Structural–Functional Connectivity Coupling in Motor–Brain Networks Following Acute Ischemic Stroke

Structural–Functional Connectivity Coupling in Motor–Brain Networks Following Acute Ischemic Stroke | 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 Structural–Functional Connectivity Coupling in Motor–Brain Networks Following Acute Ischemic Stroke Jiannian Hua, Dongdong Chen, Yusong Sun, Zelin Liu, Xingkai Fang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7510682/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Structural connectivity (SC) and functional connectivity (FC) are pivotal for motor recovery after stroke, yet their interplay (SC-FC coupling) within the motor network during the acute phase of ischemic stroke remains poorly understood. Objective: This study aimed to investigate SC-FC coupling in the motor network of patients with acute ischemic stroke (AIS) and elucidate its relationship with motor function. Methods: We prospectively enrolled 55 patients within one week of AIS onset and 55 baseline-matched healthy controls (HC). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We compared the motor network SC and FC metrics between the two groups. Mediation analysis was employed to explore the interplay among SC, FC, and motor function and further analyze the associations between SC-FC coupling levels and motor function. Results: The study included 55 patients (mean age ± standard deviation (SD): 57.75 ± 13.00 years; 36 males) and 55 HC (mean age ± SD: 57.09 ± 10.74 years; 33 males). Compared with HC, patients with AIS demonstrated significantly reduced SC and FC strength within the motor network ( P <0.05). The altered SC and FC metrics were significantly negatively correlated with motor function scores ( P <0.05). Notably, mediation analysis revealed that the SC between the ipsilesional thalamus (THA) and contralesional putamen (PUT) influenced motor function through its effect on interhemispheric precentral gyrus (PreCG) FC. Crucially, the level of SC-FC coupling was significantly negatively correlated with motor function scores ( r = -0.27, P = 0.04). Conclusion: Our findings revealed synergistic alterations in the SC between the ipsilesional THA and contralesional PUT, as well as in the FC of the interhemispheric PreCG, in patients with AIS, indicating a pathological coupling effect. Furthermore, stronger SC-FC coupling is significantly associated with poorer motor function outcomes. Therefore, targeting this specific SC-FC coupling pattern, particularly by modulating interhemispheric PreCG FC, may represent a promising neuromodulation strategy to promote motor recovery in AIS patients. acute ischemic stroke SC-FC coupling motor networks neuromodulation 1. Introduction Ischemic stroke remains a leading cause of long-term disability worldwide 1 , 2 , with motor dysfunction affecting the majority of survivors. This motor impairment severely reduces quality of life and imposes a substantial socioeconomic burden 3 – 5 . The acute phase of ischemic stroke, often called the “golden window,” is a critical period of enhanced neuroplasticity that presents a unique opportunity for rehabilitation efforts to capitalize on endogenous recovery mechanisms 6 , 7 . Nevertheless, recovery trajectories vary widely across individuals, underscoring the urgent need to elucidate the neural mechanisms underlying this variability to develop more effective and personalized therapeutic strategies 8 . Advances in multimodal neuroimaging have driven progress in the field of network neuroscience, prompting a paradigm shift from a lesion-centric view to understanding stroke recovery as a process of large-scale brain network reorganization 9 , 10 . In patients with AIS, both structural and functional reorganization of the brain critically support functional recovery poststroke 11 , 12 . Traditionally, research on motor dysfunction poststroke has focused on two main aspects. One is the integrity of the corticospinal tract (CST), which originates from the ipsilesional primary motor cortex (M1) 13 – 16 . The other is the reorganization of FC 17 , 18 . Although both the integrity of the M1-CST and FC reorganization are valuable predictors, they fail to fully account for the wide spectrum of functional recovery observed in patients. This finding suggests that additional structural and functional patterns, including the coupling between structural and functional connectivity within the contralesional hemisphere and across hemispheres 19 , may significantly contribute to recovery. One emerging area of interest is the coupling between SC derived from DTI and FC measured by rs-fMRI 20 . The level of SC‒FC coupling represents the correspondence between anatomical pathways and dynamic functional networks, offering a powerful tool to investigate the complex interplay between brain structure and function after neurological injury 21 , 22 . In this context, studying the level of SC-FC coupling in patients with AIS holds significant promise for identifying potential targets for early and individualized rehabilitation. Brain structural and functional reorganization plays a pivotal role in poststroke functional recovery 23 – 25 , as the structural foundation of the brain provides the necessary substrate for the restoration or rerouting of functional pathways 22 . A recent study in chronic ischemic stroke patients demonstrated that structural damage in motor areas impairs functional recovery by disrupting FC 16 . Therefore, it is important to understand the causal relationship between preserved SC and reorganized FC 26 . Specifically, it is necessary to determine whether structural changes directly drive functional normalization or if functional adaptations occur first and subsequently influence structural plasticity. Clarifying these mechanisms will greatly aid in identifying precise targets for rehabilitation. Nevertheless, few studies have rigorously examined SC-FC interactions in the acute phase of ischemic stroke using robust statistical methods, such as analysis models 27 . Furthermore, while the level of SC-FC coupling has been linked to clinical symptoms and outcomes in stroke patients 27 , 28 , most existing research has focused on the chronic phase of ischemic stroke 28 , 29 . There remains a critical gap in the understanding of how SC-FC coupling is related to motor function during the acute stage of AIS, when intervention may be most impactful. Addressing this gap could facilitate the identification of reliable neuroimaging biomarkers predictive of recovery potential and help guide therapeutic interventions. In this study, we aimed to investigate SC-FC interactions in AIS patients using multimodal neuroimaging (DTI and rs-fMRI) in combination with mediation analysis. Specifically, we sought to elucidate how preserved SC and reorganized FC interact and how these interactions are related to motor function outcomes. Additionally, we assessed the potential of SC-FC coupling metrics as predictive biomarkers for motor recovery. By identifying specific SC-FC coupling patterns associated with motor impairment and recovery potential, this study aimed to identify novel neurobiological markers and therapeutic targets. These insights may pave the way for more precise, individualized rehabilitation strategies, ultimately improving outcomes for patients with AIS. 2. Methods 2.1 Study Participants The participants enrolled in this study were patients who received treatment at the Stroke Center at the First Affiliated Hospital of Soochow University between January 2019 and June 2021. A total of 55 patients with motor dysfunction caused by AIS were included in this study. The inclusion criteria for stroke patients were as follows: (1) first-ever ischemic stroke; (2) stroke onset within one week prior to enrollment; (3) unilateral limb motor dysfunction; (4) cerebral infarction in the territory of the middle cerebral artery, confirmed by magnetic resonance imaging (MRI); (5) age between 18 and 80 years; (6) right-handedness as assessed by the Edinburgh Handedness Inventory 30 ; and (7) normal auditory function. The exclusion criteria were as follows: (1) hemorrhagic stroke; (2) other neurological diseases; (3) severe aphasia or cognitive impairment 31 ; (4) current or past treatment with antidepressants or benzodiazepines; (5) concurrent cancer, pulmonary disease, or heart disease; and (6) contraindications for MRI examination, including pacemaker implantation or epilepsy. Furthermore, a group of HC was recruited. The inclusion criteria for HC were as follows: (1) aged 18–80 years; (2) right-handed; and (3) had no history of neurological or psychiatric diseases. The exclusion criteria for HC were as follows: (1) any contraindications for MRI scanning and (2) severe cognitive impairment. After screening, 55 right-handed stroke patients (36 males, 19 females; mean age ± SD: 57.75 ± 13.00 years) and 55 age- and sex-matched right-handed HC (33 males, 22 females; mean age ± SD: 57.09 ± 10.74 years) were included in the analysis (demographic and clinical characteristics are detailed in Table 1 and eTable 1 ). Both groups underwent the same neuroimaging protocol to ensure comparability between groups. 2.2 Ethical Approval, Registration, and Informed Consent This study was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University (IRB No. 2019-069). The study was registered with the Chinese Clinical Trial Registry (Registration Number: ChiCTR1900028355). 2.3 MRI Data Acquisition 2.3.1 MRI Scan Contents All participants underwent MRI examinations at the Department of Radiology, the First Affiliated Hospital of Soochow University. High-resolution 3D T1-weighted anatomical images, whole-brain rs-fMRI data, T2-weighted images, and DTI data were acquired. 2.3.2 MRI Acquisition Parameters MRI scans were performed on a Philips Ingenia 3.0T scanner (Philips Medical Systems Nederland B.V.) with a standard 15-channel head coil for transmission and reception. Conventional sequences, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI), were acquired for diagnostic purposes and to exclude other organic lesions. For the resting-state functional image (echo planar imaging, EPI) sequences, the acquisition was oriented parallel to the anterior‒posterior commissure; repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; slice thickness = 4 mm; slice gap = 0.4 mm; flip angle (FA) = 90°; matrix size = 64 × 64; field of view (FOV) = 240 × 240 mm²; and number of slices = 30. A total of 250 time points were acquired. DTI scan sequences and their parameters were as follows: a single-shot echo‒planar imaging sequence with TR = 3898 ms; TE = 93.0 ms; flip angle (FA) = 90°; FOV = 240 × 240 mm; matrix size = 240 × 240; slice thickness = 4 mm; number of slices = 30; and 32 noncollinear diffusion gradient directions (b = 1000 s/mm²). 