Exploring the Interrelationship of Intra- and Inter-network Alteration in Motor Recovery After Stroke

Exploring the Interrelationship of Intra- and Inter-network Alteration in Motor Recovery After 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 Article Exploring the Interrelationship of Intra- and Inter-network Alteration in Motor Recovery After Stroke Jungsoo Lee, Yun-Hee Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5258130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Brain networks demonstrate various dynamics during recovery after stroke. Recovery of interhemispheric interaction and balance and the occurrence of network reorganization have also been reported during stroke recovery. This study aimed to investigate the dynamics of brain networks after stroke. At the large-scale brain network level, this study focuses on determining whether changes in brain networks during the functional recovery period following a stroke, along with concurrent changes in connected networks, facilitate functional recovery. Eighty-three subacute ischemic stroke patients participated. All patients underwent resting-state functional MRI and motor function assessments at two weeks and three months after stroke onset. Intra- and inter-networks from 12 resting-state networks were extracted from functional MRI data. The interrelationship between changes in intra-network values and changes in inter-network values between the corresponding network and other networks during the recovery period was investigated. The interrelationship between the good and poor recovery subgroups was compared. The interrelationship of intra- and inter-network alterations could be observed in both groups. This interrelationship was more pronounced across multiple networks in the good recovery group compared to the poor recovery group. The group differences in the interrelationship of intra- and inter-network alteration were shown in diverse sub-networks, including cognitive and sensory networks as well as motor networks. This study suggests the importance of adopting a plasticity-oriented perspective focused on changes in intra- and inter-network connectivity throughout the entire brain rather than solely emphasizing the motor function area for predicting and treating motor function recovery. Biological sciences/Neuroscience Health sciences/Neurology Figures Figure 1 Figure 2 Introduction The brain networks are functionally specialized and reciprocally connected to each other 1 , 2 . Therefore, brain damage caused by a stroke can be explained from the perspective of the brain network 3 – 5 . The brain network is disrupted caused by stroke onset, and the network is reorganized during recovery period 6 . Previous neuroimaging studies have reported about brain network reorganization during recovery period of stroke patients with motor impairment, such as recovery of interhemispheric interaction, increased randomness of global network topology, and alteration of local connectivity strength including functional connectivity of cognitive and sensory regions as well as motor regions 7 – 10 . In motor recovery-related neuroimaging biomarker studies, several cognitive networks of resting-state functional networks and functional connectivity of cognitive-sensory regions, including the motor network and connectivity of motor-related regions, played a role in prognosis prediction of motor recovery in stroke patients 11 , 12 . Therefore, even the recovery of a specific function in stroke patients needs to be understood from a whole-brain perspective. In other words, understanding the reciprocal global influence among functionally specialized networks from the large-scale brain network perspective is crucial in the process of motor function recovery. In this context, this study aimed to investigate additional dynamics of brain networks. We investigated the reciprocal alterations between brain networks at the large-scale brain network level during the recovery period following stroke and explored their potential relevance to functional recovery. This investigation examines whether changes in synchronization between connected brain networks during the recovery period are related to functional recovery. In this study, we utilized resting-state fMRI data acquired at two weeks and three months post-stroke onset from ischemic stroke patients with significant motor impairment to extract large-scale brain networks. During the subacute recovery phase, we investigated whether the interplay of changes between specific resting-state functional network and their connected networks relates to differences in motor function recovery levels between patients with good and poor recovery. Methods Study participants One-hundred twenty-four ischemic stroke patients who received comprehensive inpatient rehabilitation therapy for about three weeks during the subacute phase from the database of the Department of Physical and Rehabilitation Medicine, Samsung Medical Center, were screened retrospectively. The inclusion criteria of this study were: 1) age 19 years or older at the time of stroke onset, 2) the first-ever unilateral stroke, 3) T1-weighted MRI and rs-fMRI data acquisition at two weeks and three months after stroke onset, and 4) Fugl-Meyer Assessment upper extremity (FMA-UE) score at two weeks and three months. The exclusion criteria were: 1) clinically significant neuropsychiatric comorbidities in addition to stroke, 2) metallic implants in the brain, 3) hemorrhagic stroke, 4) bilateral lesions, 5) recurrent stroke, and 6) patients with mild impairment of upper extremity (FMA-UE score > 42) 13 at two weeks after stroke onset. Eighty-three stroke patients were included in this analysis. According to the minimal clinically important difference (MCID) between FMA-UE scores at two weeks and three months (FMA-UE 3m – FMA-UE 2w > 12) 14 , participants who achieved MCID were assigned to the good recovery group ( n = 55), and the rest of the patients were assigned to the poor recovery group ( n = 28). All participants’ demographic and clinical information is listed in Table 1 . Table 1 Demographic and clinical characteristics in participants Group Good recovery ( n = 55) Poor recovery ( n = 28) p -value Age (years) Mean ± SD 55.1 ± 13.1 66.9 ± 9.9 < 0.001 Sex ( n ) Male 34 17 0.9222 Female 21 11 Lesion side ( n ) Right 30 13 0.4841 Left 25 15 Lesion location ( n ) Supratentorial 42 23 0.5458 Infratentorial 13 5 Education level ( n ) Bachelor’s degree or higher 17 9 0.9088 Less than bachelor’s degree 38 19 FMA-UE, mean ± SD Two weeks after