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Currently, the whole\u2010brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation\u2010based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high\u2010resolution network (~50,000\u2010nodes overall) to identify changes at the local level (\n                    <jats:italic>within<\/jats:italic>\n                    brain regions) and test its relation with duration and surgical outcome. Patients with TLE (\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a033) and age\u2010, sex\u2010matched healthy subjects (\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a036) underwent high\u2010resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan\u2013Killany atlas. The connectivity\n                    <jats:italic>within<\/jats:italic>\n                    regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/nicara.eu\">https:\/\/nicara.eu<\/jats:ext-link>\n                    ). Lower clustering coefficient and higher edge density were found for local connectivity\n                    <jats:italic>within<\/jats:italic>\n                    regions in patients, but were absent for the global network\n                    <jats:italic>between<\/jats:italic>\n                    regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity\n                    <jats:italic>within<\/jats:italic>\n                    regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.\n                  <\/jats:p>","DOI":"10.1002\/hbm.25464","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T13:07:01Z","timestamp":1620738421000},"page":"3777-3791","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Connectivity within regions characterizes epilepsy duration and treatment outcome"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4062-9009","authenticated-orcid":false,"given":"Xue","family":"Chen","sequence":"first","affiliation":[{"name":"College of Control Science and Engineering China University of Petroleum (East China)  Qingdao China"},{"name":"School of Computing Newcastle University  Newcastle upon Tyne UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9910-7884","authenticated-orcid":false,"given":"Yanjiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering China University of Petroleum (East China)  Qingdao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian J.","family":"Kopetzky","sequence":"additional","affiliation":[{"name":"Biomax Informatics AG, Brain Science  Planegg Germany"},{"name":"TUM School of Life Sciences Weihenstephan Technical University of Munich  Freising Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Butz\u2010Ostendorf","sequence":"additional","affiliation":[{"name":"Biomax Informatics AG, Brain Science  Planegg 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