Persistent metabolic youth in the aging female brain

Manu S. Goyal; Tyler M. Blazey; Yi Su; Lars E. Couture; Tony J. Durbin; Randall J. Bateman; Tammie L.S. Benzinger; John C. Morris; Marcus E. Raichle; Andrei G. Vlassenko

doi:10.1073/pnas.1815917116

Persistent metabolic youth in the aging female brain

Manu S. Goyal, Tyler M. Blazey, Yi Su, Lars E. Couture, Tony J. Durbin, Randall J. Bateman, Tammie L.S. Benzinger, John C. Morris, Marcus E. Raichle, Andrei G. Vlassenko

Research output: Contribution to journal › Article › peer-review

119 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 language	English
Pages (from-to)	3251-3255
Number of pages	5
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	116
Issue number	8
DOIs	https://doi.org/10.1073/pnas.1815917116
State	Published - Feb 19 2019

Keywords

Brain aging
Brain metabolism
Machine learning
Neoteny
Sex differences

Access to Document

10.1073/pnas.1815917116

Cite this

@article{0257619134fc4d5b8cd972e72a533a78,

title = "Persistent metabolic youth in the aging female brain",

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.",

keywords = "Brain aging, Brain metabolism, Machine learning, Neoteny, Sex differences",

author = "Goyal, {Manu S.} and Blazey, {Tyler M.} and Yi Su and Couture, {Lars E.} and Durbin, {Tony J.} and Bateman, {Randall J.} and Benzinger, {Tammie L.S.} and Morris, {John C.} and Raichle, {Marcus E.} and Vlassenko, {Andrei G.}",

year = "2019",

month = feb,

day = "19",

doi = "10.1073/pnas.1815917116",

language = "English",

volume = "116",

pages = "3251--3255",

journal = "Proceedings of the National Academy of Sciences of the United States of America",

issn = "0027-8424",

number = "8",

}

TY - JOUR

T1 - Persistent metabolic youth in the aging female brain

AU - Goyal, Manu S.

AU - Blazey, Tyler M.

AU - Su, Yi

AU - Couture, Lars E.

AU - Durbin, Tony J.

AU - Bateman, Randall J.

AU - Benzinger, Tammie L.S.

AU - Morris, John C.

AU - Raichle, Marcus E.

AU - Vlassenko, Andrei G.

PY - 2019/2/19

Y1 - 2019/2/19

N2 - 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.

AB - 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.

KW - Brain aging

KW - Brain metabolism

KW - Machine learning

KW - Neoteny

KW - Sex differences

UR - http://www.scopus.com/inward/record.url?scp=85061869820&partnerID=8YFLogxK

U2 - 10.1073/pnas.1815917116

DO - 10.1073/pnas.1815917116

M3 - Article

C2 - 30718410

AN - SCOPUS:85061869820

SN - 0027-8424

VL - 116

SP - 3251

EP - 3255

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 8

ER -

Persistent metabolic youth in the aging female brain

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this