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

Area of Science:

• Neuroscience
• Psychiatry
• Computational Neuroscience

Background:

• The distinction between neurasthenia and major depressive disorder (MDD) remains a diagnostic challenge, often relying solely on symptom-based classifications.
• Objective biomarkers are needed to accurately differentiate these conditions.

Purpose of the Study:

• To identify objective electroencephalography (EEG) measures capable of distinguishing neurasthenia from MDD.
• To explore differences in brain activity patterns between neurasthenia and MDD patients.

Main Methods:

• Retrospective analysis of electronic medical records and EEG data from neurasthenia and MDD patients.
• Comparison of demographic, clinical characteristics, EEG power spectral density, and functional connectivity.
• Application of machine learning algorithms (random forest, logistic regression, SVM, kNN) for classification.

Main Results:

• Analysis included 305 neurasthenia and 45 MDD patients.
• Neurasthenia patients exhibited more somatic and fewer emotional symptoms compared to MDD patients.
• Significantly lower theta connectivity was observed in neurasthenia patients versus MDD patients.
• Random forest model achieved high accuracy (0.93) in classification, with theta connectivity identified as a key feature.

Conclusions:

• Neurasthenia and MDD are distinguishable not only by their clinical symptoms but also by distinct patterns of brain activity.
• EEG-based measures, particularly theta connectivity, show promise as objective biomarkers for differentiating neurasthenia from MDD.