Are neurasthenia and depression the same disease entity? An electroencephalography study
Abstract Background 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...
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2025-01-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-06468-1 |
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| author | Ge Dang Lin Zhu Chongyuan Lian Silin Zeng Xue Shi Zian Pei Xiaoyong Lan Jian Qing Shi Nan Yan Yi Guo Xiaolin Su |
| author_facet | Ge Dang Lin Zhu Chongyuan Lian Silin Zeng Xue Shi Zian Pei Xiaoyong Lan Jian Qing Shi Nan Yan Yi Guo Xiaolin Su |
| author_sort | Ge Dang |
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| description | Abstract Background 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). Methods 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. Results 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. 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. Conclusion Neurasthenia and MDD are different not only in symptoms but also in brain activities. |
| format | Article |
| id | doaj-art-d5531e25c35b49b9a6e09b12a6dfe5d5 |
| institution | Kabale University |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Psychiatry |
| spelling | doaj-art-d5531e25c35b49b9a6e09b12a6dfe5d52025-01-19T12:34:26ZengBMCBMC Psychiatry1471-244X2025-01-0125111110.1186/s12888-025-06468-1Are neurasthenia and depression the same disease entity? An electroencephalography studyGe Dang0Lin Zhu1Chongyuan Lian2Silin Zeng3Xue Shi4Zian Pei5Xiaoyong Lan6Jian Qing Shi7Nan Yan8Yi Guo9Xiaolin Su10Department of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology)Department of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology)Institute of Neurological and Psychiatric Disorders, Shenzhen Bay LaboratoryDepartment of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology)Department of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology) Department of Electronic and Electrical Engineering, Southern University of Science and TechnologyInstitute of Neurological and Psychiatric Disorders, Shenzhen Bay LaboratoryDepartment of Statistics and Data Science, College of Science, Southern University of Science and TechnologyCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of SciencesDepartment of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology)Department of Neurology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology)Abstract Background 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). Methods 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. Results 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. 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. Conclusion Neurasthenia and MDD are different not only in symptoms but also in brain activities.https://doi.org/10.1186/s12888-025-06468-1NeurastheniaMajor depressive disorderElectroencephalogramPower spectral densityFunctional connectivityClassification |
| spellingShingle | Ge Dang Lin Zhu Chongyuan Lian Silin Zeng Xue Shi Zian Pei Xiaoyong Lan Jian Qing Shi Nan Yan Yi Guo Xiaolin Su Are neurasthenia and depression the same disease entity? An electroencephalography study BMC Psychiatry Neurasthenia Major depressive disorder Electroencephalogram Power spectral density Functional connectivity Classification |
| title | Are neurasthenia and depression the same disease entity? An electroencephalography study |
| title_full | Are neurasthenia and depression the same disease entity? An electroencephalography study |
| title_fullStr | Are neurasthenia and depression the same disease entity? An electroencephalography study |
| title_full_unstemmed | Are neurasthenia and depression the same disease entity? An electroencephalography study |
| title_short | Are neurasthenia and depression the same disease entity? An electroencephalography study |
| title_sort | are neurasthenia and depression the same disease entity an electroencephalography study |
| topic | Neurasthenia Major depressive disorder Electroencephalogram Power spectral density Functional connectivity Classification |
| url | https://doi.org/10.1186/s12888-025-06468-1 |
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