Background

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms.

Methods

Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models.

Results

Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03).

Conclusion

The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history.

Significance

Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression.

1. A. Baskaran et al. The neurobiology of the EEG biomarker as a predictor of treatment response in depression. Neuropharmacology (2012)
2. G.E. Bruder et al. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: pre- and post-treatment findings. Biol Psychiatry (2008)
3. A. Carvalho et al. EEG frontal asymmetry in the depressed and remitted elderly: is it related to the trait or to the state of depression? J Affect Disord (2011)
4. A. Dutta et al. Resting state networks in major depressive disorder. Psychiatry Res (2014)
5. A.A. Fingelkurts et al. Composition of brain oscillations in ongoing EEG during major depression disorder. Neurosci Res (2006)
6. P. Flor-Henry et al. A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated males with depression. Psychiatry Res (2004)
7. J.M. Gatt et al. Association between BDNF Val66Met polymorphism and trait depression is mediated via resting EEG alpha band activity. Biol Psychol (2008)
8. A.K. Ibrahim et al. A systematic review of studies of depression prevalence in university students. J Psychiatr Res (2013)
9. N. Jagadeesan et al. Brain monitoring during general anesthesia. Trends Anaesth Crit Care (2013)
10. N. Jaworska et al. alpha Power, alpha asymmetry and anterior cingulate cortex activity in depressed males and females. J Psychiatr Res (2012)
11. A.H. Kemp et al. Disorder specificity despite comorbidity: resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder. Biol Psychol (2010)
12. B.E. Kilavik et al. The ups and downs of beta oscillations in sensorimotor cortex. Exp Neurol (2013)
13. W. Klimesch EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev (1999)
14. G.G. Knyazev et al. The default mode network and EEG alpha oscillations: an independent component analysis. Brain Res (2011)
15. J. Lee et al. Differential resting-state EEG patterns associated with comorbid depression in Internet addiction. Prog Neuro-Psychopharmacol Biol Psychiatry (2014)
16. C.P. Lewis et al. Cortical inhibitory and excitatory correlates of depression severity in children and adolescents. J Affect Disord (2016)
17. M.R. Nuwer et al. Routine and quantitative EEG in mild traumatic brain injury. Clin Neurophysiol (2005)
18. N. Radhu et al. A meta-analysis of cortical inhibition and excitability using transcranial magnetic stimulation in psychiatric disorders. Clin Neurophysiol (2013)
19. J.L. Rushton et al. Epidemiology of depressive symptoms in the National Longitudinal Study of Adolescent Health. J Am Acad Child Adolesc Psychiatry (2002)
20. K. Shamsuddin et al. Correlates of depression, anxiety and stress among Malaysian university students. Asian J Psychiatr (2013)
21. S. Tement et al. EEG alpha frequency correlates of burnout and depression: the role of gender. Biological psychology (2016)
22. D. Begic et al. Quantitative electroencephalography in schizophrenia and depression. Psychiatr Danub (2011)
23. M.M. Bolton et al. Brain-derived neurotrophic factor differentially regulates excitatory and inhibitory synaptic transmission in hippocampal cultures. J Neurosci (2000)