2.3.3 MRI Scanning Procedure The participants lay supine with their eyes closed in the MRI scanner and were instructed to stay still and keep their minds relaxed. 2.3.4 Head Motion Control To mitigate the potential impact of head motion, we confirmed the absence of severe head motion by calculating the individual mean and maximum framewise displacement (FD). The mean framewise displacement was calculated using three translational and three rotational parameters. Maximum displacement was defined as the greatest absolute movement of any volume compared with a reference volume. Participants whose mean framewise displacement exceeded 0.5 mm or whose maximum displacement was greater than 1 mm were excluded. 2.4 MRI Data Preprocessing 2.4.1 Lesion Mapping and Image Flipping Before preprocessing, we manually delineated lesions on each patient’s high-resolution T1-weighted images using Mricron (https://www.nitrc.org/projects/mricron). The binary lesion masks were subsequently spatially normalized to the Montreal Neurological Institute (MNI) brain template. Finally, the lesion masks were overlaid and displayed on the MNI template. Images with lesions in the right hemisphere were flipped to the left hemisphere. The left hemisphere was consistently defined as the affected hemisphere on the basis of lesion location, and the right hemisphere was defined as the unaffected hemisphere. To avoid interhemispheric asymmetry, equivalent proportions of healthy control subject data were processed similarly. 2.4.2 Resting-State fMRI Data Preprocessing rs-fMRI data preprocessing was performed using DPARSF software, which is based on the MATLAB 9.2 platform 32 . The initial 10 time points of each patient’s data were discarded before preprocessing, and the remaining 240 time points were retained for preprocessing to ensure the stability of the acquired data. The specific preprocessing steps included slice timing correction, head motion correction (translation < 2 mm and rotation < 2° along the X, Y, and Z axes), spatial normalization to the MNI template, and spatial smoothing using a Gaussian kernel with a full width at half maximum (FWHM) of 8 mm. The nuisance signals, including Friston 24 head motion parameters, white matter (WM) signals, cerebrospinal fluid signals, and global mean brain signals, were subsequently regressed out. Finally, a bandpass filter of 0.01-0.08 Hz was applied to the data. 2.4.3 DTI Data Preprocessing This study utilized the PANDA (Pipeline for Analyzing Neuroimaging with Diffusion) toolkit to preprocess DTI data in a standardized manner 33 . The preprocessing ultimately generated the SC matrices. Specifically, raw DICOM DTI data were converted to the NIfTI format. Head motion and distortion were subsequently corrected to remove artifacts caused by participant motion or magnetic field inhomogeneities. Subsequently, a brain tissue extraction algorithm was used to remove nonbrain tissues such as the skull, ensuring accuracy in the analysis region. The diffusion characteristics of each voxel were subsequently estimated using a tensor model, which calculated metrics such as fractional anisotropy (FA). Afterward, the standard AAL atlas was registered to individual space to ensure the comparability of network nodes across different participants. Using the registration results, we reconstructed WM tracts with a whole-brain fiber tractography method and analyzed fiber connectivity between brain regions. This process ultimately produced SC matrices. 2.5 Imaging Data Analysis 2.5.1 rs-fMRI Data Analysis This study used the automated anatomical labeling (AAL) atlas for brain parcellation. First, we registered the preprocessed fMRI data to the AAL template using FSL software and then constructed whole-brain FC networks. Then, twelve cortical regions of interest (ROIs) commonly associated with motor function were selected on the basis of the findings of Wang L et al. 34 ( Table 2 ). The time series of all voxels within each ROI were extracted and averaged to obtain the mean time series for each ROI. Subsequently, Pearson correlation analysis was used to calculate the correlation coefficients between every pair of ROIs, resulting in a 12×12, symmetric connectivity matrix for each subject. 2.5.2 DTI Data Analysis DTI data analysis involved applying the PANDA toolkit to preprocess the data and generate SC maps. For the purpose of this study, the average SC values for the 12 motor-related ROIs were calculated to assess WM structural integrity. 2.5.3 SC-FC Coupling Analysis First, the SC matrix and FC matrix were computed for each participant; then, the nonzero elements of the SC matrix and their corresponding elements in the FC matrix were correlated. This yielded the SC-FC coupling value, defined as the correlation coefficient between structural and functional connectivity, for each participant ( Figure 1 ). 2.6 Statistical Analysis 2.6.1 Data Statistical Analysis Statistical analysis was performed using SPSS version 22 for Windows (IBM Corp., Armonk, NY, USA). Baseline data statistical analysis included comparing continuous variables that met normal distribution criteria between groups using independent samples t tests. For nonnormally distributed data, Mann-Whitney U tests were used. Categorical variables were compared using chi-square tests. For MRI data, statistical analysis was conducted using Python version 3.6 and one-way ANOVA to analyze between-group differences. Pearson correlation analysis was used to investigate the correlation between imaging data and motor function. A P value < 0.05 was considered statistically significant. 2.6.2 Mediation Analysis To assess FC as a mediator of the relationship between SC and motor functional outcome, a mediation analysis was performed using a bootstrap method within the PROCESS macro of SPSS 35 . A bootstrap sample size of 10,000 was used to estimate the significance of the indirect or mediating effect, yielding an estimated β coefficient and confidence interval (CI). The indirect effect was considered significant if the 95% CI did not include zero. Mediation analysis involves testing five pathways: (1) Path c, the total effect of SC on motor function; (2) Path a, the effect of SC on FC; (3) Path b, the effect of FC on motor function controlling for SC; (4) Path c’, the direct effect of SC on motor function controlling for FC; and (5) the indirect effect (a × b), the product of paths a and b. The total effect (c) is the sum of the indirect effect (a × b) and the direct effect (c’). 2.7 Data Accessibility Data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to ethical approval and data sharing agreements. 3. Results 3.1 Participant Characteristics This study included 55 patients with AIS and 55 HC. There were no statistically significant differences in demographic or clinical characteristics between the two groups (Table 1). The mean Fugl-Meyer assessment (FMA) score of the patients was 38.82 ± 20.38, and the mean time since stroke onset was 3.96 ± 0.86 days. Among the patients, 36 had left-sided ischemic stroke (Table 1 and eTable 1 ). 3.2 SC and Its Correlation with Clinical Motor Function in Patients with AIS Compared with HC, patients presented significantly reduced WM SC integrity. The specific structural connections that showed significant differences between the two groups included PoCG_L-PreCG_L ( P = 0.0500); PoCG_L-THA_L ( P = 0.0063); PoCG_L-PUT_L ( P = 0.0001); PoCG_R-THA_R ( P = 0.0133); PoCG_R-PUT_R ( P = 0.0396); PreCG_L-PreCG_R ( P = 0.0084); PreCG_R-THA_R ( P = 0.0329); THA_L-SPG_L ( P = 0.0048); THA_L-PUT_R ( P = 0.0332); SMA_L-SMA_R ( P = 0.0000); and SMA_R-PUT_R ( P = 0.0021) (Table 3, Fig. 2, eFigure 1 and eFigure 2 ). Notably, a significant negative correlation was found between the strength of certain structural connections and motor function scores. Specifically, the SC strengths of PoCG_R-THA_R ( r = -0.27542, P = 0.04183) and THA_L-PUT_R ( r = -0.27854, P = 0.03947) were negatively correlated with FMA scores (Table 3, Fig. 3, and eFigure 3 ). These findings suggest that stronger connectivity in these structural circuits may be associated with poorer motor function, indicating their crucial role in motor recovery following AIS. 3.3 FC and Its Correlation with Motor Function in Patients with AIS In addition to SC, FC was also assessed. Compared with HC, patients with AIS demonstrated significantly decreased functional connectivity. The specific functional connections showing significant differences between the two groups were as follows: PoCG_L-PoCG_R ( P = 0.00000); PoCG_L-PreCG_R ( P = 0.00001); PoCG_L-THA_L ( P = 0.01604); PoCG_L-THA_R ( P = 0.03532); PoCG_L-SPG_L ( P = 0.00010); PoCG_R-THA_L ( P = 0.00411); PoCG_R-THA_R ( P = 0.00655); PoCG_R-SMA_L ( P = 0.03269); PreCG_L-PreCG_R ( P = 0.00876); PreCG_R-THA_L ( P = 0.00351); PreCG_R-THA_R ( P = 0.00107); THA_L-THA_R ( P = 0.00029); THA_L-PUT_R ( P = 0.04013); SPG_L-SPG_R ( P = 0.00641); SMA_L-PUT_L ( P = 0.02397); SMA_R-PUT_L ( P = 0.01620); and PUT_L-PUT_R ( P = 0.00000) (Table 4, Fig. 4, eFigure 4 and eFigure 5 ). Furthermore, the FC strengths of PoCG_L-PoCG_R ( r = -0.27855, P = 0.03947), PoCG_L-SPG_L ( r = -0.31831, P = 0.01786), and PreCG_L-PreCG_R ( r = -0.33423, P = 0.01263) were significantly negatively correlated with FMA scores. In contrast, the FC strength of PoCG_R-SMA_L was positively correlated with FMA scores ( r = 0.29572, P = 0.02838) (Table 4, Fig. 5, and eFigure 6 ). These findings indicate that these specific functional circuits may be important factors influencing motor function in patients with AIS. 3.4 Mediation Analysis of Structural Connectivity, Functional Connectivity, and Motor Function We investigated the relationships among SC, FC, and motor function in AIS patients using multimodal fMRI and mediation analysis. The analysis revealed that the indirect effect of the THA_L-PUT_R SC on motor function, mediated by the PreCG_L-PreCG_R FC, was significant (β = -11.13589; 95% CI, -0.5391 to -0.9842; P <0.0500), whereas the direct effect (path c’) was not significant (β = -29.7349; 95% CI, -68.8586 to 9.3887; P = 0.1333). Meanwhile, the total effect (path c) was significant (β = -40.8704; 95% CI, -79.6960 to -2.0447; P = 0.0395) (Table 5 and Fig. 6). These results indicate that SC affects motor function solely through FC and has no direct effect. Thus, the SC between the ipsilesional THA and contralesional PUT regulates clinical motor function levels through the mediating effect of bilateral PreCG FC. 3.5 Relationship Between SC-FC Coupling and Motor Function in AIS Patients Compared with HC, patients presented a significant reduction in SC-FC coupling strength (Fig. 7). Additionally, in patients, the SC strength between the ipsilesional THA and contralesional PUT was positively correlated with bilateral PreCG FC strength (r = 0.27, P = 0.04; Fig. 7), whereas no such correlation was observed in the HC group. Importantly, higher levels of SC-FC coupling were significantly associated with poorer motor function ((r = -0.27, P = 0.04;Figure 7), suggesting a notable relationship between SC-FC coupling and motor impairment in AIS patients. 