Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

Abstract

Objectives: Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis.

Methods: Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication.

Results: High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning.

Conclusions: While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

Trial registration: ClinicalTrials.gov NCT01031407.

Keywords: Autism; Biomarkers; Machine learning classification; Social brain.

Fig. S1

Connectivity matrices from the two subject cohorts show significant whole-brain differences during ASD vs. TD comparisons. A series of thresholded t-tests (p < .005) on the connectivity matrices from the DiMartino (left column), Power (middle column), and Destrieux (right column) ROI sets are shown. Matrices show t-values from the comparisons. Comparing all of the NIMH data to all of the ABIDE data (top row), regardless of ASD or TD distinction, reveals whole-brain connectivity differences in all three ROI sets. The t-test of ASD-TD reveals incongruent group differences in the NIMH cohort (middle row) and ABIDE cohort (bottom row).

Fig. S2

Feature weights from the top performing linear classifiers are highly correlated. The Spearman correlation coefficient for each pair comparison is shown above the scatter plots (p < 10^− 15).

Fig. S3

Default mode network and frontal–parietal control network regions are highly prevalent in the most important features from the Power ROI set. The frequency of regions within particular functional networks among the top ranked features (red bars) chosen by RFECV are plotted along with the expected frequency of these functional networks if features were chosen at random (*s). The observed frequency of regions was significantly different from the expected frequency (p < .01).

Fig. 1

Optimal feature subsets chosen via recursive feature elimination (RFE) for each ROI set by L-SVM. The feature weights shown are the average weights from LOO cross-validation. Spheres are centered at the ROI's center of mass, and sphere radius represents the number of features coincident on that region. Comparing sphere radii across ROI sets is not advised due to the difference in the number of regions, in the number of features chosen, and in the cross-validation accuracy stated in the text. Edge thickness indicates absolute value of feature weight in the L-SVM, and color indicates the sign of the feature. ‘Hotter’ edges indicate stronger connectivity in ASD individuals while ‘cooler’ edges represent indicate stronger connectivity in TD individuals.

Fig. 2

The most predictive features from the Power and Destrieux ROI sets correlate with subjects' SRS sum scores. Spheres are centered at each ROI's center of mass, and sphere radius represents the number of significantly correlated features coincident on that region. Edge thickness indicates absolute value of the r-statistic. Edge color indicates the sign and magnitude of the r-statistic. Cooler colors indicate a negative correlation while warmer colors indicate a positive correlation. All correlations are significant at FDR < .05.