Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data.

Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.