Evidence map›Paper›PMID 39825342›Full record

ArticleBMC psychiatry2025

Are neurasthenia and depression the same disease entity? An electroencephalography study.

Ge Dang, Lin Zhu, Chongyuan Lian, Silin Zeng, Xue Shi, Zian Pei, Xiaoyong Lan, Jian Qing Shi, Nan Yan, Yi Guo and 1 more

Abstract read
In one paragraph

Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Ge Dang *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.
Lin Zhu *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.
Chongyuan LianInstitute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
Silin ZengDepartment 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.
Xue ShiDepartment 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.
Zian PeiDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China.
Xiaoyong LanInstitute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
Jian Qing ShiDepartment of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Nan YanCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. nan.yan@siat.ac.cn.
Yi GuoDepartment 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. xuanyi_guo@163.com.
Xiaolin SuDepartment 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. suxiaolin111@126.com.

Funding

National Key Research and Development Program of China 2020YFC2004100National Natural Science Foundation of China NSFC12271239National Natural Science Foundation of China NSFC62271477National Natural Science Foundation of China NSFC81901208National Natural Science Foundation of China NSFC82371471Shenzhen Fundamental Research Program JCYJ20220818100602005Shenzhen Science and Technology Program JCYJ20210324115810030
6 · The paper itself

Abstract

backgroundThe 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).

methodsBoth 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.

resultsWe 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. LIMITATIONS: 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.

conclusionNeurasthenia and MDD are different not only in symptoms but also in brain activities.

Indexed as

ElectroencephalographyMajor Depressive DisorderNeurastheniaAdultDiagnosis, DifferentialFemaleHumansMachine LearningMaleMiddle AgedClassificationElectroencephalogramFunctional connectivityMajor depressive disorderNeurastheniaPower spectral density

Identifiers

PMID39825342
PMCPMC11742223

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.