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The human brain's white matter (WM) structure is of immense interest to thescientific community. Diffusion MRI gives a powerful tool to describe the brainWM structure noninvasively. To potentially enable monitoring of age-relatedchanges and investigation of sex-related brain structure differences on themapping between the brain connectome and healthy subjects' age and sex, weextract fiber-cluster-based diffusion features and predict sex and age with anovel ensembled neural network classifier. We conduct experiments on the HumanConnectome Project (HCP) young adult dataset and show that our model achieves94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We alsoshow that the fractional anisotropy (FA) is the most predictive of sex, whilethe number of fibers is the most predictive of age and the combination ofdifferent features can improve the model performance.

Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach