Multi-target transcranial alternating current stimulation (tACS) enhances motor learning and brain network connection in middle-aged adults

Multi-target transcranial alternating current stimulation (tACS) enhances motor learning and brain network connection in middle-aged adults | 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 Multi-target transcranial alternating current stimulation (tACS) enhances motor learning and brain network connection in middle-aged adults xiaoming Yu, minghui Lai, Cong Wang, yan Lu, En-Bang Zhang, Fu Wang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7378253/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 Background 40 Hz transcranial alternating current stimulation (tACS) enhances motor learning, but single-target effects and mechanisms remain unclear. We proposed multi-target tACS to improve efficacy. Methods Twenty-five healthy adults (>45 years) were randomized into sham (A), single-target (B), double-target (C), and multi-target (D) tACS groups. Outcomes included sequence reaction time task (SRTT), transcranial magnetic stimulation (TMS), MRI (gray matter density, activation and functional connectivity (FC)), and RNA sequencing. Results SRTT showed that group D significantly shortened the reaction time and error rate compared to baseline. TMS results indicate increased cortical excitability before and after tACS intervention, but no significant difference exists. MRI results showed that the gray matter density in the right middle frontal gyrus (MFG), including the dorsolateral prefrontal cortex (DLPFC) of group D, significantly increased. The activation value of group D in the frontal lobe (left) and cerebellum (left) is substantially higher than that of the other three groups. The functional connection (FC) of motor-cognitive-related brain networks, including primary motor cortex (M1) and frontal lobe and supplementary motor area (SMA), was significantly improved in group D. RNA sequencing analysis revealed a significant increase in oxygen metabolism of group D when compared to group C. Conclusion Multi-target tACS enhances motor learning, likely by activating left frontal and cerebellar regions, strengthening M1-frontal-SMA connectivity, and boosting oxygen metabolism. Biological sciences/Neuroscience/Learning and memory/Cortex Health sciences/Medical research/Clinical trial design/Randomized controlled trials Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Motor learning, the process of acquiring and refining motor skills through practice and experience, plays a crucial role in motor function[ 1 ]. Healthy aging is associated with a decline in motor function that can seriously affect the quality of life and safety [ 2 ]. Motor learning is a complex process that relies on the dynamic interaction of distributed neural networks, particularly those involving the primary motor cortex (M1), supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC), and cerebellum (CB)[ 3 – 5 ]. Recent evidence suggests that synchronized oscillatory activity, especially in the gamma (30-50Hz) frequency bands, facilitates neuroplasticity and inter-regional communication during skill acquisition[ 6 , 7 ]. 40 Hz oscillations have enhanced synaptic plasticity and long-term potentiation (LTP), critical for motor memory consolidation[ 8 , 9 ]. Therefore, recent research has focused on artificially modulating oscillatory activity in the gamma bands in the motor-related area of the brain to improve motor performance[ 10 , 11 ]. Transcranial alternating current stimulation (tACS) has emerged as a promising non-invasive tool to modulate these oscillatory dynamics. 40-Hz tACS has been confirmed to have benefits for motor learning [ 12 ], motor skills [ 13 ], and cognitive [ 14 ] by the injection of sinusoidal currents (1-2mA) to modulate cortical excitability and brain electrical activity [ 15 ]. Many studies on tACS have consistently confirmed that 40 Hz can significantly enhance cognitive function in healthy people and individuals with Alzheimer’s disease (AD) [ 16 – 19 ]. It is worth noting that the newest research affirms a significant correlation between cognitive ability and motor learning function in the elderly[ 20 ]. A ‘‘binding theory’’ has been proposed, in which neural populations excited in different cortical regions synchronize with the gamma oscillation, strengthening the intercortical neural network [ 21 ]. Previous studies have reported that the neural activities of the primary and secondary somatosensory cortex are synchronized in the gamma band in the perceptual process [ 22 ], and bilateral M1 are synchronized in the gamma band in the bilateral handed motor tasks [ 23 ]. These reports suggest that the outcome of motor control may be improved by modulating the activity of several brain cortex regions rather than modulating M1 alone. Several brain networks' functional connectivity (FC) plays a crucial role in motor learning by facilitating information integration, enhancing neural plasticity, strengthening inter-regional communication, and dynamically optimizing resource allocation to support skill acquisition and adaptation. Motor learning involves the integrated contribution of cortical and subcortical brain systems, each supporting different aspects of the process[ 24 ]. This leads to changes across multiple brain regions [ 25 ], which may be limited to functional adaptations or extend to structural modifications, depending on the timescale of motor learning[ 26 , 27 ]. Fully synchronous 40 Hz tACS is hypothesized to strengthen corticocortical and corticostriatal coherence, facilitating more efficient information transfer and neuroplastic adaptations. Fully synchronous 40 Hz tACS is hypothesized to strengthen corticocortical and corticostriatal coherence, facilitating more efficient information transfer and neuroplastic adaptations. Previous studies have demonstrated that gamma-band tACS over M1 can improve sequence learning in the serial reaction time task (SRTT)[ 28 ]. Yet the effects of multi-target stimulation (e.g., combined M1 and prefrontal or cerebellar stimulation) remain unexplored in middle-aged adults. This study aims to investigate the potential of multi-target 40 Hz tACS to enhance motor learning in middle-aged adults through three complementary mechanisms: (1) changes in corticospinal tract integrity and central motor conduction efficiency; (2) regional alterations in brain functional and structural organization, along with modified connectivity patterns within motor learning-associated brain networks; (3) induction of molecular signatures associated with neuroplasticity. This multimodal approach promises to yield novel insights into the neuromodulatory mechanisms and molecular correlates of age-related changes in motor learning. 2. Methods 2.1 Study design This study was a randomized controlled trial utilizing a parallel-group design. The comprehensive study flowchart is depicted in Fig. 1 . The experimental procedure is illustrated in Fig. 2 . Participants were recruited from the logistics department of Shanghai Seventh People’s Hospital in China and nearby communities. All participants met the following inclusion criteria: (1) age between 45 and 60 years old, possessing normal or corrected-to-normal vision; (2) Right-handedness, as indicated by a score greater than 40 points on the Edinburgh Handedness Scale [ 29 ]; (3) Exhibiting clear consciousness and absence of mental disorders, with a score exceeding 23 points on the Mini-Mental State Examination (MMSE) with no history of neurological and psychiatric diseases or medication intake; (4) Demonstrating a willingness to cooperate in the research and providing voluntary consent by signing the informed consent form. And participants with the following were excluded: (1) Proficiency in demanding finger movements, such as piano players, professional game players, and individuals with prior experience in SRTT; (2) Ongoing participation in concurrent clinical studies; (3) Presence of contraindications for tACS, TMS, or MRI, such as implantable electronic device or compromised skin in the stimulation area resulting from damage or hyperalgesia. 2.2 Participants A total of 25 participants were recruited into the study, meeting the inclusion criteria, and were randomized into one of four groups. Three participants withdrew from the experiment: two due to a severe cold and another due to a conflict between work and experimental time. Eligible participants were randomly assigned to four groups in a 1:1:1:1 using stratified block wise randomization (based on age and gender), including, (1) group A (M1 + SMA + DLPFC + CB are all sham stimulation, n = 5) ; (2) group B (40-Hz tACS on M1; sham stimulation on SMA + DLPFC + CB, n = 6) ; (3) group C (40-Hz tACS on M1 + SMA; sham stimulation on DLPFC + CB, n = 7); (4) group D (40-Hz tACS on M1 + SMA + DLPFC + CB, n = 7). To achieve blinding of participants, sham tACS stimulation was applied by delivering the current for 30 seconds at the beginning of the session, then turning it off. This process caused a skin sensation like real stimulation, without any observable effect on brain state, which can achieve blinding of participants. To blind research personnel to patient treatment and assessment, the individual responsible for operating the tACS machine and allocating participants did not participate in patient contact or data analysis processes. 2.3 Protocol for tACS intervention The four independent channels of the transcranial electrical stimulator (YingChi, Shenzhen) were utilized for the tACS protocol, allowing each channel to independently adjust the current output (Fig. 2 A). A skilled and independent physiotherapist conducted the tACS intervention on participants by adjusting the current output of the four channels. The four anode electrodes were placed in the M1, SMA, DLPFC, and CB of the right brain (Fig. 2 B). In comparison, the four cathode electrodes were placed in the contralateral brain area for each corresponding anode electrode [ 30 ]The tACS session were delivered to the cortex via surface sponge electrodes (5cm×7cm) soaked in 0.9% NaCl, which will be employed and secured in place using gauze head cover [ 31 ]. The peak current and stimulation frequency were set at 1 mA and 40 Hz. tACS application in this study complied with recent safety guidelines [ 32 ]. All participants performed five tACS sessions per week for two weeks, resulting in a total of 10 tACS sessions. 2.4 Serial Reaction Time Task (SRTT) The serial reaction time task (SRTT) is the most used method to assess motor learning. The task was administered using a standardized software (Deary-Liewald, UK) [ 39 ] running on a Windows 10 PC with an Intel i5 processor. The setup used a standard QWERTY keyboard (integrated into the experimental computer), connected directly to the computer to minimize input latency. The monitor, with a display resolution set at 1920 × 1080 and a refresh rate of 60 Hz, was positioned 15 cm from the participant. Reaction time (RT) and error rate (ER) were measured at baseline (T0), 7th day (T1), and 14th day (T2). Participants sat in front of the screen with their left hand resting on the keyboard, with fingers mapped as follows: index = V, middle = C, ring = X, little = Z (Fig. 2 C). The task required pressing the corresponding key as quickly as possible when a diagonal cross appeared in one of four squares. The software recorded RTs with millisecond precision, accounting for hardware limitations. The task included 10 practice trials followed by 40 experimental trials, divided into 8 blocks with 30-second intervals. The interstimulus interval (ISI) varied randomly between 1000–3000 ms. Based on manufacturer specifications and empirical tests, RTs outside the 200–1500 ms range were excluded as outliers[ 33 ]. 