Effects of repeated blocks of split-belt walking on locomotor adaptation, physiological arousal response and cortical activation

Effects of repeated blocks of split-belt walking on locomotor adaptation, physiological arousal response and cortical activation | 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 Research Article Effects of repeated blocks of split-belt walking on locomotor adaptation, physiological arousal response and cortical activation Kaya J. Yoshida, Shannon B. Lim, Lara A. Boyd, Janice J. Eng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7670653/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Abstract Adaptability of motor control of gait is fundamental to navigating obstacles and variable environments. While the central nervous system (CNS) is recognized as the primary driver of gait adaptation, the extent to which the autonomic nervous system (ANS) co-modulates with cortical activity and motor output during gait adaptation remains unclear. Thus, this study examined how cortical activation, physiological arousal, and motor adaptation co-modulate during repeated exposure to split-belt treadmill walking. Twenty unimpaired young adults (10F, 10M; 26.8 \(\:\pm\:\) 3.3yrs) completed a single-session, repeated-block split-belt treadmill protocol (three, 3.5-min, 2:1 speed adaptation blocks, interspersed with tied-belt walking). Physiological arousal response (electrodermal activity (EDA)), step length symmetry (SLS), Rating of Perceived Stability (RPS) and cortical activation (via functional near-infrared spectroscopy oxyhemoglobin (HbO)) of the prefrontal, premotor, sensorimotor and posterior parietal cortices were assessed. Linear-mixed-effects models assessed block- and phase-dependent changes in SLS, EDA, HbO response for each region, and RPS. Split-block 1 was perceived as the most destabilizing by RPS scores ( p ≤0.05) and elicited the largest within-block changes in SLS, EDA, and HbO activation in all regions ( p ≤ 0.05), suggesting that split-block 1 encompassed the largest adaptation response across the CNS and ANS. CNS and ANS savings were noted in blocks 2 and 3. Pearson’s correlations revealed that greater gait asymmetry was associated with heightened arousal during early adaptation ( r =-0.569, p <0.001), suggesting an association between error detection and ANS response. Together, these findings suggest cross-system adaptation, with reduced cortical demand, physiological arousal, and perceived challenge and more efficient locomotor adaptation with practice. functional near-infrared spectroscopy (fNIRS) split-belt walking locomotor adaptation physiological arousal response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Stability and energy efficiency in forward propulsion are widely regarded as the primary objectives of gait (Kuo & Donelan, 2009 ). Changing environments and walking conditions require the adaptation and recalibration of motor commands to continue to meet these demands. The act of fine-tuning previously learned movements to meet new requirements through repetitive practice and error-driven feedback is known as motor adaptation (Martin et al., 1996 ; Reisman et al., 2010 ). Adaptation of motor strategies is governed by the central nervous system (CNS), integrating sensory input, cortical drive and motor output (Doyon et al., 2003 ). Contributions from the autonomic nervous system (ANS) to motor adaptation are less understood. Physiological arousal response, mediated by the ANS, is known to modulate with postural control strategies in response to threats to postural stability, raising questions about how physiological arousal response impacts motor adaptation (Adkin & Carpenter, 2018 ; Carpenter et al., 2006 ; Pollock et al., 2017 ). Furthermore, emotionally evoked ANS-mediated physiological arousal response has been associated with activity at the cortical level (Dolcos et al., 2020 ), influencing the motor output. When the physiological arousal response remains elevated, adaptation of motor control strategies during standing is delayed or absent (Carpenter et al., 2006 ; Pollock et al., 2017 ; Sibley et al., 2008 , 2010 ). The physiological arousal response may be related to the perceived task challenge and the consequence of error in task performance, specifically during loss of standing balance. However, research to date has been limited to standing balance tasks, leaving the interactions between physiological arousal response and motor control unexplored during a dynamic continuous task, such as walking. Cortical involvement in motor control of walking can be measured by techniques such as functional near-infrared spectroscopy (fNIRS), electro-encephalography (EEG) or positron emission tomography (PET). According to a review by Herold et al., (2017), fNIRS is regarded as a valuable and promising method for monitoring cortical activity during walking tasks in freely moving individuals, both in laboratory settings and real-world environments. For example, fNIRS studies have shown that prefrontal activation (PFC) tends to increase during more cognitively demanding gait tasks (Bishnoi et al., 2021 ; Pelicioni et al., 2019; Vitorio et al., 2017 ). Split-belt treadmill walking paradigms have been used to experimentally investigate the physiology underpinning the motor control of walking (Severini & Zych, 2022 ). Split-belt treadmills provide a controlled perturbation to probe motor adaptation, as the belts under each leg can move either at the same speed (tied-belt) or different speeds (split-belt), which creates a continuous walking challenge. When asymmetrical walking conditions are imposed, individuals adapt step length bilaterally to achieve a symmetrical gait pattern despite the asymmetrical velocities of the belts (Dietz et al., 1994 ). Hinton et al., ( 2019 ) performed PET scans directly following the completion of a split-belt protocol consisting of continuous adjustment to belt speed ratios (every 15 seconds) over 30 minutes, inhibiting sustained adaptation to any one condition. The authors found that the medial PFC, supplementary motor area (SMA), posterior parietal cortex (PPC), anterior cingulate cortex (ACC), and cerebellum were active post-training (Hinton et al., 2019 ). Jacobsen et al., (2023) used EEG during a single, 15-minute split-belt walking block and found that activity in the PPC, sensorimotor cortex (SMC) and cingulate cortices were associated with adaptation to split-belt walking. During exposure to split-belt walking, the largest disturbance in step length symmetry is typically noted in the first 30 strides of exposure (Jacobsen & Ferris, 2023 ; Malone & Bastian, 2010 ; Roemmich & Bastian, 2015 ). The destabilizing nature of the split-belt walking provides a unique opportunity to study ANS-mediated physiological arousal response and an individual’s perception of the balance challenge, together with associated cortical activity and motor adaptation in step length symmetry. The primary aim of this research is to investigate the modulation of motor adaptation, physiological arousal response, cortical activity, and perceived challenge during repeated bouts of split-belt walking. This research aims to gain insights into how the CNS and ANS co-modulate during motor adaptation to three repeated blocks of split-belt walking. Specifically, we aim to investigate changes between early adaptation (first 30 strides) and late adaptation (last 30 strides) within and between each split-block. Secondly, we aim to probe the potential influence of biological sex on locomotor adaptation, as it has been suggested that sex differences play a role in postural control strategies and balance in older adults (Kim et al., 2010 ; Šarabon et al., 2022 ), but remains unexplored in split-belt training. Within each split-belt training block, we anticipate locomotor adaptation to occur together with reductions in mean cortical activation within the frontal lobe and motor planning areas, accompanied by a simultaneous decrease in physiological arousal response from the early to the late phases. Following the first exposure to split-belt walking, we anticipate that adaptation captured by measures of step-length symmetry, cortical activation, and physiological arousal response during the first exposure (split-block 1) will carry over to subsequent split-belt blocks, suggestive of savings. Specifically, we expect this to present as a smaller magnitude change in step length symmetry (SLS), physiological arousal response, measured by electrodermal activity (EDA) and cortical hemodynamics measures across the regions of interest. In this young and unimpaired cohort, no significant effect of biological sex is expected. 2. METHODS 2.1. Participants Twenty young adults without physical impairment (26.8 ± 3.3 years; 10 females, 10 males) were recruited through purposive sampling via word of mouth and online platforms. Data on age, sex, gender, height, weight, dominant foot, medication use, and comorbidities were collected (Table 1 ). Participants completed the State-Trait Anxiety Inventory (STAI) (Spielberger, 2010 ) and the International Physical Activity Questionnaire (IPAQ) (Craig et al., 2003 ). All participants performed two trials of the 10 Meter Walk Test (10MWT) at both comfortable gait speed (CGS) and fast gait speed (FGS). Groups were stratified by sex for sub-analyses. Inclusion criteria were ages 18–35, with no current neurological, musculoskeletal, or cardiovascular disorders that might interfere with walking, and naïve to split-belt treadmill walking. Exclusion criteria included cardiac or respiratory conditions, neurological and musculoskeletal disorders, and clinically diagnosed depression or anxiety requiring medication. The study adhered to the latest Declaration of Helsinki and was approved by the University of British Columbia Clinical Research Ethics Board, with all participants providing written informed consent. 2.2. Measures 2.2.1. Locomotor adaptation Kinetic and kinematic data were collected from embedded force plates within the instrumented split-belt treadmill (M-GAIT, MOTEK Medical; Fig. 1 a). A 10 Hz low-pass filter was applied to all signals. Step length was defined as the anterior-posterior distance between the center of pressure for each foot at the start of double leg stance, marked by a vertical ground reaction force (GRF) over 15% body weight on both force plates. Locomotor adaptation was measured using step length symmetry (SLS), calculated as a normalized ratio of fast to slow belt step lengths (Eq. 1; Malone & Bastian, 2010 ). This normalization allowed comparison across participants with different heights and baseline step lengths, where a value of zero indicates perfect symmetry, positive values reflect a longer fast step, and negative values indicate a longer slow step (Day et al., 2018 ; Finley et al., 2013 ; Reisman et al., 2010 , 2013 ). Equation 1. \(\:SLS=\frac{{(SL}_{fast}-{SL}_{slow})}{{(SL}_{fast}+{SL}_{slow})}\:\) (Malone & Bastian, 2010 ) 2.2.2. Cortical activity monitoring A portable fNIRS device (NIRSport, NIRx Medical Technologies, Germany) with 16 emitters and 16 detectors was worn by all participants. Near-infrared light at 760 and 850 nm was emitted to detect both oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). An fNIRS cap, sized to each participant's head, was used to measure bilateral regions of interest (ROIs) associated with motor learning (Fig. 1 b), created using fOLD software (Zimeo Morais et al., 2018 ). Source-detector pairs were set at an approximate distance of 30 mm. Eight short-separation channels were included across the probe, with a distance of 7.5mm to account for extracerebral signals. Data were sampled at 5.41 Hz using Aurora software (NIRx Medical Technologies, Germany). 2.2.3. Physiological arousal response and perception of task challenge Electrodermal activity was recorded using bipolar electrodes placed on the palmar surface of the right hand over the thenar and hypothenar eminences. A 50 mV current was applied between the electrodes, and skin conductance was sampled at 300 Hz as an index of physiological arousal. Electrode cables were organized to allow for participants’ normal arm swing during the protocol. Upon completion of the entire protocol, a rating of perceived stability (RPS) was self-reported for each of the split-blocks. The RPS scale (Supplementary material, Fig. S1 ) is a numerical scale providing a validated measure of balance challenge during walking, from 1–10, with 1 being “Completely Stable”, and 10 being “About to Fall” (Espy et al., 2017 ). 2.3. Experimental protocol Participants walked on the split-belt treadmill for 32.5 minutes continuously while secured to an overhead safety harness, which provided no bodyweight support and did not interfere with normal walking gait patterns (Fig. 1 a). Handrails were available on either side of the treadmill; however, participants were encouraged to avoid using them and to walk as they typically would. Participants were instructed not to talk or look down at the belts while walking. Hemodynamic and EDA data were collected for the entirety of the split-belt treadmill protocol. Embedded force plates under each belt recorded ground reaction forces (GRF), which were used to calculate step length bilaterally. All signals were time-synchronized using external triggers. Participants were first familiarized with the treadmill and the two speeds they would be exposed to in 3.5-minute blocks at the beginning of the protocol, in the “Baseline” portion (Fig. 1 c). Then, the protocol alternated between split-belt (adaptation) and tied-belt (de-adaptation) blocks in 3.5-minute blocks continuously, to allow enough time for motor adaptation. A consistent, 2:1 speed ratio was used in repeated split-belt blocks to probe within-session adaptation across repeated exposures. Walking speed during the blocks was individualized based on each person’s fast gait speed (FGS) measured during the 10-Meter Walk Test, using 90% of their average fast speed to account for differences between overground and treadmill walking (Dal et al., 2010). 2.4. Data processing EDA data were zero-phase low-pass filtered at 10 Hz, and a 500 ms median filter was applied to remove motion artifacts (Barua et al., 2020 ). Data were then baseline corrected to the average of a quiet period from the baseline block before the split-blocks (Horvers et al., 2021 ), and peak normalized to the maximum within the session (%max) to allow for comparisons across subjects (Braithwaite et al., 2015 ). SLS and EDA data were divided into “Early” and “Late” phases of adaptation for each of the three split-belt blocks and de-adaptation for each of the three interspersed tied-belt blocks. The Early phase consisted of data for the first 30 strides, excluding the first step, and the Late phase included the last 30 strides, following guidelines from prior studies showing the largest change in SLS during initial adaptation (Malone et al., 2011 ; Malone & Bastian, 2010 ). The early EDA signal accounted for the approximate three-second delay in detection of the physiological arousal response at the palmar surface of the hand (Alexander et al., 2005 ). Raw fNIRS data were analyzed using the MATLAB R2022b (Mathworks, Natick, MA, USA) NIRS AnalyzIR toolbox (Santosa et al., 2018). Scalp-coupling indices (SCI) were calculated for each participant’s data. Channels with SCI values < 0.75 were excluded from the analysis (Pollonini et al., 2014). Optical density data were converted to HbO and HbR using the modified Beer-Lambert law, using a partial pathlength factor of 0.1 (Jacques, 2013). Data were divided into blocks, as defined by stimulus event triggers for belt speed changes by the treadmill. 