Accurate prediction of individual subject identity and task, but not autism diagnosis, from functional connectomes

Abstract

Despite enthusiasm about the potential for using fMRI-based functional connectomes in the development of biomarkers for autism spectrum disorder (ASD), the literature is full of negative findings-failures to distinguish ASD functional connectomes from those of typically developing controls (TD)-and positive findings that are inconsistent across studies. Here, we report on a new study designed to either better differentiate ASD from TD functional connectomes-or, alternatively, to refine our understanding of the factors underlying the current state of affairs. We scanned individuals with ASD and controls both at rest and while watching videos with social content. Using multiband fMRI across repeat sessions, we improved both data quantity and scanning duration by collecting up to 2 hr of data per individual. This is about 50 times the typical number of temporal samples per individual in ASD fcMRI studies. We obtained functional connectomes that were discriminable, allowing for near-perfect individual identification regardless of diagnosis, and equally reliable in both groups. However, contrary to what one might expect, we did not consistently or robustly observe in the ASD group either reductions in similarity to TD functional connectivity (FC) patterns or shared atypical FC patterns. Accordingly, FC-based predictions of diagnosis group achieved accuracy levels around chance. However, using the same approaches to predict scan type (rest vs. video) achieved near-perfect accuracy. Our findings suggest that neither the limitations of resting state as a "task," data resolution, data quantity, or scan duration can be considered solely responsible for failures to differentiate ASD from TD functional connectomes.

Keywords: autism; fcMRI; functional connectivity; individual differences; naturalistic viewing; resting state.

Figure 1

Multidimensional scaling (MDS) visualizations of pairwise similarity among all rest scans (top row) and among all video scans (bottom row). Both MDS computations were conducted using all subjects (left column); results are also re‐plotted separately for each group (ASD, middle column; TD, right column) for closer inspection. The middle and right columns plot scans from the same subject in the same color (note that colors are recycled across groups, i.e., the orange points in the middle and in the right plots represent different subjects). Note that these visualizations present pairwise similarity before regressing out FD_filt

Figure 2

Median and difference functional connectivity matrices for each scan task across all subjects (Row 1), for each diagnosis group (Rows 2 and 3), and differences between diagnosis groups for each task (Row 4). Task differences (Column 3) are more apparent upon visual inspection than group differences (Row 4). Differences are ordered as ASD‐TD (Row 4) and Video‐Rest (Column 3). Left hemisphere ROIs are plotted to the left and above right hemisphere ROIs

Figure 3

MDS visualizations of pairwise similarity among all scans. Left: scans color‐coded according to diagnosis (red = ASD, blue = TD). Right: scans color‐coded according to scan type (green = video; black = rest). Note that these visualizations present pairwise similarity before regressing out FD_filt