  The goal of the present study is to identify autism using machine learning
techniques and resting-state brain imaging data, leveraging the temporal
variability of the functional connections (FC) as the only information. We
estimated and compared the FC variability across brain regions between typical,
healthy subjects and autistic population by analyzing brain imaging data from a
world-wide multi-site database known as ABIDE (Autism Brain Imaging Data
Exchange). Our analysis revealed that patients diagnosed with autism spectrum
disorder (ASD) show increased FC variability in several brain regions that are
associated with low FC variability in the typical brain. We then used the
enhanced FC variability of brain regions as features for training machine
learning models for ASD classification and achieved 65% accuracy in
identification of ASD versus control subjects within the dataset. We also used
node strength estimated from number of functional connections per node averaged
over the whole scan as features for ASD classification.The results reveal that
the dynamic FC measures outperform or are comparable with the static FC
measures in predicting ASD.
