Brain adaptations in demanding walking environments of adults with stroke: an experimental study

Brain adaptations in demanding walking environments of adults with stroke: an experimental study | 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 Brain adaptations in demanding walking environments of adults with stroke: an experimental study Jing Zhao, Xin Zhuang, Haochong Wang, Hua Xu, Qian Zhang, Lixia Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6428738/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Post-stroke gait rehabilitation strategies predominantly target steady-state patterns. Demanding walking environments more closely approximate real-world motor control demands than stable conditions, yet their neural correlates in stroke patients remain unclear. Methods: Sixty stroke patients completed three walking tasks: stable level-ground walking, asymmetrical walking task, and visual-deprived ambulation, with synchronized electroencephalography (EEG) recordings. Spectral power was computed across delta, theta, alpha, beta, and gamma frequency bands. Brain functional connectivity was assessed via weighted phase lag index, with graph theory metrics quantifying brain functional network features. Results: During the asymmetrical walking task, spectral power analysis exhibited reduced theta-band power and increased power in beta and gamma frequency bands. Brain functional networks showed weakened theta-band functional connectivity, and enhanced frontal-occipital connections in alpha, beta, and gamma frequency bands, accompanied by prolonged character path length in the delta frequency band and diminished clustering coefficients in alpha and gamma frequency bands. Under visual-deprivation ambulation, spectral power analysis exhibited suppressed delta and theta power and attenuated dominance in alpha, beta, and gamma frequency bands. The corresponding brain functional networks showed decoupled functional connectivity in delta and theta frequency bands, enhanced alpha-band frontal-parietal-temporal-occipital connections, and frontal-parietal-temporal interactions in the beta band, accompanied by increased delta character path length, diminished clustering coefficient, longer character path lengths and smaller clustering coefficients shown in alpha, beta, and gamma frequency bands. Conclusions: Demanding environmental challenges drive beneficial brain adaptations and could be harnessed to promote adaptive neuroplasticity in stroke rehabilitation. Trial registration: The study protocol was registered on ClinicalTrials.gov (No. NCT06395142). Stroke Electroencephalogram Environment Power spectral density Brain functional network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Stroke has emerged as a leading contributor to adult disability worldwide, with over two-thirds of survivors experiencing persistent gait impairments that significantly compromise post-stroke community participation [1-3]. Current rehabilitation strategies predominantly target steady-state walking patterns and demonstrate efficacy in restoring lower-limb kinematics under controlled conditions [4,5]. However, human locomotion inherently involves dynamic interactions with environmental constraints [6]. Stroke patients frequently encounter context-dependent mobility challenges during physically demanding ambulation [6,7], yet the neuroplastic mechanisms enabling adaptive movement remain poorly understood [8,9]. This critical disparity underscores the necessity to investigate neurophysiological mechanisms underlying adaptive locomotion in stroke patients under environmental challenges. Recent advancements in mobile electroencephalography (EEG) technology provide opportunities to characterize cortical dynamics during locomotor adaptation. Neural oscillations, which are rhythmically synchronized neuronal assemblies operating across distinct frequency bands [10,11], serve as fundamental mechanisms for information processing [12]. Crucially, brain functions as a closely network which facilitates distributed information integration through correlated inter-regional activities [13-15]. EEG offers millisecond-scale temporal resolution and non-invasive monitoring of cortical dynamics, enabling the characterization of brain adaptations during movement through time-frequency analysis and brain functional network construction [16]. To fill this gap, we designed an experimental protocol comprising three conditions: 1) Steady level-ground walking; 2) demanding asymmetrical walking task requiring affected limb elevation; 3) demanding visually deprived ambulation. By integrating spectral power analysis and brain functional network analysis, this study aimed to characterize how stroke survivors dynamically engage cognitive resources and reconfigure brain functional networks to meet environmental demands. We hypothesized that successful task performance in challenging environments requires enhanced cognitive control to optimize sensorimotor integration processes. 2. Methods 2.1 Subjects Subjects with first-ever stroke confirmed by MRI were enrolled from the Neurorehabilitation Unit of Geriatric Hospital of Nanjing Medical University. Inclusion criteria were: Unilateral hemiparesis; Stroke onset between 1 and 12 months prior; Age 18-70 years; Functional Ambulation Category ≥4. Exclusion criteria were: cranial defects; Orthopedic comorbidities; Cognitive impairment (Mini-Mental State Examination ≤19). Seventy-six participants were initially enrolled, and sixty participants were considered eligible for the study. The mean post-stroke duration was 5.17±3.78 months with heterogeneous lesion profiles (Table 1). Table 1. Demographics of participants Characters Value Age (year) 56.32 ± 12.72 Sex 17 female, 43 male Time since stroke (month) 5.17 ± 3.78 Type of stroke Ischemic stroke: 39 cases Hemorrhagic stroke: 21 cases Affected brain lesion Left brain lesion: 25 cases Right brain lesion: 35 cases 2.2 Experiment design Participants first completed a 60-second baseline walking trial on a flat, obstacle-free surface (level-ground walking, LDW) as a control condition, as depicted in Fig.1(A). Then, participants performed an asymmetrical gait task requiring dynamic weight shifting and paretic limb elevation control for 60 seconds. During this task, the paretic limb was positioned on a raised board (dimensions: 5 cm height × 12.5 cm width × 10 m length), whereas the non-paretic limb remained on the adjacent level surface (board walking, BW), as depicted in Fig.1(B). In the final condition, participants walked for 60 seconds under visual occlusion with eyes maintained in an open state (visual deprivation walking, VDW), as depicted in Fig.1(C). All trials were performed at the self-selected normal walking speed. A 3-minute seated rest interval was enforced between trails. Prior to formal testing, participants underwent a 5-minute practice session to familiarize themselves with the apparatus and task requirements. Two licensed physical therapists provided continuous safety supervision during all trials, and minimal tactile guidance were administered only when necessary to ensure safety. 