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Article: Connectome-based models can predict processing speed in older adults

TitleConnectome-based models can predict processing speed in older adults
Authors
KeywordsConnectome-based predictive models
Functional connectivity
Processing speed
Resting-state
Older adults
Issue Date2020
PublisherElsevier: Creative Commons. The Journal's web site is located at http://www.elsevier.com/locate/ynimg
Citation
NeuroImage, 2020, v. 223, p. article no. 117290 How to Cite?
AbstractDecrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.
Persistent Identifierhttp://hdl.handle.net/10722/305755
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 2.436
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGao, M-
dc.contributor.authorWong, CHY-
dc.contributor.authorHuang, H-
dc.contributor.authorShao, R-
dc.contributor.authorHuang, R-
dc.contributor.authorChan, CCH-
dc.contributor.authorLee, TMC-
dc.date.accessioned2021-10-20T10:13:52Z-
dc.date.available2021-10-20T10:13:52Z-
dc.date.issued2020-
dc.identifier.citationNeuroImage, 2020, v. 223, p. article no. 117290-
dc.identifier.issn1053-8119-
dc.identifier.urihttp://hdl.handle.net/10722/305755-
dc.description.abstractDecrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.-
dc.languageeng-
dc.publisherElsevier: Creative Commons. The Journal's web site is located at http://www.elsevier.com/locate/ynimg-
dc.relation.ispartofNeuroImage-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectConnectome-based predictive models-
dc.subjectFunctional connectivity-
dc.subjectProcessing speed-
dc.subjectResting-state-
dc.subjectOlder adults-
dc.titleConnectome-based models can predict processing speed in older adults-
dc.typeArticle-
dc.identifier.emailWong, CHY: hycwong@hku.hk-
dc.identifier.emailShao, R: rshao@hku.hk-
dc.identifier.emailLee, TMC: tmclee@hku.hk-
dc.identifier.authorityShao, R=rp02519-
dc.identifier.authorityLee, TMC=rp00564-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.neuroimage.2020.117290-
dc.identifier.pmid32871259-
dc.identifier.scopuseid_2-s2.0-85090914531-
dc.identifier.hkuros328011-
dc.identifier.hkuros326537-
dc.identifier.volume223-
dc.identifier.spagearticle no. 117290-
dc.identifier.epagearticle no. 117290-
dc.identifier.isiWOS:000582799600017-
dc.publisher.placeUnited States-

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