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

Global brain age has been used as an effective biomarker to study the correlation between brain aging and neurological disorders. However, 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. The proposed model outperforms the model existing in the literature and yields valuable clinical insights when applied to both healthy and diseased populations. Most findings from the analysis align with existing studies on aging, whereas other findings are intriguing and could be potential biomarkers of early-stage neurodegeneration detection. 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. A comparative analysis with traditional deep learning interpretability methods showed that the proposed voxel-level approach to brain age prediction is an effective way to understand regional aging trajectories while being quantitative in nature. The source code is publicly available at https://github.com/nehagianchandani/Voxel-level-brain-age-prediction.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Engineering – Biomedical

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2023

Author dc:creator

• Gianchandani, Neha

Advisors dc:contributor.advisor

• Souza, Roberto
• MacDonald, M. Ethan

Committee members dc:contributor.committeemember

• Bayat, Sayeh
• Pike, Bruce
• Harris, Ashley
• Tan, Benjamin

• machine learning, brain aging, regional brain aging

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

en

DOI

https://doi.org/10.11575/PRISM/42465

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/117622