{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lombardo MV"],"funding":["Medical Research Council","National Institute for Health Research (NIHR)","Wellcome Trust"],"pagination":["35333"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5067562"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6"],"pubmed_abstract":["Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectiv"],"journal":["Scientific reports"],"pubmed_title":["Unsupervised data-driven stratification of mentalizing heterogeneity in autism."],"pmcid":["PMC5067562"],"funding_grant_id":["091774/Z/10/Z","G0600977","RP-PG-0606-1045","NF-SI-0515-10097","MR/N026063/1","NF-SI-0513-10051","G9817803B","G9817803","G1000183","G1000183B","093875"],"pubmed_authors":["MRC AIMS Consortium","Spain D","Murphy CM","Ellie Wilson C","Daly EM","Chakrabarti B","Bullmore ET","Jones DK","Craig MC","Carrington S","Holt RJ","Allison C","Lombardo MV","Jezzard P","Williams SC","Ruigrok AN","Bailey AJ","Baron-Cohen S","Suckling J","Stewart R","Auyeung B","Bolton PF","Deoni SC","Wheelwright SJ","Ecker C","Mullins D","Pasco G","Smith P","Sadek SA","Madden A","Catani M","Lai MC","Happe F","Murphy DG","Johnston P","Henty J"],"additional_accession":[]},"is_claimable":false,"name":"Unsupervised data-driven stratification of mentalizing heterogeneity in autism.","description":"Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectiv","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Oct","modification":"2025-04-27T01:56:19.106Z","creation":"2019-03-27T02:26:54Z"},"accession":"S-EPMC5067562","cross_references":{"pubmed":["27752054"],"doi":["10.1038/srep35333"]}}