Cai Jiaye, Xu Mengru, Cai Huaying, Jiang Yun, Zheng Xu, Sun Hongru, Sun Yu, Sun Yi
Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China.
Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
Brain Sci. 2023 Jul 29;13(8):1143. doi: 10.3390/brainsci13081143.
Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS ( = 0.011), while local efficiency was significantly increased in SS than in CS ( = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS ( = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.
人们已经付出了越来越多的努力来研究中风患者的认知障碍,但很少有人关注轻度中风。关于轻度中风和不同病变部位对认知障碍影响的研究仍然有限。为了研究不同病变部位轻度中风患者认知功能障碍的潜在机制,在视觉Oddball任务期间,对三组患者(40例皮质中风患者(CS)、40例皮质下中风患者(SS)和40例健康对照者(HC))进行了脑电图(EEG)记录。基于EEG源信号构建功率包络连接性(PEC),然后进行图论分析以定量评估功能性脑网络特性。进一步应用分类框架来探索PEC在识别轻度中风方面的可行性。结果显示患者组的行为表现较差,三组之间存在显著差异的PEC在频段和皮层中呈现出复杂的分布模式。在δ频段,HC组的全局效率显著高于CS组(P = 0.011),而SS组的局部效率显著高于CS组(P = 0.038)。在β频段,HC组的小世界特性相较于CS组显著增加(P = 0.004)。此外,令人满意的分类结果(HC与CS组相比为76.25%,HC与SS组相比为80.00%)验证了PEC作为检测轻度中风生物标志物的潜力。我们的研究结果为不同病变部位轻度中风患者认知障碍的复杂机制提供了一些新的定量见解,这可能有助于中风后认知康复。