4. Discussion A central challenge in understanding motor recovery after AIS is to clarify how the brain’s structural and functional networks are coupled. In this study, we found that patients with AIS exhibited widespread reductions in both intra- and interhemispheric SC and FC. We identified a coupling effect between the SC of between the ipsilesional THA and contralesional PUT and the FC of the interhemispheric PreCG. The SC pathway between the ipsilesional THA and contralesional PUT affects motor recovery by mediating interhemispheric PreCG FC, where stronger SC-FC coupling is correlated with worse motor outcomes. First, our findings indicate that patients with AIS exhibit significantly reduced intra- and interhemispheric SC and FC compared with HC, which is consistent with previous reports showing that stroke not only causes local structural damage but also leads to widespread weakening of remote motor networks 12 , 29 , 36 , 37 . Cheng et al. reported that, compared with healthy individuals, patients with chronic stroke have decreased SC and FC in the ipsilesional motor areas, accompanied by severely impaired interhemispheric coordination and information transfer 37 , 38 . Therefore, early rehabilitation strategies for stroke should focus on rebuilding not only locally damaged areas but also connections within remote areas. Second, in contrast to some previous studies 37 , we found that increased activation in interhemispheric motor areas was significantly correlated with worse motor outcomes. Our findings provide novel insights into the neural mechanisms underlying motor recovery in AIS patients. Recent studies suggest that severe damage to the CST significantly reduces motor execution efficiency in the ipsilesional network 39 . This reduction leads to compensatory activation in the contralesional hemisphere 40 , 41 . However, this compensatory activation may neither lead to effective network reorganization nor support poststroke network remodeling; instead, it might even interfere with these processes 42 , 43 . Our study further confirms that enhanced interhemispheric FC of the PreCG is associated with poorer motor performance. Some studies have proposed that this interhemispheric compensatory activation may be accompanied by microstructural damage to the corpus callosum motor fibers, thereby hindering the functional reconstruction of the ipsilesional motor network. Moreover, the levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) are significantly elevated in the ipsilesional M1 of stroke patients. This increase in GABA may reduce neural plasticity, thereby exacerbating the interhemispheric imbalance 44 . Therefore, early neurorehabilitation should focus on inhibiting excessive interhemispheric activation. Interhemispheric balance should also be restored by targeting specific network nodes, such as optimizing interhemispheric connections or modulating GABA levels. These strategies can effectively help rescue lost motor function. Furthermore, we aimed to explore the interplay between SC and FC in depth. To achieve this goal, we employed a multimodal neuroimaging approach alongside a mediation analysis model. We found that the SC between the ipsilesional THA and contralesional PUT did not directly affect motor recovery but rather exerted its influence through the mediating role of interhemispheric PreCG FC. This finding confirms that the SC provides the anatomical substrate for functional reorganization. Therefore, clinical interventions should focus on the SC-FC coupling mechanism. Notably, this study is the first to demonstrate that the anatomical connection between the ipsilesional THA and contralesional PUT influences motor function by activating interhemispheric PreCG FC, serving as its anatomical foundation. Additionally, FC serves as the neuromodulation for remodeling SC. Recent research has indicated that cross-hemispheric information integration relies on the integrity of cortical structural pathways and the synergies of functional networks 45 . Importantly, after controlling for FC, the direct effect of SC on motor function disappeared, suggesting that functional reorganization is the key mediating pathway through which structural remodeling influences motor recovery. On the basis of these mechanisms, we propose rehabilitation strategies that target specific neural circuits involved in SC‒FC coupling, particularly by modulating interhemispheric PreCG functional circuits to promote motor recovery in patients with AIS. Finally, our study revealed the remodeling characteristics of SC-FC coupling in the motor network of AIS patients and its clinical significance. We observed that stronger SC-FC coupling was significantly associated with poorer motor outcomes, suggesting that excessive network coupling may reflect an abnormal compensatory pattern during early neural remodeling 46 . This finding is consistent with previous research indicating that overly strong SC-FC coupling can hinder effective network reorganization 47 . This abnormal compensatory pattern is particularly pronounced in patients with severe CST damage. The loss of CST integrity may force the brain to adopt inefficient compensatory strategies 48 – 50 . Our study specifically highlights the crucial role of the ipsilesional THA and contralesional PUT SC in the compensatory mechanisms of the motor network. Consistent with previous research, when M1 function is severely impaired, higher-order motor control functions dominated by the PUT become essential for maintaining residual motor function and developing compensatory strategies 51 . Additionally, the THA is a critical hub for information flow in the brain. It serves not only as a relay station for sensorimotor information to the cortex but also as a core hub that integrates outputs from multiple motor systems (the cortex, basal ganglia, and cerebellum) and modulates cortical excitability 52 . When the M1 and its primary descending pathways are severely compromised, the THA may facilitate the activation and strengthening of alternative motor pathways through its extensive cortical and subcortical connections, aiding in motor planning and execution 53 . The patients included in this study predominantly exhibited moderate-to-severe motor deficits. This circuit plays an important compensatory role by addressing interhemispheric inhibition (IHI) imbalance and recruiting resources from the contralesional hemisphere for effective motor output. Compared with focusing solely on M1 modulation, our study underscores the vital role of t between the ipsilesional THA and the contralesional PUT structural circuit in the motor network of AIS patients. However, this compensatory circuit was unexpectedly associated with poorer, rather than better, clinical motor outcomes, which may suggest that overcompensation could reflect maladaptive plasticity or inefficient motor control. Therefore, future interventions might aim to inhibit the overcompensation of this neural circuit to improve motor function in AIS patients. Despite its contributions, this study has several limitations. First, the relatively small sample size may have reduced the statistical power needed to robustly detect coupling effects between the ipsilesional THA and contralesional PUT structural circuit and the interhemispheric PreCG functional circuit. As the data were collected from a single center, the study represents a preliminary investigation. Future multicenter studies with larger sample sizes are warranted to validate these findings. Second, we used a cross-sectional design to reveal the mediating mechanisms among structural damage, functional abnormalities, and clinical motor performance. To establish the stability of these mediating effects and assess how these mediating effects apply throughout the dynamic changes in clinical presentation, longitudinal studies are necessary to provide more robust evidence for the clinical application of SC‒FC coupling in AIS treatment. 5. Conclusion In conclusion, our study revealed a coupling between the structural circuit of the ipsilesional THA and the contralesional PUT and the functional circuit of the interhemispheric PreCG. Moreover, we demonstrated that stronger coupling is significantly correlated with poorer motor outcomes in patents with AIS. This research provides crucial evidence for understanding the mechanisms of motor network remodeling in AIS patients and lays the groundwork for future clinical interventions. For example, targeted neuromodulation strategies could be developed to adjust the SC-FC coupling of specific brain networks, aiming to maximize motor recovery in patients with AIS. Declarations 6. Acknowledgement We wish to thank Department of Medical Imaging, The First Affiliated Hospital of Soochow University providing technical support for the scanning. 7. Funding This work was supported by the National Natural Science Foundation of China (Project No. 82202819, Qingmei Chen) ，Suzhou Science and Technology Planning Project (SKY2022123, Qingmei Chen) and Suzhou Basic Research Special Project, (Project No. SSD2024074, Qingmei Chen) . 8. Conflicts of interest There are no competing interests for all the other authors of this manuscript. 9. Contribution Statement HJN ,ZLL,FXK,SHWand CDD conceived the study and designed the statistical analyses. SYSorganized the database. HJN, CDD, and CQM did the statistical analyses and prepared the draft of the manuscript. CY, ZLC and CQM substantively revised the manuscript. All authors contributed to the interpretation of data. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors read and approved the final manuscript. References Chuzheng P, Feng C, Yan Y, Haiwen L, Chengfeng Q. 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Cell Rep. 2025;44. 10.1016/j.celrep.2025.115570 . Haojie Z, Jun Z, Lingzhong F, Chaohong G, Fang L, Jingya L, Chen B, Xingzhu L, Bingjie L, Tong Z. Somatosensory-Thalamic Functional Dysconnectivity Associated With Poststroke Motor Function Rehabilitation: A Resting-State fMRI Study. Brain Behav. 2025;15. 