{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang C"],"funding":["Beijing Nova Program","National Natural Science Foundation of China","China Postdoctoral Science Foundation","Jiangsu Province Social Development Project","Beijing Municipal Commission of Education","Natural Science Foundation of Beijing","Beijing Nova Program (CN)"],"pagination":["1356"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11902853"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(5)"],"pubmed_abstract":["Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (<i>p</i> < 0.05). Comp"],"journal":["Sensors (Basel, Switzerland)"],"pubmed_title":["Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study."],"pmcid":["PMC11902853"],"funding_grant_id":["KM201710005013","XX2016120","20230484469","31640035","82172022","2022M720326","4162008","BE2023795"],"pubmed_authors":["Li G","Hu W","Wu T","Zhang C","Yang C","Wu Y"],"additional_accession":[]},"is_claimable":false,"name":"Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study.","description":"Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (<i>p</i> < 0.05). Comp","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-04T01:29:06.605Z","creation":"2025-04-04T01:29:06.605Z"},"accession":"S-EPMC11902853","cross_references":{"pubmed":["40096174"],"doi":["10.3390/s25051356"]}}