<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Lombardo MV</submitter><funding>Medical Research Council</funding><funding>National Institute for Health Research (NIHR)</funding><funding>Wellcome Trust</funding><pagination>35333</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC5067562</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>6</volume><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</pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Unsupervised data-driven stratification of mentalizing heterogeneity in autism.</pubmed_title><pmcid>PMC5067562</pmcid><funding_grant_id>091774/Z/10/Z</funding_grant_id><funding_grant_id>G0600977</funding_grant_id><funding_grant_id>RP-PG-0606-1045</funding_grant_id><funding_grant_id>NF-SI-0515-10097</funding_grant_id><funding_grant_id>MR/N026063/1</funding_grant_id><funding_grant_id>NF-SI-0513-10051</funding_grant_id><funding_grant_id>G9817803B</funding_grant_id><funding_grant_id>G9817803</funding_grant_id><funding_grant_id>G1000183</funding_grant_id><funding_grant_id>G1000183B</funding_grant_id><funding_grant_id>093875</funding_grant_id><pubmed_authors>MRC AIMS Consortium</pubmed_authors><pubmed_authors>Spain D</pubmed_authors><pubmed_authors>Murphy CM</pubmed_authors><pubmed_authors>Ellie Wilson C</pubmed_authors><pubmed_authors>Daly EM</pubmed_authors><pubmed_authors>Chakrabarti B</pubmed_authors><pubmed_authors>Bullmore ET</pubmed_authors><pubmed_authors>Jones DK</pubmed_authors><pubmed_authors>Craig MC</pubmed_authors><pubmed_authors>Carrington S</pubmed_authors><pubmed_authors>Holt RJ</pubmed_authors><pubmed_authors>Allison C</pubmed_authors><pubmed_authors>Lombardo MV</pubmed_authors><pubmed_authors>Jezzard P</pubmed_authors><pubmed_authors>Williams SC</pubmed_authors><pubmed_authors>Ruigrok AN</pubmed_authors><pubmed_authors>Bailey AJ</pubmed_authors><pubmed_authors>Baron-Cohen S</pubmed_authors><pubmed_authors>Suckling J</pubmed_authors><pubmed_authors>Stewart R</pubmed_authors><pubmed_authors>Auyeung B</pubmed_authors><pubmed_authors>Bolton PF</pubmed_authors><pubmed_authors>Deoni SC</pubmed_authors><pubmed_authors>Wheelwright SJ</pubmed_authors><pubmed_authors>Ecker C</pubmed_authors><pubmed_authors>Mullins D</pubmed_authors><pubmed_authors>Pasco G</pubmed_authors><pubmed_authors>Smith P</pubmed_authors><pubmed_authors>Sadek SA</pubmed_authors><pubmed_authors>Madden A</pubmed_authors><pubmed_authors>Catani M</pubmed_authors><pubmed_authors>Lai MC</pubmed_authors><pubmed_authors>Happe F</pubmed_authors><pubmed_authors>Murphy DG</pubmed_authors><pubmed_authors>Johnston P</pubmed_authors><pubmed_authors>Henty J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Unsupervised data-driven stratification of mentalizing heterogeneity in autism.</name><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</description><dates><release>2016-01-01T00:00:00Z</release><publication>2016 Oct</publication><modification>2025-04-27T01:56:19.106Z</modification><creation>2019-03-27T02:26:54Z</creation></dates><accession>S-EPMC5067562</accession><cross_references><pubmed>27752054</pubmed><doi>10.1038/srep35333</doi></cross_references></HashMap>