Persistent metabolic youth in the aging female brain

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25 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)3251-3255
Number of pages5
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number8
DOIs
StatePublished - Feb 19 2019

Keywords

  • Brain aging
  • Brain metabolism
  • Machine learning
  • Neoteny
  • Sex differences

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