onset 20.4 ± 12.0 12.3 ± 11.7 0.0043 Three months after onset 44.0 ± 13.5 15.4 ± 14.6 < 0.001 SD, standard deviation; FMA-UE, Fugl-Meyer assessment-upper extremity Ethical approval was obtained from the Institutional Review Board (IRB) of Kumoh National Institute of Technology, Republic of Korea. The IRB granted an exemption for informed consent as we utilized only previously collected data, and the study did not exceed minimal risk. Data acquisition The neuroimaging data were acquired using a 3T Philips ACHIEVA® MR scanner (Philips Medical Systems, Best, the Netherlands). During resting-state fMRI and T1-weighted imaging acquisition, participants were instructed to keep their eyes closed without thinking about anything and to remain motionless during scanning. Resting-state fMRI data were acquired using a T2*-weighted gradient echo-planar imaging (EPI) sequence with the following parameters: 35 axial slices, 4 mm slice thickness with no gap, matrix size of 128 × 128, voxel size of 1.72 × 1.72 mm, repetition time of 3000 ms, echo time of 35 ms, flip angle of 90°, and field of view of 220 × 220 mm. T1-weighted imaging data were acquired with the following parameters: 124 axial slices, 1.6 mm slice thickness with no gap, matrix size of 512 × 512, voxel size of 0.47 × 0.47 mm, repetition time of 13.9 ms, echo time of 6.89 ms, flip angle of 8°, and field of view of 240 × 240 mm. Data processing Resting-state networks were extracted from resting-state fMRI data using CONN (McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA, http://www.nitrc.org/projects/conn ) and CAT12 (Structural Brain Mapping Group, Jena University Hospital, Jena, Germany, http://www.neuro.uni-jena.de/cat/ ) toolboxes, based on SPM12 (Wellcome Trust Center for Neuroimaging, University College London, London, UK, https://www.fil.ion.ucl.ac.uk/spm/software/spm12 ). Preprocessing included head motion correction, slice timing correction, outlier detection for scrubbing, registration of structural images, segmentation, removal of nuisance sources, band-pass filtering, and linear detrending. Nuisance sources, such as motion artifacts and other confounding signals (six head motion parameters, six first-order temporal derivatives of the motion parameters, each of the five parameters obtained from a principal component analysis of the temporal components of white matter and ventricle signals, and scrubbing parameters for outlier volumes) were removed using linear regression. The frequency band of the band-pass filter was 0.008–0.09 Hz. Morphological parameters were computed from T1-weighted images in the subject's native space using the projection method of CAT12. This involved surface-based spherical registration based on the previously generated surfaces of the individual T1-weighted images. The fMRI data for each subject and the cortical surface parcellation atlas were then mapped to the individual surface using the weighted-mean method provided by CAT12. The parcellation atlas, which consists of 12 resting-state networks, was generated by Gordon et al. 15 . Twelve resting-state networks included visual network (VIS), parieto-occipital network (PO), dorsal somato-motor network (SMD), ventral somato-motor network (SMV), auditory network (AUD), cingulo-opercular network (CO), ventral attention/language network (VAN), salience network (SA), cingulo-parietal network (CP), dorsal attention network (DAN), fronto-parietal network (FP), and default network (DEF). Resting-state network edges were assessed by calculating Pearson’s correlation coefficients between the mean time courses of regions in the parcellation atlas. To investigate the interplay of changes between specific resting-state functional network and their connected networks relates to differences, intra- and inter-network values were extracted from the large-scale network. Intra-network values were calculated by averaging the edges within each of the 12 networks. Inter-network values were calculated by averaging the edges between each pair of 12 networks. Statistical analysis The differences in participants’ demographics and clinical characteristics between groups were investigated using an independent t-test for continuous variables and the chi-square test for categorical variables. To investigate the interrelationship between changes in intra-network and inter-network values, the partial correlation analysis between changes in intra-network values and changes in inter-network values between the corresponding network and other networks during the recovery period was performed, controlling for age, sex, lesion side, and education level. Multiple regression was additionally utilized to examine group differences in the interrelationship between changes in intra-network and inter-network, with the same covariates controlled. Results In the demographics and clinical characteristics of participants (Table 1 ), there were group differences in age and FMA-UE scores at two weeks and three months after onset. On the other hand, there were no differences in sex, lesion side, lesion location, and education level. The interrelationship between changes in intra-network and inter-network values during the recovery period was investigated. This analysis focused on the relationship between changes in a specific intra-network and the inter-network changes between that the specific network and other networks. For clarity, we use the notation NET A :(NET A - NET B ). NET A :( NET A - NET B ) means the interrelationship between change in the A intra-network and change in the inter-network between A and B networks. Figure 1 shows the significant interrelationship between changes in intra-network and inter-network values during the recovery period in the good recovery group. Altered intra-network of 12 resting-state networks was associated with altered inter-network. The inter-network associated with the change in each intra-network accounted for 49.2% of the total network in the good recovery group. Figure 2 shows the significant interrelationship between changes in intra-network and inter-network values during recovery in the poor recovery group. The inter-network associated with the change in each intra-network accounted for 31.1% of the total network. The interrelationship of intra- and inter-network alterations in the good recovery group was 18.1% higher than in the poor recovery group. In terms of sub-networks, the proportion of intra- and inter-network changes that showed an interrelationship in the good recovery group was higher than that in the poor recovery group in all