2.5 Single-pulse transcranial magnetic stimulation evaluation To investigate the changes in motor cortex physiology before and after intervention, we selected the four indicators for single-pulse transcranial magnetic stimulation (TMS, Xiang Yu Medical, China) with a figure-of-eight magnetic coil (Fig. 2 D), including the resting motor threshold (RMT) [ 34 ], central motor conduction time (CMCT) [ 35 ], and latency of MEP [ 36 – 38 ]. The recording electrode is attached to the abdominal part of the abductor pollicis brevis of the right thumb. The reference electrode is attached to the abductor pollicis brevis tendon. The ground wire is set on the wrist. The recording muscle is the abductor pollicis brevis muscle of the finger [ 39 ]. Participants were comfortably seated, relaxing their heads and arms. The independent assessor placed the coil over the M1 of the right hemisphere. The location of cortical stimulation points is at the intersection of the line connecting the two ears and the line connecting the nasal root and the skull crest to the occipital protuberance, and then along the line connecting the two ears to the left 5–7 cm in the forward 1.5 cm area (M1), aligning at a 45-degree angle from the brain midline, with the handle pointing backward [ 40 ]. The RMT was determined as the lowest stimulus intensity that produced MEPs exceeding 50 µV in 5 of 10 trials. The TMS operator will adjust the intensity of the magnetic cortical stimulus to elicit MEPs with a peak-to-peak amplitude of approximately 1 mV. Sequential stimulation will be administered five times at each intensity, and the average of the resulting five MEP traces will be used as the outcome for data analysis [ 41 ]. 2.6 MRI Data Acquisition MRI data were acquired by a 3T Siemens Verio scanner with a 32-channel head coil (Siemens, Erlangen, Germany). Three distinct scanning sequences were implemented as follows: ① Resting state functional MRI images (rs-fMRI): Recurrence time (TR) = 2100 ms, echo time (TE) = 30 ms, flip angle = 90°, voxel size = 0.9 isotropic, 42 axial slices, field of view (FOV) = 200 mm×200 mm, and phases = 230. ② High-resolution T1-weighted structural images (T1WI): TR = 8.2 ms, TE = 3.2 ms, flip angle = 12°, FOV = 220 mm×220 mm, matrix = 256,256, slice thickness = 1 mm. ③ Blood oxygenation level-dependent (BOLD) signal: TR = 2000 ms, TE = 30 ms, flip angle = 90°, FOV = 240 mm×240 mm, thickness = 4 mm, no interval scanning. Each participant underwent an MRI scan for approximately 25 minutes, performed with their eyes closed but not sleeping, and with extra padding around the ears to reduce noise interference during the MRI scanning process. Before scanning, participants were informed that the scanning was about to commence and were instructed to maintain a stable state while minimizing head and body movement. 2.7 mRNA-sequencing of the Blood Sample All participants were sampled 2 ml of blood before and after 14 days of tACS intervention to discern alterations in neuroinflammatory factors, nerve growth factors, and synaptic plasticity-related genes in the blood milieu [ 42 ]. Participants will be instructed to abstain from food consumption after 20:00 the previous evening, along with refraining from drinking and high-intensity exercise before the blood draw. A volume of 2ml venous blood was collected into an EDTA anticoagulant tube and 3ml of TRIzol Reagent (LMAl Bio, China) was added, ensuring thorough mixing and then stored at − 80°C [ 42 ]. This process facilitates the preservation and stability of the blood samples for subsequent biochemical analysis. 2.8 Sample Size The sample size was computed with G*power software (v3.1.9.2) [ 43 ], by considering F tests for with-in between interaction with four groups (placebo-TENS, placebo tACS and control) and three sessions (baseline, training and final). The effect size expected is 0.526, based on a study conducted by Jaberzadeh et al. who investigated the differential effects of unihemispheric concurrent dual-site and conventional tACS on motor learning using SRTT [ 44 ]. According to a prior two-way analysis of variance (ANOVA) F test, with a power of 0.8 and an alpha (α) level of 0.05, an estimated 20 participants will be needed. Considering a 20% drop-out rate, the final sample size of each group will be 7, with a total of 28. 2.9 Statistical Analysis Non-imaging data analyses were performed using IBM SPSS Statistics 25 ( http://www.spss.com.hk ). Categorical variables were analyzed using the Chi-square test. Continuous variables with a normal distribution were described as mean ± standard deviation (SD). Two-way ANOVA was performed for continuous variables that met the assumptions of normality (assessed by the Shapiro-Wilk test) and homogeneity of variance (assessed by Levene’s test). Pearson’s correlation coefficients were calculated to investigate the relationships between reaction time (RT) in the serial reaction time task (SRTT) and MRI measures (e.g., functional connectivity or regional volume). Statistical significance was set at p < 0.05, and the Bonferroni correction was applied to adjust for multiple comparisons when necessary. Post-hoc comparisons were performed using the Bonferroni correction if significant main or interaction effects were found. The spatial preprocessing and analysis procedures performed on imaging data analyses utilized the RESTplus V1.2 (the Resting-State fMRI Data Analysis Toolkit plus V1.2, http://restfmri.net/forum/RESTp lusV1.2) carried out on the MATLAB (Version 2016b, The MathWorks, Inc., Natick, MA, United States) [ 45 ]. The Data preprocessing consisted of removing the first five time points, slice timing, realignment, reorientation, normalization, smoothing, detrending, nuisance covariates regression, filtering (0.01 Hz-0.08 Hz), and ALFF/Degree Centrality (DC) calculation. Both ALFF and DC values were transformed into Z-scores for group-level analysis. The MRI data were preprocessed and analyzed using Statistical Parametric Mapping (SPM12; The Wellcome Centre for Human Neuroimaging, London, UK, http://www.fil.ion.ucl.ac.uk ) implemented in MATLAB version 2022b (The Mathworks Inc., MA, USA, http://matlab.p2hp.com ). 3. Results 3.1 Baseline characteristics of the participants The baseline characteristics of the participants are presented in Table 1 . All variables in all groups were normally distributed according to K-S test. Based on the results, there were no statistically significant differences among participants in four groups regarding gender, age, years of education, and MMSE ( P > 0.05). Similarly, there were no baseline differences for reaction time, error rate and motor evoked potential ( P > 0.05). Table 1 Baseline assessments of participant characteristics. Values are in mean ± standard deviation (mean ± SD). MMSE: Mini-Mental State Examination, RT: Reaction Time, ER: Error Rate, MEP: Motor Evoked Potential. * P < 0.05. Sham (group A) Single (group B) Double (group C) Multiple (group D) P value Sample n = 5 n = 6 n = 7 n = 7 / Gender (males/females) 2/3 2/4 3/4 4/3 0.945 Age (years) 58.60 ± 1.67 56.50 ± 3.62 54.86 ± 6.51 57.29 ± 4.39 0.565 Years of Education 3.20 ± 1.64 4.17 ± 2.14 3.57 ± 1.27 4.57 ± 1.33 0.444 MMSE test 25.60 ± 2.30 24.33 ± 3.67 26.43 ± 2.51 26.00 ± 1.29 0.509 RT 831.99 ± 160.69 860.87 ± 187.12 679.61 ± 92.81 828.61 ± 114.59 0.107 ER 0.08 ± 0.07 0.05 ± 0.05 0.44 ± 0.31 0.08 ± 0.02 0.249 RMT 57.20 ± 13.29 61.00 ± 17.10 61.00 ± 11.76 54.86 ± 12.65 0.790 3.2 Multi-target tACS enhances the motor learning behaviour. Figure 3 shows the mean RT and error rates of all blocks in the four groups. The results indicated that there was no significant interaction between group and time ( P = 0.7267), and showed significant main effects of group ( P < 0.001) and time ( P = 0.0312). Furthermore, post-hoc comparisons revealed that the RT ( P = 0.013) and ER ( P = 0.0341) of group D significantly decreased after the 40 Hz tACS intervention. After 14 days of tACS intervention, the RT of group C was significantly lower than group B. 3.3 Multi-target tACS enhances cortical excitability of M1. Figure 4 shows the resting motor threshold (RMT, %MSO) and central motor conduction time (CMCT) at various time points. %MSO (percentage of maximum stimulator output) represents the intensity of TMS as a percentage of the device's maximum output, used to standardize stimulation strength across individuals and studies. There is no significant difference in RMT (Fig. 4 A) and CMCT (Fig. 4 B), but as the number of targets increases, there is a clear downward trend in MEP, after 14 days of tACS intervention, which might imply that multi-target tACS stimulation potentially enhances the cortical excitability of participants. 3.4 Multi-target tACS changed GMD, ALFF and FC in specific brain regions. In the study, we mainly analyzed the brain structure and function among four groups, as well as voxel-level functional connectivity indicators, including the gray matter density (GMD), amplitude of low-frequency fluctuation (ALFF), FC between M1 and whole brain voxels, and density center (DC). Two-way ANOVA was conducted to examine the differences in GMD among the four groups. The results revealed a significant decrease of group D in the Left Calcarine Sulcus and Surrounding Primary Visual Cortex (Calcarine_L) compared to group C; while as significant increase the Right Middle Frontal Gyrus (MFG), Including Dorsolateral Prefrontal Cortex (DLPFC)(Frontal_Mid_R) compared to other three groups (Table 2 ). We employed the z-score transformed amplitude of low-frequency fluctuation (zALFF) to investigate regional spontaneous brain activity. Resting-state fMRI data were preprocessed using standard procedures, including slice timing correction, realignment, normalization to the Montreal Neurological Institute (MNI) space, spatial smoothing with a Gaussian kernel of 6 mm full-width at half-maximum (FWHM), and band-pass filtering (0.01–0.08 Hz). The ALFF was calculated as the square root of the power spectrum within the low-frequency range (0.01–0.08 Hz) for each voxel, and then transformed to z-scores (zALFF) by subtracting the global mean and dividing by the standard deviation across the whole brain. A two-way ANOVA was performed on the zALFF values of the four groups, followed by post-hoc analysis. The results revealed significant differences at the group level, but no significant differences were observed over time or in the group*time interaction. Specifically, the activation value of the group D in the Left Middle Occipital Gyrus (Occipital_Mid_L); Left Inferior Frontal Gyrus, Triangular Part (Frontal_Inf_Tri_L); Left Calcarine Cortex (Calcarine_L); Right Middle Temporal Gyrus (Temporal_Mid_R) is significantly higher than the other three groups. Conversely, the activation values of several brain regions were significantly reduced in group D compared to other groups, including Right Cerebellum Lobule VI (Cerebellum_6_R); Left Superior Temporal Pole (Temporal_Pole_Sup_L); Left Lingual Gyrus (Lingual_L); Right Lingual Gyrus (Lingual_R); Left Middle Temporal Gyrus (Temporal_Mid_L); Right Inferior Frontal Gyrus, Opercular Part (Frontal_Inf_Oper_R) (Table 3 ). We also computed the DC to investigate the FC density of each voxel across the whole brain. The fMRI data were preprocessed using standard procedures. The DC value for each voxel was calculated by summing the functional connectivity strengths (Pearson correlation coefficients) between the voxel and all other voxels in the brain, with a threshold of r > 0.25 r > 0.25 applied to exclude weak correlations. The resulting DC maps were standardized to z-scores to facilitate group-level comparisons. Significant positive FC was observed between M1 and several brain regions, including the Frontal_Inf_Orb_L, Frontal_Inf_Oper_R, and Supp_Motor_Area_R in group D (Table 4 ). These findings suggest that M1 is functionally integrated with these regions, which are known to be involved in motor planning, execution, and coordination. Table 2 The post-analysis comparison of GMD. Cluster Name (AAL) MNI Coordinates (x, y, z) F value Cluster size Post-hoc Comparisons (group) Calcarine_L (aal) -30 -48 22.5 209.6937 155 D A D > B D > C Table 3 The post-analysis comparison of zALFF. Cluster Name (AAL) MNI Coordinates (x, y, z) F value Cluster size Post-hoc Comparisons (group) Vermis_7 0 -75 -27 57.558 42 A<B, C B<C D C, D Cerebelum_6_R 30 -48 -21 43.9486 20 A>B B>C, D Lingual_L -15 -96 -12 114.1273 108 A>B, C, D Lingual_R 18 -96 -9 58.8128 40 A>C, D B>D Occipital_Mid_L -30 -78 0 65.7053 30 D > A, B, C Frontal_Inf_Tri_L -57 15 3 99.2553 88 D > A Calcarine_L -15 -51 12 59.801 94 D > A Temporal_Mid_R 45 -48 15 69.8061 34 A A, C, D Temporal_Mid_L -48 -57 15 39.9888 22 A>B, D B>D Frontal_Inf_Oper_R 33 12 30 44.1791 52 A > C, D Supp_Motor_Area_R 9 12 54 17 26 D > A, B, C Table 4 The post-analysis comparison of FC. Cluster Name (AAL) MNI Coordinates (x, y, z) F value Cluster size Post-hoc Comparisons (group) Frontal_Inf_Orb_L -51 -24 -6 15.2569 9 B > A C > A D > A Frontal_Inf_Oper_R 60 15 3 27.8852 13 D > A D > B D > C Supp_Motor_Area_R 12 12 72 18.4108 68 B > A A > C D > A D > B 3.5 RT is negatively correlated with GMD and ALFF values in multi-target tACS We further evaluated the association between the changes in differential brain area structure and activation values and motor learning performance through Pearson analysis. Utilizing the Extract ROI Signals of RESTplus in MATLAB to extract differential cluster, we computed the correlation between the ΔRT and ΔGMD, ΔzALFF. For the group D, we observed a negative correlation between the mean ΔRT and ΔVBM in the Frontal_Mid_R (R=-0.839, P = 0.018) (Fig. 6 A), indicating that motor performance improved with the increased gray matter density. With the 40-Hz multi-target tACS, another negative correlation was found between the ΔRT and ΔzALFF in Vermis_7 (R=-0.759, P = 0.048); Supp_Motor_Area_R (R= -0.798, P = 0.032) (Fig. 6 B, C), indicating that as the increased activation values of brain regions in the Vermis_7 and Supp_Motor_Area_R, the RT decreases. These findings suggest that the changes in gray matter density/brain activation values may be an essential factor influencing motor performance in the studied population. 3.6 Results of RNA Sequencing We performed RNA sequencing on peripheral blood samples to characterize the transcriptomic profiles of the healthy participants. A total of 300 differentially expressed genes (DEGs) were identified, with 217 upregulated and 83 downregulated genes in group D compared to group A (|log2 fold change| >1, adjusted P value < 0.05) (Fig. 7 A). Compared group C, group D upregulated 68 genes and downregulated 73 genes (Fig. 8 A). Gene Ontology (GO) show that compared group A (Fig. 7 B), group D potential involvement of these genes in biological theme, e.g., D-alanine transport, chromaffin granule lumen, dopamine beta-monooxygenase activity, etc. When compared group C, the differentially expressed genes in group D were mainly involved in oxygen transport, hemoglobin complex, oxygen carrier activity (Fig. 8 B). And GO enrichment set (Fig. 7 C, D) shows that group D upregulated immune response (38%) genes, downregulated material metabolism (28.9%). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (Fig. 7 E, F) highlighted that the upregulated DEGs were predominantly enriched in pathways related to Antigen processing and presentation, Complement and coagulation cascades, Wnt signaling pathway, Neuroactive ligand-receptor interaction, suggesting their potential roles in the pathogenesis of dynamic regulation of synaptic plasticity. And Fig. 8 C, D indicated that compared with group C, group D upregulated DEGs were predominantly enriched in pathways related to ribosome biogenesis in eukaryotes, downregulated DEGs enriched in antigen processing and presentation. In the analysis of significantly downregulated genes, KEGG pathway enrichment identified several key pathways that were notably suppressed, including gap junction, glycosphingolipid biosynthesis-globo and isoglobo series, and Phagosome. 4. Discussion Improving motor learning in healthy middle-aged adults is crucial for maintaining functional independence, reducing the risk of age-related motor decline, and enhancing the quality of life. The 40-Hz tACS can synchronize neural activity by applying an external rhythmic electrical current that matches the intrinsic frequency of brain oscillations, presenting a promising treatment for improving motor learning. Numerous research articles have confirmed the beneficial impact of 40-Hz tACS in exploring memory [ 46 ], learning [ 47 ], and higher cognitive function [ 48 , 49 ], all of which are pivotal for effective motor learning. In the study, we successfully recruited 25 participants. We divided them into four groups to explore the effect of 40-Hz tACS on motor learning by measuring RT and ER of SRTT and ascertain whether the number of stimulation targets will affect the improvement effect. Furthermore, we elucidated the brain mechanism and physiological underpinnings that drive the improvement effect through TMS, MRI, and blood RNA sequencing. The study demonstrated that after the RT had been significantly decreased between group B and C after 2-week tACS intervention (T2). This finding aligns with previous studies suggesting that non-invasive brain stimulation, particularly tACS, can enhance cognitive-motor functions in aging populations [ 50 , 51 ]. The RT and ER had been significantly decreased compared to baseline in the group D. The above results indicate that within a short period of 40-Hz tACS intervention, the number of targets has a significant impact on the improvement of motor learning in middle-aged and older adults, as evidenced by RT and ER of SRTT. To investigate the neural mechanism underlying the enhancement of motor learning by 40-Hz tACS in healthy middle-aged and older adults, we employed a multimodal neuroimaging approach, combining TMS and MRI. TMS was used to assess cortical excitability and plasticity in the M1, while MRI, including both structural and functional imaging, provided insights into changes in ALFF, GMD, and FC within the brain network. The results of TMS revealed no significant decrease in RMT or CMCT following the intervention. However, a trend toward reduction was observed in both RMT and CMCT of groups B, C, and D, which suggests a potential modulation of cortical excitability and corticospinal tract efficiency. The lack of statistical significance may be attributed to the RMT and CMCT being indirect measures of cortical excitability and corticospinal tract function, respectively [ 52 ]. More sensitive measures, such as paired-pulse TMS or combined TMS-EEG, may be needed to capture subtle changes in neural activity. Additionally, structural MRI revealed a significant reduction of group D in the Left Calcarine Sulcus and Surrounding Primary Visual Cortex (Calcarine_L). In contrast, Group D significantly increased in the Right Middle Frontal Gyrus (MFG), Including Dorsolateral Prefrontal Cortex (DLPFC) (Frontal_Mid_R). The tACS-induced gray matter reduction in left calcarine cortex and increase in right DLPFC may reflect stimulus-driven neuroplastic reorganization, potentially representing a shift from sensory processing to cognitive control[ 53 ]. This may optimize the functional connectivity and neural plasticity of the neural network, thereby promoting motor learning. This phenomenon reflects the complexity of neural plasticity and the dynamic reorganization of brain networks. As for brain activation, the activation value of the group D in the Occipital_Mid_L; Frontal_Inf_Tri_L; Calcarine_L; Temporal_Mid_R is significantly higher than the other three groups. These brain regions play an important role in visual processing and may be involved in motor tasks involving visual feedback, optimizing the role of visual feedback in SRTT. We further analyzed the indicators related to FC, and the results showed the significant positive FC between M1 and several brain regions, including the Frontal_Inf_Orb_L, Frontal_Inf_Oper_R, and Supp_Motor_Area_R in group D. The enhancement of FC between M1 and these brain regions reflects the reorganization and optimization of neural networks, thereby improving the efficiency and effectiveness of motor learning. Multi-target tACS may further enhance these functional connections by regulating neural oscillations and promoting neural plasticity, compared to single-target stimulation. These findings suggest that tACS may improve motor learning by modulating cortical excitability, enhancing functional integration within the motor network, and promoting neuroplastic changes in critical motor regions. The combination of TMS and MRI provided a comprehensive understanding of the neural mechanisms underlying tACS-induced improvements in motor learning. The results of behavioral and neural correlates in group D showed the negative correlation of ΔRT and ΔVBM of Frontal_Mid_R. The changes in RT and brain activation values of Vermis_7 and Supp_Motor_Area_R before and after multi-target tACS intervention further demonstrate the importance of neural plasticity and dynamic reorganization of brain networks in motor learning. The SRTT results were interpreted further by the knowledge of the underlying biological drivers of the measured change. RNA sequencing is a high-throughput genomic technology that can simultaneously determine the entire gene expression information of RNA, so as to gain insights into gene expression patterns, cell states and functions of different cell types [ 54 ]. The 2-week tACS intervention may induce subtle changes in gray matter structure, particularly in the stimulated cortical regions, through mechanisms such as synaptic plasticity and neurotrophic factor release. The GO results showed that group D potential involvement of D-alanine transport, chromaffin granule lumen, dopamine beta-monooxygenase activity, etc, compared group A. These pathways play critical roles in modulating neurotransmitter synthesis, storage, and release, which may indirectly enhance motor learning. D-alanine transport could influence neurotransmitter regulation or provide neuroprotective effects, supporting neural plasticity essential for motor learning. The chromaffin granule lumen is crucial for storing and releasing catecholamines like dopamine and norepinephrine, which are key to reward processing, motivation, and attention—processes vital for optimizing motor learning. Dopamine beta-monooxygenase activity, which converts dopamine to norepinephrine, further enhances attention and arousal states, improving cognitive resource allocation during motor tasks. Together, these pathways optimize neurotransmitter levels, strengthen neural network plasticity, and enhance functional connectivity in motor-related circuits, ultimately promoting more efficient and effective motor learning. Multi-target tACS may further amplify these effects by regulating neural oscillations and enhancing the release and functional integration of these neurotransmitters. However, compared group C, the differentially expressed genes in group D were mainly involved in oxygen transport, hemoglobin complex, oxygen carrier activity, which is likely due to the broader and more comprehensive modulation of brain metabolism and neural activity by multi-target tACS. It was leading to increased oxygen utilization and transport. This widespread stimulation could elevate neuronal electrical activity and synaptic plasticity, thereby increasing the brain's oxygen requirements and activating related pathways. Additionally, multi-target