Year: 2023
Type: preprint
Abstract: In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.
Source: PubMed
Institutions: University of Pennsylvania, University of Rochester
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1423396920, G2189010064, G2423387114, G2835601961, G29951154, G3216574877, G4119534169, G4475667458, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASE, Alzheimer's Disease Neuroimaging Initiative, G5850682332, Healthy Aging and Senile Dementia, Washington University Institute of Clinical and Translational Sciences, G764699806, G8895907669, G8897063270, G9000039018, G9000112288, G9000115080, G9000122335, G9000217785, G9000266300, Alzheimer's Disease Research Center, THE XNAT IMAGING INFORMATICS PLATFORM, Antecedent Biomarkers for Alzheimer Disease: The Adult Children Study
OpenAlex ID: W4387439614