sub-networks (Figs. 1 and 2 ). In the good recovery group, the network with the most significant interrelationship was the CO:(CO-AUD). Next, the interrelationship was strongest in the order AUD:(AUD-CO), SA:(SA-CO), SMV:(SMV–SMD), CO:(CO-SA), and SMD:(SMD-SMV). In the poor recovery group, the network with the most significant interrelationship was also the CO:(CO-AUD). Next, the interrelationship was strongest in the order CO:(CO-SA), AUD:(AUD-CO), SMV:(SMV–SMD), SMD:(SMD-SMV), and SA:(SA-CO). In two different recovery groups with initial motor impairment, the interrelationship of intra- and inter-network alteration was observed in networks related to motor and cognitive functions. In terms of sub-networks, sub-networks showing a difference of over 20% between groups included not only the motor network (SMD + SMV) but also the cognitive network (PO + CO + CP). The group differences in the interrelationship of intra- and inter-network alteration were additionally investigated. Out of the total, six interrelationships (SMV:(SMV-FP), SA:(SA-DEF), CP:(CP-SMD), CP:(CP-AUD), CP:(CP-CO), and CP:(CP-VAN)) exhibited group differences. The significant group difference indicates that the magnitude of inter-network changes in response to intra-network alterations is greater in the good recovery group compared to the poor recovery group. These networks mostly corresponded to cognitive function-related networks. Discussion Focusing on the interrelationship of intra- and inter-network in the large-scale networks in subacute stroke patients with motor impairment, alterations of brain networks were investigated during the subacute recovery phase. The interrelationship of intra- and inter-network alterations could be observed, and this interrelationship was more pronounced across multiple networks in the good recovery group compared to the poor recovery group. In this study, the demonstration of the interrelationship of intra- and inter-network alterations can be interpreted as indicating that changes are interacting within the connected networks beyond the local change of the specific network during the recovery period. Previous studies reported that the brain networks are structurally interconnected and functionally specialized, and therefore, damage to the brain structure by a focal lesion can diffuse through the brain network 1 , 2 , 5 , 16 . Considering the characteristics of brain networks and stroke from previous studies, such interrelationship between network changes is expected. However, there are few cases where interplay of intra- and inter-networks have been observed during the recovery period of stroke patients. The interrelationship of intra- and inter-network alterations was more prevalent across multiple networks in the good recovery group compared to the poor recovery group. According to previous neuroimaging studies, brain networks in patients with neurological disorders, including stroke, have higher random properties because damage to normal brain networks results in suboptimal rewired connections during the recovery period 17 – 19 , 10 . The plasticity of brain networks during the recovery period likely varies at the local connectivity level among individual patients. However, similar to previous studies that observed global characteristics, our study’s findings contribute to understanding one of these characteristics. Particularly in the good recovery group, this feature was more pronounced, suggesting that multiple network changes interact synergistically. This interplay between connected networks could potentially contribute to a more positive recovery. The interrelationship of intra- and inter-network alterations was observed in networks related to motor, sensory, and cognitive functions. This feature occurs not limited to specific networks but across the entire brain network. When comparing group results at the sub-network level, the good recovery group demonstrated a great interrelationship of intra- and inter-network alterations across all sub-networks compared to the poor recovery group. Particularly noteworthy, sub-networks showing a difference of over 20% between groups included not only the motor network but also the cognitive network. Also, the group differences in the interrelationship of intra- and inter-network alteration were shown in cognitive function-related networks. In two groups divided by levels of motor function recovery, notable differences between groups were observed in the interrelationship of intra- and inter-network alterations. These differences were prominent not only in the motor network but also in cognitive networks. According to previous stroke studies, cognitive function is related to motor function and recovery 20 , 21 . Previous neuroimaging studies related to motor recovery in stroke patients reported changes in connectivity strength and neural correlates in cognition-related networks and regions 11 , 22 , 7 . Frontal cortical regions are connected to secondary motor regions, posterior parietal cortex, basal ganglia, and cerebellum, and these regions are also functionally related to motor learning 23 , 24 . It is reasonable for cognitive networks to be involved in this study. The results suggest that understanding the reciprocal global influence among functionally specialized networks from the large-scale brain network perspective is crucial in the process of motor function recovery. This study has several limitations. First, it compares two groups based on their level of functional recovery, which allows for the description of group characteristics but does not identify specific biomarkers that can predict the recovery level of individual patients. Second, the study focuses on comparing groups based on the recovery levels of upper limb motor function. Due to data limitations, we were unable to conduct additional comparisons of groups based on other functional recovery levels. This study demonstrated that in the good recovery group, the interrelationship of intra- and inter-network alterations is more active than in the poor recovery group during the recovery period. Through the results of group differences in the interrelationship of intra- and inter-network alterations, this study also highlights the importance of understanding the reciprocal global influence among functionally specialized networks from a large-scale brain network perspective in the process of a specific function recovery. From this perspective, this study suggests the need for research on the diversity of intervention strategies, such as exploring multiple target areas and electrical current modes in the non-invasive brain stimulation for functional recovery in stroke patients by considering concurrent changes in connected networks. The findings could potentially contribute to the design of future studies aimed at enhancing functional recovery by integrating the initial state of the brain network with patient characteristics. Declarations Ethics Approval and Consent to Participate The study has been approved by the Institutional Review Board (IRB) of Kumoh National Institute of Technology, Republic of Korea. (202408-HR-005) and conformed to the principles of the Declaration of Helsinki. The IRB granted an exemption for informed consent because the study did not exceed minimal risk according to the retrospective study design. Competing Interests The authors have no competing interests to declare. Funding This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT). (No. RS-2023-00208884) and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (KMDF-RS-2022-00140478). Author Contribution J.L. contributed to the design and conceptualization of the study, methodology, data curation, investigation, analysis, funding acquisition, and drafting the manuscript. Y-H. K. contributed to the design and conceptualization of the study, data curation, supervision, funding acquisition, project administration, and critical revision of the manuscript and final approval. All authors have read and approved the final manuscript. Data Availability The data are available from the corresponding authors upon reasonable request. References Sporns, O., Chialvo, D. R., Kaiser, M. & Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn. Sci. 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Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Dec, 2024 Reviews received at journal 04 Dec, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviews received at journal 01 Nov, 2024 Reviewers agreed at journal 17 Oct, 2024 Reviewers invited by journal 16 Oct, 2024 Editor assigned by journal 16 Oct, 2024 Editor invited by journal 15 Oct, 2024 Submission checks completed at journal 15 Oct, 2024 First submitted to journal 14 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5258130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386517220,"identity":"6f15a306-b6ba-4a4d-b78c-da88bc8c88ab","order_by":0,"name":"Jungsoo Lee","email":"","orcid":"","institution":"Kumoh National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jungsoo","middleName":"","lastName":"Lee","suffix":""},{"id":386517221,"identity":"eff2aa9f-4433-482d-ab09-8b3aff081984","order_by":1,"name":"Yun-Hee Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYHACZoYEBhsGCR4Qm414LWmkamFgOEyCFoMbuYcNHuact5fsOWPA8KHsMDFa8pITErfdTpzN22PAOOMcUVpyjA8AtSTI8fMYMPO2Ea/lnD1Yy19itQAddoAR5DBmRmK0SJ55Y2yQuC05cWbPsYKDPefSCWvhO55jLPlzm529xJnkjQ9+lFkT1qJwAIlzAIciVCDfQJSyUTAKRsEoGNEAAMcNOwFxxjmWAAAAAElFTkSuQmCC","orcid":"","institution":"Sungkyunkwan University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yun-Hee","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-10-14 05:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5258130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5258130/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87164-8","type":"published","date":"2025-04-15T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72281383,"identity":"d72e2731-810a-42d6-8ef7-9fe86c6f21c6","added_by":"auto","created_at":"2024-12-24 16:28:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5319615,"visible":true,"origin":"","legend":"\u003cp\u003eThe interrelationship between changes in intra-network and inter-network values in the good recovery group. In the matrix, matrix(i, j) value means a t value with statistical significance of NET\u003csub\u003eAi\u003c/sub\u003e:( NET\u003csub\u003eAi\u003c/sub\u003e - NET\u003csub\u003eBj\u003c/sub\u003e) that indicates interrelationship between change in the i\u003csub\u003eth\u003c/sub\u003e row intra-network values and change in inter-network between the i\u003csub\u003eth\u003c/sub\u003e row and j\u003csub\u003eth\u003c/sub\u003e column network values. The circular graph shows the interrelationship between the intra-network with a colored circle node and the inter-network between the corresponding network and other networks. In the circular graph, edge thicknesses are proportional to the interrelationship between changes in intra-network and inter-network values. The percentage value indicates the proportion of intra- and inter-network changes that show an interrelationship within sub-networks. The figure was conducted with a MATLAB function, circularGraph, shared by Paul Kassebaum (http://www.mathworks.com/matlabcentral/fileexchange/48576-circulargraph). VIS, visual network; PO, parieto-occipital network; SMD, dorsal somato-motor network; SMV, ventral somato-motor network; AUD, auditory network; CO, cingulo-opercular network; VAN, ventral attention/language network; SA, salience network; CP, cingulo-parietal network; DAN, dorsal attention network; FP, fronto-parietal network; DEF, default network.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5258130/v1/cc8ae2712b88faa5790be6af.png"},{"id":72281384,"identity":"7d44174f-e0a7-4b03-b75c-916f725b57ab","added_by":"auto","created_at":"2024-12-24 16:28:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4447769,"visible":true,"origin":"","legend":"\u003cp\u003eThe interrelationship between changes in intra-network and inter-network values in the poor recovery group. In the matrix, matrix(i, j) value means a t value with statistical significance of NET\u003csub\u003eAi\u003c/sub\u003e:( NET\u003csub\u003eAi\u003c/sub\u003e - NET\u003csub\u003eBj\u003c/sub\u003e) that indicates interrelationship between change in the i\u003csub\u003eth\u003c/sub\u003e row intra-network values and change in inter-network between the i\u003csub\u003eth\u003c/sub\u003e row and j\u003csub\u003eth\u003c/sub\u003e column network values. The circular graph shows the interrelationship between the intra-network with a colored circle node and the inter-network between the corresponding network and other networks. In the circular graph, edge thicknesses are proportional to the interrelationship between changes in intra-network and inter-network values. The percentage value indicates the proportion of intra- and inter-network changes that show an interrelationship within sub-networks. VIS, visual network; PO, parieto-occipital network; SMD, dorsal somato-motor network; SMV, ventral somato-motor network; AUD, auditory network; CO, cingulo-opercular network; VAN, ventral attention/language network; SA, salience network; CP, cingulo-parietal network; DAN, dorsal attention network; FP, fronto-parietal network; DEF, default network.