O2‐02‐05: PREDICTING BRAIN AGE AS A BIOMARKER FOR RISK OF DEMENTIA Article Swipe

PDF

Related Concepts

Dementia Intraclass correlation Magnetic resonance imaging Medicine Biomarker Population Grey matter Rotterdam Study Psychology Internal medicine White matter Prospective cohort study Radiology Psychometrics Clinical psychology Disease Environmental health Biochemistry Chemistry

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 ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1016/j.jalz.2019.06.4461 · OA: W2980446596

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as biomarker for dementia. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia in a general elderly population, using a deep learning (DL) approach for predicting brain age based on MRI-derived grey matter maps. From the population-based Rotterdam Study, 5656 dementia-free and stroke-free participants (mean age 64.67±9.82, 54.73% women) underwent brain MRI at 1.5T, including three-dimensional T1-weighted sequence, between 2006 and 2015. All participants were followed for incident dementia until 2016. During 6.66±2.46 years of follow-up, 159 subjects developed dementia. Subjects were split into control (N=5497) and incident dementia (N=159) groups. Control group data was split into training, validation and test sets, and used to train a convolutional neural network (CNN) model to predict brain age. We built a CNN model to predict brain age based on its MRI. We used mean absolute error (MAE) to measure model performance in predicting brain age. Reproducibility of prediction was tested using the intraclass correlation coefficient (ICC) computed on a subset of 80 subjects. Cox proportional hazards models were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoEe4 allele carriership, grey matter volume and intracranial volume. Additionally, we computed the Grad-CAM attention maps of CNN, which shows which brain regions are important for age prediction. MAE of brain age prediction was 4.45±3.59 years and ICC was 0.97 (95% confidence interval CI=0.96-0.98). Cox proportional hazards models showed that the age gap was significantly related to incident dementia (hazard ratio HR=1.11 and 95% CI=1.06-1.15). Attention maps indicated that grey matter density around the amygdalae and hippocampi primarily drive the age estimation. These brain regions are relevant to brain aging and are also affected by neurodegenerative processes.