2.5. Statistical analysis 2.5.1. SLS and EDA data To compare the effect of block (1–3) and phase (early, late) on the dependent variables (DVs) of SLS, EDA and RPS data, linear mixed-effects models (LMM) were conducted for each DV (Package lme4 version 1.1–34, R Studio, 2022) to compare the effects of repeated adaptation (split) and de-adaptation (tied) blocks. The models for SLS and EDA (Eq. 2) included Block, Phase, and their interaction, with participant as a random intercept in all models. Sex was initially tested as a covariate in the SLS model (our primary DV), but its inclusion did not significantly improve model fit (likelihood ratio test, p = 0.815). As a result, sex was not included in the final models for all DVs. The RPS model (Eq. 3) assessed the change in RPS as a function of split. For the SLS and EDA models, estimated marginal means (EMMs), 95% confidence intervals (CI) and pairwise contrasts for the interaction of Block and Phase were calculated (Package emmeans version 4.2-2, R Studio, 2022) and adjusted for multiple comparisons using the Bejamini-Hochberg correction (presented as q -values). For the RPS model (Eq. 3), EMMs, CIs and contrasts were calculated for the effect of split-block. Results from the LMM model can be found in the supplementary material (Table S1 ). Effect sizes (Cohen’s d ) were calculated for all pairwise condition comparisons. Equation 2. \(\:\text{D}\text{V}\:\sim\:-1\:+\:\text{P}\text{h}\text{a}\text{s}\text{e}\:+\:\text{B}\text{l}\text{o}\text{c}\text{k}\:+\text{P}\text{h}\text{a}\text{s}\text{e}\text{*}\text{B}\text{l}\text{o}\text{c}\text{k}\:+\:\left(1\right|\text{P}\text{a}\text{r}\text{t}\text{i}\text{c}\text{i}\text{p}\text{a}\text{n}\text{t})\) Equation 3. \(\:\text{R}\text{P}\text{S}\:\sim\:-1\:+\:\text{S}\text{p}\text{l}\text{i}\text{t}-\text{B}\text{l}\text{o}\text{c}\text{k}\:+\:\left(1\right|\text{P}\text{a}\text{r}\text{t}\text{i}\text{c}\text{i}\text{p}\text{a}\text{n}\text{t})\) 2.5.2. Hemodynamic data analysis Given the temporal dynamics of hemodynamic responses, we quantified activation in each split and tied-block as (1) the overall amplitude of activation and (2) the within-block change in amplitude of activation over time . For clarity, the following description refers to split-blocks; identical procedures were applied to tied-blocks. Hemodynamic data from the three repeated split-blocks were analyzed with a parametric general linear model (GLM) to examine modulation of cortical activity across blocks and over time. Two regressors of interest were included in the GLM for each of the three split-block conditions. The first regressor for each split-block modelled the average hemodynamic brain response over the 210-second block, represented as a boxcar function convolved with a canonical hemodynamic response function ( amplitude of activation ). The second regressor modelled the first-order (linear) modulation of the response over time within the block ( change in amplitude of activation over time ), allowing assessment of whether cortical activation changed systematically over the block and reflecting learning-related effects. In other words, our regression model included one regressor that was constant over time (capturing the average hemodynamic response) and one that varied linearly over time (capturing changes in the response within a block). This approach allowed us to test both the overall amplitude of activation and its modulation over time. Additionally, eight fNIRS short-separation regressors were included as regressors of no interest to further reduce noise from systemic physiological signals (Santosa et al., 2020). The GLM was solved with an autoregressive (AR) pre-whitening filter and iteratively reweighted least squares (AR-ILS) and applied to the pre-processed hemodynamic data to remove serially correlated systemic physiological noise and motion artifacts from the data (Barker et al., 2013). This approach yields three coefficients for the average amplitude of activation (S1, S2, S3) and three coefficients for the change in amplitude of activation over the block (S1_time, S2_time, S3_time), at the individual level, corresponding to the three split-blocks, per ROI. Second-level statistical analysis used a mixed-effects model within the Brain AnalyzIR toolbox, with the first-level regression coefficients ( amplitude of activation and change in amplitude of activation over time coefficients for each split-block) serving as the dependent variables to obtain group-level estimates. Participant ID was included as a random effect on the intercept (Santosa et al., 2018). The second-level statistical model was solved using a weighted regression analysis using the estimated covariances per participant from the first-level GLM as a noise whitening matrix (Santosa et al., 2018). This allows modelling of the heterogeneity of noise and fNIRS signal quality between participants. The estimated group-level model coefficients for the six terms for each fNIRS channel were then averaged across channels into the four regions of interest (ROI). The contrasts between ROIs for each block condition and modulation over time were tested. To account for multiple comparisons, corrected p -values ( q -values) were calculated using a Benjamini-Hochberg correction. Effect sizes (Cohen’s d ) were calculated for all pairwise condition comparisons using the second-level coefficients estimated from the mixed effects model. 2.5.3. Correlations Additionally, correlations were calculated to explore the relationship between each group-estimated brain activity value for each ROI from the fNIRS second-level mixed effects model and measures of performance (SLS), physiological arousal response (EDA) and rating of perceived stability (RPS). Pearson’s correlations were used for all variables found to be normally distributed using the Shapiro-Wilks test, and Spearman’s rank correlations were used for any variables that were not. To control for multiple comparisons in the correlation analyses, p -values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction. The resulting adjusted p -values are referred to as q -values throughout the analysis. 3. RESULTS All participants in this study reported being cisgender. No statistically significant differences were found between reported sex for walking speed, RPS scores reported during split-blocks, self-reported anxiety, or physical activity levels (Table 1 ). Both state and trait anxiety fell within the range of “no or low anxiety” (20–37) to “moderate anxiety” (38–44) (Kayikcioglu et al., 2017 ). Four of the participants reported left-foot dominance, while the rest reported right-foot dominance. Activity levels were classified as moderately to highly active based on the IPAQ. Table 1 Descriptive statistics for the 20 participants, separated by sex (10 per group) shown as mean (95% Confidence Interval (CI)) and results of the between-group ANOVA. RPS = Rating of perceived stability; IPAQ = International Physical Activity Questionnaire; MET = metabolic equivalent Males ( n = 10) Females ( n = 10) Between-group analysis Variable Mean (95% CI) Mean (95% CI) F-statistic p -value Age (years) 28.3 (25.56, 31.04) 25.2 (24.04, 26.36) 5.555 0.097 Height (cm) 184.7 (180.92, 188.48) 168.35 (161.68, 175.02) 31.098 0.000 Weight (kg) 83.46 (76.37, 90.55) 65.54 (56.25, 74.83) 18.286 0.002 Slow belt speed (m/s) 0.96 (0.90, 1.01) 0.95 (0.90, 1.00) 0.048 1.000 Fast belt speed(m/s) 1.91 (1.80, 2.02) 1.9 (1.80, 1.99) 0.037 1.000 RPS S1 (/10) 6 (4.88, 7.12) 6.4 (4.81, 7.99) 0.217 0.935 RPS S2 (/10) 4.7 (3.53, 5.87) 4.7 (3.31, 6.09) 0.000 1.000 RPS S3 (/10) 3.6 (2.58, 4.62) 3.6 (2.29, 4.91) 0.000 1.000 State anxiety (/80) 28 (24.45, 31.55) 32.8 (27.26, 38.34) 2.723 0.252 Trait anxiety (/80) 37.9 (34.11, 41.69) 36.3 (33.21, 39.39) 0.547 0.871 IPAQ Total MET min/week 5525.65 (2014.70, 9036.60) 2868 (1892.73, 3843.27) 2.722 0.252 Baseline step length and SLS were measured during the slow, tied belt block, immediately before block split-block 1 (S1). The average tied-belt walking baseline SLS for this cohort was 0.023 +/- 0.033, indicating that baseline walking was symmetrical. Raw data from all participants followed a similar pattern. For all participants, the step length from the leg on the slow belt (right) was demonstrably shorter than the step length from the stepping leg on the fast belt (left) in the Early phase of S1. By the end of the Early phase of split 1 (> 30 strides), SLS improved but remained lower than baseline symmetry during baseline tied-belt walking. With repeated exposure, symmetry approached baseline levels. During de-adaptation, there was an initial increase in SLS ratio, demonstrating the opposite pattern as in the adaptation blocks (i.e., a larger fast-belt leg step, a shorter slow-belt leg step), indicative of after-effects (Fig. 2 ). EDA data for one participant were removed due to poor signal quality related to loss of electrode adhesion during data collection. While fNIRS data for 2 of the 20 participants were removed due to technical problems, no imputations or statistical corrections were applied, given the small sample size. Hemodynamic data for one participant were removed due to poor signal quality upon visual inspection. The channels excluded for each participant can be found in Table S2 of the Supplementary Material. Only one participant intermittently used the handrails to stabilize during the first split-block; this did not affect the EDA signal. 3.1. Step length symmetry, physiological arousal and perceived stability Linear mixed models (LMM) were calculated for all variables. Although sex was included in the model as a modifier, it did not significantly improve the model for any of the dependent variables ( p > 0.05). SLS demonstrated evidence of adaptation across all blocks of split-belt walking ( q ≤ 0.05 for all), with the greatest magnitude of change in split-block 1 as described below. EDA demonstrated evidence of adaptation during split-blocks 1 and 2 ( q ≤ 0.05 for both), with the greatest magnitude of change in split-block 1 (S1). Amplitude of cortical activation and change in amplitude of activation for HbO were also greater in S1, compared to the second and third split-blocks across all ROIs ( q ≤ 0.05 for all). A full table of model results and pairwise comparisons can be found in the supplementary material (Tables S1 and S3). 3.1.1. SLS and EDA adaptation Statistically significant differences were observed between Early and Late phases for all three split-blocks (S1, S2 and S3), as mean SLS increased from early to late phases becoming significantly more symmetrical by the end of each split (Fig. 2 ; S1: q < 0.001, d = -2.07; S2: q < 0.001, d = -1.22; S3: q = 0.02, d = -0.75). SLS observed during the Early phase of S2 and S3 were both significantly more symmetrical than the Early phase of S1 (S1-S2: q < 0.001, d = -1.72, S1-S3: q < 0.001, d = -1.99), suggestive of savings in S2 and S3. Across S1, SLS increased from − 0.22, 95%CI [-0.26, -0.19] to -0.08, 95%CI [-0.11, -0.05]. Across S2, SLS increased from − 0.10, 95%CI [-0.13, -0.07], to -0.05, 95%CI [-0.08, -0.02]. Across S3, SLS increased from − 0.07, 95%CI [-0.10, -0.04], to -0.04, 95%CI [-0.07, -0.01]. Statistically significant differences in physiological arousal response measured by EDA were observed between Early and Late phases for blocks S1 and S2 (Fig. 2 ). Between the Early and Late phases of S1, average EDA decreased from 70.84%, 95%CI [62.63%, 79.04%] to 23.54%, 95%CI [15.45%, 31.63%], maximum, a decrease of 47.3% maximum, by the end of the Late phase ( q < 0.001, d = 2.67). Between the Early and Late phases of S2, average EDA decreased from 37.89%, 95%CI [29.68%, 46.09%] to 24.46%, 95%CI [16.37%, 32.56%] maximum, a decrease by 13.4% maximum, by the end of the Late phase ( q = 0.002, d = 0.80). There was no significant difference by phase during S3 ( q > 0.05). Compared to the early phase of S1, both subsequent split-blocks showed a significant decrease in physiological arousal response at the initiation of each block (S2, 32.95%, S3, 35.84%), with both late phases approaching baseline EDA measurements. 3.1.2. SLS and EDA de-adaptation De-adaptation during the tied belt blocks showed statistically significant changes from early to late in blocks T1 and T2 in SLS, and T1, T2, and T3 for EDA (Fig. 2 ). Mean SLS during Early T1 (0.12, 95%CI [0.10, 0.14]) was significantly different from Early T2 (0.08, 95%CI [0.06, 0.10]) and Early T3 (0.05, 95%CI [0.03, 0.07]), indicating increased symmetry with repeated de-adaptation blocks (T1-T2: q < 0.001, d = 0.91; T1-T3: q < 0.001, d = 1.62). Mean SLS during early T1 decreased to 0.03 (95%CI [0.01, 0.05]) in late T1 ( q < 0.001, d = 1.42). Similarly, early T2 decreased from 0.08 (95%CI [0.06, 0.10]) to 0.03 (95%CI [0.01, 0.05], q < 0.001, d = 1.55). No significant difference was found between the early and late phases of T3. Mean EDA decreased from Early to Late across the tied-blocks by 30.5%, 18.0%, and 14.6%, respectively. Across T1, EDA changed significantly from 40.52% (95%CI [30.77%, 50.28%]) to 10.05% (95%CI [0.41%, 19.70%]) ( q < 0.001, d = 1.83). Across T2, EDA decreased significantly from 32.70% (95%CI [22.94%, 42.45%]) to 14.74% (95%CI [5.09%, 24.39%]) ( q < 0.001, d = 1.17). Across T3, EDA decreased significantly from 35.41% (95%CI [25.65%, 45.17%]) to 20.81% (95%CI [11.16%, 30.46%]) ( q = 0.001 , d = 1.03). There was no difference between the early phases of tied-blocks 1, 2, and 3. Perception of stability measured by the RPS scale was significantly higher in S1 than in S2 and S3 (S1-S2: q < 0.001, d = 1.31; S1-S3: q < 0.001, d = 1.56). The average reported RPS score for S1 was 6.2, 95% CI [5.44, 6.96]. For S2 and S3, the average reported RPS scores were 4.7, 95% CI [3.94, 5.46] and 3.6, 95% CI [2.84, 4.36], respectively. 3.2. Cortical hemodynamic response A statistically significant effect of amplitude of activation between split-blocks was found across all ROIs (Fig. 3 ). Specifically, the amplitude of HbO response for S1 was significantly higher than that in S2 and S3 for the PFC, PMC, SMC and PPC ( q < 0.05 for all). Further, a statistically significant interaction effect was found for the time-modulation term ( change in amplitude of activation over time ) for the PFC only. There was a significantly greater change in amplitude of activation over time in the PFC during S1 and S3, compared to S2 (S1-S2: q = 0.002, d = 0.726; S2-S3: q = 0.032, d = 0.361, Supplementary Fig. S2). No significant changes in amplitude of activation over time were found in HbO for the PMC, SMC, or PPC. A table of all contrasts for second-level model coefficients by ROI can be found in the supplementary material (Table S4). Heatmaps by channel for all split-block comparisons can be found in the supplementary material (Figs. S4 and S5). A statistically significant effect of amplitude of cortical activation between tied blocks was found across all ROIs (Fig. 4 ). Specifically, the amplitude of HbO activation for T1 was significantly higher than those in T3 for all ROIs ( q < 0.01 for all), and compared to T2 for the PFC, PMC and SMC regions ( q = 0.007, d = 0.203 for PFC; q = 0.003, d = 0.176 for PMC; and q = 0.007, d = 0.396 for SMC). The amplitude of activation for T2 was significantly higher than T3 for the PMC and SMC regions ( q = 0.001, d = 0.498; q = 0.018, d = 0.731, respectively). No statistically significant interaction effect was found for change in amplitude of activation over time, for all ROIs (Supplementary Fig. S3). A table of all contrasts for second-level model coefficients by ROI can be found in the supplementary material (Table S5). Heatmaps by channel for all split-block comparisons can be found in the supplementary material (Figs. S6 and S7). 3.3. Correlations Correlations were calculated to explore relationships between SLS, EDA, RPS values and the HbO data from each ROI during adaptation (split) and de-adaptation (tied) blocks. The full table of results can be found in the Supplementary Material (Table S6); however, only comparisons with relationships stronger than |0.3|, as a cut-off for physiological relevance (Mukaka, 2012 ) will be discussed (Fig. 5 ). 4. DISCUSSION Our findings are the first to describe locomotor adaptation and the associated changes in cortical activity, the ANS-mediated physiological arousal response and perceived instability during repeated blocks of split-belt walking. The first block of split-belt walking was reported by participants as the most destabilizing and elicited the greatest magnitude of significant adaptation across both the CNS and the ANS. Specifically, split-block 1 showed the greatest magnitude of motor adaptation of SLS and adaptation of EDA between early and late phases. Further, the magnitude of hemodynamic response was significantly greater in all ROIs (PFC, PMC, SMC and PPC) in split-block 1 compared to split-blocks 2 and 3. With repeated exposure, there was a lower magnitude of adaptation, consistent with our primary hypothesis. In this sample, participants were comparable in age, physical activity, and state and trait anxiety levels, and did not demonstrate between-sex differences in adaptation of SLS, supporting our secondary hypotheses. A similar pattern of response was noted in de-adaptation (tied-belt blocks) across variables. Notably, SLS values in the early phases of split-blocks 2 and 3 resembled those of the late phase of split-block 1. These findings suggest that the first exposure to split-belt walking elicited the largest adaptation response, followed by evidence of savings in locomotor performance, corresponding to decreases in cortical response, and ANS-mediated physiological arousal with repeated exposure during a single training session. Split-belt walking challenges the stability and symmetry of gait, particularly at the initial onset of belt speed change. This was evident in physiological signals of both the CNS and the ANS, as the onset of the first split-belt block was associated with significant within-block adaptation of SLS, EDA, and hemodynamic response across all ROIs. This was further reflected by participants’ perceived instability (RPS scores), which were significantly higher during the initial block of split-belt walking compared to split-blocks 2 and 3. This observed first-exposure effect across variables is in line with postural control literature that has described the significantly larger evoked response and adaptation in the first trial of a postural perturbation relative to subsequent trials with the same stimulus, in standing balance (Adkin et al., 2000 ; Allum et al., 2011 ; Campbell et al., 2013 ). Although smaller in magnitude, the responses of the CNS and ANS to the onset of tied-blocks show similarity in patterns to the responses noted across split-blocks. During de-adaptation (tied-blocks), differences in SLS reflect after-effects, whereupon returning to symmetrical belt speeds, the modified walking pattern is briefly retained, leading to asymmetry (Malone et al., 2011 ; Malone & Bastian, 2010 , 2014 ; Reisman et al., 2007 ). While the magnitude of SLS after-effects are thought to reflect the extent of initial motor adaptation (Malone et al., 2011 ), the heightened EDA and cortical activity during the first tied-block likely reflect increased task-related demands associated with dynamic balance challenge and re-establishing symmetrical gait following a change in belt speeds. The demands of de-adaptation appeared to elicit parallel responses across systems, as SLS, EDA, and HbO cortical activity were all greatest during the first tied-block, supporting the presence of a first-exposure effect across both adaptation and de-adaptation processes. Moreover, though not statistically compared in this study, this similar pattern of cortical responses observed during split-belt and tied-belt walking aligns with findings by Hulzinga et al., (2025), who reported no difference in activation between these two conditions among their healthy control cohort or people with Parkinson’s disease. Physiological savings refer to the neuromotor system’s ability to adapt more rapidly when a perturbation is reintroduced, even after the initial adaptation has been washed out ໿ (Krakauer et al., 2005 ; Malone et al., 2011 ) and is proposed as evidence for the nervous system learning to solve a problem more efficiently (Braun et al., 2009). Savings are evident in SLS, between the late phase of split-block 1 and the early phase of split-block 2, and again between late split-block 2 and early split-block 3, despite the washout periods between. Our findings in cortical activity are indicative of a similar pattern of savings, notable in split-blocks 2 and 3 across all ROIs, where the lack of significant difference between the amplitude of response in split-blocks 2 and 3, furthered by the minimal time-modulation response within split-blocks 2 and 3, provides evidence for a CNS analogue of physiological savings in this task. In EDA, some habituation is noted between repeated blocks, most clearly in reduced magnitude between early split-blocks. This pattern of