Open AccessSex differences in predictors and regional patterns of brain age gap estimates
Author(s) -
Sanford Nicole,
Ge Ruiyang,
Antoniades Mathilde,
Modabbernia Amirhossein,
Haas Shalaila S.,
Whalley Heather C.,
Galea Liisa,
Popescu Sebastian G.,
Cole James H.,
Frangou Sophia
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25983
Subject(s) - insula , neuroimaging , psychology , fractional anisotropy , sex characteristics , brain size , white matter , magnetic resonance imaging , medicine , neuroscience , radiology
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.