Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.
Alzheimer’s disease
brain atrophy
deep learning
mild cognitive impairment
neurodegeneration
subtyping
Journal
Cell reports. Medicine
ISSN: 2666-3791
Titre abrégé: Cell Rep Med
Pays: United States
ID NLM: 101766894
Informations de publication
Date de publication:
21 12 2021
21 12 2021
Historique:
received:
29
05
2021
revised:
08
09
2021
accepted:
13
11
2021
entrez:
14
1
2022
pubmed:
15
1
2022
medline:
4
3
2022
Statut:
epublish
Résumé
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
Identifiants
pubmed: 35028609
doi: 10.1016/j.xcrm.2021.100467
pii: S2666-3791(21)00339-6
pmc: PMC8714856
doi:
Substances chimiques
Biomarkers
0
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
100467Subventions
Organisme : NIA NIH HHS
ID : R01 AG062590
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG003991
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG026276
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG043434
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB009352
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000448
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002345
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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