Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex.
Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex.
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认知表现的个体差异可以通过大脑皮层的整体而不是局部的大胆活动模式更好地预测。
DOI：
10.1093/cercor/bhaa290
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Fan,ChunChieh
中科院分区：
文献类型：
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作者：
Zhao,Weiqi;Palmer,ClareE;Thompson,WesleyK;Chaarani,Bader;Garavan,HughP;Casey,BJ;Jernigan,TerryL;Dale,AndersM;Fan,ChunChieh
尽管神经影像学研究在揭示行为的神经生物学机制方面发挥着核心作用，但它仍面临着为认知过程和临床结果产生可靠生物标志物的挑战。统计学上显著的脑区，确定的质量单变量统计模型中常用的神经影像学研究，解释最小的表型变异，限制了神经影像表型的翻译效用。这可能是由于观察到行为特征受到神经成像表型变化的影响，这些表型在大脑皮层中分布广泛，因此不能被神经成像研究中通常报告的阈值统计参数图捕获。在这里，我们开发了一种新的多变量预测方法，贝叶斯polyvertex评分，它将一个无阈值的统计参数图转化为一个汇总评分，汇总了大脑皮层中许多但很小的影响，用于行为预测。通过明确假设全球分布的效应量模式并对质量单变量汇总统计量进行操作，它能够实现比质量单变量和流行的多变量方法更高的样本外方差解释，同时仍然保留生成模型的可解释性。我们的研究结果表明，与遗传学领域观察到的多源性类似，复杂行为的神经基础可能在于神经成像表型效应大小变化的全局模式，而不是局部的候选大脑区域和网络。
Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.