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The brain's white matter (WM) undergoes developmental and degenerativeprocesses during the human lifespan. To investigate the relationship between WManatomical regions and age, we study diffusion magnetic resonance imagingtractography that is finely parcellated into fiber clusters in the deep,superficial, and cerebellar WM. We propose a deep-learning-based age predictionmodel that leverages large convolutional kernels and inverted bottlenecks. Weimprove performance using novel discrete multi-faceted mix data augmentationand a novel prior-knowledge-based loss function that encourages age predictionsin the expected range. We study a dataset of 965 healthy young adults (22-37years) derived from the Human Connectome Project (HCP). Experimental resultsdemonstrate that the proposed model achieves a mean absolute error of 2.59years and outperforms compared methods. We find that the deep WM is the mostinformative for age prediction in this cohort, while the superficial WM is theleast informative. Overall, the most predictive WM tracts are thethalamo-frontal tract from the deep WM and the intracerebellar input andPurkinje tract from the cerebellar WM.

Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections