Identifying Hierarchical Individual Functional Network under Naturalistic Paradigm via Two-stage DBN with Neural Architecture Search

Abstract

Functional magnetic resonance imaging under naturalistic paradigm (NfMRI) is gaining increasing attraction, as it offers an ecologically-valid condition to understand brain function in real life. Characterizing the hierarchical organization of brain function while taking the nature of fMRI activities under naturalistic condition into account has been a critical issue in identifying naturalistic functional networks. Recent studies have made efforts on characterizing the brain's hierarchical organizations from fMRI data via a variety of deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN framework) to model both the group-consistent and individual-specific naturalistic functional brain networks. Our results demonstrated that the optimized DBN-based framework can characterize meaningful group-wise and individual-level naturalistic functional networks, which reflected the hierarchical organization of brain function and the properties of brain functional activities under naturalistic paradigm.