tACS may improve neurovascular coupling, enhancing cerebral blood flow and oxygen delivery to meet the heightened metabolic demands of stimulated regions. In contrast, dual-target tACS, with its more limited scope, may not sufficiently activate these metabolic pathways. The increased oxygen transport and hemoglobin complex activity in the multitarget group likely support enhanced neural plasticity, optimized brain network connectivity, and improved motor learning efficiency, reflecting the broader and more robust effects of multitarget stimulation on brain metabolism and function. Despite these promising findings, several limitations should be noted. First, the sample size was relatively small, which may limit the generalizability of the results. Nevertheless, it should be noted that the original effect size was set as 0.526, based on a previous study with the same primary outcome[ 44 ]. We assume that the chance of false-negative study results is minimal. Second, the long-term effects of multi-target tACS on motor learning remain unclear. Longitudinal studies are required to determine whether the observed improvements persist over time. Finally, the optimal stimulation parameters (e.g., frequency, intensity, duration) for multi-target tACS in aging populations warrant further investigation. In conclusion, the study has demonstrated that 40 Hz tACS effectively enhances motor learning in middle-aged and elderly healthy individuals, with a more significant improvement effect observed with multi-target stimulation. We provide insight into the mechanisms underlying neuromodulatory interventions, suggesting that the underlying mechanisms of multi-target tACS in improving motor learning more effectively can be attributed to its ability to enhance cortical excitability, entrain neural oscillations, enhance synaptic plasticity, and optimize FC within motor-cognitive-related brain networks, including M1, frontal lobe, and SMA. These findings advance the mechanistic understanding of neural tACS effects, thereby contributing to more targeted modulation of neural networks in future experimental and translational tACS applications. In addition, these findings of the effects of regional neuromodulators on multi-target tACS may provide additional guidelines for its clinical use in motor recovery after stroke. 5. Conclusions The multi-target tACS can significantly improve the motor learning of healthy participants compared to sham stimulation. The potential mechanism underlying the improvement may involve enhancing cortical excitability, activating the frontal lobe (left) and cerebellum (left) brain region, increasing FC between M1 and frontal lobe and SMA, and promoting synaptic plasticity. Declarations Ethics approval and consent to participate The Ethics Committee of Shanghai Seventh People’s Hospital (2023-7th-HIRB-043) approved the project in May 2023. All participants provided informed consent, and their safety and respect were protected. Consent for publication Not applicable Availability of data and materials The datasets used during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no conflict of interest. Acknowledgements This work was supported by the Scientific Research Program of Shanghai Pudong New Area Health Commission (the Youth Program) (No. PW2024B-09), the Discipline Construction of Pudong Health Bureau of Shanghai (No. PWZzb2022–11), the National Natural Science Foundation of China (No. 82202787 and 82272612), the Discipline Construction of Pudong Health Bureau of Shanghai——Discipline group of cerebrovascular system diseases (No. PWZxq2022-01) and 2024 Pujiang Talents Program (24PJA065). Authors' contributions MH L and CW: have designed this trial protocol and drafted the manuscript. 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Akgun Y, Soysal A, Atakli D, Yuksel B, Dayan C, Arpaci B: Cortical excitability in juvenile myoclonic epileptic patients and their asymptomatic siblings: a transcranial magnetic stimulation study . Seizure 2009, 18 (6):387-391. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A: Neuroplasticity: changes in grey matter induced by training . Nature 2004, 427 (6972):311-312. Chen G, Ning B, Shi T: Single-Cell RNA-Seq Technologies and Related Computational Data Analysis . Front Genet 2019, 10 :317. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7378253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":507335851,"identity":"352c016a-ba13-423c-841e-e2403a5931fd","order_by":0,"name":"xiaoming 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chart.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/a8ed1dec56cb1f3a8529427e.png"},{"id":90812398,"identity":"aaaf3d5f-5eff-4825-ae53-2c2893aac92c","added_by":"auto","created_at":"2025-09-08 12:19:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249713,"visible":true,"origin":"","legend":"\u003cp\u003eExperiment Setup. A. The setup of multi-target tACS device. B. International 10-10 system for EEG electrode placement, the red part corresponds to the tACS anode placement area. C. The schematic diagram of SSRT. D. TMS and a figure-of-eight magnetic coil. E. MRI scanning process.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/2ae41e7f58fe308c6baaf324.jpeg"},{"id":90812403,"identity":"861f4456-fe64-4635-a954-76f26516c30a","added_by":"auto","created_at":"2025-09-08 12:19:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76083,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean (A) RT and (B) ER for each block in four groups. *Indicates significant difference at \u0026lt;0.05 level.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/a7eba4732d151777561bc300.png"},{"id":90813234,"identity":"c8a68d05-25e8-43d4-8d2a-bace95367e54","added_by":"auto","created_at":"2025-09-08 12:27:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74424,"visible":true,"origin":"","legend":"\u003cp\u003eThe MEP threshold of the (A) RMT and (B) CMCT in four groups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/b844040449ba82b5ed6279f2.png"},{"id":90812407,"identity":"67295156-5cfb-4c7f-83dc-e2346b205313","added_by":"auto","created_at":"2025-09-08 12:19:07","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":506063,"visible":true,"origin":"","legend":"\u003cp\u003eThe brain regions with significant differences among the four groups. (A) Compare the brain regions with significant differences in GMD. (B) Compare the brain regions with significant differences in zALFF values. (C) Compare the brain regions with significant differences in FC. (D) Compare the brain regions with significant differences in DC. All results were thresholded at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, corrected for multiple comparisons using FDR.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/cd11fe53a52af77482bd07bc.jpeg"},{"id":90813653,"identity":"84994275-2a25-4153-88a0-276607bbdaee","added_by":"auto","created_at":"2025-09-08 12:35:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":703381,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between the ΔRT and ΔGMD/ΔzALFF in group D. A. The correlation between the ΔRT and ΔGMD in Frontal_Mid_R. B. The correlation between the ΔRT and ΔzALFF in Vermis_7. C. The correlation between the ΔRT and ΔzALFF in Supp_Motor_Area_R. All results were thresholded at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and corrected for multiple comparisons using FDR.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/95be5c82d159d527478c57a6.png"},{"id":90813239,"identity":"6937d65e-331b-402f-afaf-516f86840c69","added_by":"auto","created_at":"2025-09-08 12:27:07","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":630300,"visible":true,"origin":"","legend":"\u003cp\u003eA. Volcano plot of differentially expressed genes between Group D and Group A. B. Visualization of GO enrichment results for candidate genes. C. Significant enrichment upregulation of GO set. D. Significant enrichment downregulation of GO set. E. Significant enrichment upregulation of KEGG enrichment. F. Significant enrichment downregulation of KEGG enrichment.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/84deb18a3eb4dbcbf5d6b12a.jpeg"},{"id":90813241,"identity":"fe327899-a2a6-45fb-9d39-b7d50f1e6b75","added_by":"auto","created_at":"2025-09-08 12:27:07","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":441907,"visible":true,"origin":"","legend":"\u003cp\u003eA. Volcano plot of differentially expressed genes between group C and group D. B. Visualization of GO enrichment results for candidate genes. C. Significant enrichment upregulation of KEGG enrichment. D. Significant enrichment downregulation of KEGG enrichment.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/f77338176a2a7dc5fb8d45c9.jpeg"},{"id":94490017,"identity":"3c21de4a-0cc7-4932-9a26-92c628bb8f99","added_by":"auto","created_at":"2025-10-27 17:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6225992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7378253/v1/1359de48-8ab8-47d3-9afc-c625584abe20.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-target transcranial alternating current stimulation (tACS) enhances motor learning and brain network connection in middle-aged adults","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMotor learning, the process of acquiring and refining motor skills through practice and experience, plays a crucial role in motor function[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Healthy aging is associated with a decline in motor function that can seriously affect the quality of life and safety [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Motor learning is a complex process that relies on the dynamic interaction of distributed neural networks, particularly those involving the primary motor cortex (M1), supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC), and cerebellum (CB)[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent evidence suggests that synchronized oscillatory activity, especially in the gamma (30-50Hz) frequency bands, facilitates neuroplasticity and inter-regional communication during skill acquisition[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 40 Hz oscillations have enhanced synaptic plasticity and long-term potentiation (LTP), critical for motor memory consolidation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, recent research has focused on artificially modulating oscillatory activity in the gamma bands in the motor-related area of the brain to improve motor performance[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Transcranial alternating current stimulation (tACS) has emerged as a promising non-invasive tool to modulate these oscillatory dynamics. 