A 5D approach to study spatio-temporal dynamism of resting-state brain networks in schizophrenia.

Abstract

Schizophrenia is a serious brain disorder that can affect all aspects of patient's life such as thinking, behaving and even feeling. The principal cause of schizophrenia is still unknown, but there is some evidence that differences in brain networks interactions along with functional dysconnectivity may play a significant role. Prior work has mostly focused on static summaries of functional data, or more recently changes in temporal coupling between fixed networks. Here, we study differences in spatio-temporal brain dynamics using resting state fMRI images in a dataset including 510 control and 708 schizophrenia patients. To do this, we utilized a deep residual network to extract 5 different spatiotemporal networks each of which captures spatial and temporal dynamics within sensory-motor, auditory, and default mode domains. Our analysis shows significant group differences in various aspects of spatio-temporal dynamics including magnitude, voxel-wise variability, and temporal functional network connectivity. Clinical relevance- Our study explores effects of spatio-temporal brain dynamism in schizophrenia, which is rarely taken into account, but could provide unique and more sensitive information about the disorder. Here we incorporate a novel 5D brain parcellation model, that enables us to encode spatio-temporal dynamics, to extract and characterize multiple resting fMRI brain networks.