PMID- 36855286 OWN - NLM STAT- MEDLINE DCOM- 20230511 LR - 20240504 IS - 1528-1167 (Electronic) IS - 0013-9580 (Linking) VI - 64 IP - 5 DP - 2023 May TI - Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy. PG - 1305-1317 LID - 10.1111/epi.17565 [doi] AB - OBJECTIVE: 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). METHODS: In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain 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 "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction. RESULTS: Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation 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. SIGNIFICANCE: Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation. CI - (c) 2023 International League Against Epilepsy. FAU - Lucas, Alfredo AU - Lucas A AUID- ORCID: 0000-0001-9439-735X AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Cornblath, Eli J AU - Cornblath EJ AUID- ORCID: 0000-0002-2619-8778 AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Sinha, Nishant AU - Sinha N AUID- ORCID: 0000-0002-2090-4889 AD - Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Hadar, Peter AU - Hadar P AUID- ORCID: 0000-0002-1681-4717 AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Caciagli, Lorenzo AU - Caciagli L AD - Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Keller, Simon S AU - Keller SS AUID- ORCID: 0000-0001-5247-9795 AD - Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. FAU - Bonilha, Leonardo AU - Bonilha L AD - Department of Neurology, Emory University, Atlanta, Georgia, USA. FAU - Shinohara, Russell T AU - Shinohara RT AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Stein, Joel M AU - Stein JM AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Das, Sandhitsu AU - Das S AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. FAU - Gleichgerrcht, Ezequiel AU - Gleichgerrcht E AUID- ORCID: 0000-0002-4212-4146 AD - Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA. FAU - Davis, Kathryn A AU - Davis KA AD - Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. AD - Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. LA - eng GR - MR/S00355X/1/MRC_/Medical Research Council/United Kingdom GR - R01 MH123550/MH/NIMH NIH HHS/United States PT - Journal Article PT - Multicenter Study PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't DEP - 20230320 PL - United States TA - Epilepsia JT - Epilepsia JID - 2983306R SB - IM MH - Humans MH - *Epilepsy, Temporal Lobe MH - Magnetic Resonance Imaging MH - Brain MH - Brain Mapping/methods MH - Electrocorticography OTO - NOTNLM OT - biomarker OT - data set OT - integration OT - multicenter OT - network OT - neuroimaging EDAT- 2023/03/02 06:00 MHDA- 2023/05/11 06:42 CRDT- 2023/03/01 01:24 PHST- 2023/02/26 00:00 [revised] PHST- 2022/10/04 00:00 [received] PHST- 2023/02/27 00:00 [accepted] PHST- 2023/05/11 06:42 [medline] PHST- 2023/03/02 06:00 [pubmed] PHST- 2023/03/01 01:24 [entrez] AID - 10.1111/epi.17565 [doi] PST - ppublish SO - Epilepsia. 2023 May;64(5):1305-1317. doi: 10.1111/epi.17565. Epub 2023 Mar 20.