Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6062-6065. doi: 10.1109/EMBC46164.2021.9630923.
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).
本文分析了 10 名人类被试的局部场电位（LFP），以发现认知冲突的频域相关生物标志物。我们利用被试在认知任务（称为多源干扰任务（MSIT））期间的皮质和皮质下 LFP 记录来解码任务参与情况，并发现可能有助于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用于以 88.1%的中位数准确度对任务和非任务段进行分类。在这里，首先使用贝叶斯因子对特征进行排序，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。其次，我们表明，θ（4-8 Hz）频段和高γ（65-200 Hz）频段的振荡在任务执行期间被调制。第三，通过从特定的大脑感兴趣区域隔离时间序列，我们观察到背外侧前额叶皮层的特征子集足以解码任务状态。本文表明，认知控制会引起强大的神经学特征，尤其是在前额叶皮层（PFC）中。