2.3 EEG recording and preprocessing EEG data were recorded synchronously during three walking conditions using a 32-channel wireless EEG recorder amplifier (ZhenTec-NT1, Xi’an ZhenTec Intelligence Technology Co., Ltd., Xi'an, China), as shown in Fig.2(A). The EEG scalp electrodes were arranged according to the international 10-10 system. The reference electrode is positioned at CPz, and the ground electrode is positioned at AFz. All electrodes are Ag/AgCl semi-dry, made from a highly absorbent sponge wetted with a 3%-NaCl solution. Additional electrooculography (EOG) and mastoid electrodes collect relevant voltages using Ag/AgCl electrodes and conductive hydrogel. The EEG signals were sampled at 500 Hz, and electrode impedance was kept below 20 kΩ, as exhibited in Fig.2(B). All 60 participants completed the LGW protocol, with 57 completing the asymmetrical BW protocol and 59 the VDW conditions. EEG preprocessing was implemented in Matlab 2021b and the EEGlab toolbox. First, a 6th-order Butterworth bandpass filter (1-45 Hz) followed by ERPLAB-implemented 50 Hz notch filter were applied on the recorded EEG data. Then each channel of intercepted EEG was decomposed into the five EEG sub-frequency bands of interest: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), and gamma (30-45 Hz) via the frequency band-passed filter. EEG data was screened for extreme values by visual inspection, and bad channels were interpolated. Independent component analysis was applied to remove artifact-related components. EEG data were average re-referenced, and non-EEG electrodes were excluded, such as EOG electrodes. Finally, EEG data were segmented into a window of 5 seconds and any electrode's amplitude exceeding 75 μV was discarded. 2.4 Analysis methods 2.4.1 Power Spectral Density Power Spectral Density (PSD) is an effective method to differentiate between noise and features in a signal by making spectral representation of the power distribution of its frequency components. We used the Welch method to compute PSD [17]. Additionally, we took a sliding Hamming window with a length of 250 sampling points (500 ms) and an overlap of 125 sampling points (250 ms) to improve the performance of the spectral estimation. Firstly, we calculated the Fast Fourier Transform (FFT) of each windowed data segment and obtained its periodogram. Secondly, the average value of the periodic graph of all windowed data segments was used as the final spectral estimate of the signal. Then the PSD results of each frequency band were normalized to obtain the relative PSD of one frequency band to the whole frequency band. where [ f L , f H ] = [1, 45] and [f 1 , f 2 ]----- is determined by the frequency sub-frequency band selected. 2.4.2 Weighted Phase Lag Index In this study, we used the weighted Phase Lag Index (wPLI) method, which was first proposed by Vinck et.al [18] and was soon widely applied to estimate phase synchronization between two time-series signals to measure synchronization on a trial-by-trial basis. For each channel pair x and y, at time t, and for all the trials (n=1, 2, …, N), the FFT was performed on the two time-domain signals x(t) and y(t) to obtain their frequency-domain representations X(f) and Y(f) . Then, we calculated the cross spectrum C xy (f) , which is the product of the conjugate of X(f) and Y(f) . Finally, the imaginary component of C xy (f) was extracted. We obtained the wPLI value from the time series as follows: where is the imaginary component of the cross-spectrum C xy (f) . The wPLI ranges from 0 to 1; a wPLI of 0 indicates no phase difference or no coupling centered around 0 mod π, and a PLI of 1 implies a perfect phase locking. The larger value of wPLI indicates the stronger coupling of nonzero weight phase lag. With this measure, a synchronization matrix containing wPLI values of all pair-wise EEG channels can be obtained. The 30×30 wPLI-based matrix can be taken as a correlation matrix of the whole brain in five sub-frequency frequency bands of interest. By threshold processing, we investigated a set of proportional thresholds over the range 0.2 to 0.5. As the mean wPLI values of three tasks were significantly different, the uniform threshold value was not suitable for network construction. We adopt the proportional threshold by which the same number of links is reserved in the network and analyzed the topological difference of networks [19-21]. The thresholds were also chosen to make sure that there are no isolated nodes in the network. A proportional threshold of 0.3 means that the strongest 30% of the connections were selected. Based on this, a weighted functional brain network under three different tasks was reconstructed. Graph theory was further used to gain insight into the network features. 2.4.3 Graph Theory Metric In graph theory, a network is represented by a geometric model with nodes (processing centers) and edges (information transmission between nodes). In this context, character path length represents the average optimal path length of all pairs of nodes, which is the average value of the minimum number of edges traversed from one node to another, and it also reflects the efficiency of overall information transfer and global efficiency within the network. Thus, global efficiency is inverse of character path length. Another metric, which is the clustering coefficient, indicates network functional segmentation. Low global efficiency and high clustering coefficient suggest regular networks, whereas high global efficiency and low clustering coefficient suggest random networks. Fig.3 shows the data processing and analysis flow chart. 2.5 Statistical analysis All analyses were performed using the SPSS 22.0 statistical software. Quantitative data were expressed as the mean ± standard deviation. Qualitative data were expressed by frequency. The relative power values, character path length values, global efficiency values, clustering coefficient values, and clustering coefficient values were considered as continuous variables. The Shapiro-Wilk test was used to assess the normal distribution of these continuous variables, and Levene's test was employed to evaluate the homogeneity of variance. Three-way repeated measures analysis of variance (ANOVA) was conducted on the relative power between the factors: frequency band (delta, theta, alpha, beta, gamma), brain region (prefrontal, frontal, parietal, temporal, occipital), and group (LGW, BW, VDW). Two-way repeated measures ANOVA was performed on the network properties (clustering coefficient, character path length, global efficiency, local efficiency) between the factors: frequency band (delta, theta, alpha, beta, gamma), and group (LGW, BW, VDW). S-N-K was used to perform post hoc analysis and statistical significance was set at P <0.05. 3. Results 3.1 Relative PSD Analysis The three-way repeated-measures ANOVA (tasks: LGW, BW, VDW; frequency bands: delta, theta, alpha, beta, gamma; brain regions: prefrontal, frontal, parietal, temporal, occipital) revealed a dominant main effect of frequency band ( F = 511.81, P 0.05). Significant interactions emerged between task and frequency band ( F = 10.37, P < 0.0001, ηp² = 0.0033) and frequency band and brain region ( F = 36.42, P 0.05). Compared to LGW, both BW and VDW showed a decrease in relative power in delta and theta frequency bands, whereas showing an increase in relative power in alpha, beta, and gamma frequency bands. Specifically, in delta frequency band, VDW had lower relative power than BW. In contrast, in alpha frequency band, VDW exhibited higher relative power than BW. Furthermore, in gamma frequency band, VDW had lower relative power than BW across all brain regions. However, in beta frequency band, VDW only had lower relative power than BW in the occipital region. Specifically, compared to LGW, BW showed significant differences in relative power in theta and beta frequency bands in the parietal, temporal, and occipital brain regions ( P <0.05), and in gamma frequency band in the frontal, parietal, and temporal brain regions ( P <0.05). Additionally, VDW exhibited significant differences in relative power in delta and alpha frequency bands across all five brain regions ( P <0.05), in beta and gamma frequency bands except for the prefrontal region ( P <0.05), and in theta frequency band in the temporal and occipital regions ( P <0.05). Additionally, significant differences in relative power between BW and VDW were observed in delta and alpha frequency bands over the parietal and temporal regions ( P <0.05), in theta, alpha, and beta frequency bands over the occipital region ( P <0.05), and solely in alpha frequency band over the frontal region ( P <0.05). Comparisons on relative power for five bands during different walking tasks are demonstrated in Fig.4. 3.2 Brain functional network Compared to LGW, BW and VDW resulted in reduced functional network connectivity in delta and theta frequency bands. VDW further attenuated functional connectivity in delta and theta frequency bands versus BW. In contrast, functional networks were augmented in alpha, beta, and gamma frequency bands. Specially, BW selectively amplified frontal-parietal connections in alpha, theta and gamma frequency bands. The VDW significantly strengthened brain functional connectivity in alpha frequency band and enhanced frontal-parietal-temporal functional connectivity in beta frequency band. Brian functional network connectivity changes during different walking tasks are illustrated in Fig.5. Brain functional network characteristic metrics are shown in Fig.6. High-frequency bands (alpha, beta, and gamma) universally exhibited shorter character path lengths, smaller clustering coefficients, and lower global and local efficiencies. Conversely, in delta frequency band, both groups showed significant increases in character path length (BW: t = -2.1731, P = 0.0320; VDW: t = -2.1769, P = 0.0319), and VDW additionally exhibited a reduced clustering coefficient ( t = 2.8223, P = 0.0056). Within alpha frequency band, VDW induced pronounced network reorganization, manifesting as shorter character path length ( t = 3.0586, P = 0.0028), larger clustering coefficient ( t = -2.0698, P = 0.0103), and elevated global and local efficiencies (both t = -2.7242, P = 0.0075). BW also significantly enhanced the clustering coefficient in alpha frequency band ( t = -3.0797, P = 0.0026). Beta frequency band analyses revealed consistent reductions in character path length for both BW ( t = 2.0547, P = 0.0426) and VDW ( t = 3.0418, P = 0.0030), accompanied by decreased clustering coefficient in the VDW ( t = -2.1372, P = 0.0347). VDW produced shorter character path length ( t = 2.9940, P = 0.0035) and reduced clustering coefficients ( t = -2.9509, P = 0.0038) in gamma frequency band. Notably, no statistically significant differences were observed in any network characteristic metrics in theta frequency band. Discussion Environmental challenges dynamically suppressed low-frequency activities whereas enhancing high-frequency oscillations. This study elucidated the neurodynamic mechanisms by which post-stroke participants adapt to environmentally demanding walking tasks, revealing that the brain underwent frequency- and region-specific reorganization to meet heightened cognitive and sensorimotor demands. The suppression of the delta frequency band is associated with attentional engagement in sustained vigilance modes [22]. Theta power reduction further correlates with improved spatial navigation performance [23] and spatial memory [24]. The alpha frequency band mediates sustained top-down attention by suppressing task-irrelevant sensory inputs [25]. The beta frequency band is responsible for sensorimotor integration and maintenance of motor state stability [26,27]. The gamma frequency band enables rapid bottom-up sensory processing, with its activity dynamically modulated by coordinated alpha-beta oscillations to ensure temporally precise integration of motor planning and environmental feedback [27]. Asymmetrical gait pattern during BW triggered changes in the theta, beta, and gamma frequency bands hinted that the brain enhanced task-related sensory processing to promote sensorimotor integration. Under visual deprivation conditions, the brain relied on visual working memory to mediate spatial navigation [24,28], with heightened attentional alertness enhancing task-relevant information processing while suppressing distractors to stabilize motor performance. Delta and theta rhythms during wakefulness are classically associated with neurological damage [29,30]. Elevated delta and theta functional connectivity during low-demand walking (LDW) may signify maladaptive neural hyperactivity and inefficient cognitive resource utilization during steady-state locomotion [31]. Conversely, the reduction in delta and theta functional connectivity during walking in complex environments might reflect improved adaptive neural resource allocation to meet sensorimotor challenges. The frontal cortex is responsible for higher-order cognitive control and top-down integration [32,33]. The parietal cortex orchestrates spatial attention and sensorimotor integration [34]. Enhanced frontal-parietal connections indicated compensatory network reconfiguration to prioritize task-relevant integration under environmental constraints. Furthermore, VDW elicited strengthened temporo-parietal connectivity, potentially facilitating multisensory convergence of vestibular and proprioceptive inputs for dynamic postural adaptation [35,36]. Stroke not only causes localized damage but also exerts widespread effects on distant brain regions [37]. Low-frequency bands are associated with distant brain regions interaction, whereas high-frequency bands are associated with local information interaction[38]. The longer character path length and smaller clustering coefficient in delta frequency band revealed that the brain promoted information transmission between distant brain regions through less-clustered longer-range functional networks. The shorter character path lengths, larger clustering coefficients, higher global efficiencies and higher local efficiencies in high-frequency bands implied the brain adopt more-clustered shorter-range functional networks to promote localized information integration with high efficiency and low cost [39,40]. Theta-frequency band network exhibited a trend toward randomized configurations characterized by increased character path length and reduced clustering coefficient, suggesting that theta-mediated network randomization supports adaptive behavioral adjustments during complex tasks [41]. After stroke, elevated delta and theta power coupled with diminished alpha and beta oscillations were identified as biomarkers predictive of unfavorable functional recovery [42,43]. Liu et al. found transcranial direct current stimulation improved residual dysfunction after stroke by reducing delta-band power and functional connectivity and enhancing alpha-band power and functional connectivity [44]. Our findings revealed that exposure to ecologically valid challenging walking environments counteracts maladaptive neuroplasticity patterns following stroke, achieving comparable modulatory effects to device-assisted interventions without technological dependency. The principal limitation lies in the undetermined sustainability of environmentally induced neuroadaptations. Future investigations should establish parametric models of challenge progression to optimize personalized rehabilitation protocols. Conclusions Our findings revealed that low-frequency bands exhibited diminished relative power, attenuated functional connectivity, and increased network randomization, whereas high-frequency bands demonstrated enhanced power, strengthened connectivity, and promoted network efficiency during demanding walking environments. Brain adaptations in challenging environments are conducive to brain remodeling after stroke with further long-term intervention is needed to determine the specific effect. Abbreviations EEG: Electroencephalogram EOG: electrooculography LGW: Level-ground walking BW: Board walking VDW: Visual deprivation walking PSD: Power Spectral Density FFT: Fast Fourier Transform wPLI: weighted Phase Lag Index Declarations Ethics approval and consent to participate: Ethical approval was obtained from the institutional review board of the Geriatric Hospital of Nanjing Medical University (No. 2024018-1). All participants provided written informed consent following Declaration of Helsinki. Consent for publication: Not applicable. Availability of data and materials: The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding : This work was supported by the Jiangsu Commission of Health under Grant ZD2022065. Authors' contributions: JZ wrote the manuscript. JZ collected data in consultation with XZ and HX. HCW analyzed the EEG data. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 14 Apr, 2025 First submitted to journal 11 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6428738","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466517435,"identity":"74722790-f44f-4173-97bc-ff7f1654f263","order_by":0,"name":"Jing Zhao","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhao","suffix":""},{"id":466517436,"identity":"32cd336f-9ccd-4463-b55d-182a416c29cb","order_by":1,"name":"Xin Zhuang","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhuang","suffix":""},{"id":466517437,"identity":"81042988-6fe4-4fc3-91e6-84dad09cf845","order_by":2,"name":"Haochong Wang","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Haochong","middleName":"","lastName":"Wang","suffix":""},{"id":466517438,"identity":"3cbe8121-715c-48d6-a4ad-6af75134c44c","order_by":3,"name":"Hua Xu","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Xu","suffix":""},{"id":466517439,"identity":"8265011c-06dd-40db-b299-9c9f9209decc","order_by":4,"name":"Qian Zhang","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""},{"id":466517440,"identity":"cf1c4737-39df-463d-b316-b380cb9a0d55","order_by":5,"name":"Lixia Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACAwYGNhDNw8/ewHCAJC0ykj0HSNRiYzAjgViHSaQ/e/BzRy2PgeTzh4cLahjk+cUIWGbAcyDdsPfMcR5z6RyDwzOOMRjOnE3AOgP2hmMSvG3HeCxn5zAc5mFjSDC4TUgLM2Ob5F+gFoObxx8c5vlHjBb2ZjZp3rYaHoMbDAaHeduI0cJzjE1atu0Aj2QP0C+8fRKE/WI/I/2Z5Nu2Ont+9uOPP/N8s5HnlyagBQoOwxgSRCkHgTqiVY6CUTAKRsEIBAAFPz+42w4LvgAAAABJRU5ErkJggg==","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-11 13:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6428738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6428738/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12984-026-01951-6","type":"published","date":"2026-03-27T16:09:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84252895,"identity":"aecb693c-e374-455e-9582-9b0085e376e3","added_by":"auto","created_at":"2025-06-09 18:59:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":841958,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrations of the study design. A Participants with stroke walked on the level-ground. B Participants with stroke walked with the paretic limb positioned on a raised board. C Participants with stroke walked with visual deprivation situation. EEG signals were recorded currently by a wireless EEG recorder amplifier system.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/44f3b993f785f5847062187b.png"},{"id":84253108,"identity":"621a92a7-f944-4684-92c6-380861996b82","added_by":"auto","created_at":"2025-06-09 19:07:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":310143,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrations of the study setup. A 32-channel wireless EEG recorder amplifier. B Electrode impedance adjustment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/b28d1e212805b20c8fc40577.png"},{"id":84252532,"identity":"00da0727-b0c9-4670-81b5-1d0541b3f88e","added_by":"auto","created_at":"2025-06-09 18:51:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141089,"visible":true,"origin":"","legend":"\u003cp\u003eThe data analysis flow chart.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/ebd93bc1604d74b1767fc76a.png"},{"id":84252534,"identity":"1b10fa88-2653-45ac-9b11-ff22ab1528df","added_by":"auto","created_at":"2025-06-09 18:51:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152581,"visible":true,"origin":"","legend":"\u003cp\u003eRelative power for five bands during different walking tasks. ∗represents \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, ∗∗represents \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01. Abbreviations: LDW: level-ground walking; BW: board walking; VDW: visual deprivation walking.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/0356c26c5bf914a596358523.png"},{"id":84252530,"identity":"015e7472-c85a-418d-bd54-fb1de7c097df","added_by":"auto","created_at":"2025-06-09 18:51:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":625584,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional connectivity changes during different walking tasks. Abbreviations: LDW: level-ground walking; BW: board walking; VDW: visual deprivation walking.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/7f4bb64bbbd92a797dc411ec.png"},{"id":84252896,"identity":"c6ecb569-5a3b-42bf-a84b-2ce3f8ef9e75","added_by":"auto","created_at":"2025-06-09 18:59:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130550,"visible":true,"origin":"","legend":"\u003cp\u003eBrain functional network characteristic metrics changes. ∗ represents \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. Abbreviations: LDW: level-ground walking; BW: board walking; VDW: visual deprivation walking.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/bc191c581aaddfc1367b2486.png"},{"id":105904056,"identity":"035ad315-4aa5-4fd1-b192-fd2579b921ce","added_by":"auto","created_at":"2026-04-01 10:02:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2598302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6428738/v1/641cab62-19cf-445a-8751-e2a9ecd3620c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain adaptations in demanding walking environments of adults with stroke: an experimental study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke\u0026nbsp;has emerged as a leading contributor to adult disability worldwide, with over two-thirds of survivors experiencing persistent gait impairments that significantly compromise post-stroke community participation [1-3]. Current rehabilitation strategies predominantly target steady-state walking patterns and demonstrate efficacy in restoring lower-limb kinematics under controlled conditions [4,5]. However, human locomotion inherently involves dynamic interactions with environmental constraints [6]. Stroke patients frequently encounter context-dependent mobility challenges during physically demanding ambulation [6,7], yet the neuroplastic mechanisms enabling adaptive movement remain poorly understood [8,9]. This critical disparity underscores the necessity to investigate neurophysiological mechanisms underlying adaptive locomotion in stroke patients under environmental challenges.\u003c/p\u003e\n\u003cp\u003eRecent advancements in mobile electroencephalography (EEG) technology provide opportunities to characterize cortical dynamics during locomotor adaptation. Neural oscillations, which are rhythmically synchronized neuronal assemblies operating across distinct frequency bands [10,11], serve as fundamental mechanisms for information processing [12]. Crucially, brain functions as a closely network which facilitates distributed information integration through correlated inter-regional activities [13-15]. EEG offers millisecond-scale temporal resolution and non-invasive monitoring of cortical dynamics, enabling the characterization of brain adaptations during movement through time-frequency analysis and brain functional network construction [16].