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The objective of this study is to derive functional networks for the autismspectrum disorder (ASD) population using the group ICA and dictionary learningmodel together and to classify ASD and typically developing (TD) participantsusing the functional connectivity calculated from the derived functionalnetworks. In our experiments, the ASD functional networks were derived fromresting-state functional magnetic resonance imaging (rs-fMRI) data. Wedownloaded a total of 120 training samples, including 58 ASD and 62 TDparticipants, which were obtained from the public repository: Autism BrainImaging Data Exchange I (ABIDE I). Our methodology and results have five mainparts. First, we utilize a group ICA model to extract functional networks fromthe ASD group and rank the top 20 regions of interest (ROIs). Second, weutilize a dictionary learning model to extract functional networks from the ASDgroup and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from thetwo models together as the ASD functional networks. Fourth, we generate threecorresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIsselected 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 TDclassification. The classification results showed that the functional networksderived from ICA and dictionary learning together outperform those derived froma single ICA model or a single dictionary learning model.

Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning