Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Abstract

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.

Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder

“…In order to maintain consistency in the number of time points for all subjects from different sites as suggested in other studies [Easson, Fatima, & McIntosh, ; Rashid et al, ], the fMRI data of Trinity Centre for Health Sciences were trimmed to 180 time points so that every subject had 180 volumes. Then, rsfMRI data were preprocessed by Data Processing & Analysis for Brain Imaging (DPABI V3.0, http://rfmri.org/) [Yan, Wang, Zuo, & Zang, ], an open‐source package based on Statistical Parametric Mapping (SPM8, https://www.fil.ion.ucl.ac.uk/spm/) and MATLAB (MathWorks).…”

“…In order to maintain consistency in the number of time points for all subjects from different sites as suggested in other studies [Easson, Fatima, & McIntosh, 2018;Rashid et al, 2018], the fMRI data of Trinity Centre for Health Sciences were trimmed to 180 time points so that every subject had 180 volumes.…”

Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

“…For example, unique regional patterns of group differences have been demonstrated in transient states, which differ from those observed in static connectivity analyses (Chen, Nomi, Uddin, Duan, & Chen, ; de Lacy, Doherty, King, Rachakonda, & Calhoun, ). Furthermore, dynamic connectivity measures have been found to improve diagnostic prediction of ASDs (Wee, Yap, & Shen, ; Zhu et al, ), and have also been explored as a potential subtype classifier (Easson et al, ). However, findings with respect to transient state duration and flexibility have been more difficult to interpret.…”

Reconciling Dimensional and Categorical Models of Autism Heterogeneity: a Brain Connectomics & Behavioral Study

“…Most studies have focused on variability of behavioral or cognitive characteristics (7)(8)(9)(10). Studies focusing on brain features are emerging (11)(12)(13)(14). Here, we propose a Bayesian framework to decompose whole-brain resting-state functional connectivity (RSFC) patterns in ASD individuals into multiple hypo/hyper RSFC patterns, which we will refer to as "factors" ( Figure 1A).…”

“…Here, we propose a Bayesian framework to decompose whole-brain resting-state functional connectivity (RSFC) patterns in ASD individuals into multiple hypo/hyper RSFC patterns, which we will refer to as "factors" ( Figure 1A). This approach allows an individual to express one or more factors (categorical subtypes) to varying degrees (continuous), thus potentially reconciling dimensional (13)(14)(15) and categorical (11,12,17) models of ASD heterogeneity. This approach is motivated by two important considerations.…”