Wu Jennifer, Srinivasan Ramesh, Burke Quinlan Erin, Solodkin Ana, Small Steven L, Cramer Steven C
Department of Anatomy and Neurobiology, University of California, Irvine, California;
Department of Cognitive Sciences, University of California, Irvine, California; and.
J Neurophysiol. 2016 Jun 1;115(5):2399-405. doi: 10.1152/jn.00978.2015. Epub 2016 Mar 2.
EEG has been used to study acute stroke for decades; however, because of several limitations EEG-based measures rarely inform clinical decision-making in this setting. Recent advances in EEG hardware, recording electrodes, and EEG software could overcome these limitations. The present study examined how well dense-array (256 electrodes) EEG, acquired with a saline-lead net and analyzed with whole brain partial least squares (PLS) modeling, captured extent of acute stroke behavioral deficits and varied in relation to acute brain injury. In 24 patients admitted for acute ischemic stroke, 3 min of resting-state EEG was acquired at bedside, including in the ER and ICU. Traditional quantitative EEG measures (power in a specific lead, in any frequency band) showed a modest association with behavioral deficits [NIH Stroke Scale (NIHSS) score] in bivariate models. However, PLS models of delta or beta power across whole brain correlated strongly with NIHSS score (R(2) = 0.85-0.90) and remained robust when further analyzed with cross-validation models (R(2) = 0.72-0.73). Larger infarct volume was associated with higher delta power, bilaterally; the contralesional findings were not attributable to mass effect, indicating that EEG captures significant information about acute stroke effects not available from MRI. We conclude that 1) dense-array EEG data are feasible as a bedside measure of brain function in patients with acute stroke; 2) high-dimension EEG data are strongly correlated with acute stroke behavioral deficits and are superior to traditional single-lead metrics in this regard; and 3) EEG captures significant information about acute stroke injury not available from structural brain imaging.
脑电图（EEG）已被用于研究急性中风数十年；然而，由于存在一些局限性，基于脑电图的测量方法在这种情况下很少能为临床决策提供信息。脑电图硬件、记录电极和脑电图软件的最新进展可能会克服这些局限性。本研究考察了使用盐水导联网采集并用全脑偏最小二乘法（PLS）建模分析的密集阵列（256个电极）脑电图，在捕捉急性中风行为缺陷程度以及与急性脑损伤相关变化方面的效果。在24例因急性缺血性中风入院的患者中，在床边采集了3分钟的静息状态脑电图，包括在急诊室和重症监护病房。传统的定量脑电图测量方法（特定导联在任何频段的功率）在双变量模型中与行为缺陷[美国国立卫生研究院卒中量表（NIHSS）评分]显示出适度的相关性。然而，全脑δ或β功率的PLS模型与NIHSS评分密切相关（R² = 0.85 - 0.90），在用交叉验证模型进一步分析时仍然稳健（R² = 0.72 - 0.73）。较大的梗死体积与双侧较高的δ功率相关；对侧的结果并非由占位效应引起，这表明脑电图捕捉到了有关急性中风影响的重要信息，而这些信息是磁共振成像（MRI）无法提供的。我们得出以下结论：1）密集阵列脑电图数据作为急性中风患者脑功能的床边测量方法是可行的；2）高维脑电图数据与急性中风行为缺陷密切相关，在这方面优于传统的单导联指标；3）脑电图捕捉到了有关急性中风损伤的重要信息，而这些信息是脑结构成像无法提供的。