ANS and CNS co-modulation is further supported by the negative relationship observed between SLS and EDA during early split-blocks, indicating that individuals with greater symmetry in early adaptation exhibited a lower physiological arousal response to the perturbation. These findings raise questions regarding the contribution of the ANS in locomotor adaptation to conditions that challenge walking. The link between the CNS and ANS has been previously characterized in behavioural literature using functional brain imaging (Cheng et al., 2003 ; Critchley et al., 2000 ; Knight et al., 2005 ; MacIntosh et al., 2007 ). Specifically, regions of the PFC, anterior cingulate cortex (ACC), parietal lobe, insula and amygdala have been shown to be associated with modulation of physiological arousal response in a context-specific manner (Critchley et al., 2000 ). During motor task performance (finger movement task), MacIntosh et al., ( 2007 ) found that physiological arousal response, measured by EDA, habituated alongside functional magnetic resonance imaging, blood-oxygen-level dependent (BOLD) response in a network of regions associated with motor learning. Furthermore, the nature of the task (i.e., finger movements vs. risk-taking) has been shown to influence the level of modulation between physiological arousal response, measured by EDA and the PFC (Holper, Ten Brincke, et al., 2014; Holper, Wolf, et al., 2014). During tasks with risk-taking behaviours (risky financial decisions), coherence between PFC hemodynamics and physiological arousal response is shown to increase as the level of risk rises (Holper, Ten Brincke, et al., 2014; Holper, Wolf, et al., 2014). In the current study, the highest level of perceived instability was reported in the first split-block, with an average participant-reported score of 6.2/10. This coincided with the greatest within-block change in physiological arousal response and hemodynamic cortical activation in the PFC. In the second and third split-blocks, RPS scores describe a decreased, but not absent, perceived instability with scores of 4.7 and 3.6, respectively ( i.e., a general sense of unsteadiness but no need for additional support perceived by the individual) (Espy et al., 2017 ). Similarly, EDA response and level of activity in all cortical ROIs were lower in split-blocks 2 and 3. These findings suggest the co-modulation between CNS-ANS noted in standing postural tasks extends to dynamic walking tasks. The pattern of cortical response across the PFC, PMC, PPC and SMC suggests a coordinated network supporting locomotor adaptation, particularly at the initial exposure to split-belt walking. Our findings showed that the initial exposure evoked the largest cortical response across all ROIs, with activation attenuating in subsequent blocks. This pattern suggests that the PFC, PMC, PPC, and SMC are most engaged during early error-detection and adaptation, when the task is novel and perceived as more challenging. The PFC, associated with attentional control and executive function, is often shown to be active during gait initiation and complex walking conditions, such as dual-task walking (Koenraadt et al., 2014; Mirelman et al., 2014 ; Pelicioni et al., 2019). The PMC may contribute to step length adaptation and serve as a bridge between cognitive and motor networks (Hanakawa, 2011 ), with co-activation of the PFC and PMC reflective of top-down attentional control, often observed during motor learning (Abe & Hanakawa, 2009). Further, a moderate negative correlation between HbO activity in the PPC and EDA response across the early tied-blocks may be attributed to the PPC’s role in attentional shifts and arousal (Brown et al., 2023 ). Interestingly, inhibition of the PPC has been shown to impair adaptation and enhance after-effects in split-belt walking (Young et al., 2020), highlighting its broader involvement in locomotor control. The SMC supports sensorimotor integration necessary for gait adjustments, and its activity, along with that of the PPC and ACC, measured by EEG, was found to be most prominent in the first 30 strides of adaptation to a single, prolonged block of split-belt walking (Jacobsen & Ferris, 2023 ). Similar findings were reported by Hinton et al., ( 2019 ), where PET imaging following a 30-minute split-belt protocol with continuously shifting belt speed ratios found activation in the PFC, SMA, PPC, ACC, and cerebellum, further supporting the involvement of a distributed cortical–subcortical network in locomotor adaptation. The observed attenuation of activity across repeated exposures supports the idea that these regions form a flexible, adaptive network, likely in concert with subcortical structures, that facilitates the transition from inclusion of increased input from conscious control during initial error-detection to more automatic gait regulation as adaptation progresses. While this study provides evidence for cortical involvement in novel locomotor adaptation, this does not imply a complete causal role in driving the adaptation process. The neural control of simple, rhythmic gait, such as maintaining tied-belt treadmill walking, is known to be subcortically driven by structures including the basal ganglia, cerebellum and brainstem, and cannot be probed by fNIRS (Nakazawa et al., 2012 ; Takakusaki, 2017 ). During walking, Central Pattern Generators (CPGs) within each limb are thought to control the rhythmic movement of the ipsilateral limb, in coordination with the contralateral limb via inhibitory and excitatory commissural interneurons within the spinal cord (Rybak et al., 2015 ). Cross-limb communication via CPGs likely somewhat underpins the bilateral step length adaptation observed during split-belt walking. However, intentional or complex walking tasks engage more supraspinal control (Mirelman et al., 2018 ; Nakazawa et al., 2012 ; Takakusaki, 2017 ), the lack of significant change in cortical activation during subsequent split-belt adaptation and tied-belt de-adaptation blocks may suggest that higher-order cortical control is less critical following initial error-detection and adaptation. 4.1. Limitations As the use of fNIRS during walking continues to evolve, certain methodological limitations warrant consideration. One inherent challenge is the introduction of motion artifacts during ambulatory tasks. Motion artifacts arise from slippage of the head cap on the scalp, rather than movement of the participant per se, but these artifacts are more likely during walking. The GLM model used in this study uses a robust estimator (outlier down weighting) and has been demonstrated to be less sensitive to these artifacts (Barker et al., 2013). In addition, we used the scalp coupling index to exclude poor channels of data on a per participant basis in order to ensure that only data that provided physiologically relevant signals was used. Secondly, data from three participants were excluded due to technical issues during data collection, which may have reduced the overall statistical power and sensitivity to detect effects. Participant data describing diversity was limited to age, gender, sex and physical activity. Additional research is required to test the effect of other variables. 5. CONCLUSION Our study provides new insight into the modulation of the CNS and ANS during a repeated split-belt walking challenge. Taken together, these metrics point to cross-system modulation with locomotor adaptation and lower perceived challenge leading to a lower magnitude of cortical involvement and lower physiological arousal response during the task due to practice. The reduction in magnitude of cortical involvement may be suggestive of increased neural efficiency, a concept to be explored in future work. Future work should also explore these relationships in other cohorts, including people with gait-related impairments, decreased walking balance confidence and in older adults to probe the impact of motor impairment, levels of balance confidence and aging on the co-modulation between the CNS and ANS. Declarations The authors have no competing interests to disclose. Funding Kaya J. Yoshida is supported by the Canadian Institute for Health Research Doctoral award (#186421). Courtney L. Pollock is supported by a Michael Smith Health Research British Columbia research award. This project was funded by the Natural Sciences and Engineering Research Council of Canada, and a Canadian Foundation for Innovation grant. Author Contribution KJY conceived and designed the study, curated data, performed analyses, and drafted the manuscript. SBL, LAB, and JJE contributed to study conception and manuscript revision, with JJE also providing resources. AS and TJH contributed to methodology, conducted analyses, and revised the manuscript. CLP conceived and supervised the study, acquired funding and resources, and revised the manuscript. All authors approved the final version of the manuscript. Data Availability The data and code used in the study can be requested by sending a research proposal to the principal investigator (PI) Dr. Courtney Pollock (email: [email protected] ). References Adkin, A. L., & Carpenter, M. G. (2018). New insights on emotional contributions to human postural control. Frontiers in Neurology , 9 (789), 1–8. https://doi.org/10.3200/JMBR.36.2.212-224 Adkin, A. L., Frank, J. S., Carpenter, M. G., & Peysar, G. W. (2000). Postural control is scaled to level of postural threat. Gait & Posture , 12 (2), 87–93. https://doi.org/10.1016/S0966-6362(00)00057-6 Alexander, D. M., Trengove, C., Johnston, P., Cooper, T., August, J. P., & Gordon, E. (2005). Separating individual skin conductance responses in a short interstimulus-interval paradigm. Journal of Neuroscience Methods , 146 (1), 116–123. https://doi.org/10.1016/j.jneumeth.2005.02.001 Allum, J. H. J., Tang, K.-S., Carpenter, M. G., Oude Nijhuis, L. B., & Bloem, B. R. (2011). Review of first trial responses in balance control: Influence of vestibular loss and Parkinson’s disease. Human Movement Science , 30 (2), 279–295. https://doi.org/10.1016/j.humov.2010.11.009 Barua, S., Ahmed, M. U., & Begum, S. (2020). Towards intelligent data analytics: A case study in driver cognitive load classification. Brain Sciences , 10 (8), 1–19. https://doi.org/10.3390/brainsci10080526 Betschart, M., Lauzière, S., Miéville, C., McFadyen, B. J., & Nadeau, S. (2017). Changes in lower limb muscle activity after walking on a split-belt treadmill in individuals post-stroke. Journal of Electromyography and Kinesiology , 32 , 93–100. https://doi.org/10.1016/j.jelekin.2016.12.007 Bishnoi, A., Holtzer, R., & Hernandez, M. E. (2021). Brain activation changes while walking in adults with and without neurological disease: Systematic review and meta-analysis of functional near-infrared spectroscopy studies. Brain Sciences , 11 (3), 1–22. https://doi.org/10.3390/brainsci11030291 Braithwaite, J. J., Watson, D. G., Jones, R., & Rowe, M. (2015). A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments [Technical Report]. Brown, L., White, L. K., Makhoul, W., Teferi, M., Sheline, Y. I., & Balderston, N. L. (2023). Role of the intraparietal sulcus (IPS) in anxiety and cognition: Opportunities for intervention for anxiety-related disorders. International Journal of Clinical and Health Psychology , 23 (4). https://doi.org/10.1016/j.ijchp.2023.100385 Campbell, A. D., Squair, J. W., Chua, R., Inglis, J. T., & Carpenter, M. G. (2013). First trial and StartReact effects induced by balance perturbations to upright stance. Journal of Neurophysiology , 110 (9), 2236–2245. https://doi.org/10.1152/jn.00766.2012 Carpenter, M. G., Adkin, A. L., Brawley, L. R., & Frank, J. S. (2006). Postural, physiological and psychological reactions to challenging balance: Does age make a difference? Age and Ageing , 35 (3), 298–303. https://doi.org/10.1093/ageing/afl002 Cheng, D. T., Knight, D. C., Smith, C. N., Stein, E. A., & Helmstetter, F. J. (2003). Functional MRI of human amygdala activity during Pavlovian fear conditioning: Stimulus processing versus response expression. Behavioral Neuroscience , 117 (1), 3–10. https://doi.org/10.1037/0735-7044.117.1.3 Craig, C. L., Marshall, A. L., Sj??Str??M, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., Pratt, M., Ekelund, U., Yngve, A., Sallis, J. F., & Oja, P. (2003). International Physical Activity Questionnaire: 12-Country Reliability and Validity: Medicine & Science in Sports & Exercise , 35 (8), 1381–1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB Critchley, H. D., Elliott, R., Mathias, C. J., & Dolan, R. J. (2000). Neural activity relating to generation and representation of galvanic skin conductance responses: A functional magnetic resonance imaging study. Journal of Neuroscience , 20 (8), 3033–3040. https://doi.org/10.1523/jneurosci.20-08-03033.2000 Day, K. A., Leech, K. A., Roemmich, R. T., & Bastian, A. J. (2018). Accelerating locomotor savings in learning: Compressing four training days to one. Journal of Neurophysiology , 119 (6), 2100–2113. https://doi.org/10.1152/jn.00903.2017 Dietz, V., Zijlstra, W., & Duysens, J. (1994). Human neuronal interlimb coordination during split-belt locomotion. Experimental Brain Research , 101 (3), 513–520. https://doi.org/10.1007/BF00227344 Dolcos, F., Katsumi, Y., Moore, M., Berggren, N., de Gelder, B., Derakshan, N., Hamm, A. O., Koster, E. H. W., Ladouceur, C. D., Okon-Singer, H., Pegna, A. J., Richter, T., Schweizer, S., Van den Stock, J., Ventura-Bort, C., Weymar, M., & Dolcos, S. (2020). Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions. Neuroscience and Biobehavioral Reviews , 108 (August 2019), 559–601. https://doi.org/10.1016/j.neubiorev.2019.08.017 Doyon, J., Penhune, V., & Ungerleider, L. G. (2003). Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia , 41 (3), 252–262. https://doi.org/10.1016/S0028-3932(02)00158-6 Espy, D., Reinthal, A., & Meisel, S. (2017). Intensity of balance task intensity, as measured by the Rate of Perceived Stability, is independent of physical exertion as measured by heart rate. Journal of Novel Physiotherapies , 7 (S4). https://doi.org/10.4172/2165-7025.1000343 Finley, J. M., Bastian, A. J., & Gottschall, J. S. (2013). Learning to be economical: The energy cost of walking tracks motor adaptation. Journal of Physiology , 591 (4), 1081–1095. https://doi.org/10.1113/jphysiol.2012.245506 Hanakawa, T. (2011). Rostral premotor cortex as a gateway between motor and cognitive networks. Neuroscience Research , 70 (2), 144–154. https://doi.org/10.1016/j.neures.2011.02.010 Hinton, D. C., Thiel, A., Soucy, J. P., Bouyer, L., & Paquette, C. (2019). Adjusting gait step-by-step: Brain activation during split-belt treadmill walking. NeuroImage , 202 (August), 116095. https://doi.org/10.1016/j.neuroimage.2019.116095 Holper, L., Ten Brincke, R. H. W., Wolf, M., & Murphy, R. O. (2014). fNIRS derived hemodynamic signals and electrodermal responses in a sequential risk-taking task. Brain Research , 1557 , 141–154. https://doi.org/10.1016/j.brainres.2014.02.013 Holper, L., Wolf, M., & Tobler, P. N. (2014). Comparison of functional near-infrared spectroscopy and electrodermal activity in assessing objective versus subjective risk during risky financial decisions. NeuroImage , 84 , 833–842. https://doi.org/10.1016/j.neuroimage.2013.09.047 Holtzer, R., Izzetoglu, M., Chen, M., & Wang, C. (2019). Distinct fNIRS-Derived HbO2 Trajectories during the Course and over Repeated Walking Trials under Single-and Dual-Task Conditions: Implications for Within Session Learning and Prefrontal Cortex Efficiency in Older Adults. Journals of Gerontology - Series A Biological Sciences and Medical Sciences , 74 (7), 1076–1083. https://doi.org/10.1093/gerona/gly181 Horvers, A., Tombeng, N., Bosse, T., Lazonder, A. W., & Molenaar, I. (2021). Detecting emotions through electrodermal activity in learning contexts: A systematic review. Sensors , 21 (23). https://doi.org/10.3390/s21237869 Jacobsen, N. A., & Ferris, D. P. (2023). Electrocortical activity correlated with locomotor adaptation during split‐belt treadmill walking. The Journal of Physiology , 601 (17), 3921–3944. https://doi.org/10.1113/JP284505 Kayikcioglu, O., Bilgin, S., Seymenoglu, G., & Deveci, A. (2017). State and Trait Anxiety Scores of Patients Receiving Intravitreal Injections. Biomedicine Hub , 2 (2), 1–5. https://doi.org/10.1159/000478993 Kim, J., Eom, G., Kim, C., Kim, D., Lee, J., Park, B. K., & Hong, J. (2010). Sex differences in the postural sway characteristics of young and elderly subjects during quiet natural standing. Geriatrics & Gerontology International , 10 (2), 191–198. https://doi.org/10.1111/j.1447-0594.2009.00582.x Knight, D. C., Nguyen, H. T., & Bandettini, P. A. (2005). The role of the human amygdala in the production of conditioned fear responses. NeuroImage , 26 (4), 1193–1200. https://doi.org/10.1016/j.neuroimage.2005.03.020 Krakauer, J. W., Ghez, C., & Ghilardi, M. F. (2005). Adaptation to visuomotor transformations: Consolidation, interference, and forgetting. Journal of Neuroscience , 25 (2), 473–478. https://doi.org/10.1523/JNEUROSCI.4218-04.2005 Kuo, A. D., & Donelan, J. M. (2009). Dynamic principles of gait and their clinical implications. Physical Therapy , 90 (2), 157–174. Kurz, M. J., Wilson, T. W., & Arpin, D. J. (2012). Stride-time variability and sensorimotor cortical activation during walking. NeuroImage , 59 (2), 1602–1607. https://doi.org/10.1016/j.neuroimage.2011.08.084 Lauzière, S., Miéville, C., Betschart, M., Duclos, C., Aissaoui, R., & Nadeau, S. (2014). Plantarflexion moment is a contributor to step length after-effect following walking on a split-belt treadmill in individuals with stroke and healthy individuals. Journal of Rehabilitation Medicine , 46 (9), 849–857. https://doi.org/10.2340/16501977-1845 Lim, S. B., Peters, S., Yang, C. L., Boyd, L. A., Liu-Ambrose, T., & Eng, J. J. (2022). Frontal, sensorimotor, and posterior parietal regions are involved in dual-task walking after stroke. Frontiers in Neurology , 13 (June), 1–13. https://doi.org/10.3389/fneur.2022.904145 Lim, S. B., Yang, C. L., Peters, S., Liu-Ambrose, T., Boyd, L. A., & Eng, J. J. (2022). Phase-dependent brain activation of the Frontal and Parietal regions during walking after stroke—An fNIRS study. Frontiers in Neurology , 13 (July), 1–14. https://doi.org/10.3389/fneur.2022.904722 MacIntosh, B. J., Mraz, R., McIlroy, W. E., & Graham, S. J. (2007). Brain activity during a motor learning task: An fMRI and skin conductance study. Human Brain Mapping , 28 (12), 1359–1367. https://doi.org/10.1002/hbm.20351 Malone, L. A., & Bastian, A. J. (2010). Thinking about walking: Effects of conscious correction versus distraction on locomotor adaptation. Journal of Neurophysiology , 103 (4), 1954–1962. https://doi.org/10.1152/jn.00832.2009 Malone, L. A., & Bastian, A. J. (2014). Spatial and temporal asymmetries in gait predict split-belt adaptation behavior in stroke. Neurorehabilitation and Neural Repair , 28 (3), 230–240. https://doi.org/10.1177/1545968313505912 Malone, L. A., Vasudevan, E. V. L. L., & Bastian, A. J. (2011). Motor adaptation training for faster relearning. Journal of Neuroscience , 31 (42), 15136–15143. https://doi.org/10.1523/JNEUROSCI.1367-11.2011 Martin, T. A., Keating, J. G., Goodkin, H. P., Bastian, A. J., & Thach, W. T. (1996). Throwing while looking through prisms: II. Specificity and storage of multiple gaze--throw calibrations. Brain , 119 (4), 1199–1211. https://doi.org/10.1093/brain/119.4.1199 Mirelman, A., Maidan, I., Bernad-Elazari, H., Nieuwhof, F., Reelick, M., Giladi, N., & Hausdorff, J. M. (2014). Increased frontal brain activation during walking while dual tasking: An fNIRS study in healthy young adults. Journal of NeuroEngineering and Rehabilitation , 11 (1), 1–7. https://doi.org/10.1186/1743-0003-11-85 Mirelman, A., Maidan, I., & Hausdorff, J. M. (2018). Chapter 7—Gait. In B. L. Day & S. R. Lord (Eds.), Handbook of Clinical Neurology (Vol. 159, pp. 119–134). Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal , 24 (3), 69–71. Nakazawa, K., Obata, H., & Sasagawa, S. (2012). Neural control of human gait and posture. The Journal of Physical Fitness and Sports Medicine , 1 (2), 263–269. https://doi.org/10.7600/jpfsm.1.263 Pollock, C. L., Carpenter, M. G., Hunt, M. A., Gallina, A., Vieira, T. M., Ivanova, T. D., & Garland, S. J. (2017). Physiological arousal accompanying postural responses to external perturbations after stroke. Clinical Neurophysiology , 128 (6), 935–944. https://doi.org/10.1016/j.clinph.2017.03.008 Reisman, D. S., Bastian, A. J., & Morton, S. M. (2010). Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. Physical Therapy , 90 (2), 187–195. https://doi.org/10.2522/ptj.20090073 Reisman, D. S., McLean, H., Keller, J., Danks, K. A., & Bastian, A. J. (2013). Repeated split-belt treadmill training improves poststroke step length asymmetry. Neurorehabilitation and Neural Repair , 27 (5), 460–468. https://doi.org/10.1177/1545968312474118 Reisman, D. S., Wityk, R., Silver, K., & Bastian, A. J. (2007). Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. Brain , 130 (Pt 7), 1861–1872. https://doi.org/10.1093/brain/awm035.LOCOMOTOR Roemmich, R. T., & Bastian, A. J. (2015). Two ways to save a newly learned motor pattern. Journal of Neurophysiology , 113 (10), 3519–3530. https://doi.org/10.1152/jn.00965.2014 Rybak, I. A., Dougherty, K. J., & Shevtsova, N. A. (2015). Organization of the mammalian locomotor CPG: Review of computational model and circuit architectures based on genetically identified spinal interneurons. Eneuro , 2 (5), ENEURO.0069-15.2015. https://doi.org/10.1523/ENEURO.0069-15.2015 Šarabon, N., Kozinc, Ž., & Marković, G. (2022). Effects of age, sex and task on postural sway during quiet stance. Gait and Posture , 92 (October 2021), 60–64. https://doi.org/10.1016/j.gaitpost.2021.11.020 Severini, G., & Zych, M. (2022). Locomotor adaptations: Paradigms, principles and perspectives. Progress in Biomedical Engineering , 4 (4), 042003. https://doi.org/10.1088/2516-1091/ac91b6 Sibley, K. M., Mochizuki, G., Esposito, J. G., Camilleri, J. M., & McIlroy, W. E. (2008). Phasic electrodermal responses associated with whole-body instability: Presence and influence of expectation. Brain Research , 1216 , 38–45. https://doi.org/10.1016/j.brainres.2008.04.002 Sibley, K. M., Mochizuki, G., Frank, J. S., & McIlroy, W. E. (2010). The relationship between physiological arousal and cortical and autonomic responses to postural instability. Experimental Brain Research , 203 (3), 533–540. https://doi.org/10.1007/s00221-010-2257-8 Spielberger, C. D. (2010). State-Trait Anxiety Inventory. In Corsini Encyclopedia of Psychology . John Wiley & Sons, Inc. https://doi.org/10.1002/9780470479216.corpsy0943 Takakusaki, K. (2017). Functional neuroanatomy for posture and gait control. Journal of Movement Disorders , 10 (1), 1–17. https://doi.org/10.14802/jmd.16062 Vitorio, R., Stuart, S., Rochester, L., Alcock, L., & Pantall, A. (2017). fNIRS response during walking—Artefact or cortical activity? A systematic review. Neuroscience and Biobehavioral Reviews , 83 (October), 160–172. https://doi.org/10.1016/j.neubiorev.2017.10.002 Zimeo Morais, G. A., Balardin, J. B., & Sato, J. R. (2018). FNIRS Optodes’ Location Decider (fOLD): A toolbox for probe arrangement guided by brain regions-of-interest. Scientific Reports , 8 (1), 1–11. https://doi.org/10.1038/s41598-018-21716-z Additional Declarations No competing interests reported. Supplementary Files KJYESM1.docx Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Experimental Brain Research → 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7670653","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527889165,"identity":"cb2c6bd2-7551-4ac1-a5d0-13067db1d785","order_by":0,"name":"Kaya J. Yoshida","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Kaya","middleName":"J.","lastName":"Yoshida","suffix":""},{"id":527889166,"identity":"db7f2859-20a7-422b-9653-b93fbf921a6c","order_by":1,"name":"Shannon B. Lim","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Shannon","middleName":"B.","lastName":"Lim","suffix":""},{"id":527889168,"identity":"580a540f-b90e-4d86-9e95-805b87255ca1","order_by":2,"name":"Lara A. Boyd","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"A.","lastName":"Boyd","suffix":""},{"id":527889170,"identity":"91405879-f430-484c-bef8-ef50795ec280","order_by":3,"name":"Janice J. Eng","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Janice","middleName":"J.","lastName":"Eng","suffix":""},{"id":527889172,"identity":"4d30496d-636f-40cb-890e-e5619af6e9f3","order_by":4,"name":"Amy Schneeberg","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Schneeberg","suffix":""},{"id":527889174,"identity":"9dc6a359-640d-42e3-b95d-1f392faaa04a","order_by":5,"name":"Theodore J. Huppert","email":"","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Theodore","middleName":"J.","lastName":"Huppert","suffix":""},{"id":527889177,"identity":"77a870c7-f2e4-4223-aa66-fe468702abff","order_by":6,"name":"Courtney L. Pollock","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3QPQrCMBTA8SeuQteIeodKIJNUvElKwKm6uCgKBgrpIrh6DxfHV4S6xL2jJ5CKB9Aqfi01ugnmv4SE9xteAGy2H6+EOwDXgcoHs3g/eU6q8mviooE40YwdD6uWL2shIh96lKa9OIOJV0iI1nQR664v6wlHrgVjaV8QSEQhcUlAIVZrX5LARV9hi6UBg5Ism8jpSejiSqYmgg/C8pcLWb/ZJRnAVgmqbrtQoveC8GRTSJwoXMJIeY15LYx32dBrzqP8x7LJuJDc6yjyeuVGANAGYh6y2Wy2P+0MvGNXws8PzSMAAAAASUVORK5CYII=","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Courtney","middleName":"L.","lastName":"Pollock","suffix":""}],"badges":[],"createdAt":"2025-09-22 09:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7670653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7670653/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00221-025-07218-1","type":"published","date":"2025-12-27T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93530596,"identity":"f16e7767-7011-4ed0-82b8-d40755187df0","added_by":"auto","created_at":"2025-10-14 20:58:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":934546,"visible":true,"origin":"","legend":"","description":"","filename":"KJYManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/36a034ac3fd8047284552a47.docx"},{"id":93529454,"identity":"720bb796-3270-498b-a7b0-79b8bfc28815","added_by":"auto","created_at":"2025-10-14 20:50:41","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8712,"visible":true,"origin":"","legend":"","description":"","filename":"3a6d14f95597431788b4d6d31910062e.json","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/b5f7140feb08972d841f337b.json"},{"id":93529462,"identity":"4cafa634-02e8-44dc-8747-adb10909f56f","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1673933,"visible":true,"origin":"","legend":"","description":"","filename":"KJYESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/5ceac031399bb10aa46f1f1d.docx"},{"id":93529459,"identity":"3ed1f35f-58ef-4fd2-a673-b9b456017474","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165750,"visible":true,"origin":"","legend":"","description":"","filename":"3a6d14f95597431788b4d6d31910062e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/be8de51ae337bdacd576225d.xml"},{"id":93529452,"identity":"d9e72507-7392-4ade-b548-ec93da082767","added_by":"auto","created_at":"2025-10-14 20:50:41","extension":"eps","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":390,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage1.eps","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/49c7082473cb927460a27190.eps"},{"id":93530598,"identity":"20e9dab5-7b42-46d5-87a7-650b39c8831d","added_by":"auto","created_at":"2025-10-14 20:58:42","extension":"eps","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":384,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage2.eps","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/6a2c3952117491ba0cd67b16.eps"},{"id":93529465,"identity":"8312622f-a73a-440c-8faa-0b2df4d66858","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109115,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/6e3d50921f88da33b7e24203.png"},{"id":93530599,"identity":"f98a4292-4153-4bba-93dc-0b19f8a61cd0","added_by":"auto","created_at":"2025-10-14 20:58:42","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30646,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/6cff1f53294b0f18fe35f630.png"},{"id":93529469,"identity":"99acc1bd-1d93-4d4b-929e-0fc7344d3834","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24445,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/5385e90067ccb837316e016f.png"},{"id":93529464,"identity":"32531240-4d06-4c80-862d-440fbe5125b0","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43654,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/1e9e9a2554781a740fdcff73.png"},{"id":93529460,"identity":"fe8a488e-00dd-4c22-a178-530946c1b212","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30759,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/297f4969ea1f5c97705c0156.png"},{"id":93529466,"identity":"7c534b23-c0d1-419c-bae8-9cd6e5a48a40","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162647,"visible":true,"origin":"","legend":"","description":"","filename":"3a6d14f95597431788b4d6d31910062e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/af7f419a43197cc97c9b600b.xml"},{"id":93529467,"identity":"f64ac42b-d756-4e4a-94fd-6c1e5aa09544","added_by":"auto","created_at":"2025-10-14 20:50:42","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177086,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/d787ed6c1e449e0bcb2e6b54.html"},{"id":93529451,"identity":"aa43512d-57d4-4807-acb3-442f59926f4c","added_by":"auto","created_at":"2025-10-14 20:50:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283954,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental protocol and probe arrangement. a) Participants walked on a split-belt treadmill while wearing a functional near-infrared spectroscopy (fNIRS) cap and electrodermal activity (EDA) electrodes on the palmar surface of the hand. Embedded force plates recorded ground reaction forces (GRF) to calculate step length. b) fNIRS probe arrangement based on the EEG 10/10 system. Red dots = sources; blue dots = detectors; yellow circles = short-separation detectors. Numbers denote source–detector channels, colored by region of interest. c) Block timing. Participants first completed baseline walking at individualized speeds, followed by alternating 3.5-min adaptation (split-belt: left fast, right slow) and de-adaptation (tied, both slow) blocks\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/bcfa482c9d39011930843b87.jpeg"},{"id":93530594,"identity":"d1521a4c-0c1c-46b3-b26b-068678716042","added_by":"auto","created_at":"2025-10-14 20:58:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126177,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in step length symmetry (SLS, top row) and physiological arousal response (bottom row) measured by electrodermal arousal (EDA), between split-blocks (S1/S2/S3, left) and tied-blocks (T1/T2/T3, right) for early (E) and late (L) phases for all participants. For SLS, a symmetry ratio of zero indicates perfect step length symmetry. EDA is shown as a percentage of the maximum response within the entire session. Error bars represent the standard error. Asterisk (*) indicates statistical significance at \u003cem\u003eq \u003c/em\u003e≤ 0.05 between phases\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/163b5fbc9390fa6a94c9d43a.jpeg"},{"id":93530595,"identity":"687864f7-d33b-4c47-ba1b-8803066cf259","added_by":"auto","created_at":"2025-10-14 20:58:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109165,"visible":true,"origin":"","legend":"\u003cp\u003eMean amplitude of response in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration across the three, 210-second split-belt walking blocks calculated for the prefrontal cortex (PFC), premotor cortex (PMC), sensorimotor cortex (SMC) and posterior parietal cortex (PPC) during split-blocks 1-3 (S1, S2, S3). Error bars represent the standard error. Asterisk (*) indicates significance at\u003cem\u003e q\u003c/em\u003e ≤ 0.05\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/43cd71157d922fb3728ff23c.jpeg"},{"id":93529455,"identity":"4a65287c-bfb5-4ece-a1d3-cae509223073","added_by":"auto","created_at":"2025-10-14 20:50:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":256429,"visible":true,"origin":"","legend":"\u003cp\u003eMean amplitude of response in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration across the three, 210-second tied-belt walking blocks calculated for the prefrontal cortex (PFC), premotor cortex (PMC), sensorimotor cortex (SMC) and posterior parietal cortex (PPC) during tied-blocks 1-3 (T1, T2, T3). Error bars represent the standard error. Asterisk (*) indicates significance at\u003cem\u003e q\u003c/em\u003e ≤ 0.05\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/a9c02a46896e11f54c9a4e8a.jpeg"},{"id":93529458,"identity":"4f43bdaf-e1e4-4998-b2b2-b204247dd6cc","added_by":"auto","created_at":"2025-10-14 20:50:41","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135630,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the early phases of each block for all variables with correlation coefficients greater than |0.3|. SLS = step length symmetry; EDA = electrodermal activity; RPS = Rating of Perceived Stability, PPC = posterior parietal cortex; PFC = prefrontal cortex; HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/997c9006a89705a94baba8f6.jpeg"},{"id":99172387,"identity":"bd80aad9-1abf-4f6b-b784-c87c4401a5f1","added_by":"auto","created_at":"2025-12-29 16:08:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1814316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/c2a7f98e-a971-4e63-aab0-f2758727a480.pdf"},{"id":93530597,"identity":"85113740-32a8-461e-a47e-38b3a47e2ecb","added_by":"auto","created_at":"2025-10-14 20:58:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1673933,"visible":true,"origin":"","legend":"","description":"","filename":"KJYESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7670653/v1/3cc7e15d207aafffd912532a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of repeated blocks of split-belt walking on locomotor adaptation, physiological arousal response and cortical activation","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eStability and energy efficiency in forward propulsion are widely regarded as the primary objectives of gait (Kuo \u0026amp; Donelan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Changing environments and walking conditions require the adaptation and recalibration of motor commands to continue to meet these demands. The act of fine-tuning previously learned movements to meet new requirements through repetitive practice and error-driven feedback is known as motor adaptation (Martin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Reisman et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Adaptation of motor strategies is governed by the central nervous system (CNS), integrating sensory input, cortical drive and motor output (Doyon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Contributions from the autonomic nervous system (ANS) to motor adaptation are less understood. Physiological arousal response, mediated by the ANS, is known to modulate with postural control strategies in response to threats to postural stability, raising questions about how physiological arousal response impacts motor adaptation (Adkin \u0026amp; Carpenter, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carpenter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pollock et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, emotionally evoked ANS-mediated physiological arousal response has been associated with activity at the cortical level (Dolcos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), influencing the motor output.