Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.
Brain reserve
Cognitive aging
Cognitive heterogeneity
Deep learning
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 05 2022
01 05 2022
Historique:
received:
22
10
2021
revised:
20
01
2022
accepted:
17
02
2022
pubmed:
24
2
2022
medline:
1
4
2022
entrez:
23
2
2022
Statut:
ppublish
Résumé
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.
Identifiants
pubmed: 35196565
pii: S1053-8119(22)00149-5
doi: 10.1016/j.neuroimage.2022.119020
pmc: PMC9045384
mid: NIHMS1792870
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
119020Subventions
Organisme : NIA NIH HHS
ID : R01 AG041851
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG056366
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG034676
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG016574
Pays : United States
Organisme : NIA NIH HHS
ID : R33 AG058738
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS097495
Pays : United States
Organisme : NIA NIH HHS
ID : R37 AG011378
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG006786
Pays : United States
Informations de copyright
Copyright © 2022. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors report no competing interests relevant to this manuscript.
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