A voxel-level approach to brain age prediction: A method to assess regional brain aging

• DOI: 10.59275/j.melba.2024-4dg2
• Publication Date: 2024-04-24T21:43:39Z
• Authors: Neha Gianchandani; Mahsa Dibaji; Johanna Ospel; Fernando Vega; Mariana Bento; M. Ethan MacDonald; Roberto Souza
• Keywords: Brain Aging; Aging brain

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