\u003c/p\u003e\u003cp\u003eWhen the physiological arousal response remains elevated, adaptation of motor control strategies during standing is delayed or absent (Carpenter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pollock et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sibley et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The physiological arousal response may be related to the perceived task challenge and the consequence of error in task performance, specifically during loss of standing balance. However, research to date has been limited to standing balance tasks, leaving the interactions between physiological arousal response and motor control unexplored during a dynamic continuous task, such as walking. Cortical involvement in motor control of walking can be measured by techniques such as functional near-infrared spectroscopy (fNIRS), electro-encephalography (EEG) or positron emission tomography (PET). According to a review by Herold et al., (2017), fNIRS is regarded as a valuable and promising method for monitoring cortical activity during walking tasks in freely moving individuals, both in laboratory settings and real-world environments. For example, fNIRS studies have shown that prefrontal activation (PFC) tends to increase during more cognitively demanding gait tasks (Bishnoi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pelicioni et al., 2019; Vitorio et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSplit-belt treadmill walking paradigms have been used to experimentally investigate the physiology underpinning the motor control of walking (Severini \u0026amp; Zych, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Split-belt treadmills provide a controlled perturbation to probe motor adaptation, as the belts under each leg can move either at the same speed (tied-belt) or different speeds (split-belt), which creates a continuous walking challenge. When asymmetrical walking conditions are imposed, individuals adapt step length bilaterally to achieve a symmetrical gait pattern despite the asymmetrical velocities of the belts (Dietz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Hinton et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) performed PET scans directly following the completion of a split-belt protocol consisting of continuous adjustment to belt speed ratios (every 15 seconds) over 30 minutes, inhibiting sustained adaptation to any one condition. The authors found that the medial PFC, supplementary motor area (SMA), posterior parietal cortex (PPC), anterior cingulate cortex (ACC), and cerebellum were active post-training (Hinton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Jacobsen et al., (2023) used EEG during a single, 15-minute split-belt walking block and found that activity in the PPC, sensorimotor cortex (SMC) and cingulate cortices were associated with adaptation to split-belt walking. During exposure to split-belt walking, the largest disturbance in step length symmetry is typically noted in the first 30 strides of exposure (Jacobsen \u0026amp; Ferris, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Malone \u0026amp; Bastian, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Roemmich \u0026amp; Bastian, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The destabilizing nature of the split-belt walking provides a unique opportunity to study ANS-mediated physiological arousal response and an individual\u0026rsquo;s perception of the balance challenge, together with associated cortical activity and motor adaptation in step length symmetry.\u003c/p\u003e\u003cp\u003eThe primary aim of this research is to investigate the modulation of motor adaptation, physiological arousal response, cortical activity, and perceived challenge during repeated bouts of split-belt walking. This research aims to gain insights into how the CNS and ANS co-modulate during motor adaptation to three repeated blocks of split-belt walking. Specifically, we aim to investigate changes between early adaptation (first 30 strides) and late adaptation (last 30 strides) within and between each split-block. Secondly, we aim to probe the potential influence of biological sex on locomotor adaptation, as it has been suggested that sex differences play a role in postural control strategies and balance in older adults (Kim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Šarabon et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but remains unexplored in split-belt training. Within each split-belt training block, we anticipate locomotor adaptation to occur together with reductions in mean cortical activation within the frontal lobe and motor planning areas, accompanied by a simultaneous decrease in physiological arousal response from the early to the late phases. Following the first exposure to split-belt walking, we anticipate that adaptation captured by measures of step-length symmetry, cortical activation, and physiological arousal response during the first exposure (split-block 1) will carry over to subsequent split-belt blocks, suggestive of savings. Specifically, we expect this to present as a smaller magnitude change in step length symmetry (SLS), physiological arousal response, measured by electrodermal activity (EDA) and cortical hemodynamics measures across the regions of interest. In this young and unimpaired cohort, no significant effect of biological sex is expected.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eTwenty young adults without physical impairment (26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 years; 10 females, 10 males) were recruited through purposive sampling via word of mouth and online platforms. Data on age, sex, gender, height, weight, dominant foot, medication use, and comorbidities were collected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants completed the State-Trait Anxiety Inventory (STAI) (Spielberger, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the International Physical Activity Questionnaire (IPAQ) (Craig et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). All participants performed two trials of the 10 Meter Walk Test (10MWT) at both comfortable gait speed (CGS) and fast gait speed (FGS). Groups were stratified by sex for sub-analyses. Inclusion criteria were ages 18\u0026ndash;35, with no current neurological, musculoskeletal, or cardiovascular disorders that might interfere with walking, and na\u0026iuml;ve to split-belt treadmill walking. Exclusion criteria included cardiac or respiratory conditions, neurological and musculoskeletal disorders, and clinically diagnosed depression or anxiety requiring medication. The study adhered to the latest Declaration of Helsinki and was approved by the University of British Columbia Clinical Research Ethics Board, with all participants providing written informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Measures\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Locomotor adaptation\u003c/h2\u003e\u003cp\u003eKinetic and kinematic data were collected from embedded force plates within the instrumented split-belt treadmill (M-GAIT, MOTEK Medical; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). A 10 Hz low-pass filter was applied to all signals. Step length was defined as the anterior-posterior distance between the center of pressure for each foot at the start of double leg stance, marked by a vertical ground reaction force (GRF) over 15% body weight on both force plates. Locomotor adaptation was measured using step length symmetry (SLS), calculated as a normalized ratio of fast to slow belt step lengths (Eq.\u0026nbsp;1; Malone \u0026amp; Bastian, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This normalization allowed comparison across participants with different heights and baseline step lengths, where a value of zero indicates perfect symmetry, positive values reflect a longer fast step, and negative values indicate a longer slow step (Day et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Finley et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reisman et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEquation 1. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SLS=\\frac{{(SL}_{fast}-{SL}_{slow})}{{(SL}_{fast}+{SL}_{slow})}\\:\\)\u003c/span\u003e\u003c/span\u003e (Malone \u0026amp; Bastian, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Cortical activity monitoring\u003c/h2\u003e\u003cp\u003eA portable fNIRS device (NIRSport, NIRx Medical Technologies, Germany) with 16 emitters and 16 detectors was worn by all participants. Near-infrared light at 760 and 850 nm was emitted to detect both oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). An fNIRS cap, sized to each participant's head, was used to measure bilateral regions of interest (ROIs) associated with motor learning (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), created using fOLD software (Zimeo Morais et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Source-detector pairs were set at an approximate distance of 30 mm. Eight short-separation channels were included across the probe, with a distance of 7.5mm to account for extracerebral signals. Data were sampled at 5.41 Hz using Aurora software (NIRx Medical Technologies, Germany).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Physiological arousal response and perception of task challenge\u003c/h2\u003e\u003cp\u003eElectrodermal activity was recorded using bipolar electrodes placed on the palmar surface of the right hand over the thenar and hypothenar eminences. A 50 mV current was applied between the electrodes, and skin conductance was sampled at 300 Hz as an index of physiological arousal. Electrode cables were organized to allow for participants\u0026rsquo; normal arm swing during the protocol. Upon completion of the entire protocol, a rating of perceived stability (RPS) was self-reported for each of the split-blocks. The RPS scale (Supplementary material, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) is a numerical scale providing a validated measure of balance challenge during walking, from 1\u0026ndash;10, with 1 being \u0026ldquo;Completely Stable\u0026rdquo;, and 10 being \u0026ldquo;About to Fall\u0026rdquo; (Espy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Experimental protocol\u003c/h2\u003e\u003cp\u003eParticipants walked on the split-belt treadmill for 32.5 minutes continuously while secured to an overhead safety harness, which provided no bodyweight support and did not interfere with normal walking gait patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Handrails were available on either side of the treadmill; however, participants were encouraged to avoid using them and to walk as they typically would. Participants were instructed not to talk or look down at the belts while walking. Hemodynamic and EDA data were collected for the entirety of the split-belt treadmill protocol. Embedded force plates under each belt recorded ground reaction forces (GRF), which were used to calculate step length bilaterally. All signals were time-synchronized using external triggers. Participants were first familiarized with the treadmill and the two speeds they would be exposed to in 3.5-minute blocks at the beginning of the protocol, in the \u0026ldquo;Baseline\u0026rdquo; portion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Then, the protocol alternated between split-belt (adaptation) and tied-belt (de-adaptation) blocks in 3.5-minute blocks continuously, to allow enough time for motor adaptation. A consistent, 2:1 speed ratio was used in repeated split-belt blocks to probe within-session adaptation across repeated exposures. Walking speed during the blocks was individualized based on each person\u0026rsquo;s fast gait speed (FGS) measured during the 10-Meter Walk Test, using 90% of their average fast speed to account for differences between overground and treadmill walking (Dal et al., 2010).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data processing\u003c/h2\u003e\u003cp\u003eEDA data were zero-phase low-pass filtered at 10 Hz, and a 500 ms median filter was applied to remove motion artifacts (Barua et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data were then baseline corrected to the average of a quiet period from the baseline block before the split-blocks (Horvers et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and peak normalized to the maximum within the session (%max) to allow for comparisons across subjects (Braithwaite et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). SLS and EDA data were divided into \u0026ldquo;Early\u0026rdquo; and \u0026ldquo;Late\u0026rdquo; phases of adaptation for each of the three split-belt blocks and de-adaptation for each of the three interspersed tied-belt blocks. The Early phase consisted of data for the first 30 strides, excluding the first step, and the Late phase included the last 30 strides, following guidelines from prior studies showing the largest change in SLS during initial adaptation (Malone et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Malone \u0026amp; Bastian, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The early EDA signal accounted for the approximate three-second delay in detection of the physiological arousal response at the palmar surface of the hand (Alexander et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Raw fNIRS data were analyzed using the MATLAB R2022b (Mathworks, Natick, MA, USA) NIRS AnalyzIR toolbox (Santosa et al., 2018). Scalp-coupling indices (SCI) were calculated for each participant\u0026rsquo;s data. Channels with SCI values\u0026thinsp;\u0026lt;\u0026thinsp;0.75 were excluded from the analysis (Pollonini et al., 2014). Optical density data were converted to HbO and HbR using the modified Beer-Lambert law, using a partial pathlength factor of 0.1 (Jacques, 2013). Data were divided into blocks, as defined by stimulus event triggers for belt speed changes by the treadmill.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1. SLS and EDA data\u003c/h2\u003e\u003cp\u003eTo compare the effect of block (1\u0026ndash;3) and phase (early, late) on the dependent variables (DVs) of SLS, EDA and RPS data, linear mixed-effects models (LMM) were conducted for each DV (Package lme4 version 1.1\u0026ndash;34, R Studio, 2022) to compare the effects of repeated adaptation (split) and de-adaptation (tied) blocks. The models for SLS and EDA (Eq.\u0026nbsp;2) included Block, Phase, and their interaction, with participant as a random intercept in all models. Sex was initially tested as a covariate in the SLS model (our primary DV), but its inclusion did not significantly improve model fit (likelihood ratio test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.815). As a result, sex was not included in the final models for all DVs. The RPS model (Eq.\u0026nbsp;3) assessed the change in RPS as a function of split. For the SLS and EDA models, estimated marginal means (EMMs), 95% confidence intervals (CI) and pairwise contrasts for the interaction of Block and Phase were calculated (Package \u003cem\u003eemmeans\u003c/em\u003e version 4.2-2, R Studio, 2022) and adjusted for multiple comparisons using the Bejamini-Hochberg correction (presented as \u003cem\u003eq\u003c/em\u003e-values). For the RPS model (Eq.\u0026nbsp;3), EMMs, CIs and contrasts were calculated for the effect of split-block. Results from the LMM model can be found in the supplementary material (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Effect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e) were calculated for all pairwise condition comparisons.\u003c/p\u003e\u003cp\u003eEquation 2. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{D}\\text{V}\\:\\sim\\:-1\\:+\\:\\text{P}\\text{h}\\text{a}\\text{s}\\text{e}\\:+\\:\\text{B}\\text{l}\\text{o}\\text{c}\\text{k}\\:+\\text{P}\\text{h}\\text{a}\\text{s}\\text{e}\\text{*}\\text{B}\\text{l}\\text{o}\\text{c}\\text{k}\\:+\\:\\left(1\\right|\\text{P}\\text{a}\\text{r}\\text{t}\\text{i}\\text{c}\\text{i}\\text{p}\\text{a}\\text{n}\\text{t})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eEquation 3. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{R}\\text{P}\\text{S}\\:\\sim\\:-1\\:+\\:\\text{S}\\text{p}\\text{l}\\text{i}\\text{t}-\\text{B}\\text{l}\\text{o}\\text{c}\\text{k}\\:+\\:\\left(1\\right|\\text{P}\\text{a}\\text{r}\\text{t}\\text{i}\\text{c}\\text{i}\\text{p}\\text{a}\\text{n}\\text{t})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2. Hemodynamic data analysis\u003c/h2\u003e\u003cp\u003eGiven the temporal dynamics of hemodynamic responses, we quantified activation in each split and tied-block as (1) the overall \u003cem\u003eamplitude of activation\u003c/em\u003e and (2) the within-block \u003cem\u003echange in amplitude of activation over time\u003c/em\u003e. For clarity, the following description refers to split-blocks; identical procedures were applied to tied-blocks. Hemodynamic data from the three repeated split-blocks were analyzed with a parametric general linear model (GLM) to examine modulation of cortical activity across blocks and over time. Two regressors of interest were included in the GLM for each of the three split-block conditions. The first regressor for each split-block modelled the average hemodynamic brain response over the 210-second block, represented as a boxcar function convolved with a canonical hemodynamic response function (\u003cem\u003eamplitude of activation\u003c/em\u003e). The second regressor modelled the first-order (linear) modulation of the response over time within the block (\u003cem\u003echange in amplitude of activation over time\u003c/em\u003e), allowing assessment of whether cortical activation changed systematically over the block and reflecting learning-related effects. In other words, our regression model included one regressor that was constant over time (capturing the average hemodynamic response) and one that varied linearly over time (capturing changes in the response within a block). This approach allowed us to test both the overall amplitude of activation and its modulation over time. Additionally, eight fNIRS short-separation regressors were included as regressors of no interest to further reduce noise from systemic physiological signals (Santosa et al., 2020). The GLM was solved with an autoregressive (AR) pre-whitening filter and iteratively reweighted least squares (AR-ILS) and applied to the pre-processed hemodynamic data to remove serially correlated systemic physiological noise and motion artifacts from the data (Barker et al., 2013). This approach yields three coefficients for the \u003cem\u003eaverage amplitude of activation\u003c/em\u003e (S1, S2, S3) and three coefficients for the \u003cem\u003echange in amplitude of activation over the block\u003c/em\u003e (S1_time, S2_time, S3_time), at the individual level, corresponding to the three split-blocks, per ROI.