Abstract: Depression is a global mental disease. Conventional diagnostic methods mainly depend on the scale and the subjective assessment of doctors, which cannot guarantee effective identification of symptoms and may have the risk of misdiagnosis. Using physiological signals, deep learning methods are expected to improve those diagnostic methods that lack the support of physiological basis. Traditional deep learning methods, however, rely on huge computing power, and most of them are end-to-end network learning. There also lacks physiological interpretability in those learning methods, limiting the clinical application of auxiliary diagnosis. This paper proposes a brain-inspired learning model for electroencephalogram (EEG) diagnosis of depression. At the functional level, a spiking neural network is constructed to classify depression and healthy individuals with an accuracy of more than 97.5%, which reduces the energy consumption compared to deep convolutional methods. At the structural level, the spatial topology of brain connectivity is established by using complex network and its graph characteristics are analyzed to find out the underlying mechanism of abnormal brain functional connectivity in individuals with depression.

Key words: brain-inspired learning, spiking neural network, complex network feature, depression, electroencephalogram (EEG)

摘要： 抑郁症是一种全球性精神疾病，传统诊断方法主要依靠量表与医生的主观评估，无法有效识别症状，甚至存在误诊的风险。基于生理信号的深度学习辅助诊断有望改善传统缺乏生理学依据的方法。然而，传统深度学习方法依赖巨大算力，且大多是端到端的网络学习。这些学习方法也缺乏生理可解释性，限制了辅助诊断临床应用。提出一种用于抑郁症脑电图（electroencephalogram，EEG）诊断的类脑学习模型，在功能层面，构建脉冲神经网络对抑郁症与健康个体进行分类，精度超过97.5%，相比深度卷积方法，脉冲方法降低了能耗；在结构层面，利用复杂网络建立脑连接的空间拓扑并分析其图特征，找出了抑郁症个体潜在的脑功能连接异常机制。

关键词: 类脑学习, 脉冲神经网络, 复杂网络特征, 抑郁症, 脑电图