Standard

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. / Bouts, Mark J.R.J.; van der Grond, Jeroen; Vernooij, Meike W.; Koini, Marisa; Schouten, Tijn M.; de Vos, Frank; Feis, Rogier A.; Lechner, Anita; Niessen, Wiro J.; More Authors.

In: Human Brain Mapping, Vol. 40, No. 9, 2019, p. 2711-2722.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Bouts, MJRJ, van der Grond, J, Vernooij, MW, Koini, M, Schouten, TM, de Vos, F, Feis, RA, Lechner, A, Niessen, WJ & More Authors 2019, 'Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification' Human Brain Mapping, vol. 40, no. 9, pp. 2711-2722. https://doi.org/10.1002/hbm.24554

APA

Bouts, M. J. R. J., van der Grond, J., Vernooij, M. W., Koini, M., Schouten, T. M., de Vos, F., ... More Authors (2019). Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Human Brain Mapping, 40(9), 2711-2722. https://doi.org/10.1002/hbm.24554

Vancouver

Author

Bouts, Mark J.R.J. ; van der Grond, Jeroen ; Vernooij, Meike W. ; Koini, Marisa ; Schouten, Tijn M. ; de Vos, Frank ; Feis, Rogier A. ; Lechner, Anita ; Niessen, Wiro J. ; More Authors. / Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. In: Human Brain Mapping. 2019 ; Vol. 40, No. 9. pp. 2711-2722.

BibTeX

@article{412600ecff9145bfb85ea891b2f2090a,
title = "Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification",
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 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.",
keywords = "Alzheimer's disease, classification, community-dwelling cohort, diffusion tensor imaging, machine learning, mild cognitive impairment, MRI",
author = "Bouts, {Mark J.R.J.} and {van der Grond}, Jeroen and Vernooij, {Meike W.} and Marisa Koini and Schouten, {Tijn M.} and {de Vos}, Frank and Feis, {Rogier A.} and Anita Lechner and Niessen, {Wiro J.} and {More Authors}",
year = "2019",
doi = "10.1002/hbm.24554",
language = "English",
volume = "40",
pages = "2711--2722",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",
number = "9",

}

RIS

TY - JOUR

T1 - Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

AU - Bouts, Mark J.R.J.

AU - van der Grond, Jeroen

AU - Vernooij, Meike W.

AU - Koini, Marisa

AU - Schouten, Tijn M.

AU - de Vos, Frank

AU - Feis, Rogier A.

AU - Lechner, Anita

AU - Niessen, Wiro J.

AU - More Authors, null

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - classification

KW - community-dwelling cohort

KW - diffusion tensor imaging

KW - machine learning

KW - mild cognitive impairment

KW - MRI

UR - http://www.scopus.com/inward/record.url?scp=85062329100&partnerID=8YFLogxK

U2 - 10.1002/hbm.24554

DO - 10.1002/hbm.24554

M3 - Article

VL - 40

SP - 2711

EP - 2722

JO - Human Brain Mapping

T2 - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 9

ER -

ID: 52090750