Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

Abstract

Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.

Keywords: autism spectrum disorder; developmental brain imaging; fMRI; functional connectivity; neural networks; resting state.

Figure 1

(A) The mean age of autism spectrum disorder (ASD) participants per study included in this rs-fMRI review (Table 1). Articles are ordered by increasing age of the study population to emphasize existing trends. Gaps can be seen in age representation from 3 to 9 years and 17 to 21 years, and again a lack of representation in the older age groups. (B) Representation of females in each study represented by dark gray bars out of total sample size represented by the light gray bar. Studies are ordered alphabetically according to Table 1. An obvious trend of underrepresentation of females with ASD research can be seen, with increasing sample size not contributing to an increase in females represented. Studies using the Autism Brain Imaging Data Exchange sample set are not shown, but replicate this overall finding.

Figure 2

A comparison of the primary methodology used for resting-state functional MRI data analysis in the autism literature and the number of journal articles that represent each analysis technique. Abbreviations: Seed, voxel-based-seed analysis; ICA, independent component analysis; GT, graph theory; VWs, voxel-wise whole brain analyses; SOM, self-organizing maps; ReHo, regional heterogeneity; Voxel Comb, voxel combinations. The use of multiple techniques is indicated with two or more abbreviations.