\u003c/p\u003e\u003cp\u003eSecond-level statistical analysis used a mixed-effects model within the Brain AnalyzIR toolbox, with the first-level regression coefficients (\u003cem\u003eamplitude of activation\u003c/em\u003e and \u003cem\u003echange in amplitude of activation over time\u003c/em\u003e coefficients for each split-block) serving as the dependent variables to obtain group-level estimates. Participant ID was included as a random effect on the intercept (Santosa et al., 2018). The second-level statistical model was solved using a weighted regression analysis using the estimated covariances per participant from the first-level GLM as a noise whitening matrix (Santosa et al., 2018). This allows modelling of the heterogeneity of noise and fNIRS signal quality between participants. The estimated group-level model coefficients for the six terms for each fNIRS channel were then averaged across channels into the four regions of interest (ROI). The contrasts between ROIs for each block condition and modulation over time were tested. To account for multiple comparisons, corrected \u003cem\u003ep\u003c/em\u003e-values (\u003cem\u003eq\u003c/em\u003e-values) were calculated using a Benjamini-Hochberg correction. Effect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e) were calculated for all pairwise condition comparisons using the second-level coefficients estimated from the mixed effects model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3. Correlations\u003c/h2\u003e\u003cp\u003eAdditionally, correlations were calculated to explore the relationship between each group-estimated brain activity value for each ROI from the fNIRS second-level mixed effects model and measures of performance (SLS), physiological arousal response (EDA) and rating of perceived stability (RPS). Pearson\u0026rsquo;s correlations were used for all variables found to be normally distributed using the Shapiro-Wilks test, and Spearman\u0026rsquo;s rank correlations were used for any variables that were not. To control for multiple comparisons in the correlation analyses, \u003cem\u003ep\u003c/em\u003e-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction. The resulting adjusted \u003cem\u003ep\u003c/em\u003e-values are referred to as \u003cem\u003eq\u003c/em\u003e-values throughout the analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eAll participants in this study reported being cisgender. No statistically significant differences were found between reported sex for walking speed, RPS scores reported during split-blocks, self-reported anxiety, or physical activity levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both state and trait anxiety fell within the range of \u0026ldquo;no or low anxiety\u0026rdquo; (20\u0026ndash;37) to \u0026ldquo;moderate anxiety\u0026rdquo; (38\u0026ndash;44) (Kayikcioglu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Four of the participants reported left-foot dominance, while the rest reported right-foot dominance. Activity levels were classified as moderately to highly active based on the IPAQ.\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\u003eDescriptive statistics for the 20 participants, separated by sex (10 per group) shown as mean (95% Confidence Interval (CI)) and results of the between-group ANOVA. RPS\u0026thinsp;=\u0026thinsp;Rating of perceived stability; IPAQ\u0026thinsp;=\u0026thinsp;International Physical Activity Questionnaire; MET\u0026thinsp;=\u0026thinsp;metabolic equivalent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMales (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemales (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBetween-group analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\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\u003e28.3 (25.56, 31.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.2 (24.04, 26.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184.7 (180.92, 188.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.35 (161.68, 175.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.46 (76.37, 90.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.54 (56.25, 74.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlow belt speed (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.90, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95 (0.90, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast belt speed(m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91 (1.80, 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9 (1.80, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPS S1 (/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4.88, 7.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4 (4.81, 7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPS S2 (/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7 (3.53, 5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7 (3.31, 6.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPS S3 (/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.6 (2.58, 4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6 (2.29, 4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState anxiety (/80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (24.45, 31.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.8 (27.26, 38.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait anxiety (/80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.9 (34.11, 41.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.3 (33.21, 39.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPAQ Total MET min/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5525.65 (2014.70, 9036.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2868 (1892.73, 3843.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.252\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\u003eBaseline step length and SLS were measured during the slow, tied belt block, immediately before block split-block 1 (S1). The average tied-belt walking baseline SLS for this cohort was 0.023 +/- 0.033, indicating that baseline walking was symmetrical. Raw data from all participants followed a similar pattern. For all participants, the step length from the leg on the slow belt (right) was demonstrably shorter than the step length from the stepping leg on the fast belt (left) in the Early phase of S1. By the end of the Early phase of split 1 (\u0026gt;\u0026thinsp;30 strides), SLS improved but remained lower than baseline symmetry during baseline tied-belt walking. With repeated exposure, symmetry approached baseline levels. During de-adaptation, there was an initial increase in SLS ratio, demonstrating the opposite pattern as in the adaptation blocks (i.e., a larger fast-belt leg step, a shorter slow-belt leg step), indicative of after-effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). EDA data for one participant were removed due to poor signal quality related to loss of electrode adhesion during data collection. While fNIRS data for 2 of the 20 participants were removed due to technical problems, no imputations or statistical corrections were applied, given the small sample size. Hemodynamic data for one participant were removed due to poor signal quality upon visual inspection. The channels excluded for each participant can be found in Table S2 of the Supplementary Material. Only one participant intermittently used the handrails to stabilize during the first split-block; this did not affect the EDA signal.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Step length symmetry, physiological arousal and perceived stability\u003c/h2\u003e\u003cp\u003eLinear mixed models (LMM) were calculated for all variables. Although sex was included in the model as a modifier, it did not significantly improve the model for any of the dependent variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). SLS demonstrated evidence of adaptation across all blocks of split-belt walking (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 for all), with the greatest magnitude of change in split-block 1 as described below. EDA demonstrated evidence of adaptation during split-blocks 1 and 2 (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 for both), with the greatest magnitude of change in split-block 1 (S1). \u003cem\u003eAmplitude of cortical activation\u003c/em\u003e and \u003cem\u003echange in amplitude of activation\u003c/em\u003e for HbO were also greater in S1, compared to the second and third split-blocks across all ROIs (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 for all). A full table of model results and pairwise comparisons can be found in the supplementary material (Tables S1 and S3).\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. SLS and EDA adaptation\u003c/h2\u003e\u003cp\u003eStatistically significant differences were observed between Early and Late phases for all three split-blocks (S1, S2 and S3), as mean SLS increased from early to late phases becoming significantly more symmetrical by the end of each split (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; S1: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -2.07; S2: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -1.22; S3: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ed\u003c/em\u003e = -0.75). SLS observed during the Early phase of S2 and S3 were both significantly more symmetrical than the Early phase of S1 (S1-S2: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -1.72, S1-S3: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -1.99), suggestive of savings in S2 and S3. Across S1, SLS increased from \u0026minus;\u0026thinsp;0.22, 95%CI [-0.26, -0.19] to -0.08, 95%CI [-0.11, -0.05]. Across S2, SLS increased from \u0026minus;\u0026thinsp;0.10, 95%CI [-0.13, -0.07], to -0.05, 95%CI [-0.08, -0.02]. Across S3, SLS increased from \u0026minus;\u0026thinsp;0.07, 95%CI [-0.10, -0.04], to -0.04, 95%CI [-0.07, -0.01].\u003c/p\u003e\u003cp\u003eStatistically significant differences in physiological arousal response measured by EDA were observed between Early and Late phases for blocks S1 and S2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Between the Early and Late phases of S1, average EDA decreased from 70.84%, 95%CI [62.63%, 79.04%] to 23.54%, 95%CI [15.45%, 31.63%], maximum, a decrease of 47.3% maximum, by the end of the Late phase (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.67). Between the Early and Late phases of S2, average EDA decreased from 37.89%, 95%CI [29.68%, 46.09%] to 24.46%, 95%CI [16.37%, 32.56%] maximum, a decrease by 13.4% maximum, by the end of the Late phase (\u003cem\u003eq\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.002, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80). There was no significant difference by phase during S3 (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Compared to the early phase of S1, both subsequent split-blocks showed a significant decrease in physiological arousal response at the initiation of each block (S2, 32.95%, S3, 35.84%), with both late phases approaching baseline EDA measurements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. SLS and EDA de-adaptation\u003c/h2\u003e\u003cp\u003eDe-adaptation during the tied belt blocks showed statistically significant changes from early to late in blocks T1 and T2 in SLS, and T1, T2, and T3 for EDA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mean SLS during Early T1 (0.12, 95%CI [0.10, 0.14]) was significantly different from Early T2 (0.08, 95%CI [0.06, 0.10]) and Early T3 (0.05, 95%CI [0.03, 0.07]), indicating increased symmetry with repeated de-adaptation blocks (T1-T2: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91; T1-T3: \u003cem\u003eq\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.62). Mean SLS during early T1 decreased to 0.03 (95%CI [0.01, 0.05]) in late T1 (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.42). Similarly, early T2 decreased from 0.08 (95%CI [0.06, 0.10]) to 0.03 (95%CI [0.01, 0.05], \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.55). No significant difference was found between the early and late phases of T3. Mean EDA decreased from Early to Late across the tied-blocks by 30.5%, 18.0%, and 14.6%, respectively. Across T1, EDA changed significantly from 40.52% (95%CI [30.77%, 50.28%]) to 10.05% (95%CI [0.41%, 19.70%]) (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.83). Across T2, EDA decreased significantly from 32.70% (95%CI [22.94%, 42.45%]) to 14.74% (95%CI [5.09%, 24.39%]) (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17). Across T3, EDA decreased significantly from 35.41% (95%CI [25.65%, 45.17%]) to 20.81% (95%CI [11.16%, 30.46%]) (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003e0.001\u003c/em\u003e, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03). There was no difference between the early phases of tied-blocks 1, 2, and 3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePerception of stability measured by the RPS scale was significantly higher in S1 than in S2 and S3 (S1-S2: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.31; S1-S3: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.56). The average reported RPS score for S1 was 6.2, 95% CI [5.44, 6.96]. For S2 and S3, the average reported RPS scores were 4.7, 95% CI [3.94, 5.46] and 3.6, 95% CI [2.84, 4.36], respectively.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Cortical hemodynamic response\u003c/h2\u003e\u003cp\u003eA statistically significant effect of amplitude of activation between split-blocks was found across all ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the amplitude of HbO response for S1 was significantly higher than that in S2 and S3 for the PFC, PMC, SMC and PPC (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). Further, a statistically significant interaction effect was found for the time-modulation term (\u003cem\u003echange in amplitude of activation over time\u003c/em\u003e) for the PFC only. There was a significantly greater change in amplitude of activation over time in the PFC during S1 and S3, compared to S2 (S1-S2: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.726; S2-S3: \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.361, Supplementary Fig. S2). No significant changes in amplitude of activation over time were found in HbO for the PMC, SMC, or PPC. A table of all contrasts for second-level model coefficients by ROI can be found in the supplementary material (Table S4). Heatmaps by channel for all split-block comparisons can be found in the supplementary material (Figs. S4 and S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA statistically significant effect of amplitude of cortical activation between tied blocks was found across all ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, the amplitude of HbO activation for T1 was significantly higher than those in T3 for all ROIs (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all), and compared to T2 for the PFC, PMC and SMC regions (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.203 for PFC; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.176 for PMC; and \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.396 for SMC). The amplitude of activation for T2 was significantly higher than T3 for the PMC and SMC regions (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.498; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.731, respectively). No statistically significant interaction effect was found for change in amplitude of activation over time, for all ROIs (Supplementary Fig. S3). A table of all contrasts for second-level model coefficients by ROI can be found in the supplementary material (Table S5). Heatmaps by channel for all split-block comparisons can be found in the supplementary material (Figs. S6 and S7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Correlations\u003c/h2\u003e\u003cp\u003eCorrelations were calculated to explore relationships between SLS, EDA, RPS values and the HbO data from each ROI during adaptation (split) and de-adaptation (tied) blocks. The full table of results can be found in the Supplementary Material (Table S6); however, only comparisons with relationships stronger than |0.3|, as a cut-off for physiological relevance (Mukaka, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) will be discussed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur findings are the first to describe locomotor adaptation and the associated changes in cortical activity, the ANS-mediated physiological arousal response and perceived instability during repeated blocks of split-belt walking. The first block of split-belt walking was reported by participants as the most destabilizing and elicited the greatest magnitude of significant adaptation across both the CNS and the ANS. Specifically, split-block 1 showed the greatest magnitude of motor adaptation of SLS and adaptation of EDA between early and late phases. Further, the magnitude of hemodynamic response was significantly greater in all ROIs (PFC, PMC, SMC and PPC) in split-block 1 compared to split-blocks 2 and 3. With repeated exposure, there was a lower magnitude of adaptation, consistent with our primary hypothesis. In this sample, participants were comparable in age, physical activity, and state and trait anxiety levels, and did not demonstrate between-sex differences in adaptation of SLS, supporting our secondary hypotheses. A similar pattern of response was noted in de-adaptation (tied-belt blocks) across variables. Notably, SLS values in the early phases of split-blocks 2 and 3 resembled those of the late phase of split-block 1. These findings suggest that the first exposure to split-belt walking elicited the largest adaptation response, followed by evidence of savings in locomotor performance, corresponding to decreases in cortical response, and ANS-mediated physiological arousal with repeated exposure during a single training session.\u003c/p\u003e\u003cp\u003eSplit-belt walking challenges the stability and symmetry of gait, particularly at the initial onset of belt speed change. This was evident in physiological signals of both the CNS and the ANS, as the onset of the first split-belt block was associated with significant within-block adaptation of SLS, EDA, and hemodynamic response across all ROIs. This was further reflected by participants\u0026rsquo; perceived instability (RPS scores), which were significantly higher during the initial block of split-belt walking compared to split-blocks 2 and 3. This observed first-exposure effect across variables is in line with postural control literature that has described the significantly larger evoked response and adaptation in the first trial of a postural perturbation relative to subsequent trials with the same stimulus, in standing balance (Adkin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Allum et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Campbell et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although smaller in magnitude, the responses of the CNS and ANS to the onset of tied-blocks show similarity in patterns to the responses noted across split-blocks. During de-adaptation (tied-blocks), differences in SLS reflect after-effects, whereupon returning to symmetrical belt speeds, the modified walking pattern is briefly retained, leading to asymmetry (Malone et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Malone \u0026amp; Bastian, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reisman et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While the magnitude of SLS after-effects are thought to reflect the extent of initial motor adaptation (Malone et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), the heightened EDA and cortical activity during the first tied-block likely reflect increased task-related demands associated with dynamic balance challenge and re-establishing symmetrical gait following a change in belt speeds. The demands of de-adaptation appeared to elicit parallel responses across systems, as SLS, EDA, and HbO cortical activity were all greatest during the first tied-block, supporting the presence of a first-exposure effect across both adaptation and de-adaptation processes. Moreover, though not statistically compared in this study, this similar pattern of cortical responses observed during split-belt and tied-belt walking aligns with findings by Hulzinga et al., (2025), who reported no difference in activation between these two conditions among their healthy control cohort or people with Parkinson\u0026rsquo;s disease.\u003c/p\u003e\u003cp\u003ePhysiological savings refer to the neuromotor system\u0026rsquo;s ability to adapt more rapidly when a perturbation is reintroduced, even after the initial adaptation has been washed out \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e໿\u003c/span\u003e (Krakauer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Malone et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and is proposed as evidence for the nervous system learning to solve a problem more efficiently (Braun et al., 2009). Savings are evident in SLS, between the late phase of split-block 1 and the early phase of split-block 2, and again between late split-block 2 and early split-block 3, despite the washout periods between. Our findings in cortical activity are indicative of a similar pattern of savings, notable in split-blocks 2 and 3 across all ROIs, where the lack of significant difference between the amplitude of response in split-blocks 2 and 3, furthered by the minimal time-modulation response within split-blocks 2 and 3, provides evidence for a CNS analogue of physiological savings in this task. In EDA, some habituation is noted between repeated blocks, most clearly in reduced magnitude between early split-blocks. This pattern of ANS and CNS co-modulation is further supported by the negative relationship observed between SLS and EDA during early split-blocks, indicating that individuals with greater symmetry in early adaptation exhibited a lower physiological arousal response to the perturbation. These findings raise questions regarding the contribution of the ANS in locomotor adaptation to conditions that challenge walking.\u003c/p\u003e\u003cp\u003eThe link between the CNS and ANS has been previously characterized in behavioural literature using functional brain imaging (Cheng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Critchley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Knight et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; MacIntosh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Specifically, regions of the PFC, anterior cingulate cortex (ACC), parietal lobe, insula and amygdala have been shown to be associated with modulation of physiological arousal response in a context-specific manner (Critchley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). During motor task performance (finger movement task), MacIntosh et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that physiological arousal response, measured by EDA, habituated alongside functional magnetic resonance imaging, blood-oxygen-level dependent (BOLD) response in a network of regions associated with motor learning. Furthermore, the nature of the task (i.e., finger movements vs. risk-taking) has been shown to influence the level of modulation between physiological arousal response, measured by EDA and the PFC (Holper, Ten Brincke, et al., 2014; Holper, Wolf, et al., 2014). During tasks with risk-taking behaviours (risky financial decisions), coherence between PFC hemodynamics and physiological arousal response is shown to increase as the level of risk rises (Holper, Ten Brincke, et al., 2014; Holper, Wolf, et al., 2014). In the current study, the highest level of perceived instability was reported in the first split-block, with an average participant-reported score of 6.2/10. This coincided with the greatest within-block change in physiological arousal response and hemodynamic cortical activation in the PFC. In the second and third split-blocks, RPS scores describe a decreased, but not absent, perceived instability with scores of 4.7 and 3.6, respectively ( i.e., a general sense of unsteadiness but no need for additional support perceived by the individual) (Espy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, EDA response and level of activity in all cortical ROIs were lower in split-blocks 2 and 3. These findings suggest the co-modulation between CNS-ANS noted in standing postural tasks extends to dynamic walking tasks.\u003c/p\u003e\u003cp\u003eThe pattern of cortical response across the PFC, PMC, PPC and SMC suggests a coordinated network supporting locomotor adaptation, particularly at the initial exposure to split-belt walking. Our findings showed that the initial exposure evoked the largest cortical response across all ROIs, with activation attenuating in subsequent blocks. This pattern suggests that the PFC, PMC, PPC, and SMC are most engaged during early error-detection and adaptation, when the task is novel and perceived as more challenging. The PFC, associated with attentional control and executive function, is often shown to be active during gait initiation and complex walking conditions, such as dual-task walking (Koenraadt et al., 2014; Mirelman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pelicioni et al., 2019). The PMC may contribute to step length adaptation and serve as a bridge between cognitive and motor networks (Hanakawa, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with co-activation of the PFC and PMC reflective of top-down attentional control, often observed during motor learning (Abe \u0026amp; Hanakawa, 2009). Further, a moderate negative correlation between HbO activity in the PPC and EDA response across the early tied-blocks may be attributed to the PPC\u0026rsquo;s role in attentional shifts and arousal (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Interestingly, inhibition of the PPC has been shown to impair adaptation and enhance after-effects in split-belt walking (Young et al., 2020), highlighting its broader involvement in locomotor control. The SMC supports sensorimotor integration necessary for gait adjustments, and its activity, along with that of the PPC and ACC, measured by EEG, was found to be most prominent in the first 30 strides of adaptation to a single, prolonged block of split-belt walking (Jacobsen \u0026amp; Ferris, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar findings were reported by Hinton et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where PET imaging following a 30-minute split-belt protocol with continuously shifting belt speed ratios found activation in the PFC, SMA, PPC, ACC, and cerebellum, further supporting the involvement of a distributed cortical\u0026ndash;subcortical network in locomotor adaptation. The observed attenuation of activity across repeated exposures supports the idea that these regions form a flexible, adaptive network, likely in concert with subcortical structures, that facilitates the transition from inclusion of increased input from conscious control during initial error-detection to more automatic gait regulation as adaptation progresses.\u003c/p\u003e\u003cp\u003eWhile this study provides evidence for cortical involvement in novel locomotor adaptation, this does not imply a complete causal role in driving the adaptation process. The neural control of simple, rhythmic gait, such as maintaining tied-belt treadmill walking, is known to be subcortically driven by structures including the basal ganglia, cerebellum and brainstem, and cannot be probed by fNIRS (Nakazawa et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Takakusaki, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During walking, Central Pattern Generators (CPGs) within each limb are thought to control the rhythmic movement of the ipsilateral limb, in coordination with the contralateral limb via inhibitory and excitatory commissural interneurons within the spinal cord (Rybak et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cross-limb communication via CPGs likely somewhat underpins the bilateral step length adaptation observed during split-belt walking. However, intentional or complex walking tasks engage more supraspinal control (Mirelman et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nakazawa et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Takakusaki, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the lack of significant change in cortical activation during subsequent split-belt adaptation and tied-belt de-adaptation blocks may suggest that higher-order cortical control is less critical following initial error-detection and adaptation.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Limitations\u003c/h2\u003e\u003cp\u003eAs the use of fNIRS during walking continues to evolve, certain methodological limitations warrant consideration. One inherent challenge is the introduction of motion artifacts during ambulatory tasks. Motion artifacts arise from slippage of the head cap on the scalp, rather than movement of the participant per se, but these artifacts are more likely during walking. The GLM model used in this study uses a robust estimator (outlier down weighting) and has been demonstrated to be less sensitive to these artifacts (Barker et al., 2013). In addition, we used the scalp coupling index to exclude poor channels of data on a per participant basis in order to ensure that only data that provided physiologically relevant signals was used. Secondly, data from three participants were excluded due to technical issues during data collection, which may have reduced the overall statistical power and sensitivity to detect effects. Participant data describing diversity was limited to age, gender, sex and physical activity. Additional research is required to test the effect of other variables.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur study provides new insight into the modulation of the CNS and ANS during a repeated split-belt walking challenge. Taken together, these metrics point to cross-system modulation with locomotor adaptation and lower perceived challenge leading to a lower magnitude of cortical involvement and lower physiological arousal response during the task due to practice. The reduction in magnitude of cortical involvement may be suggestive of increased neural efficiency, a concept to be explored in future work. Future work should also explore these relationships in other cohorts, including people with gait-related impairments, decreased walking balance confidence and in older adults to probe the impact of motor impairment, levels of balance confidence and aging on the co-modulation between the CNS and ANS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no competing interests to disclose.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eKaya J. Yoshida is supported by the Canadian Institute for Health Research Doctoral award (#186421). Courtney L. Pollock is supported by a Michael Smith Health Research British Columbia research award. This project was funded by the Natural Sciences and Engineering Research Council of Canada, and a Canadian Foundation for Innovation grant.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKJY conceived and designed the study, curated data, performed analyses, and drafted the manuscript. SBL, LAB, and JJE contributed to study conception and manuscript revision, with JJE also providing resources. AS and TJH contributed to methodology, conducted analyses, and revised the manuscript. CLP conceived and supervised the study, acquired funding and resources, and revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data and code used in the study can be requested by sending a research proposal to the principal investigator (PI) Dr. Courtney Pollock (email: [email protected] ).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdkin, A. L., \u0026amp; Carpenter, M. G. (2018). New insights on emotional contributions to human postural control. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(789), 1\u0026ndash;8. https://doi.org/10.3200/JMBR.36.2.212-224\u003c/li\u003e\n\u003cli\u003eAdkin, A. L., Frank, J. S., Carpenter, M. G., \u0026amp; Peysar, G. W. (2000). Postural control is scaled to level of postural threat. \u003cem\u003eGait \u0026amp; Posture\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), 87\u0026ndash;93. https://doi.org/10.1016/S0966-6362(00)00057-6\u003c/li\u003e\n\u003cli\u003eAlexander, D. M., Trengove, C., Johnston, P., Cooper, T., August, J. P., \u0026amp; Gordon, E. (2005). Separating individual skin conductance responses in a short interstimulus-interval paradigm. \u003cem\u003eJournal of Neuroscience Methods\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e(1), 116\u0026ndash;123. https://doi.org/10.1016/j.jneumeth.2005.02.001\u003c/li\u003e\n\u003cli\u003eAllum, J. H. J., Tang, K.-S., Carpenter, M. G., Oude Nijhuis, L. B., \u0026amp; Bloem, B. R. (2011). Review of first trial responses in balance control: Influence of vestibular loss and Parkinson\u0026rsquo;s disease. \u003cem\u003eHuman Movement Science\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(2), 279\u0026ndash;295. https://doi.org/10.1016/j.humov.2010.11.009\u003c/li\u003e\n\u003cli\u003eBarua, S., Ahmed, M. U., \u0026amp; Begum, S. (2020). Towards intelligent data analytics: A case study in driver cognitive load classification. \u003cem\u003eBrain Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(8), 1\u0026ndash;19. https://doi.org/10.3390/brainsci10080526\u003c/li\u003e\n\u003cli\u003eBetschart, M., Lauzi\u0026egrave;re, S., Mi\u0026eacute;ville, C., McFadyen, B. J., \u0026amp; Nadeau, S. (2017). Changes in lower limb muscle activity after walking on a split-belt treadmill in individuals post-stroke. \u003cem\u003eJournal of Electromyography and Kinesiology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 93\u0026ndash;100. https://doi.org/10.1016/j.jelekin.2016.12.007\u003c/li\u003e\n\u003cli\u003eBishnoi, A., Holtzer, R., \u0026amp; Hernandez, M. E. (2021). Brain activation changes while walking in adults with and without neurological disease: Systematic review and meta-analysis of functional near-infrared spectroscopy studies. \u003cem\u003eBrain Sciences\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 1\u0026ndash;22. https://doi.org/10.3390/brainsci11030291\u003c/li\u003e\n\u003cli\u003eBraithwaite, J. J., Watson, D. G., Jones, R., \u0026amp; Rowe, M. (2015). \u003cem\u003eA guide for analysing electrodermal activity (EDA) \u0026amp; skin conductance responses (SCRs) for psychological experiments\u003c/em\u003e [Technical Report].\u003c/li\u003e\n\u003cli\u003eBrown, L., White, L. K., Makhoul, W., Teferi, M., Sheline, Y. I., \u0026amp; Balderston, N. L. (2023). Role of the intraparietal sulcus (IPS) in anxiety and cognition: Opportunities for intervention for anxiety-related disorders. \u003cem\u003eInternational Journal of Clinical and Health Psychology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4). https://doi.org/10.1016/j.ijchp.2023.100385\u003c/li\u003e\n\u003cli\u003eCampbell, A. D., Squair, J. W., Chua, R., Inglis, J. T., \u0026amp; Carpenter, M. G. (2013). First trial and StartReact effects induced by balance perturbations to upright stance. \u003cem\u003eJournal of Neurophysiology\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(9), 2236\u0026ndash;2245. https://doi.org/10.1152/jn.00766.2012\u003c/li\u003e\n\u003cli\u003eCarpenter, M. G., Adkin, A. L., Brawley, L. R., \u0026amp; Frank, J. S. (2006). Postural, physiological and psychological reactions to challenging balance: Does age make a difference? \u003cem\u003eAge and Ageing\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 298\u0026ndash;303. https://doi.org/10.1093/ageing/afl002\u003c/li\u003e\n\u003cli\u003eCheng, D. T., Knight, D. C., Smith, C. N., Stein, E. A., \u0026amp; Helmstetter, F. J. (2003). Functional MRI of human amygdala activity during Pavlovian fear conditioning: Stimulus processing versus response expression. \u003cem\u003eBehavioral Neuroscience\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(1), 3\u0026ndash;10. https://doi.org/10.1037/0735-7044.117.1.3\u003c/li\u003e\n\u003cli\u003eCraig, C. L., Marshall, A. L., Sj??Str??M, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., Pratt, M., Ekelund, U., Yngve, A., Sallis, J. F., \u0026amp; Oja, P. (2003). International Physical Activity Questionnaire: 12-Country Reliability and Validity: \u003cem\u003eMedicine \u0026amp; Science in Sports \u0026amp; Exercise\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(8), 1381\u0026ndash;1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB\u003c/li\u003e\n\u003cli\u003eCritchley, H. D., Elliott, R., Mathias, C. J., \u0026amp; Dolan, R. J. (2000). Neural activity relating to generation and representation of galvanic skin conductance responses: A functional magnetic resonance imaging study. