Gray Matter Age Prediction as a Biomarker for Risk of Dementia

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 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. deep learning | dementia | age prediction | magnetic resonance imaging | voxel-based morphometry T he human brain continuously changes throughout the entire lifespan. These changes partially reflect a normal aging process and are not necessarily pathological (1). However, neurodegenerative diseases, including dementia, also affect brain structure and function (2, 3). Therefore, a better understanding and modeling of normal brain aging can help to disentangle these two processes and improve the detection of early-stage neurodegeneration.Age prediction models based on brain MRI are a popular trend in neuroscience (4-7). The difference between predicted 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, such as Alzheimer's disease (8), schizophrenia, epilepsy, or diabetes (7,9,10).In recent years, CNNs have become the methodology of choice for analyzing medical images. These models are able to learn complex relations between input data and desired outcomes. Recent studies (11, 12) were able to demonstrate that CNN models can be successfully applied in brain MRI-based age prediction (5, 6).Although cross-sectional studies have suggested that the gap between predicted and chronological age may serve as a biomarker for dementia diagnosis, it remains unclear whether this is also t...

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