10.1002/brb3.70321 . Tables Table 1 Demographic and clinical characteristics of the participates PA (n=55) HC (n = 55) t /χ 2 P Age, y 57.75±13.00 57.09±10.74 0.29 0.23 Gender, n male 36 33 0.35 0.55 Lesion side, n left 23 Poststroke time interval, d 3.96±0.86 FMA scores 38.82±20.38 Abbreviations: PA=Stroke Patients;HC=Healthy Controls;y=years; d=days; FMA=Fugl-Meyer assessment. Table 2 The list of cortical motor-related regions Region (ipsi-hemisphere) Abbreviations Region (contra-hemisphere) Abbreviations Postcentral gyrus ipsi-PoCG Postcentral gyrus contra-PoCG Precentral gyrus ipsi-PreCG Precentral gyrus contra-PreCG Thalamus ipsi-THA Thalamus contra-THA Superior parietal gyrus ipsi-SPG Superior parietal gyrus contra-SPG Supplementary motor area ipsi-SMA Supplementary motor area contra-SMA Lenticular nucleus, putamen ipsi-PUT Lenticular nucleus, putamen contra-PUT Note: The cortical motor-related network regions of interest in this study were selected. Abbreviations: ipsi = ipsilesional;contra = contralesional. Table 3 Means SC of WM tracts of PA and HC and the correlation between SC and FMA in patient group SC PA (mean SC±SD) HC (Mean SC±SD) PA vs. HC The correlation with FMA P r P PoCG_L-PreCG_L 33.56±23.45 44.11±30.85 0.05* 0.02 0.89 PoCG_L-THA_L 1.00±2.66 3.00±4.71 0.01* 0.03 0.86 PoCG_L-PUT_L 16.87±19.02 34.64±24.45 0.00* -0.12 0.38 PoCG_R-THA_R 4.04±6.02 7.85±8.95 0.01* -0.28 0.04* PoCG_R-PUT_R 26.51±20.12 34.89±23.26 0.04* -0.01 0.96 PreCG_L-PreCG_R 0.09±0.35 1.76±3.99 0.01* 0.01 0.93 PreCG_R-THA_R 10.60±10.94 16.16±20.46 0.03* -0.14 0.30 THA_L-SPG_L 1.44±3.28 3.98±5.79 0.00* 0.12 0.38 THA_L-PUT_R 0.02±0.13 0.29±0.89 0.03* -0.28 0.04* SMA_L-SMA_R 29.69±42.67 100.91±82.08 0.00* 0.01 0.95 SMA_R-PUT_R 14.27±23.58 3.58±10.37 0.00* -0.08 0.57 Abbreviations: SC=structural connectivity;WM=white matter;PA=Stroke Patients;HC=Healthy Controls; FMA=Fugl-Meyer assessment;SD=standard deviation; PoCG=Postcentral gyrus; PreCG= Precentral gyrus; THA=Thalamus; SPG,Superior parietal gyrus; SMA=Supplementary motor area;PUT=lenticular nucleus, putamen; L=left; R=right. Table 4 Means FC of PA and HC and The correlation between FC and FMA in patient group FC PA (mean FC±SD) HC (Mean FC±SD) PA vs. HC The correlation with FMA P r P PoCG_L-PoCG_R 0.61±0.40 0.93±0.26 0.00* -0.28 0.04* PoCG_L-PreCG_R 0.44±0.28 0.66±0.22 0.00* -0.25 0.07 PoCG_L-THA_L -0.05±0.17 -0.14±0.22 0.02* 0.10 0.45 PoCG_L-THA_R -0.09±0.19 -0.18±0.25 0.04* 0.09 0.49 PoCG_L-SPG_L 0.36±0.23 0.17±0.26 0.00* -0.32 0.02* PoCG_R-THA_L -0.02±0.22 -0.14±0.22 0.00* 0.08 0.56 PoCG_R-THA_R -0.03±0.23 -0.16±0.26 0.01* 0.08 0.58 PoCG_R-SMA_L 0.17±0.27 0.07±0.22 0.03* 0.30 0.03* PreCG_L-PreCG_R 0.44±0.26 0.57±0.26 0.01* -0.33 0.01* PreCG_R-THA_L 0.01±0.20 -0.10±0.18 0.00* 0.03 0.83 PreCG_R-THA_R 0.01±0.18 -0.12±0.21 0.00* -0.14 0.31 THA_L-THA_R 0.73±0.34 0.94±0.24 0.00* -0.07 0.61 THA_L-PUT_R -0.00±0.21 0.07±0.18 0.04* -0.09 0.52 SPG_L-SPG_R 0.56±0.30 0.71±0.25 0.01* -0.09 0.53 SMA_L-PUT_L 0.06±0.21 0.15±0.17 0.02* -0.17 0.23 SMA_R-PUT_L 0.02±0.21 0.12±0.18 0.02* -0.06 0.71 PUT_L-PUT_R 0.41±0.23 0.62±0.22 0.00* -0.16 0.26 Abbreviations: FC = functional connectivity;PA=Stroke Patients; HC=Healthy Controls; FMA,Fugl-Meyer assessment; PoCG, Postcentral gyrus; PreCG,Precentral gyrus; THA,Thalamus; SPG,Superior parietal gyrus; SMA, Supplementary motor area; PUT,lenticular nucleus, putamen; L,left; R,right. Table 5 SC association with motor function outcome mediated by FC Pathway b Measure Value (95%CI) P a β 0.54 (0.01 to 1.06) 0.04* b β -20.74 (-40.53 to -0.93) 0.04* c’ β -29.73 (-68.86 to 9.39) 0.13 c β -40.87(-79.70 to -2.04) 0.04* Note ： Mediation analyses of the association between SC and motor function (FMA score) by FC. Abbreviations :b Pathway a represents the regression coefficient for the association of SC with FC; b, the association of FC with the motor function outcome FMA score; c’, the association of SC with the FMA score, controlling for FC; and c, the association of SC with the FMA score. Total effect (c) = direct effect (c) + indirect effect (ab).SC=structural connectivity; FC= structural connectivity; CI=confidence interval; *represents a P Value <0.05. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eIschemic stroke remains a leading cause of long-term disability worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with motor dysfunction affecting the majority of survivors. This motor impairment severely reduces quality of life and imposes a substantial socioeconomic burden\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The acute phase of ischemic stroke, often called the \u0026ldquo;golden window,\u0026rdquo; is a critical period of enhanced neuroplasticity that presents a unique opportunity for rehabilitation efforts to capitalize on endogenous recovery mechanisms\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, recovery trajectories vary widely across individuals, underscoring the urgent need to elucidate the neural mechanisms underlying this variability to develop more effective and personalized therapeutic strategies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdvances in multimodal neuroimaging have driven progress in the field of network neuroscience, prompting a paradigm shift from a lesion-centric view to understanding stroke recovery as a process of large-scale brain network reorganization\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In patients with AIS, both structural and functional reorganization of the brain critically support functional recovery poststroke\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Traditionally, research on motor dysfunction poststroke has focused on two main aspects. One is the integrity of the corticospinal tract (CST), which originates from the ipsilesional primary motor cortex (M1) \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The other is the reorganization of FC\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although both the integrity of the M1-CST and FC reorganization are valuable predictors, they fail to fully account for the wide spectrum of functional recovery observed in patients. This finding suggests that additional structural and functional patterns, including the coupling between structural and functional connectivity within the contralesional hemisphere and across hemispheres\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, may significantly contribute to recovery. One emerging area of interest is the coupling between SC derived from DTI and FC measured by rs-fMRI\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The level of SC‒FC coupling represents the correspondence between anatomical pathways and dynamic functional networks, offering a powerful tool to investigate the complex interplay between brain structure and function after neurological injury\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In this context, studying the level of SC-FC coupling in patients with AIS holds significant promise for identifying potential targets for early and individualized rehabilitation.\u003c/p\u003e\u003cp\u003eBrain structural and functional reorganization plays a pivotal role in poststroke functional recovery\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, as the structural foundation of the brain provides the necessary substrate for the restoration or rerouting of functional pathways\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A recent study in chronic ischemic stroke patients demonstrated that structural damage in motor areas impairs functional recovery by disrupting FC\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, it is important to understand the causal relationship between preserved SC and reorganized FC\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Specifically, it is necessary to determine whether structural changes directly drive functional normalization or if functional adaptations occur first and subsequently influence structural plasticity. Clarifying these mechanisms will greatly aid in identifying precise targets for rehabilitation. Nevertheless, few studies have rigorously examined SC-FC interactions in the acute phase of ischemic stroke using robust statistical methods, such as analysis models\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, while the level of SC-FC coupling has been linked to clinical symptoms and outcomes in stroke patients\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, most existing research has focused on the chronic phase of ischemic stroke\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. There remains a critical gap in the understanding of how SC-FC coupling is related to motor function during the acute stage of AIS, when intervention may be most impactful. Addressing this gap could facilitate the identification of reliable neuroimaging biomarkers predictive of recovery potential and help guide therapeutic interventions.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to investigate SC-FC interactions in AIS patients using multimodal neuroimaging (DTI and rs-fMRI) in combination with mediation analysis. Specifically, we sought to elucidate how preserved SC and reorganized FC interact and how these interactions are related to motor function outcomes. Additionally, we assessed the potential of SC-FC coupling metrics as predictive biomarkers for motor recovery. By identifying specific SC-FC coupling patterns associated with motor impairment and recovery potential, this study aimed to identify novel neurobiological markers and therapeutic targets. These insights may pave the way for more precise, individualized rehabilitation strategies, ultimately improving outcomes for patients with AIS.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Participants\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe participants enrolled in this study were patients who received treatment at the Stroke Center at the First Affiliated Hospital of Soochow University between January 2019 and June 2021. A total of 55 patients with motor dysfunction caused by\u0026nbsp;AIS\u0026nbsp;were included in this study. The inclusion criteria for stroke patients were as follows: (1) first-ever ischemic stroke; (2) stroke onset within one week prior to enrollment; (3) unilateral limb motor dysfunction; (4) cerebral infarction in the territory of the middle cerebral artery, confirmed by magnetic resonance imaging (MRI); (5) age between 18 and 80 years; (6) right-handedness as assessed by the Edinburgh Handedness Inventory\u003csup\u003e30\u003c/sup\u003e; and (7) normal auditory function. The exclusion criteria were as follows: (1) hemorrhagic stroke; (2) other neurological diseases; (3) severe aphasia or cognitive impairment\u003csup\u003e31\u003c/sup\u003e; (4) current or past treatment with antidepressants or benzodiazepines; (5) concurrent cancer, pulmonary disease, or heart disease; and (6) contraindications for MRI examination, including pacemaker implantation or epilepsy.