Spectral Features Based Decoding of Task Engagement: The Role of Theta and High Gamma Bands in Cognitive Control

Spectral Features Based Decoding of Task Engagement: The Role of Theta and High Gamma Bands in Cognitive Control
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基于光谱特征的任务参与解码:Theta 和高伽玛波段在认知控制中的作用

DOI:
10.1109/embc46164.2021.9630923
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发表时间:
2021
期刊:
Proc. 2021 IEEE Engineering and Medicine in Biology (EMBC
影响因子:
--
通讯作者:
Parhi, Keshab K.
Parhi, Keshab K.
中科院分区:
--
文献类型:
--
作者:
Avvaru, Sandeep;Provenza, Nicole R.;Widge, Alik S.;Parhi, Keshab K.

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本文分析了10名人类受试者的局部场电位(LFP),以发现认知冲突的频率依赖性生物标志物。我们利用皮层和皮层下的LFP记录的主题在认知任务称为多源干扰任务(MSIT)。我们解码任务参与,并发现可能促进闭环神经调节以增强认知控制的生物标志物。首先,我们表明,在预定义的频带中的频谱功率特征可以用于分类任务和非任务段,平均准确率为88.1%。在这里,首先使用贝叶斯因子对特征进行排名,然后将其用作特定于主题的线性支持向量机分类器的输入。第二,我们表明,θ(4-8赫兹)带,和高伽玛(65-200赫兹)带振荡调制期间的任务执行。第三,通过将时间序列从特定的感兴趣的大脑区域中分离出来,我们观察到背外侧前额叶皮层特征的一个子集足以解码任务状态。这篇论文表明,认知控制唤起了强大的神经特征,特别是在前额叶皮层(PFC)。
This paper analyzes local field potentials (LFP) from 10 human subjects to discover frequency-dependent biomarkers of cognitive conflict. We utilize cortical and sub-cortical LFP recordings from the subjects during a cognitive task known as the Multi-Source Interference Task (MSIT). We decode the task engagement and discover biomarkers that may facilitate closed-loop neuromodulation to enhance cognitive control. First, we show that spectral power features in predefined frequency bands can be used to classify task and non-task segments with a median accuracy of 88.1%. Here the features are first ranked using the Bayes Factor and then used as inputs to subject-specific linear support vector machine classifiers. Second, we show that theta (4–8 Hz) band, and high gamma (65–200 Hz) band oscillations are modulated during the task performance. Third, by isolating time-series from specific brain regions of interest, we observe that a subset of the dorsolateral prefrontal cortex features is sufficient to decode the task states. The paper shows that cognitive control evokes robust neurological signatures, especially in the prefrontal cortex (PFC).
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