40-Hz tACS has been confirmed to have benefits for motor learning [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], motor skills [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and cognitive [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] by the injection of sinusoidal currents (1-2mA) to modulate cortical excitability and brain electrical activity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Many studies on tACS have consistently confirmed that 40 Hz can significantly enhance cognitive function in healthy people and individuals with Alzheimer\u0026rsquo;s disease (AD) [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is worth noting that the newest research affirms a significant correlation between cognitive ability and motor learning function in the elderly[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA \u0026lsquo;\u0026lsquo;binding theory\u0026rsquo;\u0026rsquo; has been proposed, in which neural populations excited in different cortical regions synchronize with the gamma oscillation, strengthening the intercortical neural network [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous studies have reported that the neural activities of the primary and secondary somatosensory cortex are synchronized in the gamma band in the perceptual process [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and bilateral M1 are synchronized in the gamma band in the bilateral handed motor tasks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These reports suggest that the outcome of motor control may be improved by modulating the activity of several brain cortex regions rather than modulating M1 alone. Several brain networks' functional connectivity (FC) plays a crucial role in motor learning by facilitating information integration, enhancing neural plasticity, strengthening inter-regional communication, and dynamically optimizing resource allocation to support skill acquisition and adaptation. Motor learning involves the integrated contribution of cortical and subcortical brain systems, each supporting different aspects of the process[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This leads to changes across multiple brain regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which may be limited to functional adaptations or extend to structural modifications, depending on the timescale of motor learning[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Fully synchronous 40 Hz tACS is hypothesized to strengthen corticocortical and corticostriatal coherence, facilitating more efficient information transfer and neuroplastic adaptations. Fully synchronous 40 Hz tACS is hypothesized to strengthen corticocortical and corticostriatal coherence, facilitating more efficient information transfer and neuroplastic adaptations. Previous studies have demonstrated that gamma-band tACS over M1 can improve sequence learning in the serial reaction time task (SRTT)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Yet the effects of multi-target stimulation (e.g., combined M1 and prefrontal or cerebellar stimulation) remain unexplored in middle-aged adults.\u003c/p\u003e\u003cp\u003eThis study aims to investigate the potential of multi-target 40 Hz tACS to enhance motor learning in middle-aged adults through three complementary mechanisms: (1) changes in corticospinal tract integrity and central motor conduction efficiency; (2) regional alterations in brain functional and structural organization, along with modified connectivity patterns within motor learning-associated brain networks; (3) induction of molecular signatures associated with neuroplasticity. This multimodal approach promises to yield novel insights into the neuromodulatory mechanisms and molecular correlates of age-related changes in motor learning.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003eThis study was a randomized controlled trial utilizing a parallel-group design. The comprehensive study flowchart is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The experimental procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Participants were recruited from the logistics department of Shanghai Seventh People\u0026rsquo;s Hospital in China and nearby communities. All participants met the following inclusion criteria: (1) age between 45 and 60 years old, possessing normal or corrected-to-normal vision; (2) Right-handedness, as indicated by a score greater than 40 points on the Edinburgh Handedness Scale [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; (3) Exhibiting clear consciousness and absence of mental disorders, with a score exceeding 23 points on the Mini-Mental State Examination (MMSE) with no history of neurological and psychiatric diseases or medication intake; (4) Demonstrating a willingness to cooperate in the research and providing voluntary consent by signing the informed consent form.\u003c/p\u003e\u003cp\u003eAnd participants with the following were excluded: (1) Proficiency in demanding finger movements, such as piano players, professional game players, and individuals with prior experience in SRTT; (2) Ongoing participation in concurrent clinical studies; (3) Presence of contraindications for tACS, TMS, or MRI, such as implantable electronic device or compromised skin in the stimulation area resulting from damage or hyperalgesia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participants\u003c/h2\u003e\u003cp\u003eA total of 25 participants were recruited into the study, meeting the inclusion criteria, and were randomized into one of four groups. Three participants withdrew from the experiment: two due to a severe cold and another due to a conflict between work and experimental time. Eligible participants were randomly assigned to four groups in a 1:1:1:1 using stratified block wise randomization (based on age and gender), including, (1) group A (M1\u0026thinsp;+\u0026thinsp;SMA\u0026thinsp;+\u0026thinsp;DLPFC\u0026thinsp;+\u0026thinsp;CB are all sham stimulation, n\u0026thinsp;=\u0026thinsp;5) ; (2) group B (40-Hz tACS on M1; sham stimulation on SMA\u0026thinsp;+\u0026thinsp;DLPFC\u0026thinsp;+\u0026thinsp;CB, n\u0026thinsp;=\u0026thinsp;6) ; (3) group C (40-Hz tACS on M1\u0026thinsp;+\u0026thinsp;SMA; sham stimulation on DLPFC\u0026thinsp;+\u0026thinsp;CB, n\u0026thinsp;=\u0026thinsp;7); (4) group D (40-Hz tACS on M1\u0026thinsp;+\u0026thinsp;SMA\u0026thinsp;+\u0026thinsp;DLPFC\u0026thinsp;+\u0026thinsp;CB, n\u0026thinsp;=\u0026thinsp;7). To achieve blinding of participants, sham tACS stimulation was applied by delivering the current for 30 seconds at the beginning of the session, then turning it off. This process caused a skin sensation like real stimulation, without any observable effect on brain state, which can achieve blinding of participants. To blind research personnel to patient treatment and assessment, the individual responsible for operating the tACS machine and allocating participants did not participate in patient contact or data analysis processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Protocol for tACS intervention\u003c/h2\u003e\u003cp\u003eThe four independent channels of the transcranial electrical stimulator (YingChi, Shenzhen) were utilized for the tACS protocol, allowing each channel to independently adjust the current output (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A skilled and independent physiotherapist conducted the tACS intervention on participants by adjusting the current output of the four channels. The four anode electrodes were placed in the M1, SMA, DLPFC, and CB of the right brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In comparison, the four cathode electrodes were placed in the contralateral brain area for each corresponding anode electrode [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]The tACS session were delivered to the cortex via surface sponge electrodes (5cm\u0026times;7cm) soaked in 0.9% NaCl, which will be employed and secured in place using gauze head cover [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The peak current and stimulation frequency were set at 1 mA and 40 Hz. tACS application in this study complied with recent safety guidelines [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All participants performed five tACS sessions per week for two weeks, resulting in a total of 10 tACS sessions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Serial Reaction Time Task (SRTT)\u003c/h2\u003e\u003cp\u003eThe serial reaction time task (SRTT) is the most used method to assess motor learning. The task was administered using a standardized software (Deary-Liewald, UK) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] running on a Windows 10 PC with an Intel i5 processor. The setup used a standard QWERTY keyboard (integrated into the experimental computer), connected directly to the computer to minimize input latency. The monitor, with a display resolution set at 1920 \u0026times; 1080 and a refresh rate of 60 Hz, was positioned 15 cm from the participant. Reaction time (RT) and error rate (ER) were measured at baseline (T0), 7th day (T1), and 14th day (T2). Participants sat in front of the screen with their left hand resting on the keyboard, with fingers mapped as follows: index\u0026thinsp;=\u0026thinsp;V, middle\u0026thinsp;=\u0026thinsp;C, ring\u0026thinsp;=\u0026thinsp;X, little\u0026thinsp;=\u0026thinsp;Z (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The task required pressing the corresponding key as quickly as possible when a diagonal cross appeared in one of four squares. The software recorded RTs with millisecond precision, accounting for hardware limitations. The task included 10 practice trials followed by 40 experimental trials, divided into 8 blocks with 30-second intervals. The interstimulus interval (ISI) varied randomly between 1000\u0026ndash;3000 ms. Based on manufacturer specifications and empirical tests, RTs outside the 200\u0026ndash;1500 ms range were excluded as outliers[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Single-pulse transcranial magnetic stimulation evaluation\u003c/h2\u003e\u003cp\u003eTo investigate the changes in motor cortex physiology before and after intervention, we selected the four indicators for single-pulse transcranial magnetic stimulation (TMS, Xiang Yu Medical, China) with a figure-of-eight magnetic coil (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), including the resting motor threshold (RMT) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], central motor conduction time (CMCT) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and latency of MEP [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The recording electrode is attached to the abdominal part of the abductor pollicis brevis of the right thumb. The reference electrode is attached to the abductor pollicis brevis tendon. The ground wire is set on the wrist. The recording muscle is the abductor pollicis brevis muscle of the finger [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Participants were comfortably seated, relaxing their heads and arms. The independent assessor placed the coil over the M1 of the right hemisphere. The location of cortical stimulation points is at the intersection of the line connecting the two ears and the line connecting the nasal root and the skull crest to the occipital protuberance, and then along the line connecting the two ears to the left 5\u0026ndash;7 cm in the forward 1.5 cm area (M1), aligning at a 45-degree angle from the brain midline, with the handle pointing backward [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The RMT was determined as the lowest stimulus intensity that produced MEPs exceeding 50 \u0026micro;V in 5 of 10 trials. The TMS operator will adjust the intensity of the magnetic cortical stimulus to elicit MEPs with a peak-to-peak amplitude of approximately 1 mV. Sequential stimulation will be administered five times at each intensity, and the average of the resulting five MEP traces will be used as the outcome for data analysis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 MRI Data Acquisition\u003c/h2\u003e\u003cp\u003eMRI data were acquired by a 3T Siemens Verio scanner with a 32-channel head coil (Siemens, Erlangen, Germany). Three distinct scanning sequences were implemented as follows: ① Resting state functional MRI images (rs-fMRI): Recurrence time (TR)\u0026thinsp;=\u0026thinsp;2100 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, voxel size\u0026thinsp;=\u0026thinsp;0.9 isotropic, 42 axial slices, field of view (FOV)\u0026thinsp;=\u0026thinsp;200 mm\u0026times;200 mm, and phases\u0026thinsp;=\u0026thinsp;230. ② High-resolution T1-weighted structural images (T1WI): TR\u0026thinsp;=\u0026thinsp;8.2 ms, TE\u0026thinsp;=\u0026thinsp;3.2 ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;220 mm\u0026times;220 mm, matrix\u0026thinsp;=\u0026thinsp;256,256, slice