.039, n\u0026thinsp;=\u0026thinsp;9). However, the regulatory strength of Na-PIC on Idec was 56.49% in SC neurons and 28.41% in MLR neurons, showing no significant difference (Fig.\u0026nbsp;7F3, p\u0026thinsp;=\u0026thinsp;0.209, n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e \u003cp\u003eThe above results demonstrated that Ca-PIC and Na-PIC played important roles in regulating the excitability of SC and MLR neurons during exercise intervention. Both Ca-PIC and Na-PIC modulated the neuronal current threshold and firing duration. Ca-PIC primarily governed the delayed firing characteristics of neurons, while Na-PIC determined the voltage threshold for action potential generation. This combined modulation resulted in a significant enhancement of SC and MLR neuronal excitability by exercise. Specifically, the modulatory strength of Ca-PIC and Na-PIC in SC neurons was higher than that in MLR neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDendritic morphology analysis of SC and MLR neurons\u003c/h3\u003e\n\u003cp\u003eOur previous studies have shown that treadmill training promote dendritic plasticity in neurons of the mouse spinal cord and midbrain (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, can this exercise-induced dendritic plasticity be observed simultaneously in both SC and MLR neurons within the same mouse? What are the morphological features of dendritic plasticity in these different regions? What potential effects does dendritic plasticity have on the intrinsic membrane properties of these neurons? We employed Sholl analysis to investigate these questions.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the morphological distribution of the stained SC and MLR neurons in this study, including data from two groups of SC neurons: Control (Fig.\u0026nbsp;8A1, black, n\u0026thinsp;=\u0026thinsp;27) and Exercise (Fig.\u0026nbsp;8A2, gree\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003en\u003c/span\u003e, n\u0026thinsp;=\u0026thinsp;42). In the Control group, ventral neurons accounted for 70%, and dorsal neurons for 30% (Fig.\u0026nbsp;8A1); in the Exercise group, ventral neurons accounted for 74%, and dorsal neurons for 26% (Fig.\u0026nbsp;8A2). Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB displays the morphological distribution of MLR neurons, including data from Control (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB1, black, n\u0026thinsp;=\u0026thinsp;29) and Exercise (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB2, orange, n\u0026thinsp;=\u0026thinsp;70) groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further quantify the dendritic morphological characteristics and differences between SC and MLR neurons, we selected 22 pairs of neurons that were simultaneously recorded from the SC and MLR regions of the same mice for morphological analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB show the typical morphological features of SC and MLR neurons from the control groups, respectively. The results of Sholl analysis revealed that within the range of 50\u0026ndash;125 \u0026micro;m from the soma, SC neurons had a significantly greater number of dendritic intersections than MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;22). However, no significant differences were found in total dendritic length (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC2, p\u0026thinsp;=\u0026thinsp;0.165, n\u0026thinsp;=\u0026thinsp;22), the number of primary dendrites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC3, p\u0026thinsp;=\u0026thinsp;0.126, n\u0026thinsp;=\u0026thinsp;22), or somatic surface area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC6, p\u0026thinsp;=\u0026thinsp;0.391, n\u0026thinsp;=\u0026thinsp;22). Regarding the number of dendritic branch points, SC neurons had 3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3, while MLR neurons had 1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3. The difference was 1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 with statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC4, p\u0026thinsp;=\u0026thinsp;0.047, n\u0026thinsp;=\u0026thinsp;22). Similarly, for the number of dendritic terminals, SC neurons had 7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 and MLR neurons had 5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26. The difference was 1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36 with statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC5, p\u0026thinsp;=\u0026thinsp;0.027, n\u0026thinsp;=\u0026thinsp;22). These results indicate that SC neurons exhibit higher dendritic complexity than MLR neurons in terms of the number of dendritic branch points and terminals, suggesting that SC neurons may possess greater structural-level plasticity compared to MLR neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExercise promotes dendritic plasticity in SC and MLR neurons\u003c/h3\u003e\n\u003cp\u003eIn order to evaluate the effect of exercise intervention on the dendritic structure of SC and MLR neurons, we measured and analyzed morphological metrics of SC and MLR neurons following exercise training. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA shows representative morphological images of SC neurons, where the exercise group (Fig.\u0026nbsp;10A1) exhibited longer dendritic distributions compared to the control group (Fig.\u0026nbsp;10A2). Sholl analysis revealed that exercise significantly increased the number of dendritic intersections in SC neurons within the range of 50\u0026ndash;225 \u0026micro;m from the soma (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further quantitative analysis indicated that exercise induced a significant increase in the total dendritic length of SC neurons from 528.82\u0026thinsp;\u0026plusmn;\u0026thinsp;200.3 \u0026micro;m in the control group (n\u0026thinsp;=\u0026thinsp;27) to 664.4\u0026thinsp;\u0026plusmn;\u0026thinsp;264.9 \u0026micro;m in the exercise group (n\u0026thinsp;=\u0026thinsp;42) with an increase of 135.58\u0026thinsp;\u0026plusmn;\u0026thinsp;59.6 \u0026micro;m (Fig.\u0026nbsp;10C1, P\u0026thinsp;=\u0026thinsp;0.026). The number of primary dendrites also increased from 4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 to 4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55 with an increase of 0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 (Fig.\u0026nbsp;10C2, P\u0026thinsp;=\u0026thinsp;0.041). Regarding the number of dendritic branch points and terminals, the exercise group showed significant increases of 2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (Fig.\u0026nbsp;10C3; Control: 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21; Exercise: 4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (Fig.\u0026nbsp;10C4; Control: 5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12; Exercise: 9.24\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. In contrast, no significant difference was observed in somatic surface area between the two groups (Fig.\u0026nbsp;10C5; Control: 314.74\u0026thinsp;\u0026plusmn;\u0026thinsp;84.39 \u0026micro;m\u0026sup2;; Exercise: 304.74\u0026thinsp;\u0026plusmn;\u0026thinsp;121.58 \u0026micro;m\u0026sup2;, P\u0026thinsp;=\u0026thinsp;0.688). These results demonstrated that exercise intervention significantly enhanced dendritic plasticity in SC neurons, particularly in parameters such as dendritic length, number of branches, and number of terminals.\u003c/p\u003e \u003cp\u003eSimilar to SC neurons, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD, the dendritic arborization of MLR neurons in the exercise group (Fig.\u0026nbsp;10D2) appeared more complex than that in the control group (Fig.\u0026nbsp;10D1), characterized by broader dendritic extension and more terminals. Sholl analysis results indicated that exercise intervention significantly increased the number of dendritic intersections in MLR neurons (Control n\u0026thinsp;=\u0026thinsp;29; Exercise n\u0026thinsp;=\u0026thinsp;70) within the range of 150\u0026ndash;325 \u0026micro;m from the soma (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Total dendritic length increased from 583.37\u0026thinsp;\u0026plusmn;\u0026thinsp;217.4 \u0026micro;m to 761.69\u0026thinsp;\u0026plusmn;\u0026thinsp;313.3 \u0026micro;m with an increase of 178.32\u0026thinsp;\u0026plusmn;\u0026thinsp;63.81 \u0026micro;m (Fig.\u0026nbsp;10F1, P\u0026thinsp;=\u0026thinsp;0.006). The number of branch points increased from 1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 to 2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 with an increase of 1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 (Fig.\u0026nbsp;10F3, P\u0026thinsp;=\u0026thinsp;0.006). The number of terminals increased from 5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19 to 6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99 with an increase of 1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 (Fig.\u0026nbsp;10F4, P\u0026thinsp;=\u0026thinsp;0.007). However, no significant change was observed in somatic surface area (Fig.\u0026nbsp;10F5; Control: 332.04\u0026thinsp;\u0026plusmn;\u0026thinsp;173.71 \u0026micro;m\u0026sup2;; Exercise: 360.13\u0026thinsp;\u0026plusmn;\u0026thinsp;172.02 \u0026micro;m\u0026sup2;, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) or number of primary dendrites (Fig.\u0026nbsp;10F2; Control: 3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18; Exercise: 3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in MLR neurons after exercise. These results indicate that exercise significantly promotes dendritic plasticity in MLR neurons, particularly in total dendritic length, number of dendritic branch points, and number of dendritic terminals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of exercise effects on dendritic plasticity in SC and MLR neurons\u003c/h2\u003e \u003cp\u003eTo further analyze the differential effects of exercise intervention on dendritic plasticity between SC and MLR neurons, we examined the relative percentage changes in morphological metrics from 28 pairs of SC and MLR neurons that were simultaneously recorded within the same mouse preparations from the exercise group. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA displays the structure and morphology of two representative pairs of SC and MLR neurons following exercise. Statistical analysis revealed (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB) that exercise induced a significantly greater increase in total dendritic length in SC neurons (56.3%) compared to MLR neurons (25.5%), with a mean difference of 30.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB1, P\u0026thinsp;=\u0026thinsp;0.006, n\u0026thinsp;=\u0026thinsp;28). Regarding the number of dendritic branch points, SC neurons showed an increase of 107.14%, which was significantly larger than the 39.05% increase observed in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB3, P\u0026thinsp;=\u0026thinsp;0.025, n\u0026thinsp;=\u0026thinsp;28). A similar result was observed for the number of dendritic terminals, where SC neurons exhibited a 61.2% increase, significantly greater than the 27.6% increase in MLR neurons, resulting in a difference of 33.6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB4, P\u0026thinsp;=\u0026thinsp;0.025, n\u0026thinsp;=\u0026thinsp;28). In contrast, no significant differences were found between the two neuron types in the number of primary dendrites (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB2, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;28) and somatic surface area parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB5, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;28). These results indicate that while exercise significantly enhanced dendritic plasticity in both SC and MLR neurons, the magnitude of increase was markedly greater in SC neurons than in MLR neurons. This enhanced plasticity in SC neurons was primarily evident in parameters such as total dendritic length, number of dendritic branch points, and number of dendritic terminals, suggesting that SC neurons exhibit a higher degree of structural remodeling capacity in response to exercise intervention compared to MLR neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModulation of neuronal excitability in dorsal vs. ventral SC neurons by exercise\u003c/h2\u003e \u003cp\u003eThe SC neurons recorded in this study were primarily distributed in the dorsal and ventral regions of the spinal cord. Dorsal neurons play a major role in proprioceptive signal transmission (Leiras et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), whereas ventral neurons are primarily involved in the control of locomotor activity (Hsu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kiehn, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given the functional differences between these two neuronal populations, we sought to investigate whether they also exhibit differences in neuronal plasticity in response to exercise intervention. In the subsequent experiments, we selected SC neurons with clear laminar locations, classifying those in laminae I-IV as dorsal interneurons and laminae V-X the as ventral interneurons (except for lamina IX), and analyzed their membrane properties.\u003c/p\u003e \u003cp\u003eFigures 12A1-2 show representative examples of a dorsal and a ventral neuron, respectively, with their action potentials (APs) overlapped. Compared to the dorsal neuron, the ventral neuron exhibited a more hyperpolarized threshold (Vth), a longer afterhyperpolarization (AHP) half-width, and a lower input resistance (Rin). Statistical analysis (Figs.\u0026nbsp;12A3-10) confirmed that the Vth of ventral SC neurons was significantly more hyperpolarized than that of dorsal neurons by 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83 mV (Fig.\u0026nbsp;12A5; dorsal: -33.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 mV; ventral: -39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30 mV; P\u0026thinsp;=\u0026thinsp;0.032, n\u0026thinsp;=\u0026thinsp;8). The AHP half-width was significantly longer in ventral neurons by 78.29\u0026thinsp;\u0026plusmn;\u0026thinsp;63.16 ms (Fig.\u0026nbsp;12A9; dorsal: 77.76\u0026thinsp;\u0026plusmn;\u0026thinsp;53.01 ms; ventral:133.31\u0026thinsp;\u0026plusmn;\u0026thinsp;66.23 ms; P\u0026thinsp;=\u0026thinsp;0.01, n\u0026thinsp;=\u0026thinsp;8). The Rin of ventral neurons was significantly lower than that of dorsal neurons by 443.4\u0026thinsp;\u0026plusmn;\u0026thinsp;403.8 MΩ (Fig.\u0026nbsp;12A10; dorsal: 1193.5\u0026thinsp;\u0026plusmn;\u0026thinsp;376.94 MΩ; ventral: 731.22\u0026thinsp;\u0026plusmn;\u0026thinsp;308.16 MΩ; P\u0026thinsp;=\u0026thinsp;0.02; n\u0026thinsp;=\u0026thinsp;8). No significant difference was observed in other parameters, including resting membrane potential (Em), rheobase, AP amplitude, AP half-width, or AHP amplitude (Figs.\u0026nbsp;12A3-4 \u0026amp; A6-8, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;8). These results indicated that ventral SC neurons possessed higher excitability than dorsal neurons, primarily characterized by a lower Vth, suggesting they were more easily activated and may exhibit greater plasticity during exercise.\u003c/p\u003e \u003cp\u003eWe next examined the modulatory effects of exercise on the electrophysiological properties of dorsal and ventral SC neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). Figures\u0026nbsp;12B1-2 (black for Control; green for Exercise) showed a representative example where exercise intervention significantly hyperpolarized the Vth in a dorsal neuron without altering its rheobase. Statistical analysis revealed that exercise intervention hyperpolarized the Vth of dorsal neurons by 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37 mV (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB5; Control: -33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 mV, n\u0026thinsp;=\u0026thinsp;8; Exercise: -45.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.49 mV, P\u0026thinsp;=\u0026thinsp;0.026, n\u0026thinsp;=\u0026thinsp;10). No significant change was detected in Em, rheobase, AP amplitude, AP half-width, AHP amplitude, AHP half-width, and Rin (Figs.\u0026nbsp;12B3-4 \u0026amp; B6-10, total n\u0026thinsp;=\u0026thinsp;18). In contrast, exercise induced more pronounced changes in ventral SC neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC). Figures\u0026nbsp;12C1-2 (black for Control; orange for Exercise) showed a representative example where exercise resulted in hyperpolarization of Vth and a decrease in rheobase in a ventral neuron. Statistical analysis confirmed that exercise significantly decreased the rheobase of ventral neurons by 7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69 pA (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC4; Control: 14.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 pA, n\u0026thinsp;=\u0026thinsp;8; Exercise: 6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49 pA, n\u0026thinsp;=\u0026thinsp;18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hyperpolarized their Vth by 8.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18 mV (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC5; Control: -39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82 mV; Exercise: -47.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27 mV, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant change was observed in Em, AP amplitude, AP half-width, AHP amplitude, AHP half-width, or Rin (Figs.\u0026nbsp;12C3 \u0026amp; C6-10). Further comparative analysis revealed that, compared to dorsal neurons, exercise caused significantly greater reductions in ventral neurons for rheobase (Fig.\u0026nbsp;12D2; ventral: -51.69%, n\u0026thinsp;=\u0026thinsp;18; dorsal: -5.88%, n\u0026thinsp;=\u0026thinsp;10; P\u0026thinsp;=\u0026thinsp;0.029), AP half-width (Fig.\u0026nbsp;12D5; ventral: -36.89%; dorsal: 16.75%; P\u0026thinsp;=\u0026thinsp;0.019), and AHP half-width (Fig.\u0026nbsp;12D7; ventral: -33.19%; dorsal: -28.57%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found in the relative changes in Em, Vth, AP amplitude, AHP amplitude, and Rin between the two populations (Figs.\u0026nbsp;12D1, D3-4, D6, D8, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese results demonstrated that exercise enhanced the excitability of both dorsal and ventral SC neurons. Dorsal neurons primarily exhibited a lower Vth, whereas ventral neurons showed concurrent reductions in both Vth and rheobase. Furthermore, the magnitude of excitability modulation by exercise was greater in ventral neurons than in dorsal neurons, particularly regarding the relative decreases in rheobase, AP half-width, and AHP half-width. These results suggested that ventral neurons played a dominant role in the adaptation of the motor system during exercise, indicating that the locomotor system exhibited greater plasticity than the proprioceptive system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of exercise on dendritic plasticity in dorsal vs. ventral SC neurons\u003c/h2\u003e \u003cp\u003eIn the above experiments, we examined the differential effects of exercise on the membrane properties of dorsal and ventral spinal cord neurons. We now extend our analysis to compare the influence of exercise on dendritic plasticity between these two neuronal populations. Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA illustrated the morphological characteristics of both neuron types in control and exercise groups. Qualitatively, dorsal neurons in the control group (Fig.\u0026nbsp;13A1) appeared slightly smaller in dendritic extent than ventral neurons (Fig.\u0026nbsp;13A3), though this difference was not significant. Exercise markedly promoted dendritic growth in both dorsal (Fig.\u0026nbsp;13A2, green) and ventral (Fig.\u0026nbsp;13A4, orange) neurons. Statistical analysis indicated that ventral neurons exhibited a trend towards larger values than dorsal neurons in total dendritic length, number of primary dendrites, dendritic branch points, dendritic terminals, and somatic area (Figs.\u0026nbsp;13B1-5). However, a statistically significant difference was only observed in the number of primary dendrites: ventral neurons had 1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 more primary dendrites than dorsal neurons (Fig.\u0026nbsp;13B2; ventral: 4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83, n\u0026thinsp;=\u0026thinsp;18; dorsal: 3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71, n\u0026thinsp;=\u0026thinsp;8; P\u0026thinsp;=\u0026thinsp;0.005). No significant difference was found in the other metrics. Despite the minimal baseline morphological differences between the two populations, exercise profoundly influenced dendritic plasticity. In dorsal neurons, exercise significantly increased the number of primary dendrites from 3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 to 4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 with an increase of 1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 (Fig.\u0026nbsp;13C2; Control: n\u0026thinsp;=\u0026thinsp;8; Exercise: n\u0026thinsp;=\u0026thinsp;11; P\u0026thinsp;=\u0026thinsp;0.025), the number of dendritic branch points from 1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 to 3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03 with an increase of 2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 (Fig.\u0026nbsp;13C3; P\u0026thinsp;=\u0026thinsp;0.009), and the number of dendritic terminals from 4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 to 7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 with an increase of 3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01 (Fig.\u0026nbsp;13C4; P\u0026thinsp;=\u0026thinsp;0.003). In contrast, no significant change was observed in total dendritic length or somatic area following exercise (Figs.\u0026nbsp;13C1 \u0026amp; C5; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, exercise significantly enhanced dendritic morphology in ventral neurons. Total dendritic length increased from 545.31\u0026thinsp;\u0026plusmn;\u0026thinsp;238.11 \u0026micro;m to 762.7\u0026thinsp;\u0026plusmn;\u0026thinsp;269.38 \u0026micro;m with an increase of 217.4\u0026thinsp;\u0026plusmn;\u0026thinsp;82.07 \u0026micro;m (Fig.\u0026nbsp;13D1; Control: n\u0026thinsp;=\u0026thinsp;18; Exercise: n\u0026thinsp;=\u0026thinsp;21; P\u0026thinsp;=\u0026thinsp;0.011). The number of dendritic branch points from 1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 to 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32 with an increase of 2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 (Fig.\u0026nbsp;13D3; P\u0026thinsp;=\u0026thinsp;0.004), and the number of dendritic terminals from 5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57 to 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06 with an increase of 3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 (Fig.\u0026nbsp;13D4; P\u0026thinsp;=\u0026thinsp;0.003). However, no significant change was detected in the number of primary dendrites or somatic area (Figs.\u0026nbsp;13D2 \u0026amp; D5; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Finally, we compared the relative magnitude of exercise-induced dendritic plasticity between dorsal and ventral neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE). The results showed that the percent increase in total dendritic length was significantly greater in ventral neurons (46.5%, n\u0026thinsp;=\u0026thinsp;18) than in dorsal neurons (7.7%, n\u0026thinsp;=\u0026thinsp;21; Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE1; P\u0026thinsp;=\u0026thinsp;0.009). In contrast, the relative changes in the number of primary dendrites, dendritic branch points, dendritic terminals, and somatic area were not significantly different between the two populations (Figs.