Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Human Brain Mapping
Mark J R J BoutsSerge A R B Rombouts

Abstract

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 abl...Continue Reading

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Citations

Apr 29, 2020·Alzheimer's Research & Therapy·Peter N E YoungMichael Schöll

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Methods Mentioned

BETA
feature extraction

Software Mentioned

glmnet
R Core
caret
R
FMRIB58
ROCR
pROC

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