Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression.

中文翻译：

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基于功能近红外光谱的抑郁症分析与识别


抑郁症是社会、心理和生理因素复杂相互作用的结果。对抑郁症患者脑部疾病的研究可以帮助医生了解抑郁症的发病机制并促进其诊断和治疗。功能性近红外光谱（fNIRS）是一种检测大脑功能和活动的非侵入性方法。本文首先建立了一个全面的基于 fNIRS 的凹陷处理架构，包括源层、特征层和模型层，以指导 fNIRS 的深度建模。鉴于抑郁症的复杂性，我们结合现有研究提出了一种在时域和频域进行特征提取和深度神经网络进行抑郁症识别的方法。研究发现，与非抑郁症患者相比，抑郁症患者的脑区连接性较弱，大脑活动时前额叶的激活水平较低。最后，基于原始数据、手动特征和通道相关性，AlexNet模型表现出最好的性能，特别是在相关性特征方面，准确率达到0.90，精确率达到0.91，高于ResNet18和机器学习模型。在其他数据上学习算法。因此，大脑区域的相关性可以有效地识别抑郁症（从非抑郁症病例中），对于抑郁症临床诊断和治疗中大脑功能的识别具有重要意义。