Poh Foong Lee1, Donica Pei Xin Kan2, Paul Croarkin3, Cheng Kar Phang4, Deniz Doruk3. 1. Mechatronics and BioMedical Engineering Department, Lee Kong Chien Faculty of Engineering & Science, University Tunku Abdul Rahman, Malaysia. Electronic address: leepf@utar.edu.my. 2. Mechatronics and BioMedical Engineering Department, Lee Kong Chien Faculty of Engineering & Science, University Tunku Abdul Rahman, Malaysia. 3. Mayo Clinic Depression Center, 200 First Street SW, Rochester, MN 55905, United States. 4. Sunway Medical Centre, Malaysia.
Abstract
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.
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.