Dang Ge, Zhu Lin, Lian Chongyuan, Zeng Silin, Shi Xue, Pei Zian, Lan Xiaoyong, Shi Jian Qing, Yan Nan, Guo Yi, Su Xiaolin
Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
BMC Psychiatry. 2025 Jan 17;25(1):44. doi: 10.1186/s12888-025-06468-1.
The neurasthenia-depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD).
Both electronic medical information records and EEG records from patients with neurasthenia and MDD were gathered. The demographic and clinical characteristics, EEG power spectral density, and functional connectivity were compared between the neurasthenia and MDD groups. Machine Learning methods such as random forest, logistic regression, support vector machines, and k nearest neighbors were also used for classification between groups to extend the identification that there is a significant different pattern between neurasthenia and MDD.
We analyzed 305 patients with neurasthenia and 45 patients with MDD. Compared with the MDD group, patients with neurasthenia reported more somatic symptoms and less emotional symptoms (p < 0.05). Moreover, lower theta connectivity was observed in patients with neurasthenia compared to those with MDD (p < 0.01). Among the classification models, random forest performed best with an accuracy of 0.93, area under the receiver operating characteristic curve of 0.97, and area under the precision-recall curve of 0.96. The essential feature contributing to the model was the theta connectivity.
This is a retrospective study, and medical records may not include all the details of a patient's syndrome. The sample size of the MDD group was smaller than that of the neurasthenia group.
Neurasthenia and MDD are different not only in symptoms but also in brain activities.
神经衰弱与抑郁症的争议已持续数十年。仅依据症状来解决基于综合征的疾病分类争议颇具挑战性。我们的目的是确定能够区分神经衰弱与重度抑郁症（MDD）的客观脑电图（EEG）指标。
收集了神经衰弱患者和MDD患者的电子病历信息记录以及EEG记录。比较了神经衰弱组和MDD组之间的人口统计学和临床特征、EEG功率谱密度以及功能连接性。还使用了随机森林、逻辑回归、支持向量机和k近邻等机器学习方法进行组间分类，以进一步确认神经衰弱和MDD之间存在显著不同的模式。
我们分析了305例神经衰弱患者和45例MDD患者。与MDD组相比，神经衰弱患者报告的躯体症状更多，情感症状更少（p < 0.05）。此外，与MDD患者相比，神经衰弱患者的θ波连接性更低（p < 0.01）。在分类模型中，随机森林表现最佳，准确率为0.93，受试者操作特征曲线下面积为0.97，精确召回率曲线下面积为0.96。对该模型起关键作用的特征是θ波连接性。
这是一项回顾性研究，病历可能未包含患者综合征的所有细节。MDD组的样本量小于神经衰弱组。
神经衰弱和MDD不仅在症状上不同，在脑活动方面也存在差异。