\u0026nbsp;13E2-5; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese findings indicated that exercise intervention robustly enhanced dendritic plasticity in both dorsal and ventral SC neurons, which likely had implications for the regulation of passive membrane properties and neuronal excitability. The greater increase in dendritic length observed in ventral neurons suggested that exercise may exert a stronger modulatory effect on the excitability of ventral neurons compared to dorsal neurons, consistent with the electrophysiological findings presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB, C \u0026amp; D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of functional significance of exercise-induced plasticity\u003c/h2\u003e \u003cp\u003eThe findings above indicated that exercise intervention simultaneously promoted neuronal dendritic plasticity, enhanced the persistent inward current (PIC), and increased neuronal excitability, a decrease in Vth and rheobase in particular, in both the MLR and SC neurons. However, how dendritic growth and PIC modulation collectively altered neuronal excitability remains unknown. In the following analysis, we conducted a correlational study to address this issue. Based on the physiological significance of each electrophysiological parameter and the underlying neural mechanisms they represent, we paired these parameters and performed correlation analyses on the data from the control and exercise groups.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e (A, B \u0026amp; C) presented the results of the correlation analysis for neuronal parameters in SC neurons from the control and exercise groups. The results showed that among the parameter pairs in the control group, only a weak correlation (R\u0026sup2; \u0026lt; 0.4) was observed for Vth vs. PIC onset (Fig.\u0026nbsp;14A1) and Vth vs. Na-PIC onset (Fig.\u0026nbsp;14A2). However, exercise intervention significantly enhanced the correlation between these two parameter pairs, elevating it from weak to medium (R\u0026sup2; \u0026gt; 0.4) or strong (R\u0026sup2; \u0026gt; 0.7). Similar results were observed for the parameter pairs PIC amplitude vs. total dendritic length (Fig.\u0026nbsp;14A3) and Ca-PIC amplitude vs. total dendritic length (Fig.\u0026nbsp;14A4), where exercise increased R\u0026sup2; from 0.2 to 0.7. These results suggest that the hyperpolarization of Vth induced by exercise is primarily due to the hyperpolarization of PIC onset, specifically driven by the hyperpolarization of Na-PIC. Furthermore, the exercise-induced increase in PIC amplitude, particularly in Ca-PIC amplitude, may result from the growth in dendritic length. To further validate this conclusion, we analyzed experimental data from bath application of riluzole (3 \u0026micro;M) and nimodipine (25 \u0026micro;M) in the SC neurons of the exercise group. We examined four parameter pairs directly related to neuronal excitability: Vth vs. PIC onset (Fig.\u0026nbsp;14B1), Vth vs. PIC amplitude (Fig.\u0026nbsp;14B2), rheobase vs. PIC onset (Fig.\u0026nbsp;14B3), and rheobase vs. PIC amplitude (Fig.\u0026nbsp;14B4). Riluzole reduced their correlation coefficients from strong to weak (R\u0026sup2;\u0026lt;0.4), indicating that exercise-induced hyperpolarization of Na-PIC onset and an increase in Na-PIC amplitude constituted a potential mechanism underlying the enhanced excitability of SC neurons, manifested as Vth hyperpolarization and rheobase reduction.\u003c/p\u003e \u003cp\u003eWe further analyzed the experimental data from the bath application of nimodipine (25 \u0026micro;M) in the exercise group. In contrast to the results with riluzole, nimodipine had only a minor impact on the correlations for Vth vs. PIC onset (Fig.\u0026nbsp;14C1), Vth vs. PIC amplitude (Fig.\u0026nbsp;14C2), rheobase vs. PIC onset (Fig.\u0026nbsp;14C3), and rheobase vs. PIC amplitude (Fig.\u0026nbsp;14C4), with a decrease in R\u0026sup2; of less than 0.2. Among these, Vth vs. PIC onset, Vth vs. PIC amplitude, and rheobase vs. PIC onset retained medium-to-strong correlations (R\u0026sup2; \u0026gt; 0.6). Only the correlation for rheobase vs. PIC amplitude showed a noticeable decline, with R\u0026sup2; decreasing from 0.668 to 0.475. These results suggested that while exercise-induced enhancement of Ca-PIC amplitude contributed to the reduction of rheobase in SC neurons, it had little effect on the hyperpolarization of Vth.\u003c/p\u003e \u003cp\u003eIn the subsequent study, we performed the same correlation analysis on MLR neurons, with the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e (D, E \u0026amp; F). We first conducted a correlation analysis on four parameter pairs in the MLR, including Vth vs. PIC onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD1), Vth vs. Na-PIC onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD2), PIC amplitude vs. total dendritic length (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD3), and Ca-PIC amplitude vs. total dendritic length (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD4). Similar to the findings in SC neurons, exercise enhanced the correlations for these four parameter pairs from weak (R\u0026sup2;\u0026lt;0.4) to medium or strong (R\u0026sup2;\u0026gt;0.6). This indicated that the exercise-induced hyperpolarization of PIC onset, particularly Na-PIC onset, was the primary cause of Vth hyperpolarization; whereas the increase in dendritic length resulting from exercise served as a potential mechanism underlying the increase in PIC amplitude, especially Ca-PIC amplitude.\u003c/p\u003e \u003cp\u003eWe conducted analysis on the MLR neurons with riluzole (3 \u0026micro;M) in exercise group for the four parameter pairs: Vth vs. PIC onset (Fig.\u0026nbsp;14E1), Vth vs. PIC amplitude (Fig.\u0026nbsp;14E2), rheobase vs. PIC onset (Fig.\u0026nbsp;14E3), and rheobase vs. PIC amplitude (Fig.\u0026nbsp;14E4). The results showed that the correlations decreased from strong (R\u0026sup2;\u0026gt;0.7) to weak (R\u0026sup2;\u0026lt;0.4). This finding demonstrated that exercise-induced hyperpolarization of Na-PIC onset and the increase in Na-PIC amplitude were the main contributors to the enhanced excitability of MLR neurons, manifested as hyperpolarized Vth and reduced rheobase. Further analysis revealed that nimodipine had little effect on the correlations for three parameter pairs: Vth vs. PIC onset (Fig.\u0026nbsp;14F1), Vth vs. PIC amplitude (Fig.\u0026nbsp;14F2), and rheobase vs. PIC onset (Fig.\u0026nbsp;14F3), with a decrease in R\u0026sup2; of less than 0.1. These correlations remained at a medium-to-strong level (R\u0026sup2;\u0026gt;0.6). Only the correlation for rheobase vs. PIC amplitude (Fig.\u0026nbsp;14F4) showed a decline, with R\u0026sup2; decreasing from 0.510 to 0.387. These results implicated that the exercise-induced enhancement of Ca-PIC amplitude contributed to the reduction of rheobase in MLR neurons, but had a little effect on the hyperpolarization of Vth.\u003c/p\u003e \u003cp\u003eThe above results of the correlation analysis indicate that the exercise-induced enhancement of excitability in SC and MLR neurons can be directly explained by the increased dendritic length and enhanced regulatory function of the ion channel PICs. Furthermore, this increased neuronal excitability was the potential mechanism underlying the plasticity of the motor system in response to environmental changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummary of exercise-induced plasticity in SC vs. MLR neurons\u003c/h2\u003e \u003cp\u003eThis study elucidated the mechanisms underlying adaptation of the locomotor system in response to exercise intervention from three perspectives: intrinsic membrane properties, ionic channel kinetics, and neuronal morphology. We demonstrated that the locomotor system exhibited differential plasticity in the MLR and SC neurons following exercise, with the SC neurons playing a dominant role in this process. Figure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e summarizes the percentage changes in 19 key neurophysiological parameters induced by exercise intervention in the MLR and SC neurons, along with comparative analysis of synchronized changes between these two regions. The results indicated that exercise significantly enhanced plasticity in both MLR and SC neurons, as evidenced by improved neuronal excitability (rheobase, Vth), PIC channel kinetics (onset and amplitude), and dendritic plasticity (dendritic length, branch points \u0026amp; terminals) in both regions (longitudinal data in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e). More importantly, comparative analysis of simultaneously measured parameters revealed that SC neurons exhibited significantly greater plasticity than MLR neurons, showing more pronounced changes in the following parameters: rheobase (SC: -0.45; MLR: -0.23), Vth (SC: -0.22; MLR: -0.11), PIC onset (SC: -0.14; MLR: -0.08) and amplitude (SC: 0.40; MLR: 0.18), Na-PIC onset (SC: -0.27; MLR: -0.22) and amplitude (SC: 0.29; MLR: 0.23), Ca-PIC amplitude (SC: 0.34; MLR: 0.25), dendritic length (SC: 0.56; MLR: 0.26), branch points (SC: 1.07; MLR: 0.39) and ends (SC: 0.61; MLR: 0.28). These findings collectively indicated that while exercise enhanced plasticity in both SC and MLR neurons, the SC motor system demonstrated significantly greater plasticity than the MLR motor system, suggesting that the SC motor system played a dominant role in the adaptive remodeling of the locomotor system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically elucidated the mechanisms by which moderate-intensity treadmill exercise modulated neuronal plasticity in both the MLR and SC regions. The results demonstrated that exercise intervention concurrently enhanced neuronal excitability, potentiated persistent inward currents (PICs), and promoted dendritic plasticity in these two key locomotor areas. The principal innovation of this research lies in our approach of simultaneously measuring and analyzing exercise-induced plasticity changes in both regions within the same animal preparations. Our findings revealed that the plastic changes induced by exercise intervention were more pronounced in SC neurons compared to MLR neurons. Furthermore, we demonstrated that within the spinal cord, exercise induced stronger plasticity in ventral SC neurons than in dorsal SC neurons. These two key findings indicated that during exercise-induced adaptation of the locomotor system: (1) the spinal cord motor system played a more crucial role than the MLR motor system, and (2) the ventral spinal circuitry controlling locomotor output contributed more significantly than the dorsal spinal pathways integrating proprioceptive information.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExercise enhanced neuronal excitability, PICs, and dendritic plasticity\u003c/h2\u003e \u003cp\u003eChronic exercise potentiates rodent spinal motoneuron excitability by altering key electrophysiological properties (Em, Vth, rheobase, firing frequency, AHP), which heightens synaptic sensitivity and optimizes motor output (Dai et al. 2025; Gardiner et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang \u0026amp; Dai 2020). This plasticity extends to spinal interneurons, where treadmill training hyperpolarizes Vth, reduces rheobase in ventromedial/laminar X populations, and increases AP amplitude in dorsal horn interneurons. Exercise also enhances PICs and facilities dendritic plasticity in the spinal neurons (Chen \u0026amp; Dai \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, chronic exercise enhances excitability in midbrain DRN 5-HT neurons through similar changes in membrane properties, PICs and dendritic developments (Ge \u0026amp; Dai \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), demonstrating a broad effect across spinal cord and midbrain regions. Nevertheless, how exercise simultaneously affects SC and MLR neurons is still unknown.