\u003c/p\u003e\n\u003cp\u003eFurthermore, a group of HC was recruited. The inclusion criteria for HC were as follows: (1) aged 18\u0026ndash;80 years; (2) right-handed; and (3) had no history of neurological or psychiatric diseases. The exclusion criteria for HC were as follows: (1) any contraindications for MRI scanning and (2) severe cognitive impairment.\u003c/p\u003e\n\u003cp\u003eAfter screening, 55 right-handed stroke patients (36 males, 19 females; mean age \u0026plusmn; SD: 57.75 \u0026plusmn; 13.00 years) and 55 age- and sex-matched right-handed\u0026nbsp;HC\u0026nbsp;(33 males, 22 females; mean age \u0026plusmn; SD: 57.09 \u0026plusmn; 10.74 years) were included in the analysis (demographic and clinical characteristics are detailed in \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eeTable 1\u003c/strong\u003e). Both groups underwent the same neuroimaging protocol to ensure comparability between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ethical Approval, Registration, and Informed Consent\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University (IRB No. 2019-069). The study was registered with the Chinese Clinical Trial Registry (Registration Number: ChiCTR1900028355).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 MRI Data Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1 MRI Scan Contents\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants underwent MRI examinations at the Department of Radiology, the First Affiliated Hospital of Soochow University. High-resolution 3D T1-weighted anatomical images, whole-brain rs-fMRI data, T2-weighted images, and DTI data were acquired.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 MRI Acquisition Parameters\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRI scans were performed on a Philips Ingenia 3.0T scanner (Philips Medical Systems Nederland B.V.) with a standard 15-channel head coil for transmission and reception. Conventional sequences, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI), were acquired for diagnostic purposes and to exclude other organic lesions. For the resting-state functional image (echo planar imaging, EPI) sequences, the acquisition was oriented parallel to the anterior‒posterior commissure; repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; slice thickness = 4 mm; slice gap = 0.4 mm; flip angle (FA) = 90\u0026deg;; matrix size = 64 \u0026times; 64; field of view (FOV) = 240 \u0026times; 240 mm\u0026sup2;; and number of slices = 30. A total of 250 time points were acquired. DTI scan sequences and their parameters were as follows: a single-shot echo‒planar imaging sequence with TR = 3898 ms; TE = 93.0 ms; flip angle (FA) = 90\u0026deg;; FOV = 240 \u0026times; 240 mm; matrix size = 240 \u0026times; 240; slice thickness = 4 mm; number of slices = 30; and 32 noncollinear diffusion gradient directions (b = 1000 s/mm\u0026sup2;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 MRI Scanning Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants lay supine with their eyes closed in the MRI scanner and were instructed to stay still and keep their minds relaxed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.4 Head Motion Control\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo mitigate the potential impact of head motion, we confirmed the absence of severe head motion by calculating the individual mean and maximum framewise displacement (FD). The mean framewise displacement was calculated using three translational and three rotational parameters. Maximum displacement was defined as the greatest absolute movement of any volume compared with a reference volume. Participants whose mean framewise displacement exceeded 0.5 mm or whose maximum displacement was greater than 1 mm were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 MRI Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Lesion Mapping and Image Flipping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore preprocessing, we manually delineated lesions on each patient\u0026rsquo;s high-resolution T1-weighted images using Mricron (https://www.nitrc.org/projects/mricron). The binary lesion masks were subsequently spatially normalized to the Montreal Neurological Institute (MNI) brain template. Finally, the lesion masks were overlaid and displayed on the MNI template. Images with lesions in the right hemisphere were flipped to the left hemisphere. The left hemisphere was consistently defined as the affected hemisphere on the basis of lesion location, and the right hemisphere was defined as the unaffected hemisphere. To avoid interhemispheric asymmetry, equivalent proportions of healthy control subject data were processed similarly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 Resting-State fMRI Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ers-fMRI data preprocessing was performed using DPARSF software, which is based on the MATLAB 9.2 platform\u003csup\u003e32\u003c/sup\u003e. The initial 10 time points of each patient\u0026rsquo;s data were discarded before preprocessing, and the remaining 240 time points were retained for preprocessing to ensure the stability of the acquired data. The specific preprocessing steps included slice timing correction, head motion correction (translation \u0026lt; 2 mm and rotation \u0026lt; 2\u0026deg; along the X, Y, and Z axes), spatial normalization to the MNI template, and spatial smoothing using a Gaussian kernel with a full width at half maximum (FWHM) of 8 mm. The nuisance signals, including Friston 24 head motion parameters, white matter (WM) signals, cerebrospinal fluid signals, and global mean brain signals, were subsequently regressed out. Finally, a bandpass filter of 0.01-0.08 Hz was applied to the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3 DTI Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the PANDA (Pipeline for Analyzing Neuroimaging with Diffusion) toolkit to preprocess DTI data in a standardized manner\u003csup\u003e33\u003c/sup\u003e. The preprocessing ultimately generated the SC matrices. Specifically, raw DICOM DTI data were converted to the NIfTI format. Head motion and distortion were subsequently corrected to remove artifacts caused by participant motion or magnetic field inhomogeneities. Subsequently, a brain tissue extraction algorithm was used to remove nonbrain tissues such as the skull, ensuring accuracy in the analysis region. The diffusion characteristics of each voxel were subsequently estimated using a tensor model, which calculated metrics such as fractional anisotropy (FA). Afterward, the standard AAL atlas was registered to individual space to ensure the comparability of network nodes across different participants. Using the registration results, we reconstructed WM tracts with a whole-brain fiber tractography method and analyzed fiber connectivity between brain regions. This process ultimately produced SC matrices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Imaging Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1 rs-fMRI Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the\u0026nbsp;automated anatomical labeling (AAL) atlas for brain parcellation. First, we registered the preprocessed fMRI data to the AAL template using FSL software and then constructed whole-brain FC networks. Then, twelve cortical regions of interest (ROIs) commonly associated with motor function were selected on the basis of the findings of Wang L et al.\u003csup\u003e34\u003c/sup\u003e (\u003cstrong\u003eTable 2\u003c/strong\u003e). The time series of all voxels within each ROI were extracted and averaged to obtain the mean time series for each ROI. Subsequently, Pearson correlation analysis was used to calculate the correlation coefficients between every pair of ROIs, resulting in a 12\u0026times;12, symmetric connectivity matrix for each subject.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2 DTI Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDTI data analysis involved applying the PANDA toolkit to preprocess the data and generate SC maps. For the purpose of this study, the average SC values for the 12 motor-related ROIs were calculated to assess WM structural integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.3 SC-FC Coupling Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, the SC matrix and FC matrix were computed for each participant; then, the nonzero elements of the SC matrix and their corresponding elements in the FC matrix were correlated. This yielded the SC-FC coupling value, defined as the correlation coefficient between structural and functional connectivity, for each participant (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1 Data Statistical Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS version 22 for Windows (IBM Corp., Armonk, NY, USA). Baseline data statistical analysis included comparing continuous variables that met normal distribution criteria between groups using independent samples t tests. For nonnormally distributed data, Mann-Whitney U tests were used. Categorical variables were compared using chi-square tests. For MRI data, statistical analysis was conducted using Python version 3.6 and one-way ANOVA to analyze between-group differences. Pearson correlation analysis was used to investigate the correlation between imaging data and motor function. A \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.2 Mediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess FC as a mediator of the relationship between SC and motor functional outcome, a mediation analysis was performed using a bootstrap method within the PROCESS macro of SPSS\u003csup\u003e35\u003c/sup\u003e. A bootstrap sample size of 10,000 was used to estimate the significance of the indirect or mediating effect, yielding an estimated \u0026beta; coefficient and confidence interval (CI). The indirect effect was considered significant if the 95% CI did not include zero. Mediation analysis involves testing five pathways: (1) Path c, the total effect of SC on motor function; (2) Path a, the effect of SC on FC; (3) Path b, the effect of FC on motor function controlling for SC; (4) Path c\u0026rsquo;, the direct effect of SC on motor function controlling for FC; and (5) the indirect effect (a \u0026times; b), the product of paths a and b. The total effect (c) is the sum of the indirect effect (a \u0026times; b) and the direct effect (c\u0026rsquo;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Data Accessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon reasonable request, subject to ethical approval and data sharing agreements.