Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

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dc.contributor.authorMcDonough, Ian M.
dc.contributor.authorNashiro, Kaoru
dc.contributor.otherUniversity of Texas System
dc.contributor.otherUniversity of Texas Dallas
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2019-07-10T16:04:09Z
dc.date.available2019-07-10T16:04:09Z
dc.date.issued2014-06-10
dc.description.abstractAn 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.mimetypeapplication/pdf
dc.identifier.citationMcDonough, 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
dc.identifier.doi10.3389/fnhum.2014.00409
dc.identifier.orcidhttps://orcid.org/0000-0003-0907-8931
dc.identifier.orcidhttps://orcid.org/0000-0002-8227-4712
dc.identifier.urihttp://ir.ua.edu/handle/123456789/5924
dc.languageEnglish
dc.language.isoen_US
dc.publisherFrontiers Media
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectfunctional connectivity
dc.subjectHuman Connectome Project
dc.subjectinformation processing
dc.subjectmultiscale entropy
dc.subjectneural complexity
dc.subjectresting-state networks
dc.subjectINTRINSIC FUNCTIONAL CONNECTIVITY
dc.subjectINDEPENDENT COMPONENT ANALYSIS
dc.subjectBRAIN SIGNAL VARIABILITY
dc.subjectTIME-SERIES ANALYSIS
dc.subjectALZHEIMERS-DISEASE
dc.subjectMULTISCALE ENTROPY
dc.subjectCORTICAL NETWORKS
dc.subjectCEREBRAL-CORTEX
dc.subjectAPPROXIMATE ENTROPY
dc.subjectNEURONAL AVALANCHES
dc.subjectNeurosciences
dc.subjectPsychology
dc.subjectNeurosciences & Neurology
dc.titleNetwork complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Projecten_US
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dc.typeArticle
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