Noirhomme Quentin, Brecheisen Ralph, Lesenfants Damien, Antonopoulos Georgios, Laureys Steven
Brain Innovation BV, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Cyclotron Research Centre, University of Liege, Liege, Belgium.
Brain Innovation BV, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
Neuroimage. 2017 Jan 15;145(Pt B):288-303. doi: 10.1016/j.neuroimage.2015.12.006. Epub 2015 Dec 12.
Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome.
鉴于临床床边检查可能存在较高的误诊率，基于神经影像学和电生理测量的机器学习技术越来越多地被用于昏迷患者、植物状态患者、最低意识状态患者或闭锁综合征患者。机器学习技术有潜力从群体水平的统计结果转向临床环境中的个性化预测。它们已被用于以下目的：（1）检测功能性任务期间大脑激活的变化，等同于行为指令跟随测试；（2）通过分析从多个处于静息状态的受试者获得的测量数据来估计意识迹象。在本综述中，我们全面概述了关于这两种方法的文献，并讨论了将当前研究结果转化为临床实践的问题。我们发现，大多数研究都面临着建立可靠的行为评估的困难以及患者觉醒水平的波动。这两个因素在很大程度上影响了机器学习方法的训练和验证。在涉及50多名患者的研究中，发现了关于存在意识迹象或良好预后的少量到中等程度的证据，其中一项研究甚至显示了关于良好预后的有力证据。
