<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Peck FC</submitter><funding>Princeton University</funding><funding>Simons Foundation</funding><funding>NICHD NIH HHS</funding><funding>NIDCD NIH HHS</funding><funding>NIMH NIH HHS</funding><funding>Autism Speaks</funding><funding>National Institute on Deafness and Other Communication Disorders</funding><funding>National Institute of Mental Health</funding><pagination>57</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8903497</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>13(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis.&lt;h4>Methods&lt;/h4>Using EEG data collected at two different ages during a passive phoneme task in infants with hig</pubmed_abstract><journal>Journal of neurodevelopmental disorders</journal><pubmed_title>Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.</pubmed_title><pmcid>PMC8903497</pmcid><funding_grant_id>R01-DC010290</funding_grant_id><funding_grant_id>1T32MH112510</funding_grant_id><funding_grant_id>1323</funding_grant_id><funding_grant_id>R21-DC08637</funding_grant_id><funding_grant_id>R21 DC008637</funding_grant_id><funding_grant_id>T32 MH112510</funding_grant_id><funding_grant_id>1K23DC017983</funding_grant_id><funding_grant_id>P50 HD105351</funding_grant_id><funding_grant_id>K23 DC017983</funding_grant_id><funding_grant_id>None</funding_grant_id><funding_grant_id>R01 DC010290</funding_grant_id><funding_grant_id>137186</funding_grant_id><pubmed_authors>Peck FC</pubmed_authors><pubmed_authors>Gabard-Durnam LJ</pubmed_authors><pubmed_authors>Wilkinson CL</pubmed_authors><pubmed_authors>Bosl W</pubmed_authors><pubmed_authors>Tager-Flusberg H</pubmed_authors><pubmed_authors>Nelson CA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.</name><description>&lt;h4>Background&lt;/h4>Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis.&lt;h4>Methods&lt;/h4>Using EEG data collected at two different ages during a passive phoneme task in infants with hig</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Nov</publication><modification>2025-04-06T00:32:23.491Z</modification><creation>2025-02-19T03:58:21.34Z</creation></dates><accession>S-EPMC8903497</accession><cross_references><pubmed>34847887</pubmed><doi>10.1186/s11689-021-09405-x</doi></cross_references></HashMap>