\u003c/p\u003e \u003cp\u003eResults from the present study demonstrated that exercise intervention concurrently enhanced excitability in both SC and MLR neurons, primarily evidenced by reduced action potential voltage threshold and rheobase current (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparative analysis of simultaneously recorded SC and MLR neurons revealed a greater magnitude of excitability enhancement in SC neurons than in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), although the underlying mechanisms require further investigation. Similar to threshold modifications, exercise potentiated persistent inward currents (PICs) in both SC and MLR neurons, characterized by increased PIC amplitude and a hyperpolarizing shift in PIC activation voltage (onset) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Pharmacological experiments indicated that the enhanced PIC amplitude was primarily mediated by Ca-PIC, while the hyperpolarization of PIC onset was predominantly determined by Na-PIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings suggest that exercise intervention systematically modulates distinct PIC components to enhance neuronal excitability, thereby promoting adaptive plasticity within the motor system. Further analysis revealed that the exercise-induced increase in PIC amplitude was significantly larger in SC neurons than in MLR neurons, potentially explaining the greater enhancement of excitability in spinal neurons compared to MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, we observed that exercise significantly hyperpolarized Vth in both SC and MLR neurons (Fig.\u0026nbsp;7B1). However, this effect was abolished following Ca-PIC blockade (Figs.\u0026nbsp;7C1, 7E1), indicating that Ca-PIC had minimal influence on spike threshold. In contrast, exercise reduced both recruitment current (Irec) and de-recruitment current (Idec) in SC and MLR neurons (Figs.\u0026nbsp;7B2-4). Ca-PIC blockade significantly increased both Irec and Idec, with a more pronounced effect in SC neurons than in MLR neurons (Figs.\u0026nbsp;7C2-4, 7E2-3), suggesting that exercise enhanced sustained firing capacity in these neurons via upregulation of Ca-PIC. Unlike Ca-PIC, Na-PIC blockade significantly increased Vth, Irec, and Idec in both neuron types (Figs.\u0026nbsp;7D1-3), with greater changes observed in SC neurons (Figs.\u0026nbsp;7F1-3). This indicates that Na-PIC primarily governed neuronal excitability thresholds, and exercise-induced upregulation of Na-PIC enhanced both excitability and sustained firing capacity in SC and MLR neurons. These results further demonstrated that the greater exercise-induced enhancement of excitability in SC neurons compared to MLR neurons was mediated by complementary mechanisms involving both Na-PIC and Ca-PIC. This study also showed that treadmill exercise significantly enhanced dendritic plasticity in SC and MLR neurons. Sholl analysis revealed that exercise intervention increased total dendritic length, number of branch points, and number of dendritic terminals, with significantly greater enhancements in SC neurons than in MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Enhanced dendritic plasticity may strengthen the weight and efficacy of neural network connections, thereby improving motor system adaptability and learning capacity (Edgerton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Additionally, it may provide more potential sites for Cav1.3 channel expression, potentiating Ca-PIC-mediated modulation of neuronal excitability (Carlin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExercise coordinately enhanced plasticity in SC and MLR neurons\u003c/h2\u003e \u003cp\u003eLocomotor function is initiated and controlled by neural circuits located in the MLR and executed and coordinated by networks within the spinal cord. Motor commands originating from the MLR descend via brainstem pathways to spinal neural networks, where they are executed and regulated by central pattern generators (CPGs) distributing in the ventral spinal cord (Bouvier et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Caggiano et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; S. Grillner \u0026amp; A. El Manira, 2020; Leiras et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Simultaneously, somatosensory signals are integrated by dorsal spinal circuits to enable precise coordination and control of locomotion (Hsu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kiehn, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Exercise intervention essentially involves periodic acute and chronic modulation and adaptive remodeling of this entire neural pathway, from the MLR to the spinal cord, with the regulation of neuronal excitability being a core mechanism in this process (Dai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gardiner et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pearcey et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; K. E. Power et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur previous research demonstrated that exercise enhanced excitability in spinal neurons and midbrain 5-HT neurons (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, those studies specifically targeted lamina X neurons in the spinal cord and 5-HT neurons in the dorsal raphe nucleus (DRN), excluding other spinal interneurons and MLR neurons. Compared to these earlier studies, the present work introduces two major innovations: (1) The recorded SC neurons herein encompassed interneurons from both dorsal and ventral spinal regions, and the midbrain neurons cover the entire MLR area, constituting a systematic investigation of plasticity within the locomotor system. (2) We performed simultaneous measurements and analyses of MLR and SC neurons from the same mouse preparations. This approach allowed us to comprehensively and systematically study the effects of exercise on the locomotor initiation region (MLR), the execution region (ventral SC), and the proprioceptive integration region (dorsal SC). It also enabled us to investigate the mechanisms and differences in plasticity between the SC and MLR motor systems from three perspectives: intrinsic membrane properties, PIC channel characteristics, and dendritic plasticity, leading to the key discovery of the dominant role played by the SC motor system during exercise.\u003c/p\u003e \u003cp\u003eConsidering the results from SC and MLR independently, the observed neuronal plasticity closely mirrors our previous findings in lamina X neurons (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and DRN 5-HT neurons (Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, exercise enhanced excitability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), improved PIC regulatory properties (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and promoted dendritic plasticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) in both SC and MLR neurons. However, the results from simultaneous SC-MLR measurements revealed a distinctive and important finding: exercise induced greater plasticity in SC neurons than in MLR neurons. This conclusion was primarily supported by the larger magnitude of change induced by exercise in key neurophysiological parameters in SC neurons compared to MLR neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e). These parameter changes included the decrease in rheobase and Vth, the hyperpolarizing shift in the onset and the increase in amplitude of composite PIC, Na-PIC, and Ca-PIC, and the increases in dendritic length, branch points, and terminals. Decreases in rheobase and Vth altered intrinsic membrane properties, lowered the current and voltage thresholds for action potential initiation, and thereby enhanced neuronal excitability. The decrease in PIC onset and increase in amplitude enhanced sustained firing excitability through modulation of ion channel function (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The enhancement of dendritic plasticity further strengthened neural network connectivity, improving the precision and efficiency of neurotransmitter release, modulation, and motor control (Adkins et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wolpaw \u0026amp; Tennissen, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Consequently, we draw a significant conclusion: compared to the MLR motor system, the SC motor system plays a dominant role in the adaptation of the locomotor system induced by exercise.\u003c/p\u003e \u003cp\u003eNumerous factors may contribute to the greater magnitude of excitability changes in SC neurons compared to MLR neurons. These included inherent functional differences between the SC and MLR in motor control and, more importantly, their distinct positions within the neural circuitry. Spinal interneurons participate in mono- or polysynaptic spinal reflexes. Their excitability regulation involves shorter pathways with more direct responses, allowing for rapid adaptation to functional remodeling induced by motor exercise (C\u0026ocirc;t\u0026eacute; et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hultborn et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). In contrast, the MLR, as a higher-order motor center, influences motor output indirectly through multiple relays involving brainstem reticular formation neurons and spinal interneurons (Philippe Lacroix-Ouellette \u0026amp; R\u0026eacute;jean Dubuc, 2023; Noga \u0026amp; Whelan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, excitability changes in the MLR require integration and modulation through complex neural circuits, potentially resulting in a relatively slower and more moderated response to exercise intervention. The fundamental architectural differences between the SC and MLR networks likely constitute the primary reason for the more pronounced excitability changes observed in spinal neurons following exercise. This issue requires further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExercise induced adaptation of dorsal vs. ventral interneurons in the spinal cord\u003c/h2\u003e \u003cp\u003eThis study focuses on the plasticity of spinal interneurons. Functionally, the central pattern generator (CPG) networks located in the ventral spinal cord are primarily responsible for the rhythm generation and control of locomotion (Hsu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kiehn, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rossignol et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, neurons locating in the dorsal spinal cord are involved in coordinating various systemic functions, including sensory signal feedback and integration (Bourane et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Foster et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), precise regulation of CPG-mediated motor control (Goulding, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; H\u0026auml;gglund et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well as motor learning and adaptive remodeling (Edgerton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Takeoka et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In general, SC neurons in the dorsal and ventral spinal cord of rodents exhibit physiological properties highly aligned with their respective functions. Dorsal neurons are more specialized in the diverse and specific transmission as well as preliminary integration of sensory information (Usoskin et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), whereas ventral neurons\u0026mdash;particularly V2a interneurons\u0026mdash;play a pivotal role in generating motor rhythm and enabling precise control of movement intensity through their specific firing patterns and finely organized modular circuit structures (Song et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Dorsal neurons utilize diverse firing patterns and a higher action potential threshold to precisely encode complex sensory information, whereas ventral neurons ensure stable and reliable motor output through a relatively depolarized resting membrane potential, a lower action potential threshold, and regular tonic firing (Crozat et. al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results demonstrated that exercise enhanced the excitability of spinal neurons and promoted dendritic growth. However, the modulatory effects of exercise on dorsal versus ventral spinal interneurons remain poorly understood. To address this, we classified interneurons in laminae I-IV as dorsal and those in laminae V-X as ventral. The results revealed distinct differences between dorsal and ventral spinal interneurons in four key parameters: Vth, AHP half-width, Rin (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA), and the number of primary dendrites (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). These differences indicated that ventral neurons possessed higher intrinsic excitability than dorsal neurons. Exercise induced a hyperpolarization of Vth in both dorsal and ventral neurons (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB \u0026amp; C), while also causing a significant decrease in rheobase specifically in ventral neurons. Furthermore, exercise intervention enhanced dendritic plasticity in both populations (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC \u0026amp; D), with a significant increase in total dendritic length observed particularly in ventral neurons (Fig.