  The objective of this study is to derive functional networks for the autism
spectrum disorder (ASD) population using the group ICA and dictionary learning
model together and to classify ASD and typically developing (TD) participants
using the functional connectivity calculated from the derived functional
networks. In our experiments, the ASD functional networks were derived from
resting-state functional magnetic resonance imaging (rs-fMRI) data. We
downloaded a total of 120 training samples, including 58 ASD and 62 TD
participants, which were obtained from the public repository: Autism Brain
Imaging Data Exchange I (ABIDE I). Our methodology and results have five main
parts. First, we utilize a group ICA model to extract functional networks from
the ASD group and rank the top 20 regions of interest (ROIs). Second, we
utilize a dictionary learning model to extract functional networks from the ASD
group and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from the
two models together as the ASD functional networks. Fourth, we generate three
corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs
selected from dictionary learning, and the 40 combined ROIs selected from both.
Finally, we extract ROIs for all training samples using the above three masks,
and the calculated functional connectivity was used as features for ASD and TD
classification. The classification results showed that the functional networks
derived from ICA and dictionary learning together outperform those derived from
a single ICA model or a single dictionary learning model.