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003e3.1 Participant Characteristics\u003c/h2\u003e\n \u003cp\u003eThis study included 55 patients with AIS and 55 HC. There were no statistically significant differences in demographic or clinical characteristics between the two groups (Table 1). The mean Fugl-Meyer assessment (FMA) score of the patients was 38.82 ± 20.38, and the mean time since stroke onset was 3.96 ± 0.86 days. Among the patients, 36 had left-sided ischemic stroke (Table 1 \u003cstrong\u003eand eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e3.2 SC and Its Correlation with Clinical Motor Function in Patients with AIS\u003c/h2\u003e\n \u003cp\u003eCompared with HC, patients presented significantly reduced WM SC integrity. The specific structural connections that showed significant differences between the two groups included PoCG_L-PreCG_L (\u003cem\u003eP\u003c/em\u003e = 0.0500); PoCG_L-THA_L (\u003cem\u003eP\u003c/em\u003e = 0.0063); PoCG_L-PUT_L (\u003cem\u003eP\u003c/em\u003e = 0.0001); PoCG_R-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.0133); PoCG_R-PUT_R (\u003cem\u003eP\u003c/em\u003e = 0.0396); PreCG_L-PreCG_R (\u003cem\u003eP\u003c/em\u003e = 0.0084); PreCG_R-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.0329); THA_L-SPG_L (\u003cem\u003eP\u003c/em\u003e = 0.0048); THA_L-PUT_R (\u003cem\u003eP\u003c/em\u003e = 0.0332); SMA_L-SMA_R (\u003cem\u003eP\u003c/em\u003e = 0.0000); and SMA_R-PUT_R (\u003cem\u003eP\u003c/em\u003e = 0.0021) (Table 3, Fig. 2, \u003cstrong\u003eeFigure 1\u003c/strong\u003eand \u003cstrong\u003eeFigure 2\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eNotably, a significant negative correlation was found between the strength of certain structural connections and motor function scores. Specifically, the SC strengths of PoCG_R-THA_R (\u003cem\u003er\u003c/em\u003e = -0.27542, \u003cem\u003eP\u003c/em\u003e = 0.04183) and THA_L-PUT_R (\u003cem\u003er\u003c/em\u003e = -0.27854, \u003cem\u003eP\u003c/em\u003e = 0.03947) were negatively correlated with FMA scores (Table 3, Fig. 3, and \u003cstrong\u003eeFigure 3\u003c/strong\u003e). These findings suggest that stronger connectivity in these structural circuits may be associated with poorer motor function, indicating their crucial role in motor recovery following AIS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e3.3 FC and Its Correlation with Motor Function in Patients with AIS\u003c/h2\u003e\n \u003cp\u003eIn addition to SC, FC was also assessed. Compared with HC, patients with AIS demonstrated significantly decreased functional connectivity. The specific functional connections showing significant differences between the two groups were as follows: PoCG_L-PoCG_R (\u003cem\u003eP\u003c/em\u003e = 0.00000); PoCG_L-PreCG_R (\u003cem\u003eP\u003c/em\u003e = 0.00001); PoCG_L-THA_L (\u003cem\u003eP\u003c/em\u003e = 0.01604); PoCG_L-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.03532); PoCG_L-SPG_L (\u003cem\u003eP\u003c/em\u003e = 0.00010); PoCG_R-THA_L (\u003cem\u003eP\u003c/em\u003e = 0.00411); PoCG_R-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.00655); PoCG_R-SMA_L (\u003cem\u003eP\u003c/em\u003e = 0.03269); PreCG_L-PreCG_R (\u003cem\u003eP\u003c/em\u003e = 0.00876); PreCG_R-THA_L (\u003cem\u003eP\u003c/em\u003e = 0.00351); PreCG_R-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.00107); THA_L-THA_R (\u003cem\u003eP\u003c/em\u003e = 0.00029); THA_L-PUT_R (\u003cem\u003eP\u003c/em\u003e = 0.04013); SPG_L-SPG_R (\u003cem\u003eP\u003c/em\u003e = 0.00641); SMA_L-PUT_L (\u003cem\u003eP\u003c/em\u003e = 0.02397); SMA_R-PUT_L (\u003cem\u003eP\u003c/em\u003e = 0.01620); and PUT_L-PUT_R (\u003cem\u003eP\u003c/em\u003e = 0.00000) (Table 4, Fig. 4, \u003cstrong\u003eeFigure 4\u003c/strong\u003e and \u003cstrong\u003eeFigure 5\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eFurthermore, the FC strengths of PoCG_L-PoCG_R (\u003cem\u003er\u003c/em\u003e = -0.27855, \u003cem\u003eP\u003c/em\u003e = 0.03947), PoCG_L-SPG_L (\u003cem\u003er\u003c/em\u003e = -0.31831, \u003cem\u003eP\u003c/em\u003e = 0.01786), and PreCG_L-PreCG_R (\u003cem\u003er\u003c/em\u003e = -0.33423, \u003cem\u003eP\u003c/em\u003e = 0.01263) were significantly negatively correlated with FMA scores. In contrast, the FC strength of PoCG_R-SMA_L was positively correlated with FMA scores (\u003cem\u003er\u003c/em\u003e = 0.29572, \u003cem\u003eP\u003c/em\u003e = 0.02838) (Table 4, Fig. 5, and \u003cstrong\u003eeFigure 6\u003c/strong\u003e). These findings indicate that these specific functional circuits may be important factors influencing motor function in patients with AIS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e3.4 Mediation Analysis of Structural Connectivity, Functional Connectivity, and Motor Function\u003c/h2\u003e\n \u003cp\u003eWe investigated the relationships among SC, FC, and motor function in AIS patients using multimodal fMRI and mediation analysis. The analysis revealed that the indirect effect of the THA_L-PUT_R SC on motor function, mediated by the PreCG_L-PreCG_R FC, was significant (β = -11.13589; 95% CI, -0.5391 to -0.9842; \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0500), whereas the direct effect (path c’) was not significant (β = -29.7349; 95% CI, -68.8586 to 9.3887; \u003cem\u003eP\u003c/em\u003e = 0.1333). Meanwhile, the total effect (path c) was significant (β = -40.8704; 95% CI, -79.6960 to -2.0447; \u003cem\u003eP\u003c/em\u003e = 0.0395) (Table 5 and Fig. 6). These results indicate that SC affects motor function solely through FC and has no direct effect. Thus, the SC between the ipsilesional THA and contralesional PUT regulates clinical motor function levels through the mediating effect of bilateral PreCG FC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003e3.5 Relationship Between SC-FC Coupling and Motor Function in AIS Patients\u003c/h2\u003e\n \u003cp\u003eCompared with HC, patients presented a significant reduction in SC-FC coupling strength (Fig. 7). Additionally, in patients, the SC strength between the ipsilesional THA and contralesional PUT was positively correlated with bilateral PreCG FC strength (r = 0.27, \u003cem\u003eP\u003c/em\u003e = 0.04; Fig. 7), whereas no such correlation was observed in the HC group. Importantly, higher levels of SC-FC coupling were significantly associated with poorer motor function ((r = -0.27, \u003cem\u003eP\u003c/em\u003e = 0.04;Figure 7), suggesting a notable relationship between SC-FC coupling and motor impairment in AIS patients.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eA central challenge in understanding motor recovery after AIS is to clarify how the brain\u0026rsquo;s structural and functional networks are coupled. In this study, we found that patients with AIS exhibited widespread reductions in both intra- and interhemispheric SC and FC. We identified a coupling effect between the SC of between the ipsilesional THA and contralesional PUT and the FC of the interhemispheric PreCG. The SC pathway between the ipsilesional THA and contralesional PUT affects motor recovery by mediating interhemispheric PreCG FC, where stronger SC-FC coupling is correlated with worse motor outcomes.\u003c/p\u003e\u003cp\u003eFirst, our findings indicate that patients with AIS exhibit significantly reduced intra- and interhemispheric SC and FC compared with HC, which is consistent with previous reports showing that stroke not only causes local structural damage but also leads to widespread weakening of remote motor networks\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Cheng et al. reported that, compared with healthy individuals, patients with chronic stroke have decreased SC and FC in the ipsilesional motor areas, accompanied by severely impaired interhemispheric coordination and information transfer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Therefore, early rehabilitation strategies for stroke should focus on rebuilding not only locally damaged areas but also connections within remote areas.\u003c/p\u003e\u003cp\u003eSecond, in contrast to some previous studies\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, we found that increased activation in interhemispheric motor areas was significantly correlated with worse motor outcomes. Our findings provide novel insights into the neural mechanisms underlying motor recovery in AIS patients. Recent studies suggest that severe damage to the CST significantly reduces motor execution efficiency in the ipsilesional network\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This reduction leads to compensatory activation in the contralesional hemisphere\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, this compensatory activation may neither lead to effective network reorganization nor support poststroke network remodeling; instead, it might even interfere with these processes\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our study further confirms that enhanced interhemispheric FC of the PreCG is associated with poorer motor performance. Some studies have proposed that this interhemispheric compensatory activation may be accompanied by microstructural damage to the corpus callosum motor fibers, thereby hindering the functional reconstruction of the ipsilesional motor network. Moreover, the levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) are significantly elevated in the ipsilesional M1 of stroke patients. This increase in GABA may reduce neural plasticity, thereby exacerbating the interhemispheric imbalance\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Therefore, early neurorehabilitation should focus on inhibiting excessive interhemispheric activation. Interhemispheric balance should also be restored by targeting specific network nodes, such as optimizing interhemispheric connections or modulating GABA levels. These strategies can effectively help rescue lost motor function.\u003c/p\u003e\u003cp\u003eFurthermore, we aimed to explore the interplay between SC and FC in depth. To achieve this goal, we employed a multimodal neuroimaging approach alongside a mediation analysis model. We found that the SC between the ipsilesional THA and contralesional PUT did not directly affect motor recovery but rather exerted its influence through the mediating role of interhemispheric PreCG FC. This finding confirms that the SC provides the anatomical substrate for functional reorganization. Therefore, clinical interventions should focus on the SC-FC coupling mechanism. Notably, this study is the first to demonstrate that the anatomical connection between the ipsilesional THA and contralesional PUT influences motor function by activating interhemispheric PreCG FC, serving as its anatomical foundation. Additionally, FC serves as the neuromodulation for remodeling SC. Recent research has indicated that cross-hemispheric information integration relies on the integrity of cortical structural pathways and the synergies of functional networks \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Importantly, after controlling for FC, the direct effect of SC on motor function disappeared, suggesting that functional reorganization is the key mediating pathway through which structural remodeling influences motor recovery. On the basis of these mechanisms, we propose rehabilitation strategies that target specific neural circuits involved in SC‒FC coupling, particularly by modulating interhemispheric PreCG functional circuits to promote motor recovery in patients with AIS.