Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Geroscience. 2024 Apr;46(2):1713-1730. doi: 10.1007/s11357-023-00934-y. Epub 2023 Sep 21.
Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases 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 brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large 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 allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.
结构脑成像参数可以成功预测神经退行性疾病患者的认知表现，但在健康的老年人群中，这些参数大多无法预测认知能力。造成这种情况的一个重要原因可能是性别差异。行为研究发现，老年男性和女性在认知特征上存在差异，而不能通过将他们视为一个同质群体来进行检查。在当前的研究中，我们探讨了是否可以通过研究老年人群体中较大样本（1000BRAINS；N = 634；年龄范围为 55-85 岁）中特定于性别的认知特征，来提高从大脑结构（即区域灰质体积（GMV））预测认知表现的能力。使用机器学习（ML）方法评估预测性能。目标是：a）从男性和女性中提取的全样本认知成分解决方案；b）特定于性别的认知成分。结果表明，从区域 GMV 预测认知特征的能力普遍较低。在男性中，无论是整个样本还是特定于性别的认知成分，都观察到低可预测性。然而，在女性中，观察到了特定于性别的认知成分之间的可预测性差异，即视觉工作记忆（WM）和执行功能比流畅性和言语 WM 具有更高的可预测性。因此，结果强调了，解决特定于性别的认知特征可以更精细地研究可预测性差异，而这些差异在整个样本解决方案的预测中可能无法观察到。这些发现不仅强调了需要进一步研究每个认知成分的预测能力，还强调了在老年人群中进行特定于性别的分析的重要性。
