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1
ERP evidence of semantic processing in children with ASD.
DiStefano, Charlotte; Senturk, Damla; Jeste, Shafali Spurling. - : eScholarship, University of California, 2019
Abstract: 25% of children with autism spectrum disorder (ASD) remain minimally verbal (MV), despite intervention. Electroencephalography can reveal neural mechanisms underlying language impairment in ASD, potentially improving our ability to predict language outcomes and target interventions. Verbal (V) and MV children with ASD, along with an age-matched typically developing (TD) group participated in a semantic congruence ERP paradigm, during which pictures were displayed followed by the expected or unexpected word. An N400 effect was evident in all groups, with a shorter latency in the TD group. A late negative component (LNC) also differentiated conditions, with a group by condition by region interaction. Post hoc analyses revealed that the LNC was present across multiple regions in the TD group, in the mid-frontal region in MVASD, and not present in the VASD group. Cluster analysis identified subgroups within the ASD participants. Two subgroups showed markedly atypical patterns of processing, one with reversed but robust differentiation of conditions, and the other with initially reversed followed by typical differentiation. Findings indicate that children with ASD, including those with minimal language, showed EEG evidence of semantic processing, but it was characterized by delayed speed of processing and limited integration with mental representations.
Keyword: ASD; Autism; Autism Spectrum Disorder; Child; Clinical Sciences; Cognitive Sciences; EEG; Electroencephalography; ERP; Female; Humans; Language; Male; Neurosciences; Semantics
URL: https://escholarship.org/uc/item/8d84c1b4
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2
Peak alpha frequency is a neural marker of cognitive function across the autism spectrum.
In: The European journal of neuroscience, vol 47, iss 6 (2018)
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3
Peak alpha frequency is a neural marker of cognitive function across the autism spectrum
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4
Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.
In: Developmental science, vol 18, iss 1 (2015)
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