Priv.-Doz. Dr. rer. medic. Christiane Jockwitz

Structural brain imaging parameters may successfully predict cognitive performance in pathological conditions but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from grey matter volumes (GMV) of all parcels from the Julich Brain Cytoarchitectonic Atlas, implemented in the EBRAINS Research Infrastructure, would benefit from the investigation of sex-specific cognitive profiles in a large population-based sample of older adults (1000BRAINS; N = 634; age range 55-85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allow a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings emphasize the necessity for further investigation into sex-specific analyses of brain-behaviour associations in older adults. Moreover, they may also be instrumental in adapting personalized prediction and simulation approaches, such as the virtual ageing brain (TVB), to predict cognitive decline.