The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

中文翻译：

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基于不同卷积神经网络模型的重度抑郁症分类：深度学习方法

人类大脑的特点是复杂的结构、功能连接，这些连接整合了独特的认知特征。评估大脑的结构和功能连接以及对神经退行性疾病的诊断和治疗的影响存在一个基本障碍。目前，没有能够确认重度抑郁症 (MDD) 诊断的临床特异性诊断生物标志物。因此，探索基于深度学习 (DL) 的情绪障碍转化生物标志物具有宝贵的潜力，其最近强调的有希望的结果。在本文中，基于脑电图 (EEG) 的 MDD 诊断模型是通过先进的计算神经科学方法结合深度卷积神经网络 (CNN) 方法构建的。通过对 3 种不同的深度 CNN 结构（即 ResNet-50、MobileNet、Inception-v3）进行建模来分析 EEG 记录，以便对 MDD 患者和健康对照进行二分。通过从 19 个电极收集数据，收集 4 个主要频段（Δ、θ、α 和 β，伴随空间分辨率和位置信息）的 EEG 数据。在预处理步骤之后，采用不同的 DL 架构通过比较来强调识别性能分类准确率 基于位置数据的模型分类性能，MobileNet 架构产生了 89.33% 和 92.66% 的分类准确率。在频段方面，delta 频段优于其他频段，具有 90.22% 的预测准确率和曲线下面积（AUC） ResNet-50 架构的值为 0.9。该研究的主要贡献是使用各种 DL 架构将 46 名 MDD 受试者与 46 名健康受试者分开来描绘独特的空间和时间特征。基于深度学习的观点探索情绪障碍的转化生物标志物是本研究的主要重点，虽然它具有挑战性，但它具有提高我们对精神障碍的理解的有希望的潜力，计算方法对于诊断过程非常有价值，并且在术语方面很有价值与经典方法相比，速度和准确性都有所提高。