We mapped out the combined and unique contributions of 5 different biomarkers for 2 cognitive outcomes in cognitively healthy adults. Beside associations of biomarkers with cognition in the full experimental sample, we focused on how well any such associations would persist in held-out data. Three hundred thirty-five cognitively normal participants, 20-80 years older, were included in the study. Z-scores were computed for fluid reasoning and vocabulary. The following imaging data were included: regional brain volume, regional thickness, fractional anisotropy of white-matter tracts, volumes of select deep gray-matter regions, and global white-matter hyperintensity. Volume accounted for most of the variance in both cognitive domains. In out-of-sample data, fluid reasoning was best predicted by volumes, but vocabulary by the combination of all modalities. Although the predictive utility was better overall for older participants, the information gleaned relative to null models was less for older participants. An optimized set of brain biomarkers can thus predict cognition in out-of-sample data, to various degrees, for both fluid and crystallized intelligence.

中文翻译：

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基于大脑形态测量的成年认知优化预测

我们绘制了 5 种不同生物标志物对认知健康成人的 2 种认知结果的综合和独特贡献。除了完整实验样本中生物标志物与认知的关联外，我们还关注任何此类关联在保留数据中的持久性。335 名认知正常的参与者，年龄在 20-80 岁之间，被纳入研究。Z 分数是针对流畅推理和词汇计算的。包括以下成像数据：区域脑容量、区域厚度、白质束的分数各向异性、选定的深灰质区域的体积和全局白质高信号。体积占两个认知领域的大部分差异。在样本外数据中，流体推理最好通过数量来预测，而是由所有形式组合而成的词汇。尽管总体上对老年参与者的预测效用更好，但相对于空模型收集的信息对于老年参与者来说却较少。因此，一组优化的大脑生物标志物可以在不同程度上预测样本外数据中流体和结晶智力的认知。