\u0026nbsp;13D1). These findings indicated that exercise intervention concurrently enhanced excitability in both dorsal and ventral neurons, but the magnitude of this enhancement was greater in ventral neurons. A comparative analysis of the relative changes further showed that the decrease in rheobase, AP half-width, and AHP half-width, as well as the increase in dendritic length, were more pronounced in ventral neurons than in dorsal neurons after exercise (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD \u0026amp; \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE). Collectively, these results suggested that ventral interneurons played a dominant role in the adaptive remodeling of the motor system during exercise, indicating that the plasticity of the locomotor execution system surpassed that of the proprioceptive integration system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExercise induced adaptation of SC and MLR neurons vs. DRN 5-HT neurons\u003c/h2\u003e \u003cp\u003eThe MLR neurons and DRN 5-HT neurons are both located in the midbrain but reside in distinct functional regions. The MLR primarily governs the initiation and control of locomotion (Caggiano et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kiehn, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas DRN 5-HT neurons are implicated in depression and anxiety-related disorders (Albert et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gross \u0026amp; Hen, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Notably, the MLR encompasses two key subregions \u0026ndash; the cuneiform nucleus (CnF) and the pedunculopontine nucleus (PPN) \u0026ndash; with the PPN known to contain 5-HT neurons (Ge et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As our study did not differentiate between neurons from the CnF and PPN subregions, it was likely that our recorded MLR neuron sample included a subset of 5-HT neurons. Despite the distinct physiological functions of MLR neurons and DRN 5-HT neurons, the adaptive remodeling induced by exercise yielded remarkably similar outcomes in both populations. These shared enhancements included increased neuronal excitability, potentiated PIC regulation, and promoted dendritic plasticity (Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Strikingly similar results were also observed when comparing MLR and SC neurons. This consistent pattern suggests that the modulatory effects of exercise intervention selectively target fundamental neuronal membrane properties. The regulation of these core properties appears to be independent of the specific functional roles of the neural systems involved. Nevertheless, once exercise successfully remodels these intrinsic membrane properties, the functional output and adaptive capacity of the respective neural systems are consequently reshaped.\u003c/p\u003e \u003cp\u003eThe MLR, a central focus of this study, is a critical brain region for locomotor control. It integrates commands from higher brain centers and projects descending signals to the spinal cord to activate CPG networks, thereby generating rhythmic motor outputs for walking, running, and stopping (Bouvier et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Caggiano et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grillner \u0026amp; Manira, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Harris-Warrick, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Leiras et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Opris et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our findings demonstrated that exercise enhanced plasticity within both the MLR and SC motor systems. Specifically, the observed increase in neuronal excitability likely improved the speed and precision of motor control (Heckman, Hyngstrom, \u0026amp; Johnson, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The enhancement of PIC regulation probably augmented the duration and intensity of sustained neuronal firing (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), thereby refining the accuracy and efficiency of motor control (Josset et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rybak et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, the strengthening of dendritic plasticity was poised to reinforce synaptic connectivity within MLR and SC neural networks, potentially enhancing the system's adaptive capability, learning, and memory (Edgerton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Our results further indicated that the SC nervous system exhibited greater exercise-induced plasticity than the MLR system. This suggests that spinal cord-mediated locomotion plays a dominant role in the adaptive responses to motor training. However, the specific impact of this spinal dominance on the control of movement patterns (e.g., running, jumping, walking) and locomotion speed remains unclear and warrants further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional significance of exercise-induced plasticity in the MLR and SC neurons\u003c/h2\u003e \u003cp\u003eThis study found that exercise coordinately promoted neuronal plasticity in both the MLR and SC neurons, including increased neuronal excitability, enhanced persistent inward currents (PICs), and facilitated dendritic growth. Correlation analyses revealed that dendritic growth was a potential mechanism underlying the enhanced modulatory function of PICs, whereas the increase in PICs could directly increase neuronal excitability in terms of hyperpolarization of the voltage threshold and reduction in rheobase, thereby promoting adaptability of the locomotor system. Enhanced excitability of MLR neurons implicated that motor commands originating from the MLR could be initiated more rapidly and transmitted more reliably to spinal CPG neural networks, improving motor control and coordination (Caggiano et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rossignol et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Increased excitability of SC neurons could improve the stability of rhythmic generation, the adaptability of motor patterns, and the efficiency of neuromuscular coupling, ultimately recruiting more spinal motoneurons to generate stronger and more coordinated skeletal muscular force (Zhang et al. 2022; Grillner \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This study demonstrated that chronic exercise synchronously enhanced neuronal excitability in both SC and MLR neurons, promoting neuronal plasticity. Such changes in physiological function may enhance the adaptability of the locomotor system to environmental changes through multiple neurophysiological pathways including ion channel modulation, dendritic plasticity modifications, neurotransmitter release, and neurotrophic factor expression, operating at both acute and chronic states (Dai et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the perspective of neurorehabilitation, locomotor training improves post spinal cord injury (SCI) ambulation globally (Iddings et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and repetitive intensive training can activate sub-injury spinal circuits via afferent feedback (Rossignol et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our data showed that exercise training enhanced Na-PIC and Ca-PIC in MLR and SC neurons, which could facilitate post-SCI locomotor recovery. Results from this study provided a potential neurophysiological framework and foundation for optimizing exercise training strategies and developing rehabilitation paradigms for spinal cord injury.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eExercise concurrently enhanced neuronal excitability, PIC regulation, and dendritic plasticity in both MLR and SC regions. Dendritic plasticity increased PICs, and the potentiation of PICs directly elevated neuronal excitability, with Ca-PIC primarily determining the duration and frequency of sustained firing, while Na-PIC governed the threshold and capacity for action potential initiation. The enhancement of dendritic plasticity strengthened neural network connectivity and promoted the adaptive capacity of the motor system. The spinal motor system exhibited greater plasticity than the MLR motor system, whereas the spinal ventral locomotor system demonstrated stronger plasticity than the spinal dorsal proprioceptive system. This research elucidated the mechanisms underlying exercise-induced adaptation of the locomotor system from three integrated perspectives: cellular physiology, ion channel properties, and dendritic plasticity.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and ethical approval\u003c/h2\u003e \u003cp\u003eIn this study, we utilized a total of 50 healthy adult C57BL/6J mice (IMSR_JAX:000664), aged 42\u0026ndash;45 days. The mice were randomly assigned to different experimental groups at 21 days of age, prior to the start of the experiment, to minimize inter-group differences. The experimental protocol was approved by the Animal Ethics Committee of East China Normal University and strictly adhered to international guidelines for the care and use of laboratory animals (ARXM2023116). All animal procedures followed the ARRIVE guidelines. The animals were housed under controlled conditions: 3\u0026ndash;6 mice per cage, with ad libitum access to food and water, in a barrier environment maintained at 22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C and 50\u0026thinsp;\u0026plusmn;\u0026thinsp;10% humidity, under an artificially controlled light-dark cycle (light period: 07:00\u0026ndash;19:00). The inclusion criteria for the study required healthy, adult mice with no prior medical conditions, while the exclusion criteria included any mice exhibiting abnormal behavior, infection, or weight loss exceeding 20% during the acclimation period. The mice were selected with balanced gender distribution to account for sex as a biological variable. No attrition occurred during the study, as all enrolled animals completed the experimental procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eTreadmill exercise protocol\u003c/h2\u003e \u003cp\u003eThis study employed a moderate-intensity treadmill running protocol, adapted from a previously established design for mouse treadmill training (Fernando et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The experimental mice first underwent a 3-day acclimation period, running for 30 minutes daily at a speed of 5 m/min on a flat treadmill to adapt to the experimental setup. The formal training lasted for 3 weeks, conducted 6 days per week for 60 minutes each day. Each training session consisted of a 40-minute core phase followed by a 20-minute incremental phase. The core phase included a 5-minute warm-up (5 m/min) and 35 minutes of moderate-intensity training (10 m/min), aimed at enhancing endurance. The incremental phase was divided into three stages, each lasting 5 minutes at speeds of 12 m/min, 15 m/min, and 18 m/min, respectively, gradually increasing the exercise intensity, and concluded with a 5-minute cool-down at 5 m/min. All training sessions were scheduled between 08:00 and 09:30 daily to minimize circadian rhythm interference. Aside from the exercise intervention, both the control and exercise groups were housed under identical husbandry and environmental conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of slices\u003c/h2\u003e \u003cp\u003eMice of postnatal days 42\u0026ndash;45 were deeply anesthetized with 3% isoflurane, and decapitation for tissue harvesting was performed after the absence of reflex responses was confirmed. The brainstem and spinal cord were rapidly dissected in ice-cold artificial cerebrospinal fluid (aCSF), during which