Detection of mild cognitive impairment in a community‐dwelling population using quantitative, multiparametric MRI‐based classification. Issue 9 (25th February 2019)

• Record Type: Journal Article
• Authors: Bouts, Mark J. R. J.; van der Grond, Jeroen; Vernooij, Meike W.; Koini, Marisa; Schouten, Tijn M.; de Vos, Frank; Feis, Rogier A.; Cremers, Lotte G. M.; Lechner, Anita; Schmidt, Reinhold; de Rooij, Mark; Niessen, Wiro J.; Ikram, M. Arfan; Rombouts, Serge A. R. B.
• Is Part Of: Human brain mapping. Volume 40:Issue 9(2019)
• Page Start: 2711
• Page End: 2722
• Publication Date: 2019-02-25
• Subjects: Alzheimer's disease -- classification -- community‐dwelling cohort -- diffusion tensor imaging -- machine learning -- mild cognitive impairment -- MRI; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24554
• Languages: English
• ISSNs: 1065-9471

Abstract

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.