Grey Matter Age Prediction as a Biomarker for Risk of Dementia: A Population-based Study

Abstract

Importance: The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as biomarker for early-stage neurodegeneration and potentially as a risk indicator for dementia. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. Objective: We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia in a general Dutch population, using a deep learning approach for predicting brain age based on MRI-derived grey matter maps. Design: Data was collected from participants of the cohort-based Rotterdam Study who underwent brain magnetic resonance imaging between 2006 and 2015. This study was performed in a longitudinal setting and all participant were followed up for incident dementia until 2016. Setting: The Rotterdam Study is a prospective population-based study, initiated in 1990 in the suburb Ommoord of in Rotterdam, the Netherlands. Participants: At baseline, 5496 dementia- and stroke-free participants (mean age 64.67+-9.82, 54.73% women) were scanned and screened for incident dementia. During 6.66+-2.46 years of follow-up, 159 people developed dementia. Main outcomes and measures: We built a convolutional neural network (CNN) model to predict brain age based on its MRI. Model prediction performance was measured in mean absolute error (MAE). Reproducibility of prediction was tested using the intraclass correlation coefficient (ICC) computed on a subset of 80 subjects. Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoE4 allele carriership, grey matter volume and intracranial volume. Additionally, we computed the attention maps of CNN, which shows which brain regions are important for age prediction. Results: MAE of brain age prediction was 4.45+-3.59 years and ICC was 0.97 (95% confidence interval CI=0.96-0.98). Logistic regression and Cox proportional hazards 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 grey matter density around the amygdalae and hippocampi primarily drive the age estimation. Conclusion and relevance: We show that the gap between predicted and chronological brain age is a biomarker associated with risk of dementia development. This suggests that it can be used as a biomarker, complimentary to those that are known, for dementia risk screening.

Atrophy-centered subtyping of mild cognitive impairment

“…The copyright holder for this this version posted November 30, 2020. ; https://doi.org/10.1101/2020.11.28.20238964 doi: medRxiv preprint 11 utility of deep learning models based on MRI-derived features in various tasks, such as diagnostic prediction (29), image reconstruction (30) and segmentation (31), and prognostic prediction of disease progression (32). Our choice to utilize a deep learning framework was further motivated by the assumption that complex and non-linear relationships exist between whole brain structure and progression of MCI/AD.…”

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Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

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