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5258130/v1/4126592f0ec7fa20f9e09d7a.png"},{"id":81051050,"identity":"feb506ee-e2b4-4168-a65c-6ea04ed1c2dd","added_by":"auto","created_at":"2025-04-21 16:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9525336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5258130/v1/c78049ed-c98f-4aeb-bbfa-e2dfab81e452.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Interrelationship of Intra- and Inter-network Alteration in Motor Recovery After Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe brain networks are functionally specialized and reciprocally connected to each other\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, brain damage caused by a stroke can be explained from the perspective of the brain network\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 brain network is disrupted caused by stroke onset, and the network is reorganized during recovery period\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Previous neuroimaging studies have reported about brain network reorganization during recovery period of stroke patients with motor impairment, such as recovery of interhemispheric interaction, increased randomness of global network topology, and alteration of local connectivity strength including functional connectivity of cognitive and sensory regions as well as motor regions\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In motor recovery-related neuroimaging biomarker studies, several cognitive networks of resting-state functional networks and functional connectivity of cognitive-sensory regions, including the motor network and connectivity of motor-related regions, played a role in prognosis prediction of motor recovery in stroke patients\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, even the recovery of a specific function in stroke patients needs to be understood from a whole-brain perspective. In other words, understanding the reciprocal global influence among functionally specialized networks from the large-scale brain network perspective is crucial in the process of motor function recovery.\u003c/p\u003e \u003cp\u003eIn this context, this study aimed to investigate additional dynamics of brain networks. We investigated the reciprocal alterations between brain networks at the large-scale brain network level during the recovery period following stroke and explored their potential relevance to functional recovery. This investigation examines whether changes in synchronization between connected brain networks during the recovery period are related to functional recovery. In this study, we utilized resting-state fMRI data acquired at two weeks and three months post-stroke onset from ischemic stroke patients with significant motor impairment to extract large-scale brain networks. During the subacute recovery phase, we investigated whether the interplay of changes between specific resting-state functional network and their connected networks relates to differences in motor function recovery levels between patients with good and poor recovery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eOne-hundred twenty-four ischemic stroke patients who received comprehensive inpatient rehabilitation therapy for about three weeks during the subacute phase from the database of the Department of Physical and Rehabilitation Medicine, Samsung Medical Center, were screened retrospectively. The inclusion criteria of this study were: 1) age 19 years or older at the time of stroke onset, 2) the first-ever unilateral stroke, 3) T1-weighted MRI and rs-fMRI data acquisition at two weeks and three months after stroke onset, and 4) Fugl-Meyer Assessment upper extremity (FMA-UE) score at two weeks and three months. The exclusion criteria were: 1) clinically significant neuropsychiatric comorbidities in addition to stroke, 2) metallic implants in the brain, 3) hemorrhagic stroke, 4) bilateral lesions, 5) recurrent stroke, and 6) patients with mild impairment of upper extremity (FMA-UE score\u0026thinsp;\u0026gt;\u0026thinsp;42)\u003csup\u003e13\u003c/sup\u003e at two weeks after stroke onset. Eighty-three stroke patients were included in this analysis. According to the minimal clinically important difference (MCID) between FMA-UE scores at two weeks and three months (FMA-UE\u003csub\u003e3m\u003c/sub\u003e \u0026ndash; FMA-UE\u003csub\u003e2w\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;12)\u003csup\u003e14\u003c/sup\u003e, participants who achieved MCID were assigned to the good recovery group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55), and the rest of the patients were assigned to the poor recovery group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28). All participants\u0026rsquo; demographic and clinical information is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics in participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood recovery\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor recovery\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion side (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion location (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupratentorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.5458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfratentorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than bachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMA-UE, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo weeks after onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree months after onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSD, standard deviation; FMA-UE, Fugl-Meyer assessment-upper extremity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Institutional Review Board (IRB) of Kumoh National Institute of Technology, Republic of Korea. The IRB granted an exemption for informed consent as we utilized only previously collected data, and the study did not exceed minimal risk.