Multimodal Brain Age Prediction with Feature Selection and Comparison

Brain age, an estimated biological age from anatomical and/or functional brain imaging data, and its deviation from the chronological age (brain age gap) have shown the potential to serve as biomarkers for characterizing typical brain development, the abnormal aging process, and early indicators of clinical neuropsychiatric problems. In this study, we leverage multimodal brain imaging data for brain age prediction. We studied and compared the performance of individual data modalities (gray matter density in components and regions of interest, cortical and subcortical anatomical features, resting-state functional connectivity) and different combinations of multiple data modalities using data collected from 1417 participants with age between 8 and 22 years. The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least squares regression models. We have achieved a mean absolute error of 1.22 years on the test data with 188 features selected equally from all data sources, better than any individual source. After bias correction, the brain age gap was significantly associated with attention accuracy/speed and motor speed in addition to age. Our results conclude that traditional machine learning with proper feature selection can achieve similar if not better performance compared to complex deep learning neural network methods for the used sample size.

I. Introduction

Our brain continues to develop and change throughout the lifespan. Neuroimaging data serve as a helpful resource that can be used to study in vivo brain development. Brain age has been recently proposed as one of the biological aging indicators [1] and has been estimated from structural and functional brain imaging data [2],[3] in relation to the chronological age of human subjects. The estimated brain age and its divergence from the chronological age have proved to be insightful biomarkers for characterizing typical brain development [1],[8], abnormal aging process [4], and early clinical indicators of neuropsychiatric problems [5],[9]-[11].