the aCSF was continuously bubbled with 95% O₂ and 5% CO₂ (carbogen) to maintain oxygenation. Transverse slices (130 \u0026micro;m thickness) of the prepared brainstem and spinal cord were obtained using a vibrating microtome (VT1000E, Leica Microsystems, Wetzlar, Germany) under ice-cold conditions. The slices were then transferred to recording aCSF and incubated at room temperature for at least 1 hour before subsequent patch-clamp recordings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eWhole-cell patch-clamp recordings\u003c/h2\u003e \u003cp\u003eThe whole-cell patch-clamp recording methodology was adapted from our previous studies (Chen \u0026amp; Dai, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with appropriate modifications. During experiments, brainstem and spinal cord slices were transferred to a recording chamber mounted on an upright microscope (BX50, Olympus, Tokyo, Japan) equipped with differential interference contrast (DIC) optics. The slices were continuously perfused at a rate of 2 ml/min with recording aCSF saturated with 95% O₂ and 5% CO₂ using a gravity-fed perfusion system. Neurons in the spinal cord and MLR were identified and selected for whole-cell patch-clamp recordings. Recording electrodes were fabricated from borosilicate glass capillaries (1B150F-4, World Precision Instruments, USA) using a P-1000 puller (Sutter Instrument Co., USA). When filled with the intracellular solution, the electrodes had a resistance of 5\u0026ndash;8 MΩ. The recording setup consisted of a MultiClamp 700B patch-clamp amplifier, an Axon Digidata 1550B data acquisition system, and pClamp 10.7 software (RRID:SCR_011323). Bridge balance compensation was applied in current-clamp mode, and 80\u0026ndash;85% series resistance compensation (initial series resistance 10\u0026ndash;30 MΩ) was applied in voltage-clamp mode. The series resistance (Rs) was carefully monitored and kept stable throughout control and drug application periods. Signals were low-pass filtered at 3 kHz and sampled at 10 kHz. All electrophysiological data were analyzed using Clampfit 10.7 software (Molecular Devices). All recordings were performed at room temperature (20\u0026ndash;22\u0026deg;C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of membrane parameters\u003c/h2\u003e \u003cp\u003eThis study utilized the current-clamp recording technique to evaluate the effects of exercise intervention on the excitability of spinal cord (SC) and MLR neurons. To assess changes in membrane properties, we measured the following parameters: resting membrane potential (Em), rheobase, action potential voltage threshold (Vth), action potential (AP) amplitude, AP half-width, afterhyperpolarization (AHP) amplitude, AHP half-width, and input resistance (Rin). The specific calculation methods followed a previous study (Ge \u0026amp; Dai, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Briefly, the rheobase was determined by injecting a series of 1.5-second step currents with 5 pA increments; the minimum current step that elicited neuronal firing was defined as the rheobase. For some spontaneously active neurons, the rheobase was defined as 0 pA. The action potential voltage threshold (Vth) was defined as the membrane potential at which the rate of membrane potential change (dV/dt) was \u0026ge;\u0026thinsp;10 mV/ms. The Vth value used was the average measured from the first three action potentials elicited by the rheobase current stimulus. The resting membrane potential (Em) was continuously monitored during recordings; the Em value reported in this study is the average membrane potential measured over the 100 ms period preceding the rheobase current injection. AP and AHP parameters were calculated based on the average of three action potentials elicited by the rheobase current. When calculating AP amplitude and AHP depth, the Vth was used as the reference point. To evaluate the contribution of persistent inward currents (PICs) to neuronal excitability, we injected a triangular ramp current into the neurons (10-second duration, peak amplitude 50\u0026ndash;80 pA, baseline current 0 pA, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and measured the recruitment current (Irec), the decruitment current (Idec), and their difference (ΔI\u0026thinsp;=\u0026thinsp;Idec-Irec). This stimulus protocol induces a slow ramp depolarization and subsequent slow ramp hyperpolarization in the neuron, allowing for analysis of the role of PICs in sustained neuronal firing. Additionally, in this study, we also measured the Vth of action potentials evoked by the triangular ramp current to analyze the modulatory effect of PICs on neuronal excitability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWe recorded PICs in voltage-clamp mode. PICs were evoked using a protocol of five slow ramp voltage commands, starting from an initial holding potential of -70 mV, increasing in 30 mV steps to a peak of +\u0026thinsp;40 mV, with each ramp lasting 10 seconds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). To ensure data consistency, PICs evoked by the fourth or fifth voltage ramp were selected for parameter analysis. For PIC measurement, the leak current was first subtracted from the total current. A baseline was drawn along the rising phase of the current trace; the current value at the last point of tangency between this baseline and the current trace was defined as the PIC onset current (Io), and the corresponding voltage was defined as the PIC onset potential. The peak PIC current (Ip) was defined as the lowest point (maximum inward current) on the current curve. The PIC amplitude was calculated as the difference between Ip and Io (PICs\u0026thinsp;=\u0026thinsp;Ip - Io). The measurement process was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD. This study only analyzed PIC data evoked during the rising phase of the voltage ramp, including the PIC onset potential and amplitude. For the concurrently measured neuronal excitability and PIC parameters, the percentage change after the exercise intervention was calculated (Δ% = [(post-intervention value - control mean)/control mean]\u0026times;100%) to assess the contribution of the exercise intervention to changes in neuronal excitability and PICs. A paired t-test was used to determine statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eImaging and Sholl analysis\u003c/h2\u003e \u003cp\u003eSpinal cord (SC) neurons and MLR neurons were identified based on their anatomical locations and were recorded and labeled using the whole-cell patch-clamp technique. During the measurement of neuronal electrophysiological parameters, 3% tetramethylrhodamine was added to the intracellular pipette solution to simultaneously label the recorded cells. After the fluorescent dye was injected into the cells for 5\u0026ndash;10 minutes, we immediately capture images of neuronal morphology in the slices, using a Nikon Eclipse Ni fluorescence microscope (equipped with a Nikon DS-Ri2 color digital camera). Sholl analysis was employed to analyze neuronal morphology. Concentric circles were drawn at 25 \u0026micro;m intervals centered on the soma to quantitatively assess dendritic complexity. Quantification parameters included the number of intersections between dendrites and concentric circles, dendritic segment length, soma diameter, number of dendritic terminals, and primary dendrite length. Image analysis was performed using ImageJ software (version 1.52g, RRID:SCR_003070) in combination with the Sholl Analysis and NeuronJ plugins (RRID:SCR_002074) (Meijering et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003eCorrelation analysis of neurophysiological parameters in SC and MLR neurons were performed in this study. A linear regression was applied to a group of selected paraments, and the correlation coefficient R\u003csup\u003e2\u003c/sup\u003e was used to divide the correlation into three categories: strong (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7), medium (0.4\u0026le;R\u003csup\u003e2\u003c/sup\u003e\u0026le;0.7) and weak correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.4).\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eSolutions and chemicals\u003c/h2\u003e \u003cp\u003e \u003cem\u003eDissection artificial cerebrospinal fluid\u003c/em\u003e (aCSF, in mM): 25 NaCl, 188 sucrose, 1.9 KCl, 1.2 NaH₂PO₄, 10 MgSO₄, 26 NaHCO₃, 1.5 kynurenic acid, 25 glucose, and 1.0 CaCl₂.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRecording aCSF\u003c/em\u003e (in mM): 125 NaCl, 2.5 KCl, 26 NaHCO₃, 1.25 NaH₂PO₄, 25 glucose, 1.0 MgCl₂, and 2.0 CaCl₂. For the voltage-clamp experiments of measuring PICs, 10 mM TEA-Cl was added to the Recording aCSF.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe intracellular recording solution\u003c/em\u003e (in mM): 130 K-gluconate, 10 NaCl, 10 HEPES, 2 MgCl₂, 5 Mg-ATP, and 0.5 GTP. For the voltage-clamp experiments measuring PICs, 20 mM TEA-Cl was added to the intracellular recording solution.\u003c/p\u003e \u003cp\u003eThe aCSF used during tissue dissection and recording was pH-adjusted to 7.3 using HCl, and the osmolarity was maintained at approximately 305 mOsm by supplementing with sucrose. All drug stock solutions were dissolved in DMSO and stored at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe persistent sodium current (Na-PIC) was blocked by bath applying 3 \u0026micro;M riluzole (HY-B0211, MCE) to the recording solution. The persistent calcium current (Ca-PIC), specifically the L-type calcium channel current (Cav1.3), was blocked by bath administrating 25 \u0026micro;M nimodipine (HY-B0265, MCE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, Microsoft Excel (Office 2020) (RRID:SCR_016137) was used for data formatting, and Prism (RRID:SCR_002798) was employed for statistical analysis. Data are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Based on the data characteristics, both unpaired (two-tailed) and paired t-tests were performed for statistical analysis. Statistical significance was determined using a P-value threshold of \u0026lt;\u0026thinsp;0.05. The results section includes the t-statistics, p-values, degrees of freedom, and effect size with confidence intervals. All graphs and figures were created using GraphPad Prism 9.0 software (RRID:SCR_002798) and the Python (RRID:SCR_024202) environment with Seaborn V.0.10.0, DABEST, and Matplotlib v.3.1.3 (RRID:SCR_008624) (Ho et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e \u003cp\u003eConceptualization: YD; Methodology: YC, LY; Investigation: LY, YC, XW; Analysis: LY, YC; Visualization: LY, YC; Writing \u0026ndash; Original Draft: YD, LY; Writing \u0026ndash; Review \u0026amp; Editing: YD, LY; Supervision: YD, YC; Funding acquisition: YD.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study is supported by National Nature Science Foundation of China to YD (Grant No. 32171129; No. 32471187). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdkins, D. L., Boychuk, J., Remple, M. S., \u0026amp; Kleim, J. A. (2006). Motor training induces experience-specific patterns of plasticity across motor cortex and spinal cord. \u003cem\u003eJOURNAL OF APPLIED PHYSIOLOGY\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(6), 1776-1782. https://doi.org/10.1152/japplphysiol.00515.2006 \u003c/li\u003e\n\u003cli\u003eAlbert, P. R., Vahid-Ansari, F., \u0026amp; Luckhart, C. (2014). Serotonin-prefrontal cortical circuitry in anxiety and depression phenotypes: pivotal role of pre- and post-synaptic 5-HT1A receptor expression. \u003cem\u003eFrontiers in Behavioral Neuroscience\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e. https://doi.org/10.3389/fnbeh.2014.00199 \u003c/li\u003e\n\u003cli\u003eBinder, M. D., Powers, R. K., \u0026amp; Heckman, C. J. (2020). Nonlinear Input-Output Functions of Motoneurons. \u003cem\u003ePhysiology (Bethesda)\u003c/em\u003e,\u003cem\u003e 35\u003c/em\u003e(1), 31-39. https://doi.org/10.1152/physiol.00026.2019 \u003c/li\u003e\n\u003cli\u003eBourane, S., Grossmann, Katja S., Britz, O., Dalet, A., Del Barrio, Marta G., Stam, Floor J., Garcia-Campmany, L., Koch, S., \u0026amp; Goulding, M. (2015). Identification of a Spinal Circuit for Light Touch and Fine Motor Control. \u003cem\u003eCell\u003c/em\u003e,\u003cem\u003e 160\u003c/em\u003e(3), 503-515. https://doi.org/10.1016/j.cell.2015.01.011 \u003c/li\u003e\n\u003cli\u003eBouvier, J., Caggiano, V., Leiras, R., Caldeira, V., Bellardita, C., Balueva, K., Fuchs, A., \u0026amp; Kiehn, O. (2015). Descending Command Neurons in the Brainstem that Halt Locomotion. \u003cem\u003eCell\u003c/em\u003e,\u003cem\u003e 163\u003c/em\u003e(5), 1191-1203. https://doi.org/10.1016/j.cell.2015.10.074 \u003c/li\u003e\n\u003cli\u003eBrownstone, R. M., \u0026amp; Chopek, J. W. (2018). Reticulospinal Systems for Tuning Motor Commands. \u003cem\u003eFront Neural Circuits\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e, 30. https://doi.org/10.3389/fncir.2018.00030 \u003c/li\u003e\n\u003cli\u003eButton, D. C., \u0026amp; Kalmar, J. M. (2019). Understanding exercise-dependent plasticity of motoneurons using intracellular and intramuscular approaches. \u003cem\u003eApplied Physiology, Nutrition, and Metabolism\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(11), 1125-1133. https://doi.org/10.1139/apnm-2018-0862 \u003c/li\u003e\n\u003cli\u003eCaggiano, V., Leiras, R., Go\u0026ntilde;i-Erro, H., Masini, D., Bellardita, C., Bouvier, J., Caldeira, V., Fisone, G., \u0026amp; Kiehn, O. (2018). Midbrain circuits that set locomotor speed and gait selection. \u003cem\u003eNature\u003c/em\u003e,\u003cem\u003e 553\u003c/em\u003e(7689), 455-460. https://doi.org/10.1038/nature25448 \u003c/li\u003e\n\u003cli\u003eCarlin, K. P., Jiang, Z., \u0026amp; Brownstone, R. M. (2000). Characterization of calcium currents in functionally mature mouse spinal motoneurons. \u003cem\u003eEuropean Journal of Neuroscience\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(5), 1624-1634. https://doi.org/10.1046/j.1460-9568.2000.00050.x \u003c/li\u003e\n\u003cli\u003eChen, K., \u0026amp; Dai, Y. (2022). Chronic exercise increases excitability of lamina X neurons through enhancement of persistent inward currents and dendritic development in mice. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 600\u003c/em\u003e(16), 3775-3793. https://doi.org/10.1113/JP283037 \u003c/li\u003e\n\u003cli\u003eChen, K., Ge, R., Cheng, Y., \u0026amp; Dai, Y. (2019). Three-week treadmill training changes the electrophysiological properties of spinal interneurons in the mice. \u003cem\u003eExp Brain Res\u003c/em\u003e,\u003cem\u003e 237\u003c/em\u003e(11), 2925-2938. https://doi.org/10.1007/s00221-019-05647-3 \u003c/li\u003e\n\u003cli\u003eCheng, Y., Song, N., Ge, R., \u0026amp; Dai, Y. (2021). Serotonergic Modulation of Persistent Inward Currents in Serotonergic Neurons of Medulla in ePet-EYFP Mice. \u003cem\u003eFront Neural Circuits\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e, 657445. https://doi.org/10.3389/fncir.2021.657445 \u003c/li\u003e\n\u003cli\u003eCheng, Y., Wang, X., Zhang, Q., Ge, R., Zhou, M., \u0026amp; Dai, Y. (2025). Multiple patterns of persistent inward currents with multiple types of repetitive firings in medullary serotonergic neurons of mice: An experimental and modeling study. \u003cem\u003ePLoS Comput Biol\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(4), e1012918. https://doi.org/10.1371/journal.pcbi.1012918 \u003c/li\u003e\n\u003cli\u003eCheng, Y., Zhang, Q., \u0026amp; Dai, Y. (2020). Sequential activation of multiple persistent inward currents induces staircase currents in serotonergic neurons of medulla in ePet-EYFP mice. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 123\u003c/em\u003e(1), 277-288. https://doi.org/10.1152/jn.00623.2019 \u003c/li\u003e\n\u003cli\u003eCormery, B., Beaumont, E., Csukly, K., \u0026amp; Gardiner, P. (2005). Hindlimb unweighting for 2 weeks alters physiological properties of rat hindlimb motoneurones. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e,\u003cem\u003e 568\u003c/em\u003e(3), 841-850. https://doi.org/10.1113/jphysiol.2005.091835\u003c/li\u003e\n\u003cli\u003eC\u0026ocirc;t\u0026eacute;, M. P., Murray, L. M., \u0026amp; Knikou, M. (2018). Spinal Control of Locomotion: Individual Neurons, Their Circuits and Functions. \u003cem\u003eFront Physiol\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, 784. https://doi.org/10.3389/fphys.2018.00784\u003c/li\u003e\n\u003cli\u003eCrozat, E., Edith Blasco, Jorge Ramirez-Franco, Priscille Riondel, Nina Jurcic, Riad Seddik, Caroline Michelle, J\u0026eacute;r\u0026ocirc;me Trouslard \u0026amp; Nicolas Wanaverbecq (2025) Cerebrospinal fluid-contacting neurons are sensory neurons with uniform morphological and region-specific electrophysiological properties in the mouse spinal cord. \u003cem\u003ecommunications biology\u003c/em\u003e8, 1233. https://doi.org/10.1038/s42003-025-08559-x \u003c/li\u003e\n\u003cli\u003eDai, Y., Cheng, Y., Ge, R., Chen, K., \u0026amp; Yang, L. (2024). Exercise-induced adaptation of neurons in the vertebrate locomotor system. \u003cem\u003eJ Sport Health Sci\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(2), 160-171. https://doi.org/10.1016/j.jshs.2023.10.006 \u003c/li\u003e\n\u003cli\u003eDai, Y., \u0026amp; Jordan, L. M. (2010). Multiple patterns and components of persistent inward current with serotonergic modulation in locomotor activity-related neurons in Cfos-EGFP mice. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 103\u003c/em\u003e(4), 1712-1727. https://doi.org/10.1152/jn.01111.2009 \u003c/li\u003e\n\u003cli\u003eDeutsch, A. J., \u0026amp; Elbasiouny, S. M. (2024). Dysregulation of persistent inward and outward currents in spinal motoneurons of symptomatic SOD1-G93A mice. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 602\u003c/em\u003e(15), 3715-3736. https://doi.org/10.1113/jp286032 \u003c/li\u003e\n\u003cli\u003eEdgerton, V. R., Tillakaratne, N. J., Bigbee, A. J., de Leon, R. D., \u0026amp; Roy, R. R. (2004). Plasticity of the spinal neural circuitry after injury. \u003cem\u003eAnnu Rev Neurosci\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e, 145-167. https://doi.org/10.1146/annurev.neuro.27.070203.144308 \u003c/li\u003e\n\u003cli\u003eElBasiouny, S. M., Schuster, J. E., \u0026amp; Heckman, C. J. (2010). Persistent inward currents in spinal motoneurons: important for normal function but potentially harmful after spinal cord injury and in amyotrophic lateral sclerosis. \u003cem\u003eClin Neurophysiol\u003c/em\u003e,\u003cem\u003e 121\u003c/em\u003e(10), 1669-1679. https://doi.org/10.1016/j.clinph.2009.12.041 \u003c/li\u003e\n\u003cli\u003eFernando, P., Bonen, A., \u0026amp; Hoffman-Goetz, L. (1993). Predicting submaximal oxygen consumption during treadmill running in mice. \u003cem\u003eCan J Physiol Pharmacol\u003c/em\u003e,\u003cem\u003e 71\u003c/em\u003e(10-11), 854-857. https://doi.org/10.1139/y93-128 \u003c/li\u003e\n\u003cli\u003eFoster, E., Wildner, H., Tudeau, L., Haueter, S., Ralvenius, William T., Jegen, M., Johannssen, H., H\u0026ouml;sli, L., Haenraets, K., Ghanem, A., Conzelmann, K.-K., B\u0026ouml;sl, M., \u0026amp; Zeilhofer, Hanns U. (2015). Targeted Ablation, Silencing, and Activation Establish Glycinergic Dorsal Horn Neurons as Key Components of a Spinal Gate for Pain and Itch. \u003cem\u003eNeuron\u003c/em\u003e,\u003cem\u003e 85\u003c/em\u003e(6), 1289-1304. https://doi.org/10.1016/j.neuron.2015.02.028 \u003c/li\u003e\n\u003cli\u003eGardiner, P., Dai, Y., \u0026amp; Heckman, C. J. (2006). Effects of exercise training on alpha-motoneurons. \u003cem\u003eJ Appl Physiol (1985)\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(4), 1228-1236. https://doi.org/10.1152/japplphysiol.00482.2006 \u003c/li\u003e\n\u003cli\u003eGe, R., Chen, K., Cheng, Y., \u0026amp; Dai, Y. (2019). Morphological and electrophysiological properties of serotonin neurons with NMDA modulation in the mesencephalic locomotor region of neonatal ePet-EYFP mice. \u003cem\u003eExp Brain Res\u003c/em\u003e,\u003cem\u003e 237\u003c/em\u003e(12), 3333-3350. https://doi.org/10.1007/s00221-019-05675-z \u003c/li\u003e\n\u003cli\u003eGe, R., \u0026amp; Dai, Y. (2020). Three-Week Treadmill Exercise Enhances Persistent Inward Currents, Facilitates Dendritic Plasticity, and Upregulates the Excitability of Dorsal Raphe Serotonin Neurons in ePet-EYFP Mice. \u003cem\u003eFront Cell Neurosci\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e, 575626. https://doi.org/10.3389/fncel.2020.575626 \u003c/li\u003e\n\u003cli\u003eGoulding, M. (2009). Circuits controlling vertebrate locomotion: moving in a new direction. \u003cem\u003eNature Reviews Neuroscience\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(7), 507-518. https://doi.org/10.1038/nrn2608 \u003c/li\u003e\n\u003cli\u003eGrillner, S., \u0026amp; El Manira, A. (2020). Current Principles of Motor Control with Special Reference to Vertebrate Locomotion. \u003cem\u003ePhysiol Rev\u003c/em\u003e,\u003cem\u003e 100\u003c/em\u003e(1), 271-320. https://doi.org/10.1152/physrev.00015.2019 \u003c/li\u003e\n\u003cli\u003eGrillner, S. (2003) The motor infrastructure: from ion channels to neuronal networks. \u003cem\u003eNature Reviews Neuroscience\u003c/em\u003e 4(7):573-86. https://doi.org/10.1038/nrn1137\u003c/li\u003e\n\u003cli\u003eGross, C., \u0026amp; Hen, R. (2004). The developmental origins of anxiety. \u003cem\u003eNature Reviews Neuroscience\u003c/em\u003e,\u003cem\u003e 5\u003c/em\u003e(7), 545-552. https://doi.org/10.1038/nrn1429 \u003c/li\u003e\n\u003cli\u003eH\u0026auml;gglund, M., Borgius, L., Dougherty, K. J., \u0026amp; Kiehn, O. (2010). Activation of groups of excitatory neurons in the mammalian spinal cord or hindbrain evokes locomotion. \u003cem\u003eNature Neuroscience\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(2), 246-252. https://doi.org/10.1038/nn.2482 \u003c/li\u003e\n\u003cli\u003eHarris-Warrick, R. M. (2010). General Principles of Rhythmogenesis in Central Pattern Generator Networks. In \u003cem\u003eProgress in Brain Research\u003c/em\u003e (pp. 213-222): Elsevier.\u003c/li\u003e\n\u003cli\u003eHarvey, P. J., Li, Y., Li, X., \u0026amp; Bennett, D. J. (2006). Persistent sodium currents and repetitive firing in motoneurons of the sacrocaudal spinal cord of adult rats. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(3), 1141-1157. https://doi.org/10.1152/jn.00335.2005 \u003c/li\u003e\n\u003cli\u003eHassan, A. S., Fajardo, M. E., Cummings, M., McPherson, L. M., Negro, F., Dewald, J. P. A., Heckman, C. J., \u0026amp; Pearcey, G. E. P. (2021). Estimates of persistent inward currents are reduced in upper limb motor units of older adults. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 599\u003c/em\u003e(21), 4865-4882. https://doi.org/10.1113/jp282063 \u003c/li\u003e\n\u003cli\u003eHeckman, C. J., Hyngstrom, A. S., \u0026amp; Johnson, M. D. (2008). Active properties of motoneurone dendrites: diffuse descending neuromodulation, focused local inhibition. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e,\u003cem\u003e 586\u003c/em\u003e(5), 1225-1231. https://doi.org/10.1113/jphysiol.2007.145078 \u003c/li\u003e\n\u003cli\u003eHeckman, C. J., Johnson, M., Mottram, C., \u0026amp; Schuster, J. (2008). Persistent inward currents in spinal motoneurons and their influence on human motoneuron firing patterns. \u003cem\u003eNeuroscientist\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(3), 264-275. https://doi.org/10.1177/1073858408314986 \u003c/li\u003e\n\u003cli\u003eHo, J., Tumkaya, T., Aryal, S., Choi, H., \u0026amp; Claridge-Chang, A. (2019). Moving beyond P values: data analysis with estimation graphics. \u003cem\u003eNature Methods\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(7), 565-566. https://doi.org/10.1038/s41592-019-0470-3 \u003c/li\u003e\n\u003cli\u003eHsu, L. J., Bertho, M., \u0026amp; Kiehn, O. (2023). Deconstructing the modular organization and real-time dynamics of mammalian spinal locomotor networks. \u003cem\u003eNat Commun\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(1), 873. https://doi.org/10.1038/s41467-023-36587-w \u003c/li\u003e\n\u003cli\u003eHultborn, H., Jankowska, E., \u0026amp; Lindstr\u0026ouml;m, S. (1971). Recurrent inhibition of interneurones monosynaptically activated from group Ia afferents. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 215\u003c/em\u003e(3), 613-636. https://doi.org/10.1113/jphysiol.1971.sp009488\u003c/li\u003e\n\u003cli\u003eIddings JA, Zarkou A \u0026amp; Field-Fote EC. (2021). Noninvasive neuromodulation and rehabilitation to promote functional restoration in persons with spinal cord injury. \u003cem\u003eCURR OPIN NEUROL\u003c/em\u003e 34, 812-818.https://doi.org/10.1097/WCO.0000000000000997\u003c/li\u003e\n\u003cli\u003eJiang, Z., Rempel, J., Li, J., Sawchuk, M. A., Carlin, K. P., \u0026amp; Brownstone, R. M. (1999). Development of L-type calcium channels and a nifedipine-sensitive motor activity in the postnatal mouse spinal cord. \u003cem\u003eEuropean Journal of Neuroscience\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(10), 3481-3487. https://doi.org/10.1046/j.1460-9568.1999.00765.x \u003c/li\u003e\n\u003cli\u003eJosset, N., Roussel, M., Lemieux, M., Lafrance-Zoubga, D., Rastqar, A., \u0026amp; Bretzner, F. (2018). Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse. \u003cem\u003eCurrent Biology\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e(6), 884-901.e883. https://doi.org/10.1016/j.cub.2018.02.007 \u003c/li\u003e\n\u003cli\u003eKiehn, O. (2011). Development and functional organization of spinal locomotor circuits. \u003cem\u003eCurrent Opinion in Neurobiology\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(1), 100-109. https://doi.org/https://doi.org/10.1016/j.conb.2010.09.004 \u003c/li\u003e\n\u003cli\u003eKiehn, O. (2016). Decoding the organization of spinal circuits that control locomotion. \u003cem\u003eNature Reviews Neuroscience\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(4), 224-238. https://doi.org/10.1038/nrn.2016.9 \u003c/li\u003e\n\u003cli\u003eKrawitz, S., Fedirchuk, B., Dai, Y., Jordan, L. M., \u0026amp; McCrea, D. A. (2001). State-dependent hyperpolarization of voltage threshold enhances motoneurone excitability during fictive locomotion in the cat. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 532\u003c/em\u003e(Pt 1), 271-281. https://doi.org/10.1111/j.1469-7793.2001.0271g.x \u003c/li\u003e\n\u003cli\u003eKuo, J. J., Lee, R. H., Zhang, L., \u0026amp; Heckman, C. J. (2006). Essential role of the persistent sodium current in spike initiation during slowly rising inputs in mouse spinal neurones. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 574\u003c/em\u003e(Pt 3), 819-834. https://doi.org/10.1113/jphysiol.2006.107094 \u003c/li\u003e\n\u003cli\u003eLacroix-Ouellette, P., \u0026amp; Dubuc, R. (2023). Brainstem neural mechanisms controlling locomotion with special reference to basal vertebrates [Mini Review]. \u003cem\u003eFrontiers in Neural Circuits\u003c/em\u003e,\u003cem\u003e Volume 17 - 2023 \u003c/em\u003ehttps://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2023.910207 \u003c/li\u003e\n\u003cli\u003eLacroix-Ouellette, P., \u0026amp; Dubuc, R. (2023). Brainstem neural mechanisms controlling locomotion with special reference to basal vertebrates. \u003cem\u003eFront Neural Circuits\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e, 910207. https://doi.org/10.3389/fncir.2023.910207 \u003c/li\u003e\n\u003cli\u003eLee, R. H., \u0026amp; Heckman, C. J. (1999). Paradoxical effect of QX-314 on persistent inward currents and bistable behavior in spinal motoneurons in vivo. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 82\u003c/em\u003e(5), 2518-2527. https://doi.org/10.1152/jn.1999.82.5.2518 \u003c/li\u003e\n\u003cli\u003eLeiras, R., Cregg, J. M., \u0026amp; Kiehn, O. (2022). Brainstem Circuits for Locomotion. \u003cem\u003eAnnual Review of Neuroscience\u003c/em\u003e,\u003cem\u003e 45\u003c/em\u003e(1), 63-85. https://doi.org/10.1146/annurev-neuro-082321-025137 \u003c/li\u003e\n\u003cli\u003eLockyer, E. J., Alizadeh, S., Compton, C. T., Button, D. C., \u0026amp; Power, K. E. (2023). Two weeks of arm cycling sprint interval training enhances spinal excitability to the biceps brachii. \u003cem\u003eJ Appl Physiol (1985)\u003c/em\u003e,\u003cem\u003e 134\u003c/em\u003e(6), 1422-1437. https://doi.org/10.1152/japplphysiol.00367.2022 \u003c/li\u003e\n\u003cli\u003eMacDonell, C. W., Power, K. E., Chopek, J. W., Gardiner, K. R., \u0026amp; Gardiner, P. F. (2015). Extensor motoneurone properties are altered immediately before and during fictive locomotion in the adult decerebrate rat. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e,\u003cem\u003e 593\u003c/em\u003e(10), 2327-2342. https://doi.org/10.1113/jp270239 \u003c/li\u003e\n\u003cli\u003eMeijering, E., Jacob, M., Sarria, J. C. F., Steiner, P., Hirling, H., \u0026amp; Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. \u003cem\u003eCytometry Part A\u003c/em\u003e,\u003cem\u003e 58A\u003c/em\u003e(2), 167-176. https://doi.org/10.1002/cyto.a.20022 \u003c/li\u003e\n\u003cli\u003eMoritz, A. T., Newkirk, G., Powers, R. K., \u0026amp; Binder, M. D. (2007). Facilitation of somatic calcium channels can evoke prolonged tail currents in rat hypoglossal motoneurons. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 98\u003c/em\u003e(2), 1042-1047. https://doi.org/10.1152/jn.01294.2006 \u003c/li\u003e\n\u003cli\u003eNoga, B. R., \u0026amp; Whelan, P. J. (2022). The Mesencephalic Locomotor Region: Beyond Locomotor Control [Review]. \u003cem\u003eFrontiers in Neural Circuits\u003c/em\u003e,\u003cem\u003e Volume 16 - 2022\u003c/em\u003e. https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2022.884785 \u003c/li\u003e\n\u003cli\u003eOpris, I., Dai, X., Johnson, D. M. G., Sanchez, F. J., Villamil, L. M., Xie, S., Lee-Hauser, C. R., Chang, S., Jordan, L. M., \u0026amp; Noga, B. R. (2019). Activation of Brainstem Neurons During Mesencephalic Locomotor Region-Evoked Locomotion in the Cat. \u003cem\u003eFrontiers in Systems Neuroscience\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e. https://doi.org/10.3389/fnsys.2019.00069 \u003c/li\u003e\n\u003cli\u003eOrssatto, L. B. R., Fernandes, G. L., Blazevich, A. J., \u0026amp; Trajano, G. S. (2022). Facilitation-inhibition control of motor neuronal persistent inward currents in young and older adults. \u003cem\u003eJ Physiol\u003c/em\u003e,\u003cem\u003e 600\u003c/em\u003e(23), 5101-5117. https://doi.org/10.1113/jp283708 \u003c/li\u003e\n\u003cli\u003ePearcey, G. E. P., Alizedah, S., Power, K. E., \u0026amp; Button, D. C. (2021). Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain? \u003cem\u003eEur J Appl Physiol\u003c/em\u003e,\u003cem\u003e 121\u003c/em\u003e(9), 2413-2422. https://doi.org/10.1007/s00421-021-04730-4 \u003c/li\u003e\n\u003cli\u003ePower, K. E., Lockyer, E. J., Botter, A., Vieira, T., \u0026amp; Button, D. C. (2022). Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans. \u003cem\u003eEur J Appl Physiol\u003c/em\u003e,\u003cem\u003e 122\u003c/em\u003e(6), 1367-1381. https://doi.org/10.1007/s00421-022-04918-2 \u003c/li\u003e\n\u003cli\u003eRossignol S, Frigon A, Barri\u0026egrave;re G, Martinez M, Barth\u0026eacute;lemy D, Bouyer L, B\u0026eacute;langer M, Provencher J, Chau C \u0026amp; Brustein E. (2011). Spinal plasticity in the recovery of locomotion. \u003cem\u003ePROG BRAIN RES\u003c/em\u003e 188, 229-241.https://doi.org/10.1016/B978-0-444-53825-3.00021-8\u003c/li\u003e\n\u003cli\u003eRossignol, S., Dubuc, R., \u0026amp; Gossard, J.-P. (2006). Dynamic Sensorimotor Interactions in Locomotion. \u003cem\u003ePhysiological Reviews\u003c/em\u003e,\u003cem\u003e 86\u003c/em\u003e(1), 89-154. https://doi.org/10.1152/physrev.00028.2005 \u003c/li\u003e\n\u003cli\u003eRybak, I. A., Shevtsova, N. A., Lafreniere-Roula, M., \u0026amp; McCrea, D. A. (2006). Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e,\u003cem\u003e 577\u003c/em\u003e(2), 617-639. https://doi.org/10.1113/jphysiol.2006.118703 \u003c/li\u003e\n\u003cli\u003eSong, J., Ampatzis, K., Bj\u0026ouml;rnfors, E. R., \u0026amp; El Manira, A. (2018). Motor neurons control locomotor circuit function retrogradely via gap junctions. \u003cem\u003eNature Communications\u003c/em\u003e, 9(1), 5016. https://doi.org/10.1038/nature16497\u003c/li\u003e\n\u003cli\u003eSvirskis, G., \u0026amp; Hounsgaard, J. (1997). Depolarization-induced facilitation of a plateau-generating current in ventral horn neurons in the turtle spinal cord. \u003cem\u003eJ Neurophysiol\u003c/em\u003e,\u003cem\u003e 78\u003c/em\u003e(3), 1740-1742. https://doi.org/10.1152/jn.1997.78.3.1740 \u003c/li\u003e\n\u003cli\u003eTakeoka, A., Vollenweider, I., Courtine, G., \u0026amp; Arber, S. (2014). Muscle Spindle Feedback Directs Locomotor Recovery and Circuit Reorganization after Spinal Cord Injury. \u003cem\u003eCell\u003c/em\u003e,\u003cem\u003e 159\u003c/em\u003e(7), 1626-1639. https://doi.org/10.1016/j.cell.2014.11.019 \u003c/li\u003e\n\u003cli\u003eUsoskin, D., Furlan, A., Islam, S., Abdo, H., L\u0026ouml;nnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggstr\u0026ouml;m, J., Kharchenko, O., Kharchenko, P. V., Linnarsson, S., \u0026amp; \u003cstrong\u003eZhang, X\u003c/strong\u003e. (2015). Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. \u003cem\u003eCell Research\u003c/em\u003e, 25(7), 801\u0026ndash;813. https://doi.org/10.1038/nn.3881\u003c/li\u003e\n\u003cli\u003eWolpaw, J. R., \u0026amp; Tennissen, A. M. (2001). Activity-Dependent Spinal Cord Plasticity in Health and Disease. \u003cem\u003eAnnual Review of Neuroscience\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(1), 807-843. https://doi.org/10.1146/annurev.neuro.24.1.807 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Exercise, Plasticity, Motor Control, Locomotion, Neuromodulation","lastPublishedDoi":"10.21203/rs.3.rs-8363166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8363166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLocomotion involves circuits connecting mesencephalic locomotor region (MLR) and spinal cord (SC). Although chronic exercise improves neuronal adaptability, its impact on functional and structural plasticity along the MLR\u0026ndash;SC pathway remains unclear. Here, we examined exercise-induced neuroplasticity in MLR and lumbar SC neurons using whole-cell patch-clamp recordings from P42-P45 mice after three-week treadmill exercise. Key findings include: (1) Exercise increased excitability, shown by lowering rheobase and voltage threshold, with ventral SC neurons more affected than dorsal ones; (2) Exercise enhanced persistent inward currents (PICs) in terms of hyperpolarizing onset voltage and increasing amplitude, with effects stronger in SC than MLR neurons. Pharmacological data indicated calcium‑mediated PICs modulated firing duration/frequency, while sodium‑mediated PICs influenced threshold/capacity; (3) Exercise increased dendritic complexity (total length, branch points, and terminals), more markedly in SC versus MLR neurons; (4) Ventral spinal neurons displayed greater dendritic complexity than dorsal neurons, and were more modulated by exercise; (5) Correlation suggested exercise-driven dendritic plasticity potentiated PICs and excitability, collectively promoting locomotor adaptation. These results revealed an exercise-induced, coordinated plasticity throughout locomotor system, wherein spinal circuits, particularly ventral components, exhibited greater functional and structural adaptability than MLR. This study provided electrophysiological, ionic, and morphological insights into activity-dependent neural adaptation.\u003c/p\u003e","manuscriptTitle":"Exercise Coordinates Neural Plasticity from the Mesencephalic Locomotor Region to the Spinal Cord in Mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-02 07:05:56","doi":"10.21203/rs.3.rs-8363166/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"275bdf1f-baa3-4bbc-8bbe-241a2da14214","owner":[],"postedDate":"January 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59772429,"name":"Biological sciences/Physiology/Neurophysiology"},{"id":59772430,"name":"Biological sciences/Neuroscience/Motor control/Spinal cord"},{"id":59772431,"name":"Biological sciences/Neuroscience/Neuronal physiology/Excitability"}],"tags":[],"updatedAt":"2026-01-07T18:35:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-02 07:05:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8363166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8363166","identity":"rs-8363166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}