Li Zihao, Wu Meini, Yin Changhao, Wang Zhenqi, Wang Jianhang, Chen Lingyu, Zhao Weina
Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China.
Department of Neurology, Taizhou Second People's Hospital, Taizhou, Zhejiang, China.
Front Aging Neurosci. 2024 Apr 5;16:1364808. doi: 10.3389/fnagi.2024.1364808. eCollection 2024.
Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI.
A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages.
The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone.
Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.
血管性认知障碍（VCI）是老年人认知障碍的主要原因，也是大多数神经退行性疾病发生和发展的一个共同因素。随着神经影像学的不断发展，可以结合多种标志物以提供更丰富的生物学信息，但关于它们在VCI中的诊断价值却知之甚少。
共有83名受试者参与了我们的研究，包括32例无痴呆的血管性认知障碍（VCIND）患者、21例血管性痴呆（VD）患者和30名正常对照（NC）。我们利用静息态定量脑电图（qEEG）功率谱、结构磁共振成像（sMRI）进行特征筛选，并将它们与支持向量机相结合，以预测不同疾病阶段的VCI患者。
在区分VD与NC时，sMRI的分类性能优于qEEG（曲线下面积[AUC]为0.90对0.82），在区分VD与VCIND时，sMRI也优于qEEG（AUC为0.8对0.64），但在区分VCIND与NC时两者表现均不佳（AUC为0.58对0.56）。相比之下，基于qEEG和sMRI特征的联合模型在区分VCIND与NC时显示出相对较好的分类准确性（AUC为0.72），高于单独使用qEEG或sMRI的情况。
VCI不同阶段的患者表现出不同程度的脑结构和神经生理异常。脑电图是区分不同VCI阶段的一种经济且便捷的诊断手段。利用脑电图和sMRI作为复合标志物的机器学习模型在区分不同VCI阶段和进行个体化诊断方面具有很高的价值。
