Department of Electrical Engineering, Yanshan University, Qinhuangdao, People's Republic of China.
Zhuhai UM Science & Technology Research Institute, Zhuhai, People's Republic of China.
J Neural Eng. 2024 Jul 26;21(4). doi: 10.1088/1741-2552/ad618b.
To improve the understanding and diagnostic accuracy of disorders of consciousness (DOC) by quantifying transcranial magnetic stimulation (TMS) evoked electroencephalography connectivity using permutation conditional mutual information (PCMI).PCMI can characterize the functional connectivity between different brain regions. This study employed PCMI to analyze TMS-evoked cortical connectivity (TEC) in 154 DOC patients and 16 normal controls, focusing on optimizing parameter selection for PCMI (Data length, Order length, Time delay). We compared short-range and long-range PCMI values across different consciousness states-unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and normal (NOR)-and assessed various feature selection and classification techniques to distinguish these states.(1) PCMI can quantify TEC. We found optimal parameters to be Data length: 500 ms; Order: 3; Time delay: 6 ms. (2) TMS evoked potentials (TEPs) for NOR showed a rich response, while MCS patients showed only a few components, and UWS patients had almost no significant components. The values of PCMI connectivity metrics demonstrated its usefulness for measuring cortical connectivity evoked by TMS. From NOR to MCS to UWS, the number and strength of TEC decreased. Quantitative analysis revealed significant differences in the strength and number of TEC in the entire brain, local regions and inter-regions among different consciousness states. (3) A decision tree with feature selection by mutual information performed the best (balanced accuracy: 87.0% and accuracy: 83.5%). This model could accurately identify NOR (100.0%), but had lower identification accuracy for UWS (86.5%) and MCS (74.1%).The application of PCMI in measuring TMS-evoked connectivity provides a robust metric that enhances our ability to differentiate between various states of consciousness in DOC patients. This approach not only aids in clinical diagnosis but also contributes to the broader understanding of cortical connectivity and consciousness.
为了通过使用置换条件互信息（PCMI）量化经颅磁刺激（TMS）诱发的脑电图连通性来提高对意识障碍（DOC）的理解和诊断准确性，PCMI 可以描述不同脑区之间的功能连通性。本研究采用 PCMI 分析了 154 例 DOC 患者和 16 例正常对照者的 TMS 诱发皮质连通性（TEC），重点优化了 PCMI 的参数选择（数据长度、阶数、时间延迟）。我们比较了不同意识状态（无反应性觉醒综合征（UWS）、最小意识状态（MCS）和正常（NOR））下的短程和长程 PCMI 值，并评估了各种特征选择和分类技术，以区分这些状态。（1）PCMI 可以量化 TEC。我们发现最佳参数为数据长度：500ms；阶数：3；时间延迟：6ms。（2）NOR 的 TMS 诱发电位（TEP）表现出丰富的反应，而 MCS 患者仅表现出少数成分，UWS 患者几乎没有明显成分。PCMI 连通性度量值表明其可用于测量 TMS 诱发的皮质连通性。从 NOR 到 MCS 到 UWS，TEC 的数量和强度逐渐降低。定量分析显示，不同意识状态之间整个大脑、局部区域和区域间的 TEC 的强度和数量存在显著差异。（3）基于互信息的特征选择的决策树表现最佳（平衡准确率：87.0%，准确率：83.5%）。该模型可以准确识别 NOR（100.0%），但对 UWS（86.5%）和 MCS（74.1%）的识别准确率较低。PCMI 在测量 TMS 诱发连通性中的应用提供了一种强大的度量标准，增强了我们区分 DOC 患者不同意识状态的能力。这种方法不仅有助于临床诊断，还有助于更广泛地理解皮质连通性和意识。
