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