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(8), 3033\u0026ndash;3040. https://doi.org/10.1523/jneurosci.20-08-03033.2000\u003c/li\u003e\n\u003cli\u003eDay, K. A., Leech, K. A., Roemmich, R. T., \u0026amp; Bastian, A. J. (2018). Accelerating locomotor savings in learning: Compressing four training days to one. \u003cem\u003eJournal of Neurophysiology\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(6), 2100\u0026ndash;2113. https://doi.org/10.1152/jn.00903.2017\u003c/li\u003e\n\u003cli\u003eDietz, V., Zijlstra, W., \u0026amp; Duysens, J. (1994). Human neuronal interlimb coordination during split-belt locomotion. \u003cem\u003eExperimental Brain Research\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(3), 513\u0026ndash;520. https://doi.org/10.1007/BF00227344\u003c/li\u003e\n\u003cli\u003eDolcos, F., Katsumi, Y., Moore, M., Berggren, N., de Gelder, B., Derakshan, N., Hamm, A. O., Koster, E. H. W., Ladouceur, C. D., Okon-Singer, H., Pegna, A. J., Richter, T., Schweizer, S., Van den Stock, J., Ventura-Bort, C., Weymar, M., \u0026amp; Dolcos, S. (2020). Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions. \u003cem\u003eNeuroscience and Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(August 2019), 559\u0026ndash;601. https://doi.org/10.1016/j.neubiorev.2019.08.017\u003c/li\u003e\n\u003cli\u003eDoyon, J., Penhune, V., \u0026amp; Ungerleider, L. G. (2003). Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. \u003cem\u003eNeuropsychologia\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 252\u0026ndash;262. https://doi.org/10.1016/S0028-3932(02)00158-6\u003c/li\u003e\n\u003cli\u003eEspy, D., Reinthal, A., \u0026amp; Meisel, S. (2017). Intensity of balance task intensity, as measured by the Rate of Perceived Stability, is independent of physical exertion as measured by heart rate. \u003cem\u003eJournal of Novel Physiotherapies\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(S4). https://doi.org/10.4172/2165-7025.1000343\u003c/li\u003e\n\u003cli\u003eFinley, J. M., Bastian, A. J., \u0026amp; Gottschall, J. S. (2013). Learning to be economical: The energy cost of walking tracks motor adaptation. \u003cem\u003eJournal of Physiology\u003c/em\u003e, \u003cem\u003e591\u003c/em\u003e(4), 1081\u0026ndash;1095. https://doi.org/10.1113/jphysiol.2012.245506\u003c/li\u003e\n\u003cli\u003eHanakawa, T. (2011). Rostral premotor cortex as a gateway between motor and cognitive networks. \u003cem\u003eNeuroscience Research\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(2), 144\u0026ndash;154. https://doi.org/10.1016/j.neures.2011.02.010\u003c/li\u003e\n\u003cli\u003eHinton, D. C., Thiel, A., Soucy, J. P., Bouyer, L., \u0026amp; Paquette, C. (2019). Adjusting gait step-by-step: Brain activation during split-belt treadmill walking. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e202\u003c/em\u003e(August), 116095. https://doi.org/10.1016/j.neuroimage.2019.116095\u003c/li\u003e\n\u003cli\u003eHolper, L., Ten Brincke, R. H. W., Wolf, M., \u0026amp; Murphy, R. O. (2014). fNIRS derived hemodynamic signals and electrodermal responses in a sequential risk-taking task. \u003cem\u003eBrain Research\u003c/em\u003e, \u003cem\u003e1557\u003c/em\u003e, 141\u0026ndash;154. https://doi.org/10.1016/j.brainres.2014.02.013\u003c/li\u003e\n\u003cli\u003eHolper, L., Wolf, M., \u0026amp; Tobler, P. N. (2014). Comparison of functional near-infrared spectroscopy and electrodermal activity in assessing objective versus subjective risk during risky financial decisions. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 833\u0026ndash;842. https://doi.org/10.1016/j.neuroimage.2013.09.047\u003c/li\u003e\n\u003cli\u003eHoltzer, R., Izzetoglu, M., Chen, M., \u0026amp; Wang, C. (2019). Distinct fNIRS-Derived HbO2 Trajectories during the Course and over Repeated Walking Trials under Single-and Dual-Task Conditions: Implications for Within Session Learning and Prefrontal Cortex Efficiency in Older Adults. \u003cem\u003eJournals of Gerontology - Series A Biological Sciences and Medical Sciences\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(7), 1076\u0026ndash;1083. https://doi.org/10.1093/gerona/gly181\u003c/li\u003e\n\u003cli\u003eHorvers, A., Tombeng, N., Bosse, T., Lazonder, A. W., \u0026amp; Molenaar, I. (2021). Detecting emotions through electrodermal activity in learning contexts: A systematic review. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(23). https://doi.org/10.3390/s21237869\u003c/li\u003e\n\u003cli\u003eJacobsen, N. A., \u0026amp; Ferris, D. P. (2023). Electrocortical activity correlated with locomotor adaptation during split‐belt treadmill walking. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e, \u003cem\u003e601\u003c/em\u003e(17), 3921\u0026ndash;3944. https://doi.org/10.1113/JP284505\u003c/li\u003e\n\u003cli\u003eKayikcioglu, O., Bilgin, S., Seymenoglu, G., \u0026amp; Deveci, A. (2017). State and Trait Anxiety Scores of Patients Receiving Intravitreal Injections. \u003cem\u003eBiomedicine Hub\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 1\u0026ndash;5. https://doi.org/10.1159/000478993\u003c/li\u003e\n\u003cli\u003eKim, J., Eom, G., Kim, C., Kim, D., Lee, J., Park, B. K., \u0026amp; Hong, J. (2010). Sex differences in the postural sway characteristics of young and elderly subjects during quiet natural standing. \u003cem\u003eGeriatrics \u0026amp; Gerontology International\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 191\u0026ndash;198. https://doi.org/10.1111/j.1447-0594.2009.00582.x\u003c/li\u003e\n\u003cli\u003eKnight, D. C., Nguyen, H. T., \u0026amp; Bandettini, P. A. (2005). The role of the human amygdala in the production of conditioned fear responses. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 1193\u0026ndash;1200. https://doi.org/10.1016/j.neuroimage.2005.03.020\u003c/li\u003e\n\u003cli\u003eKrakauer, J. W., Ghez, C., \u0026amp; Ghilardi, M. F. (2005). Adaptation to visuomotor transformations: Consolidation, interference, and forgetting. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 473\u0026ndash;478. https://doi.org/10.1523/JNEUROSCI.4218-04.2005\u003c/li\u003e\n\u003cli\u003eKuo, A. D., \u0026amp; Donelan, J. M. (2009). Dynamic principles of gait and their clinical implications. \u003cem\u003ePhysical Therapy\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(2), 157\u0026ndash;174.\u003c/li\u003e\n\u003cli\u003eKurz, M. J., Wilson, T. W., \u0026amp; Arpin, D. J. (2012). Stride-time variability and sensorimotor cortical activation during walking. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(2), 1602\u0026ndash;1607. https://doi.org/10.1016/j.neuroimage.2011.08.084\u003c/li\u003e\n\u003cli\u003eLauzi\u0026egrave;re, S., Mi\u0026eacute;ville, C., Betschart, M., Duclos, C., Aissaoui, R., \u0026amp; Nadeau, S. (2014). Plantarflexion moment is a contributor to step length after-effect following walking on a split-belt treadmill in individuals with stroke and healthy individuals. \u003cem\u003eJournal of Rehabilitation Medicine\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(9), 849\u0026ndash;857. https://doi.org/10.2340/16501977-1845\u003c/li\u003e\n\u003cli\u003eLim, S. B., Peters, S., Yang, C. L., Boyd, L. A., Liu-Ambrose, T., \u0026amp; Eng, J. J. (2022). Frontal, sensorimotor, and posterior parietal regions are involved in dual-task walking after stroke. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(June), 1\u0026ndash;13. https://doi.org/10.3389/fneur.2022.904145\u003c/li\u003e\n\u003cli\u003eLim, S. B., Yang, C. L., Peters, S., Liu-Ambrose, T., Boyd, L. A., \u0026amp; Eng, J. J. (2022). Phase-dependent brain activation of the Frontal and Parietal regions during walking after stroke\u0026mdash;An fNIRS study. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(July), 1\u0026ndash;14. https://doi.org/10.3389/fneur.2022.904722\u003c/li\u003e\n\u003cli\u003eMacIntosh, B. J., Mraz, R., McIlroy, W. E., \u0026amp; Graham, S. J. (2007). Brain activity during a motor learning task: An fMRI and skin conductance study. \u003cem\u003eHuman Brain Mapping\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(12), 1359\u0026ndash;1367. https://doi.org/10.1002/hbm.20351\u003c/li\u003e\n\u003cli\u003eMalone, L. A., \u0026amp; Bastian, A. J. (2010). Thinking about walking: Effects of conscious correction versus distraction on locomotor adaptation. \u003cem\u003eJournal of Neurophysiology\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e(4), 1954\u0026ndash;1962. https://doi.org/10.1152/jn.00832.2009\u003c/li\u003e\n\u003cli\u003eMalone, L. A., \u0026amp; Bastian, A. J. (2014). Spatial and temporal asymmetries in gait predict split-belt adaptation behavior in stroke. \u003cem\u003eNeurorehabilitation and Neural Repair\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(3), 230\u0026ndash;240. https://doi.org/10.1177/1545968313505912\u003c/li\u003e\n\u003cli\u003eMalone, L. A., Vasudevan, E. V. L. L., \u0026amp; Bastian, A. J. (2011). Motor adaptation training for faster relearning. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(42), 15136\u0026ndash;15143. https://doi.org/10.1523/JNEUROSCI.1367-11.2011\u003c/li\u003e\n\u003cli\u003eMartin, T. A., Keating, J. G., Goodkin, H. P., Bastian, A. J., \u0026amp; Thach, W. T. (1996). Throwing while looking through prisms: II. Specificity and storage of multiple gaze--throw calibrations. \u003cem\u003eBrain\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(4), 1199\u0026ndash;1211. https://doi.org/10.1093/brain/119.4.1199\u003c/li\u003e\n\u003cli\u003eMirelman, A., Maidan, I., Bernad-Elazari, H., Nieuwhof, F., Reelick, M., Giladi, N., \u0026amp; Hausdorff, J. M. (2014). Increased frontal brain activation during walking while dual tasking: An fNIRS study in healthy young adults. \u003cem\u003eJournal of NeuroEngineering and Rehabilitation\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 1\u0026ndash;7. https://doi.org/10.1186/1743-0003-11-85\u003c/li\u003e\n\u003cli\u003eMirelman, A., Maidan, I., \u0026amp; Hausdorff, J. M. (2018). Chapter 7\u0026mdash;Gait. In B. L. Day \u0026amp; S. R. Lord (Eds.), \u003cem\u003eHandbook of Clinical Neurology\u003c/em\u003e (Vol. 159, pp. 119\u0026ndash;134).\u003c/li\u003e\n\u003cli\u003eMukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. \u003cem\u003eMalawi Medical Journal\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 69\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eNakazawa, K., Obata, H., \u0026amp; Sasagawa, S. (2012). Neural control of human gait and posture. \u003cem\u003eThe Journal of Physical Fitness and Sports Medicine\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 263\u0026ndash;269. https://doi.org/10.7600/jpfsm.1.263\u003c/li\u003e\n\u003cli\u003ePollock, C. L., Carpenter, M. G., Hunt, M. A., Gallina, A., Vieira, T. M., Ivanova, T. D., \u0026amp; Garland, S. J. (2017). Physiological arousal accompanying postural responses to external perturbations after stroke. \u003cem\u003eClinical Neurophysiology\u003c/em\u003e, \u003cem\u003e128\u003c/em\u003e(6), 935\u0026ndash;944. https://doi.org/10.1016/j.clinph.2017.03.008\u003c/li\u003e\n\u003cli\u003eReisman, D. S., Bastian, A. J., \u0026amp; Morton, S. M. (2010). Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. \u003cem\u003ePhysical Therapy\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(2), 187\u0026ndash;195. https://doi.org/10.2522/ptj.20090073\u003c/li\u003e\n\u003cli\u003eReisman, D. S., McLean, H., Keller, J., Danks, K. A., \u0026amp; Bastian, A. J. (2013). Repeated split-belt treadmill training improves poststroke step length asymmetry. \u003cem\u003eNeurorehabilitation and Neural Repair\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 460\u0026ndash;468. https://doi.org/10.1177/1545968312474118\u003c/li\u003e\n\u003cli\u003eReisman, D. S., Wityk, R., Silver, K., \u0026amp; Bastian, A. J. (2007). Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. \u003cem\u003eBrain\u003c/em\u003e, \u003cem\u003e130\u003c/em\u003e(Pt 7), 1861\u0026ndash;1872. https://doi.org/10.1093/brain/awm035.LOCOMOTOR\u003c/li\u003e\n\u003cli\u003eRoemmich, R. T., \u0026amp; Bastian, A. J. (2015). Two ways to save a newly learned motor pattern. \u003cem\u003eJournal of Neurophysiology\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(10), 3519\u0026ndash;3530. https://doi.org/10.1152/jn.00965.2014\u003c/li\u003e\n\u003cli\u003eRybak, I. A., Dougherty, K. J., \u0026amp; Shevtsova, N. A. (2015). Organization of the mammalian locomotor CPG: Review of computational model and circuit architectures based on genetically identified spinal interneurons. \u003cem\u003eEneuro\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(5), ENEURO.0069-15.2015. https://doi.org/10.1523/ENEURO.0069-15.2015\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;arabon, N., Kozinc, Ž., \u0026amp; Marković, G. (2022). Effects of age, sex and task on postural sway during quiet stance. \u003cem\u003eGait and Posture\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(October 2021), 60\u0026ndash;64. https://doi.org/10.1016/j.gaitpost.2021.11.020\u003c/li\u003e\n\u003cli\u003eSeverini, G., \u0026amp; Zych, M. (2022). Locomotor adaptations: Paradigms, principles and perspectives. \u003cem\u003eProgress in Biomedical Engineering\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(4), 042003. https://doi.org/10.1088/2516-1091/ac91b6\u003c/li\u003e\n\u003cli\u003eSibley, K. M., Mochizuki, G., Esposito, J. G., Camilleri, J. M., \u0026amp; McIlroy, W. E. (2008). Phasic electrodermal responses associated with whole-body instability: Presence and influence of expectation. \u003cem\u003eBrain Research\u003c/em\u003e, \u003cem\u003e1216\u003c/em\u003e, 38\u0026ndash;45. https://doi.org/10.1016/j.brainres.2008.04.002\u003c/li\u003e\n\u003cli\u003eSibley, K. M., Mochizuki, G., Frank, J. S., \u0026amp; McIlroy, W. E. (2010). The relationship between physiological arousal and cortical and autonomic responses to postural instability. \u003cem\u003eExperimental Brain Research\u003c/em\u003e, \u003cem\u003e203\u003c/em\u003e(3), 533\u0026ndash;540. https://doi.org/10.1007/s00221-010-2257-8\u003c/li\u003e\n\u003cli\u003eSpielberger, C. D. (2010). State-Trait Anxiety Inventory. In \u003cem\u003eCorsini Encyclopedia of Psychology\u003c/em\u003e. John Wiley \u0026amp; Sons, Inc. https://doi.org/10.1002/9780470479216.corpsy0943\u003c/li\u003e\n\u003cli\u003eTakakusaki, K. (2017). Functional neuroanatomy for posture and gait control. \u003cem\u003eJournal of Movement Disorders\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;17. https://doi.org/10.14802/jmd.16062\u003c/li\u003e\n\u003cli\u003eVitorio, R., Stuart, S., Rochester, L., Alcock, L., \u0026amp; Pantall, A. (2017). fNIRS response during walking\u0026mdash;Artefact or cortical activity? A systematic review. \u003cem\u003eNeuroscience and Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(October), 160\u0026ndash;172. https://doi.org/10.1016/j.neubiorev.2017.10.002\u003c/li\u003e\n\u003cli\u003eZimeo Morais, G. A., Balardin, J. B., \u0026amp; Sato, J. R. (2018). FNIRS Optodes\u0026rsquo; Location Decider (fOLD): A toolbox for probe arrangement guided by brain regions-of-interest. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 1\u0026ndash;11. https://doi.org/10.1038/s41598-018-21716-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"functional near-infrared spectroscopy (fNIRS), split-belt walking, locomotor adaptation, physiological arousal response","lastPublishedDoi":"10.21203/rs.3.rs-7670653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7670653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdaptability of motor control of gait is fundamental to navigating obstacles and variable environments. While the central nervous system (CNS) is recognized as the primary driver of gait adaptation, the extent to which the autonomic nervous system (ANS) co-modulates with cortical activity and motor output during gait adaptation remains unclear. Thus, this study examined how cortical activation, physiological arousal, and motor adaptation co-modulate during repeated exposure to split-belt treadmill walking. Twenty unimpaired young adults (10F, 10M; 26.8\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e3.3yrs) completed a single-session, repeated-block split-belt treadmill protocol (three, 3.5-min, 2:1 speed adaptation blocks, interspersed with tied-belt walking). Physiological arousal response (electrodermal activity (EDA)), step length symmetry (SLS), Rating of Perceived Stability (RPS) and cortical activation (via functional near-infrared spectroscopy oxyhemoglobin (HbO)) of the prefrontal, premotor, sensorimotor and posterior parietal cortices were assessed. Linear-mixed-effects models assessed block- and phase-dependent changes in SLS, EDA, HbO response for each region, and RPS. Split-block 1 was perceived as the most destabilizing by RPS scores (\u003cem\u003ep\u003c/em\u003e\u0026le;0.05) and elicited the largest within-block changes in SLS, EDA, and HbO activation in all regions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), suggesting that split-block 1 encompassed the largest adaptation response across the CNS and ANS. CNS and ANS savings were noted in blocks 2 and 3. Pearson\u0026rsquo;s correlations revealed that greater gait asymmetry was associated with heightened arousal during early adaptation (\u003cem\u003er\u003c/em\u003e=-0.569, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), suggesting an association between error detection and ANS response. Together, these findings suggest cross-system adaptation, with reduced cortical demand, physiological arousal, and perceived challenge and more efficient locomotor adaptation with practice.\u003c/p\u003e","manuscriptTitle":"Effects of repeated blocks of split-belt walking on locomotor adaptation, physiological arousal response and cortical activation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 20:50:37","doi":"10.21203/rs.3.rs-7670653/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":"25106769-7df5-4135-9eb2-fce21129041c","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:03:19+00:00","versionOfRecord":{"articleIdentity":"rs-7670653","link":"https://doi.org/10.1007/s00221-025-07218-1","journal":{"identity":"experimental-brain-research","isVorOnly":false,"title":"Experimental Brain Research"},"publishedOn":"2025-12-27 15:58:26","publishedOnDateReadable":"December 27th, 2025"},"versionCreatedAt":"2025-10-14 20:50:37","video":"","vorDoi":"10.1007/s00221-025-07218-1","vorDoiUrl":"https://doi.org/10.1007/s00221-025-07218-1","workflowStages":[]},"version":"v1","identity":"rs-7670653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7670653","identity":"rs-7670653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}