Decoding Human Cognitive Control Using Functional Connectivity of Local Field Potentials
Decoding Human Cognitive Control Using Functional Connectivity of Local Field Potentials
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DOI：
10.1109/embc46164.2021.9630706
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发表时间：
2021-11
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影响因子：
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通讯作者：
S. Avvaru;N. Provenza;A. Widge;K. Parhi
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文献类型：
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作者：
S. Avvaru;N. Provenza;A. Widge;K. Parhi
许多以认知控制受损为特征的精神疾病患者无法从黄金标准的临床治疗中得到缓解，因此迫切需要新的替代方案。本文开发了一种神经解码器来检测10名人类受试者在基于冲突的行为任务多源干扰任务(MSIT)中的任务投入。任务投入在这里特别有意义，因为在这些状态下的闭合环路大脑刺激可以增强决策能力。提取电极的功能连通性模式。对这些模式进行主成分分析，并使用排序的主成分作为输入来训练特定于主题的线性支持向量机分类器。在本文中，我们发现任务投入可以与背景脑活动区分开来，准确率的中位数为89.7%。这是通过在任务执行过程中通过记录局部场电位来构建分布式功能网络来实现的。另一个挑战是，目标导向的努力发生在更高的时间分辨率上。因此，对于主动干预，必须以类似的速度检测任务敬业度。我们表明，我们的算法可以在不到2秒的时间内从神经记录中检测到任务参与；使用特定于应用的设备可以进一步改进这一点。
Many patients with mental illnesses characterized by impaired cognitive control have no relief from gold-standard clinical treatments resulting in a pressing need for new alternatives. This paper develops a neural decoder to detect task engagement in ten human subjects during a conflict-based behavioral task known as the multi-source interference task (MSIT). Task engagement is of particular interest here because closed-loop brain stimulation during those states can augment decision-making. The functional connectivity patterns of the electrodes are extracted. A principal component analysis of these patterns is carried out and the ranked principal components are used as inputs to train subject-specific linear support vector machine classifiers. In this paper, we show that task engagement can be differentiated from background brain activity with a median accuracy of 89.7%. This was accomplished by constructing distributed functional networks from local field potentials recording during the task performance. A further challenge is that goal-directed efforts take place over higher temporal resolution. Task engagement must thus be detected at a similar rate for proactive intervention. We show that our algorithms can detect task engagement from neural recordings in less than 2 seconds; this can be further improved using an application-specific device.