Network Complexity as a Measure of Information Processing Across Resting-State Networks: Evidence from the Human Connectome Project

Show simple item record

dc.rights.license Attribution 4.0 International (CC BY 4.0) en_US
dc.contributor.author McDonough, Ian M.
dc.contributor.author Nashiro, Kaoru
dc.date.accessioned 2019-07-10T16:04:09Z
dc.date.available 2019-07-10T16:04:09Z
dc.date.issued 2014-06-10
dc.identifier.citation McDonough, I., Nashiro, K. (2014): Network Complexity as a Measure of Information Processing Across Resting-State Networks: Evidence from the Human Connectome Project. Frontiers in Human Neuroscience, vol. 8. DOI: https://doi.org/10.3389/fnhum.2014.00409 en_US
dc.identifier.uri http://ir.ua.edu/handle/123456789/5924
dc.description.abstract An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity. en_US
dc.format.mimetype application/pdf en_US
dc.language English en_US
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject functional connectivity en_US
dc.subject Human Connectome Project en_US
dc.subject information processing en_US
dc.subject multiscale entropy en_US
dc.subject neural complexity en_US
dc.subject resting-state networks en_US
dc.title Network Complexity as a Measure of Information Processing Across Resting-State Networks: Evidence from the Human Connectome Project en_US
dc.type text en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International (CC BY 4.0) Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)

Search DSpace


Browse

My Account