Resting-State Network Topology Differentiates Task Signals across the Adult Life Span

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dc.contributor.authorChan, Micaela Y.en_US
dc.contributor.authorAlhazmi, Fahd H.en_US
dc.contributor.authorPark, Denise C.en_US
dc.contributor.authorSavalia, Neil K.en_US
dc.contributor.authorWig, Gagan S.en_US
dc.contributor.utdAuthorChan, Micaela Y.en_US
dc.contributor.utdAuthorAlhazmi, Fahd H.en_US
dc.contributor.utdAuthorPark, Denice C.en_US
dc.contributor.utdAuthorSavalia, Neil K.en_US
dc.contributor.utdAuthorWig, Gagan S.en_US
dc.date.accessioned2018-08-20T16:15:30Z
dc.date.available2018-08-20T16:15:30Z
dc.date.created2017-01-31
dc.description.abstractBrain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) sub-networks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system (“non-connector” nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems (“connector” nodes). This “activation selectivity” was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the “dedifferentiation” in brain activity observed in aging.en_US
dc.description.departmentCenter for Vital Longevityen_US
dc.description.sponsorshipNIH Grant 5R37AG-006265-30en_US
dc.identifier.bibliographicCitationChan, M. Y., F. H. Alhazmi, D. C. Park, N. K. Savalia, et al. 2017. "Resting-state network topology differentiates task signals across the adult life span." Journal of Neuroscience 37(10): 2734-2745.en_US
dc.identifier.issn0270-6474en_US
dc.identifier.issue10en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/5975
dc.identifier.volume37en_US
dc.language.isoenen_US
dc.publisherSociety for Neuroscienceen_US
dc.relation.urihttp://dx.doi.org/10.1523/JNEUROSCI.2406-16.2017en_US
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2017 The Authors. All Rights Reserved.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.source.journalJournal of Neuroscienceen_US
dc.subjectAgingen_US
dc.subjectNeural networks (Neurobiology)en_US
dc.subjectAdulten_US
dc.subjectAge Factorsen_US
dc.subjectAgeden_US
dc.subjectBrain mappingen_US
dc.subjectBehavior Rating Scaleen_US
dc.subjectFemaleen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectHumansen_US
dc.subjectMaleen_US
dc.subjectNervous Systemen_US
dc.subjectSemanticsen_US
dc.subjectBrainen_US
dc.subjectConnectomeen_US
dc.subjectLongevityen_US
dc.subjectMiddle Ageden_US
dc.subjectNeuronsen_US
dc.subjectPhysiologyen_US
dc.subjectTask Performance and Analysisen_US
dc.subjectYoung Ddulten_US
dc.subjectAged, 80 and overen_US
dc.subjectNerve Neten_US
dc.subjectNeural Pathwaysen_US
dc.titleResting-State Network Topology Differentiates Task Signals across the Adult Life Spanen_US
dc.typeTexten_US
dc.type.genrearticleen_US

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