Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

Chen Yuan Kuo, Pei Lin Lee, Sheng Che Hung, Li Kuo Liu, Wei Ju Lee, Chih Ping Chung, Albert C. Yang, Shih Jen Tsai, Pei Ning Wang, Liang Kung Chen, Kun Hsien Chou*, Ching Po Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)5844-5862
Number of pages19
JournalCerebral Cortex
Volume30
Issue number11
DOIs
StatePublished - 1 Nov 2020

Keywords

  • aging
  • brain age
  • machine learning
  • neurological diseases
  • structural covariance network (SCN)

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