\u003c/p\u003e\n\u003cp\u003eTo fill this gap, we designed an experimental protocol comprising three conditions: 1) Steady level-ground walking; 2) demanding asymmetrical walking task requiring affected limb elevation; 3) demanding visually deprived ambulation. By integrating spectral power analysis and brain functional network analysis, this study aimed to characterize how stroke survivors dynamically engage cognitive resources and reconfigure brain functional networks to meet environmental demands. We hypothesized that successful task performance in challenging environments requires enhanced cognitive control to optimize sensorimotor integration processes.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubjects with first-ever stroke confirmed by MRI were enrolled from the\u0026nbsp;Neurorehabilitation Unit of Geriatric Hospital of Nanjing Medical University. Inclusion criteria were: Unilateral hemiparesis; Stroke onset between 1 and 12 months prior; Age 18-70 years; Functional Ambulation Category \u0026ge;4. Exclusion criteria were: cranial defects; Orthopedic comorbidities; Cognitive impairment (Mini-Mental State Examination \u0026le;19).\u003c/p\u003e\n\u003cp\u003eSeventy-six participants were initially enrolled, and sixty participants were considered eligible for the study. The mean post-stroke duration was 5.17\u0026plusmn;3.78 months with heterogeneous lesion profiles (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Demographics of participants\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eCharacters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003e56.32 \u0026plusmn; 12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003e17 female, 43 male\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eTime since stroke (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003e5.17 \u0026plusmn; 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eType of stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003eIschemic stroke: 39 cases\u003c/p\u003e\n \u003cp\u003eHemorrhagic stroke: 21 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4891%;\"\u003e\n \u003cp\u003eAffected brain lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65.5109%;\"\u003e\n \u003cp\u003eLeft brain lesion: 25 cases\u003c/p\u003e\n \u003cp\u003eRight brain lesion: 35 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Experiment design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants first completed a 60-second baseline walking trial on a flat, obstacle-free surface (level-ground walking, LDW) as a control condition, as depicted in Fig.1(A). Then, participants performed an asymmetrical gait task requiring dynamic weight shifting and paretic limb elevation control for 60 seconds. During this task, the paretic limb was positioned on a raised board (dimensions: 5 cm height \u0026times; 12.5 cm width \u0026times; 10 m length), whereas the non-paretic limb remained on the adjacent level surface (board walking, BW), as depicted in Fig.1(B). In the final condition, participants walked for 60 seconds under visual occlusion with eyes maintained in an open state (visual deprivation walking, VDW), as depicted in Fig.1(C). All trials were performed at the self-selected normal walking speed. A 3-minute seated rest interval was enforced between trails.\u003c/p\u003e\n\u003cp\u003ePrior to formal testing, participants underwent a 5-minute practice session to familiarize themselves with the apparatus and task requirements. Two licensed physical therapists provided continuous safety supervision during all trials, and minimal tactile guidance were administered only when necessary to ensure safety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEEG recording and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEEG data were recorded synchronously during three walking conditions using a 32-channel wireless EEG recorder amplifier (ZhenTec-NT1, Xi\u0026rsquo;an ZhenTec Intelligence Technology Co., Ltd., Xi\u0026apos;an, China), as shown in Fig.2(A). The EEG scalp electrodes were arranged according to the international 10-10 system. The reference electrode is positioned at CPz, and the ground electrode is positioned at AFz. All electrodes are Ag/AgCl semi-dry, made from a highly absorbent sponge wetted with a 3%-NaCl solution. Additional electrooculography (EOG) and mastoid electrodes collect relevant voltages using Ag/AgCl electrodes and conductive hydrogel. The EEG signals were sampled at 500 Hz, and electrode impedance was kept below 20 k\u0026Omega;, as exhibited in Fig.2(B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll 60 participants completed the LGW protocol, with 57 completing the asymmetrical BW protocol and 59 the VDW conditions. EEG preprocessing was implemented in Matlab 2021b and the EEGlab toolbox. First, a 6th-order Butterworth bandpass filter (1-45 Hz) followed by ERPLAB-implemented 50 Hz notch filter were applied on the recorded EEG data. Then each channel of intercepted EEG was decomposed into the five EEG sub-frequency bands of interest: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), and gamma (30-45 Hz) via the frequency band-passed filter. EEG data was screened for extreme values by visual inspection, and bad channels were interpolated. Independent component analysis was applied to remove artifact-related components. EEG data were average re-referenced, and non-EEG electrodes were excluded, such as EOG electrodes. Finally, EEG data were segmented into a window of 5 seconds and any electrode\u0026apos;s amplitude exceeding 75 \u0026mu;V was discarded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Analysis methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Power Spectral Density\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePower Spectral Density (PSD) is an effective method to differentiate between noise and features in a signal by making spectral representation of the power distribution of its frequency components. We used the Welch method to compute PSD [17]. Additionally, we took a sliding Hamming window with a length of 250 sampling points (500 ms) and an overlap of 125 sampling points (250 ms) to improve the performance of the spectral estimation. Firstly, we calculated the Fast Fourier Transform (FFT) of each windowed data segment and obtained its periodogram. Secondly, the average value of the periodic graph of all windowed data segments was used as the final spectral estimate of the signal. Then the PSD results of each frequency band were normalized to obtain the relative PSD of one frequency band to the whole frequency band.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"299\" height=\"84\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere [\u003cem\u003ef\u003csub\u003eL\u003c/sub\u003e, f\u003csub\u003eH\u003c/sub\u003e]\u003c/em\u003e = [1, 45] and [f\u003csub\u003e1\u003c/sub\u003e, f\u003csub\u003e2\u003c/sub\u003e]----- is determined by the frequency sub-frequency band selected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 Weighted Phase Lag Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we used the weighted Phase Lag Index (wPLI) method, which was first proposed by Vinck et.al\u0026nbsp;[18] and was soon widely applied to estimate phase synchronization between two time-series signals to measure synchronization on a trial-by-trial basis.