Varying Information Complexity in Functional Domain Interactions in Schizophrenia

Understanding the associations of the structural and functional patterns of the brain is vital. Recent studies have focused on utilizing this information within and across the different functional and anatomical domains (i.e., groups of brain networks) using neuroimaging data. In this work, we use a Bayesian optimization-based method known as the Tree Parzen Estimator (TPE) to identify variation in the nature of information encoded by different functional magnetic resonance imaging (fMRI) sub-domains of the brain. We show by repeated cross-validation on a schizophrenia classification task that specific sub-domains may require more sophisticated learning architectures to contribute optimally to classification, while others require less complicated ones. Our findings reveal the need for adaptive, hierarchical learning frameworks catering to features from different sub-domains to optimally identify features enabling the prediction of the outcome of interest.