Previous studies made progress in the early diagnosis of Alzheimer’s disease (AD) using electroencephalography (EEG) without considering EEG connectivity. To fill this gap, we explored significant differences between early AD patients and controls based on frequency domain and spatial properties using functional connectivity in mild cognitive impairment (MCI) and mild AD datasets. Four global metrics, network resilience, connection-level metrics and node versatility were used to distinguish between controls and patients. The results show that the main frequency bands that are different between MCI patients and controls are the θ\theta and low α\alpha bands, and the differently affected brain areas are the frontal, left temporal and parietal areas. Compared to MCI patients, in patients with mild AD, the main frequency bands that are different are the low and high α\alpha bands, and the main differently affected brain region is a larger right temporal area. Four LOFC bands were used as input to train the ResNet-18 model. For the MCI dataset, the average accuracy of 20 runs was 93.42% and the best accuracy was 98.33%, while for the mild AD dataset, the average accuracy was 98.54% and the best accuracy was 100%. To determine the timing of early treatment and discovering the susceptible patients, and to slow the progression of the disease, we assume that the occurrence of MCI and mild AD and their progression to more serious AD and dementia could be inferred by analyzing the topological structure of the brain network generated by EEG. Our findings provide a novel solution for connectome-based biomarker analysis to improve personalized medicine.

中文翻译：

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基于静息态脑电图的早期阿尔茨海默病拓扑网络分析


先前的研究在使用脑电图（EEG）而不考虑脑电图连接性的情况下对阿尔茨海默病（AD）进行早期诊断方面取得了进展。为了填补这一空白，我们利用轻度认知障碍 (MCI) 和轻度 AD 数据集中的功能连接，基于频域和空间特性探索了早期 AD 患者和对照组之间的显着差异。使用四个全局指标、网络弹性、连接级指标和节点多功能性来区分对照组和患者。结果显示，MCI 患者与对照组之间存在差异的主要频段是 θ\theta 和低 α\alpha 频段，受影响不同的大脑区域是额叶区、左颞叶区和顶叶区。与MCI患者相比，在轻度AD患者中，不同的主要频带是低α\α频带和高α\α频带，并且主要受到不同影响的大脑区域是较大的右颞区。使用四个 LOFC 频段作为输入来训练 ResNet-18 模型。对于MCI数据集，20次运行的平均准确率为93.42%，最佳准确率为98.33%，而对于轻度AD数据集，平均准确率为98.54%，最佳准确率为100%。为了确定早期治疗的时机和发现易感患者，减缓疾病的进展，我们假设可以通过分析神经网络的拓扑结构来推断MCI和轻度AD的发生及其进展为更严重的AD和痴呆。脑电图生成的大脑网络。我们的研究结果为基于连接组的生物标志物分析提供了一种新颖的解决方案，以改善个性化医疗。