AUTHOR=Liu Xinyu , Zhang Haoran , Cui Yi , Zhao Tong , Wang Bin , Xie Xiaomeng , Liang Sixiang , Sha Sha , Yan Yuxiang , Zhao Xixi , Zhang Ling 

TITLE=EEG-based major depressive disorder recognition by neural oscillation and asymmetry

JOURNAL=Frontiers in Neuroscience

VOLUME=18

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1362111

DOI=10.3389/fnins.2024.1362111

ISSN=1662-453X

ABSTRACT=<sec id="sec1"><title>Background</title><p>Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.</p></sec><sec id="sec2"><title>Methods</title><p>In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.</p></sec><sec id="sec3"><title>Results</title><p>Regression analysis found significant predictors of MDD to be beta2 (16–24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (<italic>B</italic> = 20.092, <italic>p</italic> = 0.011), beta3 (24–40 Hz) power in the Medial Occipital Cortex (MOC) (<italic>B</italic> = −12.050, <italic>p</italic> &lt; 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (<italic>B</italic> = 24.227, <italic>p</italic> &lt; 0.001). Asymmetries in beta1 (12–16 Hz) power with eyes open (<italic>B</italic> = 28.047, <italic>p</italic> = 0.018), and in alpha (8–12 Hz, <italic>B</italic> = 9.004, <italic>p</italic> = 0.013) and theta (4–8 Hz, <italic>B</italic> = −13.582, <italic>p</italic> = 0.008) with eyes closed were also significant predictors.</p></sec><sec id="sec4"><title>Conclusion</title><p>The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.</p></sec><sec id="sec5"><title>Limitations</title><p>The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.</p></sec>