Journal
CEREBRAL CORTEX
Volume 30, Issue 11, Pages 5844-5862Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhaa161
Keywords
aging; brain age; machine learning; neurological diseases; structural covariance network (SCN)
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Funding
- Aging and Health Research Center at National Yang Ming University, Taiwan [MOST 108-2634-F-010-001]
- Center for Geriatrics and Gerontology of Taipei Veterans General Hospital of Taiwan [MOST 108-2321-B-010-013-MY2]
- Ministry of Science and Technology, Taiwan [MOST 106-2221-E-010011, MOST 107-2221-E-010-010-MY3, MOST 108-2420H-010-001, MOST 108-2321-B-010-010-MY2]
- National Health Research Institutes [NHRI-EX108-10611EI]
- Brain Research Center, National Yang-Ming University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taipei, Taiwan
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The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
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