Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder, characterized by impairments in social communication and restricted, repetitive behaviors. Neuroimaging studies have shown complex patterns and functional connectivity (FC) in ASD, with no clear consensus on brain-behavior relationships or shared patterns of FC with typically developing controls. Here, we used a dimensional approach to characterize two distinct clusters of FC patterns across both ASD participants and controls using k-means clustering. Using multivariate statistical analyses, a categorical approach was taken to characterize differences in FC between subtypes and between diagnostic groups. One subtype was defined by increased FC within resting-state networks and decreased FC across networks compared with the other subtype. A separate FC pattern distinguished ASD from controls, particularly within default mode, cingulo-opercular, sensorimotor, and occipital networks. There was no significant interaction between subtypes and diagnostic groups. Finally, a dimensional analysis of FC patterns with behavioral measures of IQ, social responsiveness, and ASD severity showed unique brain-behavior relations in each subtype and a continuum of brain-behavior relations from ASD to controls within one subtype. These results demonstrate that distinct clusters of FC patterns exist across ASD and controls, and that FC subtypes can reveal unique information about brain-behavior relationships.

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Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder that is characterized by impairments in social communication and restricted and repetitive behaviours.Neuroimaging studies of individuals with ASD have shown complex patterns of functional connectivity (FC), with no clear consensus on defining brain-behaviour attributes of different subtypes of the disorder. In these studies, "static FC", a metric that considers correlations across the entire time series for a given region, was used. Emerging evidence shows that FC changes over time; this "dynamic FC" can allow for unique detection of the temporal variability of functional connections over time. Here, we used k-means clustering, an unsupervised machine learning algorithm, to characterize two subtypes of ASD based on distinct patterns of static and dynamic FC. A multivariate statistical approach was then implemented to determine optimal relationships between FC patterns and group membership or behaviour. The main objective was to characterize differences in static and dynamic FC between subtypes of ASD defined in a fully data-driven manner, and between subtypes and typically developing individuals. Subtype 2 was defined by increased FC within resting-state networks and decreased FC across networks for static FC, global increases in temporal stability of dynamic FC, and robust relationships between FC and several behaviours, relative to Subtype 1. Further, both ASD subtypes exhibited different patterns of static and dynamic FC compared to controls, particularly within default mode, sensorimotor, and occipital networks. Static and dynamic FC metrics provided both overlapping and unique information about the nature of FC differences between subtypes, and between both subtypes and controls. Our results demonstrate the value of considering FC-based subtypes of ASD to elucidate different relationships between brain and behaviour among individuals with this disorder, and have important clinical implications for catering treatments and behavioural interventions to specific subtypes.