Sanford, Nicole;
Ge, Ruiyang;
Antoniades, Mathilde;
Modabbernia, Amirhossein;
Haas, Shalaila S;
Whalley, Heather C;
Galea, Liisa;
... Frangou, Sophia; + view all
(2022)
Sex differences in predictors and regional patterns of brain age gap estimates.
Human Brain Mapping
10.1002/hbm.25983.
(In press).
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Abstract
The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22–37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.
| Type: | Article |
|---|---|
| Title: | Sex differences in predictors and regional patterns of brain age gap estimates |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/hbm.25983 |
| Publisher version: | https://doi.org/10.1002/hbm.25983 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Neurosciences, Neuroimaging, Radiology, Nuclear Medicine & Medical Imaging, Neurosciences & Neurology, aging, brainAGE, human connectome project, machine learning, sex differences, structural MRI, young adults, AEROBIC GLYCOLYSIS, CORTICAL THICKNESS, COGNITIVE FUNCTION, AFRICAN-AMERICANS, SLEEP, PLASTICITY, DISEASE, HEALTH, VOLUME, RISK |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152366 |
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