在最小意识状态下,自发的脑电图特征与扰动复杂度指数之间的解离
Silvia Casarotto1,2, Gabriel Hassan1, Mario Rosanova1
1Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
The European journal of neuroscience
|March 5, 2024
概括
在意识障碍 (DoC) 中评估意识是具有挑战性的. 扰乱复杂性指数 (PCI) 可靠地识别了最小意识状态 (MCS) 患者的意识,当自发电脑电图 (EEG) 没有确定性时.
科学领域:
- 神经科学是一个神经科学.
- 临床神经学 临床神经学
- 神经生理学 神经生理学
背景情况:
- 自发电脑电图 (EEG) 分析对于评估意识障碍 (DoC) 是至关重要的.
- 异常的自发EEG模式,如占主导的三角形或降低的α活性,可能难以解释,可能不准确地反映患者的意识水平.
- 意识可能会持续,尽管看似异常的EEG发现.
研究的目的:
- 为了比较扰乱复杂性指数 (PCI) 的诊断实用性,与最小意识状态 (MCS) 患者的自发EEG光谱测量.
- 为了确定PCI是否可以可靠地评估自发EEG没有结论时的意识.
主要方法:
- 研究了40名最小意识状态 (MCS) 患者的队列.
- 自发的EEG光谱测量 (delta和alpha功率) 与扰乱复杂度指数 (PCI) 进行了比较.
- 对PCI进行了评估,对意识的外部验证的切断值进行了评估.
主要成果:
- 极小意识状态 (MCS) 患者在自发EEG中表现出显著的变异性,其中一些患者表现出典型与植物状态/不响应觉醒综合征 (VS/UWS) 相关的模式 (占主导地位的三角形,减少的α).
- 所有MCS患者的PCI值都高于已确定的意识切线.
- PCI结果与可观察到的,尽管微妙,意识的行为迹象一致.
结论:
- 自发EEG节奏在评估MCS患者的意识时可能具有误导性.
- 扰乱复杂性指数 (PCI) 提供了一种可靠的方法来评估自发EEG没有确定性时的意识.
- PCI对医院的患者分层和个性化康复策略有实际意义.
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