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

doi:10.1073/pnas.1902376116

New Research In

Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved September 10, 2019 (received for review February 13, 2019)

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Significance

The difference between brain age estimated from MRI and chronological age is thought to serve as an important biomarker reflecting pathological processes in the brain. Several recent studies showed the relation between accelerated brain aging and various disorders. However, until now, the utility of such an age difference for preclinical screening using longitudinal studies was absent. To fill this gap, we first built a deep learning model using brain MRI from a population-based study including 5,496 participants. And then, using follow-up information, we observed that this age difference was significantly associated with the risk of dementia. Therefore, our study shows that the difference between MRI-brain predicted and chronological age is potentially a biomarker for early dementia risk screening.

Abstract

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 ε4 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.

Footnotes

↵¹J.W. and M.J.K. contributed equally to this work.
↵²W.J.N. and G.V.R. contributed equally to this work.
↵³To whom correspondence may be addressed. Email: g.roshchupkin{at}erasmusmc.nl.

Author contributions: J.W., M.J.K., M.W.V., M.A.I., W.J.N., and G.V.R. designed research; J.W., M.J.K., A.T., F.D., M.d.B., H.H.H.A., and G.V.R. performed research; J.W., M.J.K., A.T., F.D., and G.V.R. analyzed data; and J.W., M.J.K., M.W.V., M.A.I., W.J.N., and G.V.R. wrote the paper.
Competing interest statement: The authors declare no competing interest. W.J.N. is co-founder, scientific lead, and shareholder of Quantib BV. Other authors report no biomedical financial interests.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1902376116/-/DCSupplemental.

Published under the PNAS license.

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