Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China.
Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China.
BMC Neurosci. 2024 Jul 4;25(1):30. doi: 10.1186/s12868-024-00877-w.
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.
We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.
Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.
Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.
阿尔茨海默病（AD）和额颞叶痴呆（FTD）是两种最常见的神经退行性痴呆症，具有相似的临床特征，这给准确诊断带来了挑战。尽管进行了广泛的研究，但潜在的病理生理机制仍不清楚，有效的治疗方法也有限。本研究旨在探讨与 AD 和 FTD 相关的脑网络连接改变，以增强我们对其病理生理学的理解，并为其诊断和治疗建立科学基础。
我们分析了来自 OpenNeuro 公共数据集的预处理脑电图（EEG）数据，其中包括 36 名 AD 患者、23 名 FTD 患者和 29 名健康对照者（HC）。参与者处于闭眼静息状态。我们使用相位滞后指数（PLI）估计低频（德尔塔和 theta）的平均功能连接，使用带泄漏校正的振幅包络相关（AEC-c）估计高频（alpha、beta 和 gamma）的平均功能连接。应用图论计算拓扑参数，包括平均节点度、聚类系数、特征路径长度、全局和局部效率。然后，我们利用置换检验根据这些参数评估 AD 和 FTD 患者脑网络连接的变化。
AD 和 FTD 患者在 theta 频带中均表现出平均 PLI 值增加，同时平均节点度、聚类系数、全局效率和局部效率增加。相反，alpha 频带中的平均 AEC-c 值明显降低，同时平均节点度、聚类系数、全局效率和局部效率降低。此外，AD 患者在枕叶区域表现出 theta 频带节点度增加和 alpha 频带聚类系数和局部效率降低，而 FTD 患者则没有这种模式。
我们的研究结果揭示了 AD 和 FTD 中功能网络拓扑和连接的明显异常，这可能有助于更好地理解这些疾病的病理生理机制。具体来说，AD 患者的功能连接变化更为广泛，而 FTD 患者的枕叶连接保留。这些观察结果为开发区分这两种疾病的电生理标志物提供了有价值的见解。
