Atypical EEG in Autism Spectrum Disorder: Comparing a Dimensional and a Categorical Approach

Poster Presentation
Thursday, May 2, 2019: 5:30 PM-7:00 PM
Room: 710 (Palais des congres de Montreal)
E. Milne1, R. Gomez2, A. Giannadou2 and M. Jones1, (1)The University of Sheffield, Sheffield, United Kingdom, (2)University of Sheffield, Sheffield, United Kingdom
Background: While many studies have found group differences in neural dynamics between people with and without autism spectrum disorder (ASD), the extent to which variation in neural dynamics is related to variation in the autism phenotype across the population is not known. Objectives: The aim of this study was to establish whether neural variables, namely inter-trial phase coherence (ITC) and multiscale entropy (MSE), that have previously been shown to differ at a group level between people with and without ASD, also correlate with individual differences in the autism phenotype across the population. ITC and MSE were selected for analysis as they reflect different aspects of neural information processing, including consistency of the neural response (phase coherence) and complexity of the EEG signal (MSE). Furthermore, both variables have been suggested by previous studies to represent either an endophenotype or a biomarker for ASD. Methods: Data were obtained from ninety-nine adults, thirty-eight of whom had an ASD diagnosis and sixty-one of whom did not. Phenotypic information was obtained from the Social Responsiveness Scale (Revised), the Repetitive Behavior Questionnaire, the WHO Adult ADHD Self-Report Scale Screener and the Beck Anxiety Inventory (Trait version). Neural dynamics were computed from EEG data acquired during visual stimulation (presentation of 200 black and white checkerboard stimuli) and during a period of eyes-closed rest. Results: The phenotypic questionnaires revealed individual differences in autistic traits across the population, reflecting many previous reports that traits and behaviours associated with the autism phenotype are continuously distributed. Individual differences in both ITC and MSE were also found. However, non-parametric correlation analyses indicated that none of the phenotypic variables were related to either ITC or MSE: rho <.200; p >.05, Bayes Factors (BF01) evaluating the strength of evidence for the null hypothesis > 3. Despite finding no relationship between neural dynamics and the autism phenotype, group-level statistics showed that both ITC and MSE were more likely to be reduced in people with ASD than in those without. Discriminant function analyses indicated that associations between group (with ASD, without ASD) and both ITC and MSE were significant, ITC: χ²(1) = 7.99, Wilks Λ= .825, p =.005; MSE:, χ²(1) = 4.34, Wilks Λ= .90, p =.037, however not all participants with ASD showed reduced MSE or reduced ITC. Conclusions: These data suggest that there are likely to be multiple neural profiles underpinning ASD, and highlight that while the autism phenotype is continuously distributed across the population, this distribution is not underpinned by individual differences in these measures of neural dynamics.
See more of: Neuroimaging
See more of: Neuroimaging