{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(11)"],"submitter":["Zhou R"],"pubmed_abstract":["<h4>Objective</h4>A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored.<h4>Methods</h4>We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector "],"journal":["Aging"],"pagination":["10004-10015"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11210244"],"repository":["biostudies-literature"],"pubmed_title":["Altered intra- and inter-network connectivity in autism spectrum disorder."],"pmcid":["PMC11210244"],"pubmed_authors":["Li W","Sun M","Ruan Y","Zhou R","Sun C","Gao X"],"additional_accession":[]},"is_claimable":false,"name":"Altered intra- and inter-network connectivity in autism spectrum disorder.","description":"<h4>Objective</h4>A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored.<h4>Methods</h4>We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2025-04-21T20:18:06.18Z","creation":"2025-04-05T17:57:09.742Z"},"accession":"S-EPMC11210244","cross_references":{"pubmed":["38862259"],"doi":["10.18632/aging.205913"]}}