Characterizing Resting-State Brain Dynamics in Individuals with and without Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by impairments in social communication as well as restricted, repetitive and stereotyped behaviours. ASD is a highly heterogeneous disorder, with a broad range of the types and severities of symptoms that can be displayed. It has been proposed that ASD is characterized by abnormal functional connectivity (FC) between brain regions, which can be defined as the correlations of functional magnetic resonance imaging (fMRI) time series between pairs of regions of interest. However, studies of FC in ASD have presented mixed results. Further, it has been suggested that there is a complex developmental trajectory of FC in ASD. Inconsistent results across studies may, in part, be related to differences in fMRI data processing strategies, as well as heterogeneity of sample characteristics. Other aspects of brain dynamics, including variability and complexity of blood oxygen-level dependent (BOLD) time series, are not well characterized in ASD. The goal of this dissertation is to analyze resting-state brain dynamics in ASD, and to address the inconsistencies in previous studies of resting-state fMRI in ASD. Study 1 involves the characterization of FC-based subtypes of ASD and typically developing (TD) participants to elucidate unique relationships between FC and behaviour. In Study 2, different fMRI data processing strategies are examined to determine the effects of these processing choices on group differences in FC in children and adolescents with and without ASD. Study 3 involves examining BOLD signal variability and complexity, and relating these metrics to structural connectivity, age, and behavioural severity. Overall, these studies reveal the importance of considering subtypes of ASD and TD individuals, the effects of preprocessing strategies, and relationships between brain dynamics and brain structure, age, and behavioural severity when analyzing resting-state brain dynamics in those with and without ASD.Ph.D