期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 8, 页码 3251-3255出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1815917116
关键词
brain aging; brain metabolism; sex differences; neoteny; machine learning
资金
- Barnes-Jewish Hospital Foundation
- James S. McDonnell Foundation
- McDonnell Center for Systems Neuroscience [22-3922-26239N]
- National Institutes of Health [NS06833, NS057901, P50 AG05681, P01 AG003991, R01 AG053503, R01 AG057536, UF1AG032438]
Sex differences influence brain morphology and physiology during both development and aging. Here we apply a machine learning algorithm to a multiparametric brain PET imaging dataset acquired in a cohort of 20- to 82-year-old, cognitively normal adults (n = 205) to define their metabolic brain age. We find that throughout the adult life span the female brain has a persistently lower metabolic brain age-relative to their chronological age-compared with the male brain. The persistence of relatively younger metabolic brain age in females throughout adulthood suggests that development might in part influence sex differences in brain aging. Our results also demonstrate that trajectories of natural brain aging vary significantly among individuals and provide a method to measure this.
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