Wu Hang, Xie Qiuyou, Pan Jiahui, Liang Qimei, Lan Yue, Guo Yequn, Han Junrong, Xie Musi, Liu Yueyao, Jiang Liubei, Wu Xuehai, Li Yuanqing, Qin Pengmin
Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China.
Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China; Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China.
Neuroimage. 2023 May 15;272:120050. doi: 10.1016/j.neuroimage.2023.120050. Epub 2023 Mar 22.
Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.
利用任务相关的神经成像技术，近期研究发现，部分意识障碍（DOC）患者虽无遵循指令行为，但却表现出与健康对照者一样清晰的意识迹象，这被定义为认知运动分离（CMD）。然而，当CMD患者存在认知功能（如注意力、记忆力）损害时，现有的任务相关方法可能会失效，因为处于隐蔽意识状态的患者无法准确执行特定任务，从而被错误地认为是无意识的，这就导致了假阴性结果。近期研究表明，随着时间推移维持稳定的功能组织，即高时间稳定性，对支持意识至关重要。因此，时间稳定性可能是检测患者认知功能（如意识）的有力工具，但其在DOC中的变化情况以及识别CMD的能力尚不清楚。静息态功能磁共振成像（rs-fMRI）研究纳入了来自三个独立研究地点的119名参与者。采用滑动窗口方法来研究全局和局部时间稳定性，该方法测量大脑功能结构随时间的稳定程度。在第一个数据集中（36名/16名DOC/对照者）比较时间稳定性，然后构建支持向量机（SVM）分类器以区分DOC和对照者。此外，在第二个独立数据集中（35名/21名DOC/对照者）测试SVM分类器的可推广性。最后，将SVM分类器应用于第三个独立数据集，该数据集中的患者接受了rs-fMRI和脑机接口评估（4名/7名CMD/潜在非CMD），以测试其识别CMD的性能。我们的结果表明，DOC患者的全局和局部时间稳定性受损，尤其是在扣带回-脑岛任务控制网络、默认模式网络、额顶叶任务控制网络和突显网络区域。以时间稳定性作为特征，SVM模型不仅在第一个数据集中表现出良好性能（准确率 = 90%），在第二个数据集中也具有良好的可推广性（准确率 = 84%）。最重要的是，SVM模型在第三个数据集中识别CMD时具有良好的推广性（准确率 = 91%）。我们的初步研究结果表明，时间稳定性可能是协助诊断CMD的潜在工具。此外，本研究中所探究的时间稳定性也有助于更深入地理解意识的神经机制。
