{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jing R"],"funding":["Natural Science Foundation of Beijing Municipality","National High Technology Research and Development Program of China","R&D  Program of Beijing Municipal Education Commission","R&D Program of Beijing Municipal Education Commission","R&D Program of Beijing Municipal Education Commission","National Natural Science Foundation of China","Beijing Natural Science Foundation"],"pagination":["2744-2754"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9638404"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(6)"],"pubmed_abstract":["Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method w"],"journal":["Brain imaging and behavior"],"pubmed_title":["Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression."],"pmcid":["PMC9638404"],"funding_grant_id":["7212141","KM202011232008","KM202011232007","2019YFA0706201","4214080","7214299","KM202211232018","4214081","82101566"],"pubmed_authors":["Li H","Yu M","Huo Y","Si J","Lin X","Li P","Jing R","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.","description":"Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method w","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-03T21:30:58.864Z","creation":"2025-04-03T21:30:58.864Z"},"accession":"S-EPMC9638404","cross_references":{"pubmed":["36333522"],"doi":["10.1007/s11682-022-00739-1"]}}