Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.
Aged
Aged, 80 and over
Alzheimer Disease
/ diagnostic imaging
Cognitive Dysfunction
/ diagnostic imaging
Diffusion Tensor Imaging
/ methods
Female
Humans
Independent Living
Machine Learning
Male
Middle Aged
Models, Theoretical
Multiparametric Magnetic Resonance Imaging
/ methods
Retrospective Studies
Alzheimer's disease
MRI
classification
community-dwelling cohort
diffusion tensor imaging
machine learning
mild cognitive impairment
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
15 06 2019
15 06 2019
Historique:
received:
23
11
2018
revised:
30
01
2019
accepted:
09
02
2019
pubmed:
26
2
2019
medline:
9
4
2020
entrez:
26
2
2019
Statut:
ppublish
Résumé
Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.
Identifiants
pubmed: 30803110
doi: 10.1002/hbm.24554
pmc: PMC6563478
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2711-2722Subventions
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 016.130.677
Pays : International
Organisme : Internationale Stichting Alzheimer Onderzoek
ID : 12533
Pays : International
Organisme : Erasmus MC
ID : MRACE grant
Pays : International
Organisme : Alzheimer's Association
ID : NIRG-09-13168
Pays : International
Organisme : the Netherlands Organization for Health Research and Development (ZonMW)
ID : 916.13.054
Pays : International
Organisme : European Commission
ID : DG-XII
Pays : International
Informations de copyright
© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
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