A voxel-level approach to brain age prediction: A method to assess
  regional brain aging
Neha Gianchandani Mahsa Dibaji Johanna Ospel Fernando Vega Mariana Bento M. Ethan MacDonald Roberto Souza
Brain aging is a regional phenomenon, a facet that remains relatively
under-explored within the realm of brain age prediction research using machine
learning methods. Voxel-level predictions can provide localized brain age
estimates that can provide granular insights into the regional aging processes.
This is essential to understand the differences in aging trajectories in
healthy versus diseased subjects. In this work, a deep learning-based multitask
model is proposed for voxel-level brain age prediction from T1-weighted
magnetic resonance images. The proposed model outperforms the models existing
in the literature and yields valuable clinical insights when applied to both
healthy and diseased populations. Regional analysis is performed on the
voxel-level brain age predictions to understand aging trajectories of known
anatomical regions in the brain and show that there exist disparities in
regional aging trajectories of healthy subjects compared to ones with
underlying neurological disorders such as Dementia and more specifically,
Alzheimer's disease. Our code is available at
https://github.com/nehagianchandani/Voxel-level-brain-age-prediction.