From a deep learning model back to the brain—Identifying regional predictors and their relation to aging
Year: 2020
Type: article
Abstract: We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in "explanation maps" that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population-based, rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.
Source: Human Brain Mapping
DOI: 10.1002/hbm.25011
Institution: Ben-Gurion University of the Negev
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,200
Funders: Alzheimer's Disease Neuroimaging Initiative, Commonwealth Scientific and Industrial Research Organisation, Ben-Gurion University of the Negev, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research, U.S. National Library of Medicine, National Center for Research Resources
Awards: G104586243, G1262657692, G1360726769, Mapping Abnormal Neurodevelopment in Schizophrenia, G2305086180, Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8), G2564345267, Big Data for Discovery Science, G2717542597, Translational Methods/Facilities Core (TMF - Core) (8 of 8), G286115173, Morphometry Biomedical Informatics Research Network, G2990223308, G3291080418, Neuroimaging Informatics Tools and Resources Clearinghouse Outreach, Infrastructure, and Content Maintenance, G40706755, G4427631003, G4515475997, G4545355773, Alzheimer's Disease Neuroimaging Initiative, Memory Mechanisms and Mental Disorders, Whole Genome Assoc. Analysis Strategies for Multi. Phenotypes, Alzheimer's Disease Research Center, G7511592288, G7868656944, Alzheimer's Disease Research Center
OpenAlex ID: W3017066732