thickness\u0026thinsp;=\u0026thinsp;1 mm. ③ Blood oxygenation level-dependent (BOLD) signal: TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;240 mm\u0026times;240 mm, thickness\u0026thinsp;=\u0026thinsp;4 mm, no interval scanning. Each participant underwent an MRI scan for approximately 25 minutes, performed with their eyes closed but not sleeping, and with extra padding around the ears to reduce noise interference during the MRI scanning process. Before scanning, participants were informed that the scanning was about to commence and were instructed to maintain a stable state while minimizing head and body movement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 mRNA-sequencing of the Blood Sample\u003c/h2\u003e\u003cp\u003eAll participants were sampled 2 ml of blood before and after 14 days of tACS intervention to discern alterations in neuroinflammatory factors, nerve growth factors, and synaptic plasticity-related genes in the blood milieu [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Participants will be instructed to abstain from food consumption after 20:00 the previous evening, along with refraining from drinking and high-intensity exercise before the blood draw. A volume of 2ml venous blood was collected into an EDTA anticoagulant tube and 3ml of TRIzol Reagent (LMAl Bio, China) was added, ensuring thorough mixing and then stored at \u0026minus;\u0026thinsp;80\u0026deg;C [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This process facilitates the preservation and stability of the blood samples for subsequent biochemical analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Sample Size\u003c/h2\u003e\u003cp\u003eThe sample size was computed with G*power software (v3.1.9.2) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], by considering F tests for with-in between interaction with four groups (placebo-TENS, placebo tACS and control) and three sessions (baseline, training and final). The effect size expected is 0.526, based on a study conducted by Jaberzadeh \u003cem\u003eet al.\u003c/em\u003e who investigated the differential effects of unihemispheric concurrent dual-site and conventional tACS on motor learning using SRTT [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. According to a prior two-way analysis of variance (ANOVA) F test, with a power of 0.8 and an alpha (α) level of 0.05, an estimated 20 participants will be needed. Considering a 20% drop-out rate, the final sample size of each group will be 7, with a total of 28.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e\u003cp\u003eNon-imaging data analyses were performed using IBM SPSS Statistics 25 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spss.com.hk\u003c/span\u003e\u003cspan address=\"http://www.spss.com.hk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Categorical variables were analyzed using the Chi-square test. Continuous variables with a normal distribution were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Two-way ANOVA was performed for continuous variables that met the assumptions of normality (assessed by the Shapiro-Wilk test) and homogeneity of variance (assessed by Levene\u0026rsquo;s test). Pearson\u0026rsquo;s correlation coefficients were calculated to investigate the relationships between reaction time (RT) in the serial reaction time task (SRTT) and MRI measures (e.g., functional connectivity or regional volume). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the Bonferroni correction was applied to adjust for multiple comparisons when necessary. Post-hoc comparisons were performed using the Bonferroni correction if significant main or interaction effects were found.\u003c/p\u003e\u003cp\u003eThe spatial preprocessing and analysis procedures performed on imaging data analyses utilized the RESTplus V1.2 (the Resting-State fMRI Data Analysis Toolkit plus V1.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://restfmri.net/forum/RESTp\u003c/span\u003e\u003cspan address=\"http://restfmri.net/forum/RESTp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e lusV1.2) carried out on the MATLAB (Version 2016b, The MathWorks, Inc., Natick, MA, United States) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The Data preprocessing consisted of removing the first five time points, slice timing, realignment, reorientation, normalization, smoothing, detrending, nuisance covariates regression, filtering (0.01 Hz-0.08 Hz), and ALFF/Degree Centrality (DC) calculation. Both ALFF and DC values were transformed into Z-scores for group-level analysis. The MRI data were preprocessed and analyzed using Statistical Parametric Mapping (SPM12; The Wellcome Centre for Human Neuroimaging, London, UK, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) implemented in MATLAB version 2022b (The Mathworks Inc., MA, USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://matlab.p2hp.com\u003c/span\u003e\u003cspan address=\"http://matlab.p2hp.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of the participants\u003c/h2\u003e\u003cp\u003eThe baseline characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All variables in all groups were normally distributed according to K-S test. Based on the results, there were no statistically significant differences among participants in four groups regarding gender, age, years of education, and MMSE (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, there were no baseline differences for reaction time, error rate and motor evoked potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline assessments of participant characteristics. Values are in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). MMSE: Mini-Mental State Examination, RT: Reaction Time, ER: Error Rate, MEP: Motor Evoked Potential. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSham\u003c/p\u003e\u003cp\u003e(group A)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003cp\u003e(group B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDouble\u003c/p\u003e\u003cp\u003e(group C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultiple\u003c/p\u003e\u003cp\u003e(group D)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (males/females)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e831.99\u0026thinsp;\u0026plusmn;\u0026thinsp;160.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e860.87\u0026thinsp;\u0026plusmn;\u0026thinsp;187.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e679.61\u0026thinsp;\u0026plusmn;\u0026thinsp;92.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e828.61\u0026thinsp;\u0026plusmn;\u0026thinsp;114.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.20\u0026thinsp;\u0026plusmn;\u0026thinsp;13.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.00\u0026thinsp;\u0026plusmn;\u0026thinsp;17.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Multi-target tACS enhances the motor learning behaviour.\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean RT and error rates of all blocks in the four groups. The results indicated that there was no significant interaction between group and time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7267), and showed significant main effects of group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0312). Furthermore, post-hoc comparisons revealed that the RT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and ER (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0341) of group D significantly decreased after the 40 Hz tACS intervention. After 14 days of tACS intervention, the RT of group C was significantly lower than group B.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Multi-target tACS enhances cortical excitability of M1.\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the resting motor threshold (RMT, %MSO) and central motor conduction time (CMCT) at various time points. %MSO (percentage of maximum stimulator output) represents the intensity of TMS as a percentage of the device's maximum output, used to standardize stimulation strength across individuals and studies. There is no significant difference in RMT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and CMCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), but as the number of targets increases, there is a clear downward trend in MEP, after 14 days of tACS intervention, which might imply that multi-target tACS stimulation potentially enhances the cortical excitability of participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multi-target tACS changed GMD, ALFF and FC in specific brain regions.\u003c/h2\u003e\u003cp\u003eIn the study, we mainly analyzed the brain structure and function among four groups, as well as voxel-level functional connectivity indicators, including the gray matter density (GMD), amplitude of low-frequency fluctuation (ALFF), FC between M1 and whole brain voxels, and density center (DC). Two-way ANOVA was conducted to examine the differences in GMD among the four groups. The results revealed a significant decrease of group D in the Left Calcarine Sulcus and Surrounding Primary Visual Cortex (Calcarine_L) compared to group C; while as significant increase the Right Middle Frontal Gyrus (MFG), Including Dorsolateral Prefrontal Cortex (DLPFC)(Frontal_Mid_R) compared to other three groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We employed the z-score transformed amplitude of low-frequency fluctuation (zALFF) to investigate regional spontaneous brain activity. Resting-state fMRI data were preprocessed using standard procedures, including slice timing correction, realignment, normalization to the Montreal Neurological Institute (MNI) space, spatial smoothing with a Gaussian kernel of 6 mm full-width at half-maximum (FWHM), and band-pass filtering (0.01\u0026ndash;0.08 Hz). The ALFF was calculated as the square root of the power spectrum within the low-frequency range (0.01\u0026ndash;0.08 Hz) for each voxel, and then transformed to z-scores (zALFF) by subtracting the global mean and dividing by the standard deviation across the whole brain. A two-way ANOVA was performed on the zALFF values of the four groups, followed by post-hoc analysis. The results revealed significant differences at the group level, but no significant differences were observed over time or in the group*time interaction. Specifically, the activation value of the group D in the Left Middle Occipital Gyrus (Occipital_Mid_L); Left Inferior Frontal Gyrus, Triangular Part (Frontal_Inf_Tri_L); Left Calcarine Cortex (Calcarine_L); Right Middle Temporal Gyrus (Temporal_Mid_R) is significantly higher than the other three groups. Conversely, the activation values of several brain regions were significantly reduced in group D compared to other groups, including Right Cerebellum Lobule VI (Cerebellum_6_R); Left Superior Temporal Pole (Temporal_Pole_Sup_L); Left Lingual Gyrus (Lingual_L); Right Lingual Gyrus (Lingual_R); Left Middle Temporal Gyrus (Temporal_Mid_L); Right Inferior Frontal Gyrus, Opercular Part (Frontal_Inf_Oper_R) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also computed the DC to investigate the FC density of each voxel across the whole brain. The fMRI data were preprocessed using standard procedures. The DC value for each voxel was calculated by summing the functional connectivity strengths (Pearson correlation coefficients) between the voxel and all other voxels in the brain, with a threshold of r\u0026thinsp;\u0026gt;\u0026thinsp;0.25 r\u0026thinsp;\u0026gt;\u0026thinsp;0.25 applied to exclude weak correlations. The resulting DC maps were standardized to z-scores to facilitate group-level comparisons. Significant positive FC was observed between M1 and several brain regions, including the Frontal_Inf_Orb_L, Frontal_Inf_Oper_R, and Supp_Motor_Area_R in group D (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings suggest that M1 is functionally integrated with these regions, which are known to be involved in motor planning, execution, and coordination.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe post-analysis comparison of GMD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Name\u003c/p\u003e\u003cp\u003e(AAL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMNI Coordinates\u003c/p\u003e\u003cp\u003e(x, y, z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePost-hoc Comparisons\u003c/p\u003e\u003cp\u003e(group)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcarine_L (aal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e209.6937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026lt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal_Mid_R (aal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103.5673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;B\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe post-analysis comparison of zALFF.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Name\u003c/p\u003e\u003cp\u003e(AAL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMNI Coordinates\u003c/p\u003e\u003cp\u003e(x, y, z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePost-hoc Comparisons\u003c/p\u003e\u003cp\u003e(group)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVermis_7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026lt;B, C\u003c/p\u003e\u003cp\u003eB\u0026lt;C\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026lt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal_Pole_Sup_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.9788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026gt;C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebelum_6_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.9486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026gt;B\u003c/p\u003e\u003cp\u003eB\u0026gt;C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingual_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e114.1273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026gt;B, C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingual_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.8128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026gt;C, D\u003c/p\u003e\u003cp\u003eB\u0026gt;D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccipital_Mid_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.7053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A, B, C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal_Inf_Tri_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99.2553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcarine_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal_Mid_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.8061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026thinsp;\u0026lt;\u0026thinsp;B, C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal_Sup_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.8986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u0026thinsp;\u0026gt;\u0026thinsp;A, C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal_Mid_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.9888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026gt;B, D\u003c/p\u003e\u003cp\u003eB\u0026gt;D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal_Inf_Oper_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.1791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;C, D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupp_Motor_Area_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A, B, C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe post-analysis comparison of FC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Name\u003c/p\u003e\u003cp\u003e(AAL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMNI Coordinates\u003c/p\u003e\u003cp\u003e(x, y, z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePost-hoc Comparisons\u003c/p\u003e\u003cp\u003e(group)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal_Inf_Orb_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.2569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal_Inf_Oper_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.8852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;B\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupp_Motor_Area_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.4108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 RT is negatively correlated with GMD and ALFF values in multi-target tACS\u003c/h2\u003e\u003cp\u003eWe further evaluated the association between the changes in differential brain area structure and activation values and motor learning performance through Pearson analysis. Utilizing the Extract ROI Signals of RESTplus in MATLAB to extract differential cluster, we computed the correlation between the ΔRT and ΔGMD, ΔzALFF. For the group D, we observed a negative correlation between the mean ΔRT and ΔVBM in the Frontal_Mid_R (R=-0.839, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), indicating that motor performance improved with the increased gray matter density. With the 40-Hz multi-target tACS, another negative correlation was found between the ΔRT and ΔzALFF in Vermis_7 (R=-0.759, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048); Supp_Motor_Area_R (R= -0.798, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C), indicating that as the increased activation values of brain regions in the Vermis_7 and Supp_Motor_Area_R, the RT decreases. These findings suggest that the changes in gray matter density/brain activation values may be an essential factor influencing motor performance in the studied population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Results of RNA Sequencing\u003c/h2\u003e\u003cp\u003eWe performed RNA sequencing on peripheral blood samples to characterize the transcriptomic profiles of the healthy participants. A total of 300 differentially expressed genes (DEGs) were identified, with 217 upregulated and 83 downregulated genes in group D compared to group A (|log2 fold change| \u0026gt;1, adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Compared group C, group D upregulated 68 genes and downregulated 73 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Gene Ontology (GO) show that compared group A (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), group D potential involvement of these genes in biological theme, e.g., D-alanine transport, chromaffin granule lumen, dopamine beta-monooxygenase activity, etc. When compared group C, the differentially expressed genes in group D were mainly involved in oxygen transport, hemoglobin complex, oxygen carrier activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). And GO enrichment set (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D) shows that group D upregulated immune response (38%) genes, downregulated material metabolism (28.9%). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F) highlighted that the upregulated DEGs were predominantly enriched in pathways related to Antigen processing and presentation, Complement and coagulation cascades, Wnt signaling pathway, Neuroactive ligand-receptor interaction, suggesting their potential roles in the pathogenesis of dynamic regulation of synaptic plasticity. And Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D indicated that compared with group C, group D upregulated DEGs were predominantly enriched in pathways related to ribosome biogenesis in eukaryotes, downregulated DEGs enriched in antigen processing and presentation. In the analysis of significantly downregulated genes, KEGG pathway enrichment identified several key pathways that were notably suppressed, including gap junction, glycosphingolipid biosynthesis-globo and isoglobo series, and Phagosome.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eImproving motor learning in healthy middle-aged adults is crucial for maintaining functional independence, reducing the risk of age-related motor decline, and enhancing the quality of life. The 40-Hz tACS can synchronize neural activity by applying an external rhythmic electrical current that matches the intrinsic frequency of brain oscillations, presenting a promising treatment for improving motor learning. Numerous research articles have confirmed the beneficial impact of 40-Hz tACS in exploring memory [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], learning [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and higher cognitive function [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], all of which are pivotal for effective motor learning. In the study, we successfully recruited 25 participants. We divided them into four groups to explore the effect of 40-Hz tACS on motor learning by measuring RT and ER of SRTT and ascertain whether the number of stimulation targets will affect the improvement effect. Furthermore, we elucidated the brain mechanism and physiological underpinnings that drive the improvement effect through TMS, MRI, and blood RNA sequencing.\u003c/p\u003e\u003cp\u003eThe study demonstrated that after the RT had been significantly decreased between group B and C after 2-week tACS intervention (T2). This finding aligns with previous studies suggesting that non-invasive brain stimulation, particularly tACS, can enhance cognitive-motor functions in aging populations [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The RT and ER had been significantly decreased compared to baseline in the group D. The above results indicate that within a short period of 40-Hz tACS intervention, the number of targets has a significant impact on the improvement of motor learning in middle-aged and older adults, as evidenced by RT and ER of SRTT.\u003c/p\u003e\u003cp\u003eTo investigate the neural mechanism underlying the enhancement of motor learning by 40-Hz tACS in healthy middle-aged and older adults, we employed a multimodal neuroimaging approach, combining TMS and MRI. TMS was used to assess cortical excitability and plasticity in the M1, while MRI, including both structural and functional imaging, provided insights into changes in ALFF, GMD, and FC within the brain network. The results of TMS revealed no significant decrease in RMT or CMCT following the intervention. However, a trend toward reduction was observed in both RMT and CMCT of groups B, C, and D, which suggests a potential modulation of cortical excitability and corticospinal tract efficiency. The lack of statistical significance may be attributed to the RMT and CMCT being indirect measures of cortical excitability and corticospinal tract function, respectively [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. More sensitive measures, such as paired-pulse TMS or combined TMS-EEG, may be needed to capture subtle changes in neural activity.