Asset Details
Do you wish to reserve the book?
Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study
Do you wish to request the book?
Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study
2011
Overview
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.
Publisher
Elsevier Inc,Elsevier Limited
Subject
Afferent Pathways - physiology
/ Auditory Pathways - physiology
/ Data Interpretation, Statistical
/ Echo-Planar Imaging - statistics & numerical data
/ Efferent Pathways - physiology
/ Female
/ fMRI
/ Humans
/ Image Processing, Computer-Assisted
/ Male
/ Neural Pathways - anatomy & histology
/ Neural Pathways - physiology
/ NMR
/ Studies