\u003c/p\u003e\u003cp\u003eFinally, our study revealed the remodeling characteristics of SC-FC coupling in the motor network of AIS patients and its clinical significance. We observed that stronger SC-FC coupling was significantly associated with poorer motor outcomes, suggesting that excessive network coupling may reflect an abnormal compensatory pattern during early neural remodeling\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This finding is consistent with previous research indicating that overly strong SC-FC coupling can hinder effective network reorganization \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This abnormal compensatory pattern is particularly pronounced in patients with severe CST damage. The loss of CST integrity may force the brain to adopt inefficient compensatory strategies\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Our study specifically highlights the crucial role of the ipsilesional THA and contralesional PUT SC in the compensatory mechanisms of the motor network. Consistent with previous research, when M1 function is severely impaired, higher-order motor control functions dominated by the PUT become essential for maintaining residual motor function and developing compensatory strategies\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Additionally, the THA is a critical hub for information flow in the brain. It serves not only as a relay station for sensorimotor information to the cortex but also as a core hub that integrates outputs from multiple motor systems (the cortex, basal ganglia, and cerebellum) and modulates cortical excitability\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. When the M1 and its primary descending pathways are severely compromised, the THA may facilitate the activation and strengthening of alternative motor pathways through its extensive cortical and subcortical connections, aiding in motor planning and execution\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The patients included in this study predominantly exhibited moderate-to-severe motor deficits. This circuit plays an important compensatory role by addressing interhemispheric inhibition (IHI) imbalance and recruiting resources from the contralesional hemisphere for effective motor output. Compared with focusing solely on M1 modulation, our study underscores the vital role of t between the ipsilesional THA and the contralesional PUT structural circuit in the motor network of AIS patients. However, this compensatory circuit was unexpectedly associated with poorer, rather than better, clinical motor outcomes, which may suggest that overcompensation could reflect maladaptive plasticity or inefficient motor control. Therefore, future interventions might aim to inhibit the overcompensation of this neural circuit to improve motor function in AIS patients.\u003c/p\u003e\u003cp\u003eDespite its contributions, this study has several limitations. First, the relatively small sample size may have reduced the statistical power needed to robustly detect coupling effects between the ipsilesional THA and contralesional PUT structural circuit and the interhemispheric PreCG functional circuit. As the data were collected from a single center, the study represents a preliminary investigation. Future multicenter studies with larger sample sizes are warranted to validate these findings. Second, we used a cross-sectional design to reveal the mediating mechanisms among structural damage, functional abnormalities, and clinical motor performance. To establish the stability of these mediating effects and assess how these mediating effects apply throughout the dynamic changes in clinical presentation, longitudinal studies are necessary to provide more robust evidence for the clinical application of SC‒FC coupling in AIS treatment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study revealed a coupling between the structural circuit of the ipsilesional THA and the contralesional PUT and the functional circuit of the interhemispheric PreCG. Moreover, we demonstrated that stronger coupling is significantly correlated with poorer motor outcomes in patents with AIS. This research provides crucial evidence for understanding the mechanisms of motor network remodeling in AIS patients and lays the groundwork for future clinical interventions. For example, targeted neuromodulation strategies could be developed to adjust the SC-FC coupling of specific brain networks, aiming to maximize motor recovery in patients with AIS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank Department of Medical Imaging, The First Affiliated Hospital of Soochow University\u0026nbsp;providing technical support for the scanning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China\u0026nbsp;(Project No. 82202819, Qingmei Chen) ，Suzhou Science and Technology Planning Project (SKY2022123, Qingmei Chen) and Suzhou Basic Research Special Project, (Project No. SSD2024074, Qingmei Chen) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Conflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests for all the other authors of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJN ,ZLL,FXK,SHWand CDD conceived the study and designed the statistical analyses. SYSorganized the database. HJN, CDD, and CQM did the statistical analyses and prepared the draft of the manuscript. CY, ZLC and CQM substantively revised the manuscript. All authors contributed to the interpretation of data. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChuzheng P, Feng C, Yan Y, Haiwen L, Chengfeng Q. Tryptophan metabolites as biomarkers to predict the severity and prognosis of acute ischemic stroke patients. J Stroke Cerebrovasc Dis. 2025;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jstrokecerebrovasdis.2025.108382\u003c/span\u003e\u003cspan address=\"10.1016/j.jstrokecerebrovasdis.2025.108382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBin L, Wen X, Ying B, Xue L, Yuan Z, Lu L, Liang B, Chenchen L, Ying T, Ling S, et al. 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Disuse-driven plasticity in the human thalamus and putamen. Cell Rep. 2025;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2025.115570\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2025.115570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaojie Z, Jun Z, Lingzhong F, Chaohong G, Fang L, Jingya L, Chen B, Xingzhu L, Bingjie L, Tong Z. Somatosensory-Thalamic Functional Dysconnectivity Associated With Poststroke Motor Function Rehabilitation: A Resting-State fMRI Study. Brain Behav. 2025;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/brb3.70321\u003c/span\u003e\u003cspan address=\"10.1002/brb3.70321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDemographic and clinical characteristics of the participates\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003cp\u003e(n=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003eHC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003et /\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e57.75\u0026plusmn;13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e57.09\u0026plusmn;10.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eGender, n male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eLesion side, n left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePoststroke time interval, d \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e3.96\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eFMA scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e38.82\u0026plusmn;20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e PA=Stroke Patients;HC=Healthy Controls;y=years; d=days; FMA=Fugl-Meyer assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe list of cortical motor-related regions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion (ipsi-hemisphere) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAbbreviations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion (contra-hemisphere)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAbbreviations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePostcentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-PoCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePostcentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-PoCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrecentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-PreCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrecentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-PreCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eThalamus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-THA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-THA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior parietal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-SPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior parietal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-SPG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSupplementary motor area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSupplementary motor area\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLenticular nucleus, putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eipsi-PUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLenticular nucleus, putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003econtra-PUT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The cortical motor-related network regions of interest in this study were selected.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ipsi = ipsilesional;contra = contralesional.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Means SC of WM tracts of PA and HC and the correlation between SC and FMA in patient group \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean SC\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean SC\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePA vs. HC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe correlation with FMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-PreCG_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.56\u0026plusmn;23.