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData acquisition\u003c/h3\u003e\n\u003cp\u003eThe neuroimaging data were acquired using a 3T Philips ACHIEVA\u0026reg; MR scanner (Philips Medical Systems, Best, the Netherlands). During resting-state fMRI and T1-weighted imaging acquisition, participants were instructed to keep their eyes closed without thinking about anything and to remain motionless during scanning. Resting-state fMRI data were acquired using a T2*-weighted gradient echo-planar imaging (EPI) sequence with the following parameters: 35 axial slices, 4 mm slice thickness with no gap, matrix size of 128 \u0026times; 128, voxel size of 1.72 \u0026times; 1.72 mm, repetition time of 3000 ms, echo time of 35 ms, flip angle of 90\u0026deg;, and field of view of 220 \u0026times; 220 mm. T1-weighted imaging data were acquired with the following parameters: 124 axial slices, 1.6 mm slice thickness with no gap, matrix size of 512 \u0026times; 512, voxel size of 0.47 \u0026times; 0.47 mm, repetition time of 13.9 ms, echo time of 6.89 ms, flip angle of 8\u0026deg;, and field of view of 240 \u0026times; 240 mm.\u003c/p\u003e\n\u003ch3\u003eData processing\u003c/h3\u003e\n\u003cp\u003eResting-state networks were extracted from resting-state fMRI data using CONN (McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/conn\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/conn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CAT12 (Structural Brain Mapping Group, Jena University Hospital, Jena, Germany, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) toolboxes, based on SPM12 (Wellcome Trust Center for Neuroimaging, University College London, London, UK, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreprocessing included head motion correction, slice timing correction, outlier detection for scrubbing, registration of structural images, segmentation, removal of nuisance sources, band-pass filtering, and linear detrending. Nuisance sources, such as motion artifacts and other confounding signals (six head motion parameters, six first-order temporal derivatives of the motion parameters, each of the five parameters obtained from a principal component analysis of the temporal components of white matter and ventricle signals, and scrubbing parameters for outlier volumes) were removed using linear regression. The frequency band of the band-pass filter was 0.008\u0026ndash;0.09 Hz.\u003c/p\u003e \u003cp\u003eMorphological parameters were computed from T1-weighted images in the subject's native space using the projection method of CAT12. This involved surface-based spherical registration based on the previously generated surfaces of the individual T1-weighted images. The fMRI data for each subject and the cortical surface parcellation atlas were then mapped to the individual surface using the weighted-mean method provided by CAT12. The parcellation atlas, which consists of 12 resting-state networks, was generated by Gordon et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Twelve resting-state networks included visual network (VIS), parieto-occipital network (PO), dorsal somato-motor network (SMD), ventral somato-motor network (SMV), auditory network (AUD), cingulo-opercular network (CO), ventral attention/language network (VAN), salience network (SA), cingulo-parietal network (CP), dorsal attention network (DAN), fronto-parietal network (FP), and default network (DEF). Resting-state network edges were assessed by calculating Pearson\u0026rsquo;s correlation coefficients between the mean time courses of regions in the parcellation atlas. To investigate the interplay of changes between specific resting-state functional network and their connected networks relates to differences, intra- and inter-network values were extracted from the large-scale network. Intra-network values were calculated by averaging the edges within each of the 12 networks. Inter-network values were calculated by averaging the edges between each pair of 12 networks.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe differences in participants\u0026rsquo; demographics and clinical characteristics between groups were investigated using an independent t-test for continuous variables and the chi-square test for categorical variables. To investigate the interrelationship between changes in intra-network and inter-network values, the partial correlation analysis between changes in intra-network values and changes in inter-network values between the corresponding network and other networks during the recovery period was performed, controlling for age, sex, lesion side, and education level. Multiple regression was additionally utilized to examine group differences in the interrelationship between changes in intra-network and inter-network, with the same covariates controlled.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn the demographics and clinical characteristics of participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), there were group differences in age and FMA-UE scores at two weeks and three months after onset. On the other hand, there were no differences in sex, lesion side, lesion location, and education level.\u003c/p\u003e \u003cp\u003eThe interrelationship between changes in intra-network and inter-network values during the recovery period was investigated. This analysis focused on the relationship between changes in a specific intra-network and the inter-network changes between that the specific network and other networks. For clarity, we use the notation NET\u003csub\u003eA\u003c/sub\u003e:(NET\u003csub\u003eA\u003c/sub\u003e - NET\u003csub\u003eB\u003c/sub\u003e). NET\u003csub\u003eA\u003c/sub\u003e:( NET\u003csub\u003eA\u003c/sub\u003e - NET\u003csub\u003eB\u003c/sub\u003e) means the interrelationship between change in the A intra-network and change in the inter-network between A and B networks. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the significant interrelationship between changes in intra-network and inter-network values during the recovery period in the good recovery group. Altered intra-network of 12 resting-state networks was associated with altered inter-network. The inter-network associated with the change in each intra-network accounted for 49.2% of the total network in the good recovery group. