Modeling Life-Span Brain Age from Large-Scale Dataset Based on Multi-level Information Fusion

Predicted brain age could be used to measure individual brain status over development and degeneration, which could also indicate the potential risk of age-related brain disorders. Although various techniques for the estimation of brain age have been developed, most approaches only cover a small age range, either young or elderly period, leading to limited applications. In this work, we propose a novel approach to build a brain age prediction model on a lifespan dataset with T1-weighted magnetic resonance imaging (MRI) scans. First, we utilize different neural networks to extract features from 1) an original 3D MRI scan associated with the brain maturing and aging process, 2) three (axial, coronal, and sagittal) 2D slices selected based on prior knowledge to provide possible white matter hypointensity information, and 3) volume ratios of different brain regions related to maturing and aging. Then, these extracted features of multiple levels are fused by the transformer-based cross-attention mechanism to predict the brain age. Our experiments are conducted on a total of 5376 subjects aged from 6 to 96 years from 8 cohorts. In particular, our model is built on 3372 healthy subjects and applied to 2004 subjects with brain disorders. Experimental results show that our method achieves a mean absolute error (MAE) of 2.72 years between estimated brain age and chronological age. Furthermore, when applying our model to age-related brain disorders, it turns out that both cerebral small vessel disease (SVD) and Alzheimer's disease (AD) groups demonstrate accelerated brain aging.