Brain structure ages—A new biomarker for multi‐disease classification

• Published in: Human brain mapping Vol. 45; no. 1; pp. e26558 - n/a
• Main Authors: Nguyen, Huy‐Dung, Clément, Michaël, Mansencal, Boris, Coupé, Pierrick
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2024; Wiley
• Subjects: Age; Age composition; Age determination; age prediction; Aging; Algorithms; Alzheimer Disease - pathology; Alzheimer's disease; Anomalies; Biomarkers; Brain; Brain - diagnostic imaging; Brain - pathology; Brain research; brain structure ages; Chronology; Classification; Computer Science; Deep learning; Deviation; Differential diagnosis; Disease; DNA methylation; Estimation; frontotemporal dementia; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; multiple sclerosis; multi‐disease classification; Neuroimaging; Neuroimaging - methods; Neurological diseases; Parkinson's disease; schizophrenia; deep learning; Parkinson's disease; age prediction; multiple sclerosis; multi-disease classification; frontotemporal dementia; schizophrenia; Alzheimer's disease; brain structure ages; Deep learning; Multi-disease Classification; Multiple sclerosis; Brain Structure Ages; Schizophrenia; Age prediction; Frontotemporal dementia
• DOI: 10.1002/hbm.26558
• ISSN: 1065-9471; 1097-0193

Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel‐wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state‐of‐the‐art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi‐disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts. Our novel brain structure age biomarker enables to accurately predict the chronological age of healthy people. The deviation between the brain structure age and the subject's age can be used to improve the performance of a multi‐disease classification. Our framework provides a visualization of the 3D deviation map for a better interpretation.