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机器学习提供了新颖的神经生理学特征,可以预测抑制自动化反应的表现。

Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses.

机构信息

Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Saxony, Germany.

Department of Psychiatry, Charite University Hospital Berlin, Berlin, Germany.

出版信息

Sci Rep. 2018 Nov 2;8(1):16235. doi: 10.1038/s41598-018-34727-7.

Abstract

Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether "classical" ERPs are truly the best reflection or even causal to observable variations in behavior. Here, we used a data-driven strategy to extract features from neurophysiological data of n = 240 healthy young individuals who performed a Go/Nogo task and used machine learning methods in combination with source localization to identify the best predictors of inter-individual performance variations. Both Nogo-N2 and Nogo-P3 yielded predictions close to chance level, but a feature in between those two processes, associated with motor cortex activity (BA4), predicted group membership with up to ~68%. We further found two Nogo-associated features in the theta and alpha bands, that predicted behavioral performance with up to ~78%. Notably, the theta band feature contributed most to the prediction and occurred at the same time as the predictive ERP feature. Our approach provides a rigorous test for established neurophysiological correlates of response inhibition and suggests that other processes, which occur in between the Nogo-N2 and P3, might be of equal, if not even greater, importance.

摘要

神经生理特征,如事件相关电位(ERPs),长期以来一直被用于识别可能有助于任务表现的不同认知子过程。然而,“经典”ERPs 是否真的是对行为可观察变化的最佳反映,甚至是因果关系,仍不清楚。在这里,我们使用数据驱动的策略从 n = 240 名健康年轻个体的神经生理数据中提取特征,这些个体执行了 Go/Nogo 任务,并结合源定位使用机器学习方法来识别个体间表现变化的最佳预测因子。Nogo-N2 和 Nogo-P3 都产生了接近随机水平的预测,但介于这两个过程之间的一个特征,与运动皮层活动(BA4)相关,预测群体归属的准确率高达68%。我们进一步在 theta 和 alpha 频段中发现了两个与 Nogo 相关的特征,其预测行为表现的准确率高达78%。值得注意的是,theta 频段特征对预测的贡献最大,并且与预测性 ERP 特征同时发生。我们的方法为反应抑制的既定神经生理相关性提供了严格的测试,并表明在 Nogo-N2 和 P3 之间发生的其他过程可能具有同等甚至更大的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fa0/6215005/1018b6d4f072/41598_2018_34727_Fig1_HTML.jpg

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