Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.

ABSTRACT:

Background

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.

Methods

Using EEG data collected at two different ages during a passive phoneme task in infants with hig

SUBMITTER: Peck FC 

PROVIDER: S-EPMC8903497 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature