UC San Diego

Autism spectrum disorder (ASD) is a behaviorally diagnosed neurodevelopmental condition that is associated with atypical functional connectivity (FC). However, no consistent biomarkers have been identified. Most studies to date have focused on static FC, and relatively little is known about time-varying properties of FC. This three-paper dissertation aimed to better characterize brain networks in ASD by evaluating: 1) transient connectivity states, 2) BOLD lag structure, and 3) associations between hemodynamic and electrophysiological measures of brain function. Study 1 (Mash et al., 2019) used sliding window analysis to examine FC variability and describe transient connectivity states in children and adolescents (ages 6-18) with ASD (n = 62) and their typically developing (TD) peers (n = 57). Across all regions, the ASD group showed FC overconnectivity and hypervariability, on average. Distinct patterns of FC group differences were found in two transient states, but not in static FC analyses. Study 2 (Mash et al., under review) explored resting-state and task-related BOLD lag structure in adolescents and young adults (ages 12-21) with ASD (n = 28) and typical development (n = 22). Lag patterns did not significantly differ between groups, with common ‘early’ and ‘late’ regions emerging in both groups. However, lag structure was associated with both task condition and vascular supply, suggesting a combination of neural and vascular contributions to BOLD latency. Study 3 (Mash et al., 2020) characterized relationships between separately acquired resting-state fMRI and EEG activity in a sample of children and adolescents (ages 6-18) with ASD and typical development (EEG-only: n = 36 per group; fMRI-only: n = 66 ASD, 57 TD; EEG-fMRI: n = 17 per group). Reduced EEG alpha power, increased BOLD activity in right temporal regions, and widespread thalamocortical BOLD overconnectivity were observed in the ASD group. Multilevel modeling (with brain regions nested within individuals) revealed mostly positive relationships between EEG alpha power and regional BOLD activity in typical development, which were not observed in ASD. Overall, findings suggest that in comparison to conventional static FC studies, dynamic and multimodal analyses reveal more complex FC and activity patterns that may distinguish ASD from typical development.