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the significant interrelationship between changes in intra-network and inter-network values during recovery in the poor recovery group. The inter-network associated with the change in each intra-network accounted for 31.1% of the total network. The interrelationship of intra- and inter-network alterations in the good recovery group was 18.1% higher than in the poor recovery group. In terms of sub-networks, the proportion of intra- and inter-network changes that showed an interrelationship in the good recovery group was higher than that in the poor recovery group in all sub-networks (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the good recovery group, the network with the most significant interrelationship was the CO:(CO-AUD). Next, the interrelationship was strongest in the order AUD:(AUD-CO), SA:(SA-CO), SMV:(SMV\u0026ndash;SMD), CO:(CO-SA), and SMD:(SMD-SMV). In the poor recovery group, the network with the most significant interrelationship was also the CO:(CO-AUD). Next, the interrelationship was strongest in the order CO:(CO-SA), AUD:(AUD-CO), SMV:(SMV\u0026ndash;SMD), SMD:(SMD-SMV), and SA:(SA-CO). In two different recovery groups with initial motor impairment, the interrelationship of intra- and inter-network alteration was observed in networks related to motor and cognitive functions. In terms of sub-networks, sub-networks showing a difference of over 20% between groups included not only the motor network (SMD\u0026thinsp;+\u0026thinsp;SMV) but also the cognitive network (PO\u0026thinsp;+\u0026thinsp;CO\u0026thinsp;+\u0026thinsp;CP).\u003c/p\u003e \u003cp\u003eThe group differences in the interrelationship of intra- and inter-network alteration were additionally investigated. Out of the total, six interrelationships (SMV:(SMV-FP), SA:(SA-DEF), CP:(CP-SMD), CP:(CP-AUD), CP:(CP-CO), and CP:(CP-VAN)) exhibited group differences. The significant group difference indicates that the magnitude of inter-network changes in response to intra-network alterations is greater in the good recovery group compared to the poor recovery group. These networks mostly corresponded to cognitive function-related networks.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFocusing on the interrelationship of intra- and inter-network in the large-scale networks in subacute stroke patients with motor impairment, alterations of brain networks were investigated during the subacute recovery phase. The interrelationship of intra- and inter-network alterations could be observed, and this interrelationship was more pronounced across multiple networks in the good recovery group compared to the poor recovery group.\u003c/p\u003e \u003cp\u003eIn this study, the demonstration of the interrelationship of intra- and inter-network alterations can be interpreted as indicating that changes are interacting within the connected networks beyond the local change of the specific network during the recovery period. Previous studies reported that the brain networks are structurally interconnected and functionally specialized, and therefore, damage to the brain structure by a focal lesion can diffuse through the brain network\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Considering the characteristics of brain networks and stroke from previous studies, such interrelationship between network changes is expected. However, there are few cases where interplay of intra- and inter-networks have been observed during the recovery period of stroke patients. The interrelationship of intra- and inter-network alterations was more prevalent across multiple networks in the good recovery group compared to the poor recovery group. According to previous neuroimaging studies, brain networks in patients with neurological disorders, including stroke, have higher random properties because damage to normal brain networks results in suboptimal rewired connections during the recovery period\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The plasticity of brain networks during the recovery period likely varies at the local connectivity level among individual patients. However, similar to previous studies that observed global characteristics, our study\u0026rsquo;s findings contribute to understanding one of these characteristics. Particularly in the good recovery group, this feature was more pronounced, suggesting that multiple network changes interact synergistically. This interplay between connected networks could potentially contribute to a more positive recovery.\u003c/p\u003e \u003cp\u003eThe interrelationship of intra- and inter-network alterations was observed in networks related to motor, sensory, and cognitive functions. This feature occurs not limited to specific networks but across the entire brain network. When comparing group results at the sub-network level, the good recovery group demonstrated a great interrelationship of intra- and inter-network alterations across all sub-networks compared to the poor recovery group. Particularly noteworthy, sub-networks showing a difference of over 20% between groups included not only the motor network but also the cognitive network. Also, the group differences in the interrelationship of intra- and inter-network alteration were shown in cognitive function-related networks. In two groups divided by levels of motor function recovery, notable differences between groups were observed in the interrelationship of intra- and inter-network alterations. These differences were prominent not only in the motor network but also in cognitive networks. According to previous stroke studies, cognitive function is related to motor function and recovery\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Previous neuroimaging studies related to motor recovery in stroke patients reported changes in connectivity strength and neural correlates in cognition-related networks and regions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Frontal cortical regions are connected to secondary motor regions, posterior parietal cortex, basal ganglia, and cerebellum, and these regions are also functionally related to motor learning\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It is reasonable for cognitive networks to be involved in this study. The results suggest that understanding the reciprocal global influence among functionally specialized networks from the large-scale brain network perspective is crucial in the process of motor function recovery.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it compares two groups based on their level of functional recovery, which allows for the description of group characteristics but does not identify specific biomarkers that can predict the recovery level of individual patients. Second, the study focuses on comparing groups based on the recovery levels of upper limb motor function. Due to data limitations, we were unable to conduct additional comparisons of groups based on other functional recovery levels.