Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI.
Alzheimer’s disease neuroimaging initiative (ADNI)
CSF biomarker
Grey matter
MCI subtypes
Machine learning
Neuropsychological profile
Journal
Neurobiology of aging
ISSN: 1558-1497
Titre abrégé: Neurobiol Aging
Pays: United States
ID NLM: 8100437
Informations de publication
Date de publication:
11 2023
11 2023
Historique:
received:
06
02
2023
revised:
05
07
2023
accepted:
06
07
2023
medline:
25
9
2023
pubmed:
10
9
2023
entrez:
9
9
2023
Statut:
ppublish
Résumé
There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ
Identifiants
pubmed: 37689017
pii: S0197-4580(23)00147-1
doi: 10.1016/j.neurobiolaging.2023.07.006
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
196-208Subventions
Organisme : NIA NIH HHS
ID : U01 AG024904
Pays : United States
Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Disclosure statement The authors have no actual or potential conflicts of interest.