\u003c/p\u003e\n\u003cp\u003eFor each channel pair x and y, at time t, and for all the trials (n=1, 2, \u0026hellip;, N), the FFT was performed on the two time-domain signals \u003cem\u003ex(t)\u003c/em\u003e and \u003cem\u003ey(t)\u003c/em\u003e to obtain their frequency-domain representations\u003cem\u003e\u0026nbsp;X(f)\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Y(f)\u003c/em\u003e. Then, we calculated the cross spectrum\u003cem\u003e\u0026nbsp;C\u003csub\u003exy\u003c/sub\u003e(f)\u003c/em\u003e, which is the product of the conjugate of\u003cem\u003e\u0026nbsp;X(f)\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Y(f)\u003c/em\u003e. Finally, the imaginary component of \u003cem\u003eC\u003csub\u003exy\u003c/sub\u003e(f)\u003c/em\u003e was extracted. We obtained the wPLI value from the time series as follows:\u003c/p\u003e\n\u003cp\u003ewhere \u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADUAAAApCAYAAACV8mGQAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMYSURBVGhD7ZkxTisxEIaddwHEDZBITwNKQw/iAkHcID0SEpwgOQAkPUpQmlRwAECpQSJlbgANOUBePj//ectmvFkSKBb2kyw7u/bY/8zYa0RlOsP9MP6E+kdRiioKpaiiUIoqCqWoolCKKgq/T1Sn03HVatVVKhVfjo+P3c3NTXj7kYuLi9Ba5O3tzdt6fHwMT/5xd3fnDg8P5/b39vZcq9Xy/ZP2GMf43HBLtzg/P+f2bpbt7W3//unpyfft9XrTZrPp22no02g05n0F/bHVbrenr6+v/pn6ap4kvDs4OJj3zcIUNR6Pp7u7u77WbyZHjCZMls3NTXMyxjEmLYjnjMMxFszF+zTYYV3LhJmiHh4efLG4vb313kQIE+O99KIFC7AiqCgR4RiMtWDu2DthisITLFaRWgV527IhUVlej6Wzooz9GKYoIFJKt1jUtO+sxRHJmEe1MI2NRToGY7AfIypqGYoE6ciiSAtBWvEutmdAfVQYnzczdJjE0nfl79RgMAgt53Z2dtzl5WX45dz9/b2vNzY2fG3B52HmEDfzuP99dXXlZhGYH+lZbG1t+VrzLBDEfRp52koxnvEulrZJSFvtMRVSiwyIwbvY3LCyKNCeS6eZFpdHlCD1lFYqMWHYVR+LtUQljScFWM/ywhh9LnCaxTJRa9399vf33cy7vt3tdn2dB649z8/P4ddHsHl9fe3bs+j5+rOYophQdzLuflmb9+TkJLT+w4aH9/d3X1v0+/3QWuTo6Ci0stE8aRZEIWgymfjTjEF46+zszNVqNdO7o9HIn2Cnp6fhifMXU3h5efG1BaddLFq6+CoL0gyHQ19rngVCGppwMukko5Dr+pKzsTm1rLudTkY2voX2BPawkfw+MZbnzBu7beijH/tOLT0odOTqpNNi6vV61CjQJ2ujUxCDk5KOYwzzxQQB/bEfY63TL4usu986YA+7K939vgIuxbEUXBWiSKSy+FZR2pOk2lfA3iU9l0X/W0UBwrL+5sqLLs157Hy7KEHarBoxxrGHsg6PJOV/EotCKaoolKKKwg8U5dxf/3oJl7JichUAAAAASUVORK5CYII=\" width=\"53\" height=\"41\"\u003e is the imaginary component of the cross-spectrum \u003cem\u003eC\u003csub\u003exy\u003c/sub\u003e(f)\u003c/em\u003e. The wPLI ranges from 0 to 1; a wPLI of 0 indicates no phase difference or no coupling centered around 0 mod \u0026pi;, and a PLI of 1 implies a perfect phase locking. The larger value of wPLI indicates the stronger coupling of nonzero weight phase lag. With this measure, a synchronization matrix containing wPLI values of all pair-wise EEG channels can be obtained.\u003c/p\u003e\n\u003cp\u003eThe 30\u0026times;30 wPLI-based matrix can be taken as a correlation matrix of the whole brain in five sub-frequency frequency bands of interest. By threshold processing, we investigated a set of proportional thresholds over the range 0.2 to 0.5. As the mean wPLI values of three tasks were significantly different, the uniform threshold value was not suitable for network construction. We adopt the proportional threshold by which the same number of links is reserved in the network and analyzed the topological difference of networks\u0026nbsp;[19-21]. The thresholds were also chosen to make sure that there are no isolated nodes in the network. A proportional threshold of 0.3 means that the strongest 30% of the connections were selected. Based on this, a weighted functional brain network under three different tasks was reconstructed.\u0026nbsp;Graph theory was further used to gain insight into the network features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3 Graph Theory Metric\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn graph theory, a network is represented by a geometric model with nodes (processing centers) and edges (information transmission between nodes). In this context, character path length represents the average optimal path length of all pairs of nodes, which is the average value of the minimum number of edges traversed from one node to another, and it also reflects the efficiency of overall information transfer and global efficiency within the network. Thus, global efficiency is inverse of character path length. Another metric, which is the clustering coefficient, indicates network functional segmentation. Low global efficiency and high clustering coefficient suggest regular networks, whereas high global efficiency and low clustering coefficient suggest random networks. Fig.3 shows the data processing and analysis flow chart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using the SPSS 22.0 statistical software. Quantitative data were expressed as the mean\u0026nbsp;\u0026plusmn; standard deviation. Qualitative data were expressed by frequency. The relative power values, character path length values, global efficiency values, clustering coefficient values, and clustering coefficient values were considered as continuous variables. The Shapiro-Wilk test was used to assess the normal distribution of these continuous variables, and Levene\u0026apos;s test was employed to evaluate the homogeneity of variance. Three-way repeated measures analysis of variance (ANOVA) was conducted on the relative power between the factors: frequency band (delta, theta, alpha, beta, gamma), brain region (prefrontal, frontal, parietal, temporal, occipital), and group (LGW, BW, VDW). Two-way repeated measures ANOVA was performed on the network properties (clustering coefficient, character path length, global efficiency, local efficiency) between the factors: frequency band (delta, theta, alpha, beta, gamma), and group (LGW, BW, VDW). S-N-K was used to perform post hoc analysis and statistical significance was set at\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Relative PSD Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe three-way repeated-measures ANOVA (tasks: LGW, BW, VDW; frequency bands: delta, theta, alpha, beta, gamma; brain regions: prefrontal, frontal, parietal, temporal, occipital) revealed a dominant main effect of frequency band (\u003cem\u003eF\u003c/em\u003e = 511.81, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, \u0026eta;p\u0026sup2; = 0.8046), with no significant main effects observed for task or brain region (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Significant interactions emerged between task and frequency band (\u003cem\u003eF\u003c/em\u003e = 10.37, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, \u0026eta;p\u0026sup2; = 0.0033) and frequency band and brain region (\u003cem\u003eF\u003c/em\u003e = 36.42, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, \u0026eta;p\u0026sup2; = 0.0229), whereas three-way and task \u0026times; brain region interactions remained non-significant (\u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eCompared to LGW, both BW and VDW showed a decrease in relative power in delta and theta frequency bands, whereas showing an increase in relative power in alpha, beta, and gamma frequency bands. Specifically, in delta frequency band, VDW had lower relative power than BW. In contrast, in alpha frequency band, VDW exhibited higher relative power than BW. Furthermore, in gamma frequency band, VDW had lower relative power than BW across all brain regions. However, in beta frequency band, VDW only had lower relative power than BW in the occipital region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, compared to LGW, BW showed significant differences in relative power in theta and beta frequency bands in the parietal, temporal, and occipital brain regions (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), and in gamma frequency band in the frontal, parietal, and temporal brain regions (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). Additionally, VDW exhibited significant differences in relative power in delta and alpha frequency bands across all five brain regions (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), in beta and gamma frequency bands except for the prefrontal region (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), and in theta frequency band in the temporal and occipital regions (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). Additionally, significant differences in relative power between BW and VDW were observed in delta and alpha frequency bands over the parietal and temporal regions (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), in theta, alpha, and beta frequency bands over the occipital region (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), and solely in alpha frequency band over the frontal region (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Comparisons on relative power for five bands during different walking tasks are demonstrated in Fig.