{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:57:28Z","timestamp":1787497048770,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100001454","name":"American Epilepsy Society","doi-asserted-by":"publisher","award":["953257"],"award-info":[{"award-number":["953257"]}],"id":[{"id":"10.13039\/100001454","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS116504"],"award-info":[{"award-number":["R01NS116504"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, using a multicenter resting state functional magnetic resonance imaging (rs\u2010fMRI) data set, we constructed whole\u2010brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting\u2010state, whole\u2010brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the \u201cintegration\u2010segregation axis,\u201d by combining whole\u2010brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)\u2013based dimensionality reduction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared to UTLE, BiTLE had decreased global efficiency (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a0.031), and decreased whole brain average participation coefficient across a range of network densities (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a0.019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a0.020). Differences in network properties separate BiTLE and UTLE along the integration\u2010segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration\u2010segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>Increased interictal whole\u2010brain network segregation, as measured by rs\u2010fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non\u2010invasively identifying this patient population prior to intracranial electroencephalography or device implantation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/epi.17565","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T01:14:48Z","timestamp":1677633288000},"page":"1305-1317","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9439-735X","authenticated-orcid":false,"given":"Alfredo","family":"Lucas","sequence":"first","affiliation":[{"name":"Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Bioengineering University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2619-8778","authenticated-orcid":false,"given":"Eli 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