Department of Physical Medicine and Rehabilitation, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Korea.
Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, Korea.
Sci Rep. 2021 Jan 27;11(1):2308. doi: 10.1038/s41598-021-81912-2.
Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.
精确监测中风后的大脑对于临床决策至关重要。由于脑电图(EEG)具有非侵入性和高时间分辨率的特点,因此被广泛用于评估实时皮质活动。在这项研究中,我们研究了与中风相关的 EEG 生物标志物,并开发了一种预测模型,用于量化局灶性脑缺血大鼠模型中的结构性脑损伤。我们纳入了 31 只雄性 Sprague-Dawley 大鼠,并将其随机分为轻度中风、中度中风、重度中风和对照组。我们诱导了针对右侧听觉皮层的光血栓性中风。然后,我们获取了对声音刺激(频率从 8 到 12 kHz 线性增加,持续 750 毫秒)的 EEG 信号响应。功率谱分析显示,alpha、theta、delta、delta/alpha 比和(delta+theta)/(alpha+beta)比的相对功率与中风病变体积之间存在显著相关性。听觉诱发电位分析显示,振幅和潜伏期与中风病变体积之间存在显著关联。最后,我们开发了一个结合 EEG 预测因子的多元回归模型,用于量化缺血性病变(R=0.938,p 值<0.001)。这些发现表明 EEG 作为一种监测中风后大脑的有效方法具有潜在的应用价值。