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Brain functional network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to LGW, BW and VDW resulted in reduced functional network connectivity in delta and theta frequency bands. VDW further attenuated functional connectivity in delta and theta frequency bands versus BW. In contrast, functional networks were augmented in alpha, beta, and gamma frequency bands. Specially, BW selectively amplified frontal-parietal connections in alpha, theta and gamma frequency bands. The VDW significantly strengthened brain functional connectivity in alpha frequency band and enhanced frontal-parietal-temporal functional connectivity in beta frequency band. Brian functional network connectivity changes during different walking tasks are illustrated in Fig.5.\u003c/p\u003e\n\u003cp\u003eBrain functional network characteristic metrics are shown in Fig.6. High-frequency bands (alpha, beta, and gamma) universally exhibited shorter character path lengths, smaller clustering coefficients, and lower global and local efficiencies. Conversely, in delta frequency band, both groups showed significant increases in character path length (BW: \u003cem\u003et\u003c/em\u003e = -2.1731, \u003cem\u003eP\u003c/em\u003e = 0.0320; VDW: \u003cem\u003et\u003c/em\u003e = -2.1769, \u003cem\u003eP\u003c/em\u003e = 0.0319), and VDW additionally exhibited a reduced clustering coefficient (\u003cem\u003et\u003c/em\u003e = 2.8223, \u003cem\u003eP\u003c/em\u003e = 0.0056).\u003c/p\u003e\n\u003cp\u003eWithin alpha frequency band, VDW induced pronounced network reorganization, manifesting as shorter character path length (\u003cem\u003et\u003c/em\u003e = 3.0586, \u003cem\u003eP\u003c/em\u003e = 0.0028), larger clustering coefficient (\u003cem\u003et\u003c/em\u003e = -2.0698, \u003cem\u003eP\u003c/em\u003e = 0.0103), and elevated global and local efficiencies (both \u003cem\u003et\u003c/em\u003e = -2.7242, \u003cem\u003eP\u003c/em\u003e = 0.0075). BW also significantly enhanced the clustering coefficient in alpha frequency band (\u003cem\u003et\u003c/em\u003e = -3.0797, \u003cem\u003eP\u003c/em\u003e = 0.0026). Beta frequency band analyses revealed consistent reductions in character path length for both BW (\u003cem\u003et\u003c/em\u003e = 2.0547, \u003cem\u003eP\u003c/em\u003e = 0.0426) and VDW (\u003cem\u003et\u003c/em\u003e = 3.0418, \u003cem\u003eP\u003c/em\u003e = 0.0030), accompanied by decreased clustering coefficient in the VDW (\u003cem\u003et\u003c/em\u003e = -2.1372, \u003cem\u003eP\u003c/em\u003e = 0.0347). VDW produced shorter character path length (\u003cem\u003et\u003c/em\u003e = 2.9940, \u003cem\u003eP\u003c/em\u003e = 0.0035) and reduced clustering coefficients (\u003cem\u003et\u003c/em\u003e = -2.9509, \u003cem\u003eP\u003c/em\u003e = 0.0038) in gamma frequency band. Notably, no statistically significant differences were observed in any network characteristic metrics in theta frequency band.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eEnvironmental challenges dynamically suppressed low-frequency activities whereas enhancing high-frequency oscillations. This study elucidated the neurodynamic mechanisms by which post-stroke participants adapt to environmentally demanding walking tasks, revealing that the brain underwent frequency- and region-specific reorganization to meet heightened cognitive and sensorimotor demands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe suppression of the delta frequency band is associated with attentional engagement in sustained vigilance modes [22]. Theta power reduction further correlates with improved spatial navigation performance [23] and spatial memory [24]. The alpha frequency band mediates sustained top-down attention by suppressing task-irrelevant sensory inputs [25]. The beta frequency band is responsible for sensorimotor integration and maintenance of motor state stability [26,27]. The gamma frequency band enables rapid bottom-up sensory processing, with its activity dynamically modulated by coordinated alpha-beta oscillations to ensure temporally precise integration of motor planning and environmental feedback [27]. Asymmetrical gait pattern during BW triggered changes in the theta, beta, and gamma frequency bands hinted that the brain enhanced task-related sensory processing to promote sensorimotor integration. Under visual deprivation conditions, the brain relied on visual working memory to mediate spatial navigation [24,28], with heightened attentional alertness enhancing task-relevant information processing while suppressing distractors to stabilize motor performance.\u003c/p\u003e\n\u003cp\u003eDelta and theta rhythms during wakefulness are classically associated with neurological damage [29,30]. Elevated delta and theta functional connectivity during low-demand walking (LDW) may signify maladaptive neural hyperactivity and inefficient cognitive resource utilization during steady-state locomotion [31]. Conversely, the reduction in delta and theta functional connectivity during walking in complex environments might reflect improved adaptive neural resource allocation to meet sensorimotor challenges. The frontal cortex is responsible for higher-order cognitive control and top-down integration [32,33]. The parietal cortex orchestrates spatial attention and sensorimotor integration [34]. Enhanced frontal-parietal connections indicated compensatory network reconfiguration to prioritize task-relevant integration under environmental constraints. Furthermore, VDW elicited strengthened temporo-parietal connectivity, potentially facilitating multisensory convergence of vestibular and proprioceptive inputs for dynamic postural adaptation [35,36].\u003c/p\u003e\n\u003cp\u003eStroke not only causes localized damage but also exerts widespread effects on distant brain regions [37]. Low-frequency bands are associated with distant brain regions interaction, whereas high-frequency bands are associated with local information interaction[38]. The longer character path length and smaller clustering coefficient in delta frequency band revealed that the brain promoted information transmission between distant brain regions through less-clustered longer-range functional networks. The shorter character path lengths, larger clustering coefficients, higher global efficiencies and higher local efficiencies in high-frequency bands implied the brain adopt more-clustered shorter-range functional networks to promote localized information integration with high efficiency and low cost\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[39,40]. Theta-frequency band network exhibited a trend toward randomized configurations characterized by increased character path length and reduced clustering coefficient, suggesting that theta-mediated network randomization supports adaptive behavioral adjustments during complex tasks [41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter stroke, elevated delta and theta power coupled with diminished alpha and beta oscillations were identified as biomarkers predictive of unfavorable functional recovery [42,43]. Liu et al. found transcranial direct current stimulation improved residual dysfunction after stroke by reducing delta-band power and functional connectivity and enhancing alpha-band power and functional connectivity [44]. Our findings revealed that exposure to ecologically valid challenging walking environments counteracts maladaptive neuroplasticity patterns following stroke, achieving comparable modulatory effects to device-assisted interventions without technological dependency. The principal limitation lies in the undetermined sustainability of environmentally induced neuroadaptations. Future investigations should establish parametric models of challenge progression to optimize personalized rehabilitation protocols.