PDF(Pubmed)
Abstract：
Although auditory stimuli benefit patients with disorders of consciousness (DOC), the optimal stimulus remains unclear. We explored the most effective electroencephalography (EEG)-tracking method for eliciting brain responses to auditory stimuli and assessed its potential as a neural marker to improve DOC diagnosis. We collected 58 EEG recordings from patients with DOC to evaluate the classification model\'s performance and optimal auditory stimulus. Using non-linear dynamic analysis (approximate entropy [ApEn]), we assessed EEG responses to various auditory stimuli (resting state, preferred music, subject\'s own name [SON], and familiar music) in 40 patients. The diagnostic performance of the optimal stimulus-induced EEG classification for vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) was compared with the Coma Recovery Scale-Revision in 18 patients using the machine learning cascade forward backpropagation neural network model. Regardless of patient status, preferred music significantly activated the cerebral cortex. Patients in MCS showed increased activity in the prefrontal pole and central, occipital, and temporal cortices, whereas those in VS/UWS showed activity in the prefrontal and anterior temporal lobes. Patients in VS/UWS exhibited the lowest preferred music-induced ApEn differences in the central, middle, and posterior temporal lobes compared with those in MCS. The resting state ApEn value of the prefrontal pole (0.77) distinguished VS/UWS from MCS with 61.11% accuracy. The cascade forward backpropagation neural network tested for ApEn values in the resting state and preferred music-induced ApEn differences achieved an average of 83.33% accuracy in distinguishing VS/UWS from MCS (based on K-fold cross-validation). EEG non-linear analysis quantifies cortical responses in patients with DOC, with preferred music inducing more intense EEG responses than SON and familiar music. Machine learning algorithms combined with auditory stimuli showed strong potential for improving DOC diagnosis. Future studies should explore the optimal multimodal sensory stimuli tailored for individual patients.Trial registration: The study is registered in the Chinese Registry of Clinical Trials (Approval no: KYLL-2023-414, Registration code: ChiCTR2300079310).
摘要：
尽管听觉刺激对意识障碍（DOC）患者有益，最佳刺激仍不清楚。我们探索了最有效的脑电图（EEG）跟踪方法，用于引起大脑对听觉刺激的反应，并评估了其作为改善DOC诊断的神经标志物的潜力。我们收集了58例DOC患者的EEG记录，以评估分类模型的性能和最佳听觉刺激。使用非线性动态分析(近似熵[ApEn])，我们评估了脑电图对各种听觉刺激的反应(静息状态，喜欢的音乐，受试者自己的名字[SON]，和熟悉的音乐）在40名患者中。使用机器学习级联前向传播神经网络模型，将最佳刺激诱导的EEG分类对植物状态（VS）/无反应的觉醒综合征（UWS）和最低意识状态（MCS）的诊断性能与昏迷恢复量表修订进行了比较。不管病人状况如何,喜欢的音乐显著激活大脑皮层。MCS患者在前额叶和中央显示活动增加，枕骨,和时间皮层，而VS/UWS中的那些在前额叶和颞叶前显示活动。VS/UWS患者在中枢表现出最低的首选音乐诱导的ApEn差异，中间,和后颞叶与MCS相比。前额极点的静息状态ApEn值（0.77）将VS/UWS与MCS区分开来，准确率为61.11％。级联前向反向传播神经网络测试了静息状态下的ApEn值和首选音乐诱导的ApEn差异，在区分VS/UWS与MCS时（基于K折交叉验证），平均准确率为83.33％。EEG非线性分析量化了DOC患者的皮质反应，与SON和熟悉的音乐相比，首选音乐会引起更强烈的EEG反应。结合听觉刺激的机器学习算法显示出改善DOC诊断的强大潜力。未来的研究应该探索为个体患者量身定制的最佳多模式感觉刺激。试验注册：本研究在中国临床试验注册中心注册（批准号：KYLL-2023-414，注册码：ChiCTR2300079310）。
