BPARC: A novel spatio-temporal (4D) data-driven brain parcellation scheme based on deep residual networks

Abstract

Brain parcellation plays a significant role in computational neuroimaging by dividing the brain into meaningful anatomical sub-regions which can be used to study broad brain functionalities and structures. Most ongoing brain parcellation research has focused on fixed regions that do not vary across individuals or over time. However, brain functional organization is by its very nature dynamic and ignoring this can lead to misleading results. In this work, we have tried to address this shortcoming in fMRI-based brain parcellation by proposing a novel 4D approach using a deep residual network structure, trained to predict probabilities of voxels in each volume belonging to independent components extracted from fMRI images. Results show that the presented approach not only provides informative 4D spatiotemporal networks which are individualized but also linked across subjects, providing an important tool for further study of the human brain.