\u003c/p\u003e"},{"header":"Conclusions ","content":"\u003cp\u003eOur findings revealed that low-frequency bands exhibited diminished relative power, attenuated functional connectivity, and increased network randomization, whereas high-frequency bands demonstrated enhanced power, strengthened connectivity, and promoted network efficiency during demanding walking environments. Brain adaptations in challenging environments are conducive to brain remodeling after stroke with further long-term intervention is needed to determine the specific effect.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEEG: Electroencephalogram\u003c/p\u003e\n\u003cp\u003eEOG: electrooculography\u003c/p\u003e\n\u003cp\u003eLGW: Level-ground walking\u003c/p\u003e\n\u003cp\u003eBW: Board walking\u003c/p\u003e\n\u003cp\u003eVDW: Visual deprivation walking\u003c/p\u003e\n\u003cp\u003ePSD: Power Spectral Density\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFFT: Fast Fourier Transform\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewPLI: weighted Phase Lag Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Ethical approval was obtained from the institutional review board of the Geriatric Hospital of Nanjing Medical University (No. 2024018-1). All participants provided written informed consent following Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Jiangsu Commission of Health under Grant ZD2022065.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eJZ\u0026nbsp;wrote the manuscript. JZ\u0026nbsp;collected data in consultation with XZ and HX.\u0026nbsp;HCW analyzed the EEG data. QZ and LXZ designed the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We acknowledge Suqian Rehabilitation Hospital and Nangang Hospital for their assistance in the recruitment of subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100-32.\u003c/li\u003e\n\u003cli\u003eKersey J, Skidmore E, Hammel J, Baum C. Participation and Its Association With Health Among Community-Dwelling Adults With Chronic Stroke. Am J Occup Ther. 2023;77(6).\u003c/li\u003e\n\u003cli\u003eJang SH. The recovery of walking in stroke patients: a review. 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J Cogn Neurosci. 2022;34(6):1053-69.\u003c/li\u003e\n\u003cli\u003eZhang JJ, S\u0026aacute;nchez Vida\u0026ntilde;a DI, Chan JN, Hui ESK, Lau KK, Wang X, et al. Biomarkers for prognostic functional recovery poststroke: A narrative review. Front Cell Dev Biol. 2022;10:1062807.\u003c/li\u003e\n\u003cli\u003eAssenza G, Zappasodi F, Pasqualetti P, Vernieri F, Tecchio F. A contralesional EEG power increase mediated by interhemispheric disconnection provides negative prognosis in acute stroke. Restor Neurol Neurosci. 2013;31(2):177-88.\u003c/li\u003e\n\u003cli\u003eLiu M, Xu G, Yu H, Wang C, Sun C, Guo L. Effects of Transcranial Direct Current Stimulation on EEG Power and Brain Functional Network in Stroke Patients. IEEE Trans Neural Syst Rehabil Eng. 2023;31:335-45.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Electroencephalogram, Environment, Power spectral density, Brain functional network","lastPublishedDoi":"10.21203/rs.3.rs-6428738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6428738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Post-stroke gait rehabilitation strategies predominantly target steady-state patterns. Demanding walking environments more closely approximate real-world motor control demands than stable conditions, yet their neural correlates in stroke patients remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Sixty stroke patients completed three walking tasks: stable level-ground walking, asymmetrical walking task, and visual-deprived ambulation, with synchronized electroencephalography (EEG) recordings. Spectral power was computed across delta, theta, alpha, beta, and gamma frequency bands. Brain functional connectivity was assessed via weighted phase lag index, with graph theory metrics quantifying brain functional network features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring the asymmetrical walking task, spectral power analysis exhibited reduced theta-band power and increased power in beta and gamma frequency bands. Brain functional networks showed weakened theta-band functional connectivity, and enhanced frontal-occipital connections in alpha, beta, and gamma frequency bands, accompanied by prolonged character path length in the delta frequency band and diminished clustering coefficients in alpha and gamma frequency bands. Under visual-deprivation ambulation, spectral power analysis exhibited suppressed delta and theta power and attenuated dominance in alpha, beta, and gamma frequency bands. The corresponding brain functional networks showed decoupled functional connectivity in delta and theta frequency bands, enhanced alpha-band frontal-parietal-temporal-occipital connections, and frontal-parietal-temporal interactions in the beta band, accompanied by increased delta character path length, diminished clustering coefficient, longer character path lengths and smaller clustering coefficients shown in alpha, beta, and gamma frequency bands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Demanding environmental challenges drive beneficial brain adaptations and could be harnessed to promote adaptive neuroplasticity in stroke rehabilitation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003eThe study protocol was registered on ClinicalTrials.gov (No. NCT06395142).\u003c/p\u003e","manuscriptTitle":"Brain adaptations in demanding walking environments of adults with stroke: an experimental study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 18:51:01","doi":"10.21203/rs.3.rs-6428738/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T14:28:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T01:37:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100999610079684041612449238124825072622","date":"2025-11-04T10:38:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T13:58:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57620841015052984188222141208612855882","date":"2025-06-03T19:52:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-02T14:12:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T04:55:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-14T04:54:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-04-11T13:20:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b13def42-9eb5-49ca-b9f0-1cc0ae91ded9","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:23:12+00:00","versionOfRecord":{"articleIdentity":"rs-6428738","link":"https://doi.org/10.1186/s12984-026-01951-6","journal":{"identity":"journal-of-neuroengineering-and-rehabilitation","isVorOnly":false,"title":"Journal of NeuroEngineering and Rehabilitation"},"publishedOn":"2026-03-27 16:09:38","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2025-06-09 18:51:01","video":"","vorDoi":"10.1186/s12984-026-01951-6","vorDoiUrl":"https://doi.org/10.1186/s12984-026-01951-6","workflowStages":[]},"version":"v1","identity":"rs-6428738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6428738","identity":"rs-6428738","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}