Replicable Network-Based Diagnostic Classification of ASD in the Autism Brain Imaging Data Exchange

Saturday, May 16, 2015: 11:30 AM-1:30 PM
Imperial Ballroom (Grand America Hotel)
J. A. Richey1, M. Ghane2, M. Coffman2, A. Valdespino2 and P. Du3, (1)Virginia Tech, Blackbsurg, VA, (2)Psychology, Virginia Tech, Blacksburg, VA, (3)Statistics, Virginia Tech, Blacksburg, VA
Background:  Autism spectrum disorders (ASDs) are characterized by alterations in and heterogeneous patterns of functional brain connectivity. This heterogeneity has presented a significant obstacle to MRI/fMRI-based diagnostic classification because most optimization algorithms assume a single, mutually exclusive distinction between control and ASD groups. As a solution, in this study we present a novel classification method based on a combination of network-based effective connectivity (structural vector autoregression; SVAR) and a high-dimensional variable selection procedure (smoothly clipped absolute deviation; SCAD) to identify subsets of connections that are robust to high-dimensional heterogeneity, and as such may have promise when applied to fMRI data in order to predict ASD diagnostic status.

Objectives:  Determine the efficacy of a high-dimensional variable selection procedure (smoothly clipped absolute deviation; SCAD) in determining diagnostic status (ASD versus non-ASD) in a large, publicly available repository of resting-state data (Autism Brain Imaging Data Exchange; ABIDE), and replicate the classification procedure across 16 physical ABIDE sites. 

Methods:   We used resting state-fMRI data from 1,112 subjects (ASD N=539, Control N=573) across 16 geographic sites from ABIDE, to measure functional connectivity estimates among 12 regions of interest (ROIs) in the frontostriatal circuit. We incorporated all 12 ROIs into subject-level network maps, yielding 144 directed connectivity paths per subject. Using a leave-one-out procedure, we compared predicted diagnosis from SVAR+SCAD against actual diagnosis based on (ADOS/ADI-R). 

Results:  Results indicated that fronto-striatal network maps contained, at the individual- and site-levels, sufficient data to accurately classify 84.6% of cases, on average, across all 16 ABIDE sites. Correlational and temporally-lagged models performed relatively poorly, whereas a model that combines instantaneous and temporally-lagged effects (structural vector autoregression; SVAR) provided a superior model fit, achieving greater than 90% accuracy for sites with less than 50 subjects.

Conclusions:  Network-level information that is variably expressed as the directional connective properties of the fronto-striatal circuit can be captured by SVAR+SCAD and used to predict the diagnosis of ASD via a leave-one-out prediction algorithm. Although no significant associations between any single connectivity estimate and ASD diagnosis were observed, SVAR+SCAD captured network-level properties that are not currently incorporated into other machine-learning and prediction algorithms, which focus on a low- (or one-) dimensional distinction between groups. Replication of predictive estimates across ABIDE sites increases confidence in the utility of this method for fMRI-based classification enterprises.