The study of functional connectivity of the human brain has provided valuable insights into its organization principles. Studies have revealed consistent and reproducible patterns of activity across individuals which are referred to as resting-state networks. Although these have been studied extensively, the direction of information flow between these regions is less understood. We aimed to study this by analyzing resting state scans from 20 subjects (11 male and 9 female, all healthy) and capturing the functional interdependence of 32 regions of interest spanning the different resting state networks using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The resulting networks are then analyzed to explore patterns of directed connectivity across the subjects. Using the general linear model, we observe that the nodes of the salience network particularly shows patterns of directed influence within as well as outside the network (p<0.05, FDR corrected). Additionally, the anterior cingulate cortex exhibits a strong outgoing influence on various regions of the brain. Such directional influences of the RSNs have not been reported previously. These results suggest that our framework can effectively capture patterns of distributed and directed connectivity occurring in the brain network and can therefore serve to enhance our understanding of its organizational principles.
Anas Zainul Abidin, Adora M. D'Souza, Udaysankar Chockanathan, Giovanni Schifitto, and Axel Wismüller, "Investigating directed functional connectivity between the resting state networks of the human brain using mutual connectivity analysis," Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780A (Presented at SPIE Medical Imaging: February 11, 2018; Published: 12 March 2018); https://doi.org/10.1117/12.2293863.
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