Node identification using inter-regional correlation analysis for mapping detailed connections in resting state networks

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Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.
Publisher
FRONTIERS MEDIA SA
Issue Date
2017-05
Language
English
Article Type
Article
Keywords

INDEPENDENT COMPONENT ANALYSIS; HUMAN BRAIN NETWORKS; ALZHEIMERS-DISEASE; FUNCTIONAL CONNECTIVITY; AMYLOID BURDEN; CORTICAL HUBS; LIFE-SPAN; FMRI; DISORDERS; MODE

Citation

FRONTIERS IN NEUROSCIENCE, v.11, no.MAY

ISSN
1662-453X
DOI
10.3389/fnins.2017.00238
URI
http://hdl.handle.net/10203/225257
Appears in Collection
BiS-Journal Papers(저널논문)
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