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.11\u0026plusmn;30.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-THA_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026plusmn;2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u0026plusmn;4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-PUT_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.87\u0026plusmn;19.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.64\u0026plusmn;24.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_R-THA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.04\u0026plusmn;6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.85\u0026plusmn;8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_R-PUT_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.51\u0026plusmn;20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.89\u0026plusmn;23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePreCG_L-PreCG_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76\u0026plusmn;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePreCG_R-THA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.60\u0026plusmn;10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.16\u0026plusmn;20.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTHA_L-SPG_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44\u0026plusmn;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.98\u0026plusmn;5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTHA_L-PUT_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSMA_L-SMA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.69\u0026plusmn;42.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.91\u0026plusmn;82.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSMA_R-PUT_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.27\u0026plusmn;23.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.58\u0026plusmn;10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SC=structural connectivity;WM=white matter;PA=Stroke Patients;HC=Healthy Controls; FMA=Fugl-Meyer assessment;SD=standard deviation; PoCG=Postcentral gyrus; PreCG= Precentral gyrus; THA=Thalamus; SPG,Superior parietal gyrus; SMA=Supplementary motor area;PUT=lenticular nucleus, putamen; L=left; R=right.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eMeans FC of PA and HC and The correlation between FC and FMA in patient group \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean FC\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean FC\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA vs. HC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe correlation with FMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-PoCG_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-PreCG_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.66\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-THA_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.05\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.14\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-THA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.09\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.18\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_L-SPG_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.17\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_R-THA_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.02\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.14\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_R-THA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.03\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.16\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoCG_R-SMA_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.17\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreCG_L-PreCG_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.57\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreCG_R-THA_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.10\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreCG_R-THA_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.12\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n 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style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.00\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPG_L-SPG_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.56\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMA_L-PUT_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMA_R-PUT_L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.12\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePUT_L-PUT_R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003eFC = functional connectivity;PA=Stroke Patients; HC=Healthy Controls; FMA,Fugl-Meyer assessment; PoCG, Postcentral gyrus; PreCG,Precentral gyrus; THA,Thalamus; SPG,Superior parietal gyrus; SMA, Supplementary motor area; PUT,lenticular nucleus, putamen; L,left; R,right.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e SC association with motor function outcome mediated by FC\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePathway \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eValue (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.54 (0.01 to 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-20.74 (-40.53 to -0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ec\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-29.73 (-68.86 to 9.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-40.87(-79.70 to -2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eMediation analyses of the association between SC and motor function (FMA score) by FC. Abbreviations :b Pathway a represents the regression coefficient for the association of SC with FC; b, the association of FC with the motor function outcome FMA score; c\u0026rsquo;, the association of SC with the FMA score, controlling for FC; and c, the association of SC with the FMA score. Total effect (c) = direct effect (c) + indirect effect (ab).SC=structural connectivity; FC= structural connectivity; CI=confidence interval; *represents a P Value \u0026lt;0.05.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"acute ischemic stroke, SC-FC coupling, motor networks, neuromodulation","lastPublishedDoi":"10.21203/rs.3.rs-7510682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7510682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eStructural connectivity (SC) and functional connectivity (FC) are pivotal for motor recovery after stroke, yet their interplay (SC-FC coupling) within the motor network during the acute phase of ischemic stroke remains poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to investigate SC-FC coupling in the motor network of patients with acute ischemic stroke (AIS) and elucidate its relationship with motor function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We prospectively enrolled 55 patients within one week of AIS onset and 55 baseline-matched healthy controls (HC). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We compared the motor network SC and FC metrics between the two groups. Mediation analysis was employed to explore the interplay among SC, FC, and motor function and further analyze the associations between SC-FC coupling levels and motor function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study included 55 patients (mean age ± standard deviation (SD): 57.75 ± 13.00 years; 36 males) and 55 HC (mean age ± SD: 57.09 ± 10.74 years; 33 males). Compared with HC, patients with AIS demonstrated significantly reduced SC and FC strength within the motor network (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The altered SC and FC metrics were significantly negatively correlated with motor function scores (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Notably, mediation analysis revealed that the SC between the ipsilesional thalamus (THA) and contralesional putamen (PUT) influenced motor function through its effect on interhemispheric precentral gyrus (PreCG) FC. Crucially, the level of SC-FC coupling was significantly negatively correlated with motor function scores (\u003cem\u003er\u003c/em\u003e = -0.27, \u003cem\u003eP\u003c/em\u003e = 0.04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eOur findings revealed synergistic alterations in the SC between the ipsilesional THA and contralesional PUT, as well as in the FC of the interhemispheric PreCG, in patients with AIS, indicating a pathological coupling effect. Furthermore, stronger SC-FC coupling is significantly associated with poorer motor function outcomes. Therefore, targeting this specific SC-FC coupling pattern, particularly by modulating interhemispheric PreCG FC, may represent a promising neuromodulation strategy to promote motor recovery in AIS patients.\u003c/p\u003e","manuscriptTitle":"Structural–Functional Connectivity Coupling in Motor–Brain Networks Following Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 07:28:51","doi":"10.21203/rs.3.rs-7510682/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-30T22:23:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T22:14:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199707749369778900161562860119409206581","date":"2025-11-11T12:55:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T14:49:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201570243893508188087506847622736006256","date":"2025-09-16T14:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81282592904759862665217456597046877139","date":"2025-09-16T08:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299451311755626055128034254846875622665","date":"2025-09-08T08:18:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T20:33:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T16:50:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T01:06:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-09-01T17:12:37+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":"72048d65-81d5-4f82-b248-0512713ef33a","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:06:56+00:00","versionOfRecord":{"articleIdentity":"rs-7510682","link":"https://doi.org/10.1186/s12984-026-01981-0","journal":{"identity":"journal-of-neuroengineering-and-rehabilitation","isVorOnly":false,"title":"Journal of NeuroEngineering and Rehabilitation"},"publishedOn":"2026-04-07 15:57:23","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2025-09-03 07:28:51","video":"","vorDoi":"10.1186/s12984-026-01981-0","vorDoiUrl":"https://doi.org/10.1186/s12984-026-01981-0","workflowStages":[]},"version":"v1","identity":"rs-7510682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7510682","identity":"rs-7510682","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}