\u003c/p\u003e\u003cp\u003eAdditionally, structural MRI revealed a significant reduction of group D in the Left Calcarine Sulcus and Surrounding Primary Visual Cortex (Calcarine_L). In contrast, Group D significantly increased in the Right Middle Frontal Gyrus (MFG), Including Dorsolateral Prefrontal Cortex (DLPFC) (Frontal_Mid_R). The tACS-induced gray matter reduction in left calcarine cortex and increase in right DLPFC may reflect stimulus-driven neuroplastic reorganization, potentially representing a shift from sensory processing to cognitive control[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This may optimize the functional connectivity and neural plasticity of the neural network, thereby promoting motor learning. This phenomenon reflects the complexity of neural plasticity and the dynamic reorganization of brain networks. As for brain activation, the activation value of the group D in the Occipital_Mid_L; Frontal_Inf_Tri_L; Calcarine_L; Temporal_Mid_R is significantly higher than the other three groups. These brain regions play an important role in visual processing and may be involved in motor tasks involving visual feedback, optimizing the role of visual feedback in SRTT. We further analyzed the indicators related to FC, and the results showed the significant positive FC between M1 and several brain regions, including the Frontal_Inf_Orb_L, Frontal_Inf_Oper_R, and Supp_Motor_Area_R in group D. The enhancement of FC between M1 and these brain regions reflects the reorganization and optimization of neural networks, thereby improving the efficiency and effectiveness of motor learning. Multi-target tACS may further enhance these functional connections by regulating neural oscillations and promoting neural plasticity, compared to single-target stimulation. These findings suggest that tACS may improve motor learning by modulating cortical excitability, enhancing functional integration within the motor network, and promoting neuroplastic changes in critical motor regions. The combination of TMS and MRI provided a comprehensive understanding of the neural mechanisms underlying tACS-induced improvements in motor learning. The results of behavioral and neural correlates in group D showed the negative correlation of ΔRT and ΔVBM of Frontal_Mid_R. The changes in RT and brain activation values of Vermis_7 and Supp_Motor_Area_R before and after multi-target tACS intervention further demonstrate the importance of neural plasticity and dynamic reorganization of brain networks in motor learning.\u003c/p\u003e\u003cp\u003eThe SRTT results were interpreted further by the knowledge of the underlying biological drivers of the measured change. RNA sequencing is a high-throughput genomic technology that can simultaneously determine the entire gene expression information of RNA, so as to gain insights into gene expression patterns, cell states and functions of different cell types [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The 2-week tACS intervention may induce subtle changes in gray matter structure, particularly in the stimulated cortical regions, through mechanisms such as synaptic plasticity and neurotrophic factor release. The GO results showed that group D potential involvement of D-alanine transport, chromaffin granule lumen, dopamine beta-monooxygenase activity, etc, compared group A. These pathways play critical roles in modulating neurotransmitter synthesis, storage, and release, which may indirectly enhance motor learning. D-alanine transport could influence neurotransmitter regulation or provide neuroprotective effects, supporting neural plasticity essential for motor learning. The chromaffin granule lumen is crucial for storing and releasing catecholamines like dopamine and norepinephrine, which are key to reward processing, motivation, and attention\u0026mdash;processes vital for optimizing motor learning. Dopamine beta-monooxygenase activity, which converts dopamine to norepinephrine, further enhances attention and arousal states, improving cognitive resource allocation during motor tasks. Together, these pathways optimize neurotransmitter levels, strengthen neural network plasticity, and enhance functional connectivity in motor-related circuits, ultimately promoting more efficient and effective motor learning. Multi-target tACS may further amplify these effects by regulating neural oscillations and enhancing the release and functional integration of these neurotransmitters. However, compared group C, the differentially expressed genes in group D were mainly involved in oxygen transport, hemoglobin complex, oxygen carrier activity, which is likely due to the broader and more comprehensive modulation of brain metabolism and neural activity by multi-target tACS. It was leading to increased oxygen utilization and transport. This widespread stimulation could elevate neuronal electrical activity and synaptic plasticity, thereby increasing the brain's oxygen requirements and activating related pathways. Additionally, multi-target tACS may improve neurovascular coupling, enhancing cerebral blood flow and oxygen delivery to meet the heightened metabolic demands of stimulated regions. In contrast, dual-target tACS, with its more limited scope, may not sufficiently activate these metabolic pathways. The increased oxygen transport and hemoglobin complex activity in the multitarget group likely support enhanced neural plasticity, optimized brain network connectivity, and improved motor learning efficiency, reflecting the broader and more robust effects of multitarget stimulation on brain metabolism and function.\u003c/p\u003e\u003cp\u003eDespite these promising findings, several limitations should be noted. First, the sample size was relatively small, which may limit the generalizability of the results. Nevertheless, it should be noted that the original effect size was set as 0.526, based on a previous study with the same primary outcome[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. We assume that the chance of false-negative study results is minimal. Second, the long-term effects of multi-target tACS on motor learning remain unclear. Longitudinal studies are required to determine whether the observed improvements persist over time. Finally, the optimal stimulation parameters (e.g., frequency, intensity, duration) for multi-target tACS in aging populations warrant further investigation.\u003c/p\u003e\u003cp\u003eIn conclusion, the study has demonstrated that 40 Hz tACS effectively enhances motor learning in middle-aged and elderly healthy individuals, with a more significant improvement effect observed with multi-target stimulation. We provide insight into the mechanisms underlying neuromodulatory interventions, suggesting that the underlying mechanisms of multi-target tACS in improving motor learning more effectively can be attributed to its ability to enhance cortical excitability, entrain neural oscillations, enhance synaptic plasticity, and optimize FC within motor-cognitive-related brain networks, including M1, frontal lobe, and SMA. These findings advance the mechanistic understanding of neural tACS effects, thereby contributing to more targeted modulation of neural networks in future experimental and translational tACS applications. In addition, these findings of the effects of regional neuromodulators on multi-target tACS may provide additional guidelines for its clinical use in motor recovery after stroke.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe multi-target tACS can significantly improve the motor learning of healthy participants compared to sham stimulation. The potential mechanism underlying the improvement may involve enhancing cortical excitability, activating the frontal lobe (left) and cerebellum (left) brain region, increasing FC between M1 and frontal lobe and SMA, and promoting synaptic plasticity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Shanghai Seventh People\u0026rsquo;s Hospital (2023-7th-HIRB-043) approved the project in May 2023. All participants provided informed consent, and their safety and respect were protected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Scientific Research Program of Shanghai Pudong New Area Health Commission (the Youth Program) (No. PW2024B-09), the Discipline Construction of Pudong Health Bureau of Shanghai (No. PWZzb2022\u0026ndash;11), the National Natural Science Foundation of China (No. 82202787 and 82272612), the Discipline Construction of Pudong Health Bureau of Shanghai\u0026mdash;\u0026mdash;Discipline group of cerebrovascular system diseases (No. PWZxq2022-01) and 2024 Pujiang Talents Program (24PJA065).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMH L and CW:\u003c/strong\u003e have designed this trial protocol and drafted the manuscript. \u003cstrong\u003eYL:\u003c/strong\u003e analyzed and interpreted the patient data regarding the TMS. \u003cstrong\u003eEB Z:\u0026nbsp;\u003c/strong\u003eCollect magnetic resonance data from all subjects and conduct statistical analysis and was a major contributor in writing the manuscript. \u003cstrong\u003eWF, YL L, HL M, RR W, XY T, CL S, F W, and XM Y\u0026nbsp;\u003c/strong\u003eall contributed to the development of methods, including participant recruitment, data collection, and data analysis. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all the participants. We also appreciate the equipment supports for the Imaging Department of Shanghai Seventh People\u0026apos;s Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrafton ST, Woods RP, Tyszka M: \u003cstrong\u003eFunctional imaging of procedural motor learning: Relating cerebral blood flow with individual subject performance\u003c/strong\u003e. \u003cem\u003eHum Brain Mapp \u003c/em\u003e1994, \u003cstrong\u003e1\u003c/strong\u003e(3):221-234.\u003c/li\u003e\n\u003cli\u003eTakeuchi N, Izumi SI: \u003cstrong\u003eMotor Learning Based on Oscillatory Brain Activity Using Transcranial Alternating Current Stimulation: A Review\u003c/strong\u003e. \u003cem\u003eBrain Sci \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003eBradley C, Elliott J, Dudley S, Kieseker GA, Mattingley JB, Sale MV: \u003cstrong\u003eSlow-oscillatory tACS does not modulate human motor cortical response to repeated plasticity 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We proposed multi-target tACS to improve efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eTwenty-five healthy adults (\u0026gt;45 years) were randomized into sham (A), single-target (B), double-target (C), and multi-target (D) tACS groups. Outcomes included sequence reaction time task (SRTT), transcranial magnetic stimulation (TMS), MRI (gray matter density, activation and functional connectivity (FC)), and RNA sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eSRTT showed that group D significantly shortened the reaction time and error rate compared to baseline. TMS results indicate increased cortical excitability before and after tACS intervention, but no significant difference exists. MRI results showed that the gray matter density in the right middle frontal gyrus (MFG), including the dorsolateral prefrontal cortex (DLPFC) of group D, significantly increased. The activation value of group D in the frontal lobe (left) and cerebellum (left) is substantially higher than that of the other three groups. The functional connection (FC) of motor-cognitive-related brain networks, including primary motor cortex (M1) and frontal lobe and supplementary motor area (SMA), was significantly improved in group D. RNA sequencing analysis revealed a significant increase in oxygen metabolism of group D when compared to group C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eMulti-target tACS enhances motor learning, likely by activating left frontal and cerebellar regions, strengthening M1-frontal-SMA connectivity, and boosting oxygen metabolism.\u003c/p\u003e","manuscriptTitle":"Multi-target transcranial alternating current stimulation (tACS) enhances motor learning and brain network connection in middle-aged adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 12:19:02","doi":"10.21203/rs.3.rs-7378253/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":"2134b925-d00e-436b-b982-b69c4edbc135","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53888041,"name":"Biological sciences/Neuroscience/Learning and memory/Cortex"},{"id":53888042,"name":"Health sciences/Medical research/Clinical trial design/Randomized controlled trials"}],"tags":[],"updatedAt":"2025-10-27T15:07:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 12:19:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7378253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7378253","identity":"rs-7378253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}