Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study

• Published in: NeuroImage (Orlando, Fla.) Vol. 56; no. 3; pp. 1035 - 1042
• Main Authors: Li, Rui, Chen, Kewei, Fleisher, Adam S., Reiman, Eric M., Yao, Li, Wu, Xia
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2011; Elsevier Limited
• Subjects: Afferent Pathways - physiology; Attention; Auditory Pathways - physiology; Bayes Theorem; Bayesian network; Brain - anatomy & histology; Brain - physiology; Chinese languages; Cognition & reasoning; Cognition - physiology; Connectivity; Data Interpretation, Statistical; Echo-Planar Imaging - statistics & numerical data; Efferent Pathways - physiology; Female; fMRI; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Models, Neurological; Models, Statistical; Nerve Net - physiology; Neural Pathways - anatomy & histology; Neural Pathways - physiology; NMR; Normal Distribution; Nuclear magnetic resonance; Regression Analysis; Resting-state network; Spontaneous activity; Studies; Visual Pathways - physiology; Young Adult; fMRI; Connectivity; Resting-state network; Bayesian network; Spontaneous activity
• DOI: 10.1016/j.neuroimage.2011.03.010
• ISSN: 1053-8119; 1095-9572

This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics. ► Resting-state networks were hierarchically organized. ► Highly intra-dependent sensory RSNs and strongly intra-influential cognitive RSNs. ► Bottom-up cross-network information processing was intrinsically engaged. ► Default-mode network was pivotal for the resting-state information integration.