Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to
reveal functional biomarkers of neuropsychiatric disorders. However, extracting such
biomarkers is challenging for complex multi-faceted neuropathologies, such as autism
spectrum disorders. Large multi-site datasets increase sample sizes to compensate
for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity
raises new challenges, akin to those face in realistic diagnostic applications. Here,
we demonstrate the feasibility of inter-site classification of neuropsychiatric status,
with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a
large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines
that extract the most predictive biomarkers from the data. These R-fMRI pipelines
build participant-specific connectomes from functionally-defined brain areas. Connectomes
are then compared across participants to learn patterns of connectivity that differentiate
typical controls from individuals with autism. We predict this neuropsychiatric status
for participants from the same acquisition sites or different, unseen, ones. Good
choices of methods for the various steps of the pipeline lead to 67% prediction accuracy
on the full ABIDE data, which is significantly better than previously reported results.
We perform extensive validation on multiple subsets of the data defined by different
inclusion criteria. These enables detailed analysis of the factors contributing to
successful connectome-based prediction. First, prediction accuracy improves as we
include more subjects, up to the maximum amount of subjects available. Second, the
definition of functional brain areas is of paramount importance for biomarker discovery:
brain areas extracted from large R-fMRI datasets outperform reference atlases in the
classification tasks.