Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA.
Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA.
Neurobiol Aging. 2020 Sep;93:16-24. doi: 10.1016/j.neurobiolaging.2020.04.015. Epub 2020 Apr 24.
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.
我们绘制了 5 种不同生物标志物对认知健康成年人 2 种认知结果的综合和独特贡献。除了在全实验样本中生物标志物与认知的关联之外，我们还关注此类关联在保留数据中的表现。该研究纳入了 335 名认知正常的 20-80 岁参与者，计算了流体推理和词汇的 Z 分数。纳入了以下影像学数据：脑区体积、脑区厚度、白质束各向异性分数、特定深部灰质区体积和全脑白质高信号体积。在两个认知领域中，体积解释了大部分方差。在样本外数据中，流体推理最好由体积预测，词汇最好由所有模态的组合预测。尽管对年龄较大的参与者来说，预测效果总体上更好，但与零模型相比，年龄较大的参与者获得的信息较少。因此，优化的一组脑生物标志物可以在样本外数据中，以不同程度，预测流体和晶体智力。