\u003c/p\u003e \u003cp\u003eThis study demonstrated that in the good recovery group, the interrelationship of intra- and inter-network alterations is more active than in the poor recovery group during the recovery period. Through the results of group differences in the interrelationship of intra- and inter-network alterations, this study also highlights the importance of understanding the reciprocal global influence among functionally specialized networks from a large-scale brain network perspective in the process of a specific function recovery. From this perspective, this study suggests the need for research on the diversity of intervention strategies, such as exploring multiple target areas and electrical current modes in the non-invasive brain stimulation for functional recovery in stroke patients by considering concurrent changes in connected networks. The findings could potentially contribute to the design of future studies aimed at enhancing functional recovery by integrating the initial state of the brain network with patient characteristics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e \u003cp\u003e The study has been approved by the Institutional Review Board (IRB) of Kumoh National Institute of Technology, Republic of Korea. (202408-HR-005) and conformed to the principles of the Declaration of Helsinki. The IRB granted an exemption for informed consent because the study did not exceed minimal risk according to the retrospective study design.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT). (No. RS-2023-00208884) and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health \u0026amp; Welfare, the Ministry of Food and Drug Safety) (KMDF-RS-2022-00140478).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.L. contributed to the design and conceptualization of the study, methodology, data curation, investigation, analysis, funding acquisition, and drafting the manuscript. Y-H. K. contributed to the design and conceptualization of the study, data curation, supervision, funding acquisition, project administration, and critical revision of the manuscript and final approval. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSporns, O., Chialvo, D. R., Kaiser, M. \u0026amp; Hilgetag, C. C. Organization, development and function of complex brain networks. \u003cem\u003eTrends Cogn. Sci. (Regul Ed)\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 418\u0026ndash;425 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWig, G. S. Segregated systems of human brain networks. \u003cem\u003eTrends Cogn. Sci. (Regul Ed)\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e, 981\u0026ndash;996 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoney, C. J. \u0026amp; Sporns, O. Dynamical consequences of lesions in cortical networks. \u003cem\u003eHum. 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Brain Mapp.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 270\u0026ndash;286 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5258130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5258130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain networks demonstrate various dynamics during recovery after stroke. Recovery of interhemispheric interaction and balance and the occurrence of network reorganization have also been reported during stroke recovery. This study aimed to investigate the dynamics of brain networks after stroke. At the large-scale brain network level, this study focuses on determining whether changes in brain networks during the functional recovery period following a stroke, along with concurrent changes in connected networks, facilitate functional recovery. Eighty-three subacute ischemic stroke patients participated. All patients underwent resting-state functional MRI and motor function assessments at two weeks and three months after stroke onset. Intra- and inter-networks from 12 resting-state networks were extracted from functional MRI data. The interrelationship between changes in intra-network values and changes in inter-network values between the corresponding network and other networks during the recovery period was investigated. The interrelationship between the good and poor recovery subgroups was compared. The interrelationship of intra- and inter-network alterations could be observed in both groups. This interrelationship was more pronounced across multiple networks in the good recovery group compared to the poor recovery group. The group differences in the interrelationship of intra- and inter-network alteration were shown in diverse sub-networks, including cognitive and sensory networks as well as motor networks. This study suggests the importance of adopting a plasticity-oriented perspective focused on changes in intra- and inter-network connectivity throughout the entire brain rather than solely emphasizing the motor function area for predicting and treating motor function recovery.\u003c/p\u003e","manuscriptTitle":"Exploring the Interrelationship of Intra- and Inter-network Alteration in Motor Recovery After Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 16:27:56","doi":"10.21203/rs.3.rs-5258130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-05T06:58:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-04T19:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53602582297519101423724808891549759613","date":"2024-11-24T20:34:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"778217314666852961971430439663316880","date":"2024-11-23T14:03:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T15:55:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97681708648814721214055780813195153367","date":"2024-10-17T15:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-16T05:55:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-16T05:54:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-15T17:28:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-15T04:17:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-14T05:03:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17564685-2e4c-4498-a2b0-1fa4ab7b6ac9","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41184455,"name":"Biological sciences/Neuroscience"},{"id":41184458,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-04-21T16:05:28+00:00","versionOfRecord":{"articleIdentity":"rs-5258130","link":"https://doi.org/10.1038/s41598-025-87164-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-15 15:57:24","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2024-12-24 16:27:56","video":"","vorDoi":"10.1038/s41598-025-87164-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87164-8","workflowStages":[]},"version":"v1","identity":"rs-5258130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5258130","identity":"rs-5258130","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}