Gray matter age prediction as a biomarker for risk of dementia

Johnny Wang, Maria J. Knol, Aleksei Tiulpin, Florian Dubost, Marleen De Bruijne, Meike W. Vernooij, Hieab H.H. Adams, M. Arfan Ikram, Wiro J. Niessen, Gennady V. Roshchupkin

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

122 Citas (Scopus)

Resumen

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE e4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05–1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06–1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.

Idioma originalInglés
Páginas (desde-hasta)21213-21218
Número de páginas6
PublicaciónProceedings of the National Academy of Sciences of the United States of America
Volumen116
N.º42
DOI
EstadoPublicada - 15 oct. 2019
Publicado de forma externa

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