IEEE J Biomed Health Inform. 2021 Dec;25(12):4289-4299. doi: 10.1109/JBHI.2021.3076762. Epub 2021 Dec 6.
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.
抑郁是社会、心理和生理因素复杂相互作用的结果。对抑郁症患者大脑紊乱的研究有助于医生了解抑郁症的发病机制，促进其诊断和治疗。功能近红外光谱（fNIRS）是一种用于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基于全面 fNIRS 的抑郁处理架构，包括源、特征和模型层，以指导 fNIRS 的深度建模。鉴于抑郁的复杂性，我们结合当前的研究提出了一种在时间和频率域中进行特征提取和深度神经网络用于抑郁识别的方法。结果发现，与非抑郁者相比，抑郁患者在大脑活动期间，大脑前区的连接较弱，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 模型表现出最佳性能，特别是在相关特征方面，准确率为 0.90，精度为 0.91，高于基于其他数据的 ResNet18 和机器学习算法。因此，脑区的相关性可以有效地识别抑郁（从非抑郁病例中），这对于临床诊断和治疗抑郁症中大脑功能的识别具有重要意义。
