Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study

Journal of Clinical Neuroscience xxx (2017) xxx–xxx

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Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study Poh Foong Lee a,⇑, Donica Pei Xin Kan a, Paul Croarkin b, Cheng Kar Phang c, Deniz Doruk b a

Mechatronics and BioMedical Engineering Department, Lee Kong Chien Faculty of Engineering & Science, University Tunku Abdul Rahman, Malaysia Mayo Clinic Depression Center, 200 First Street SW, Rochester, MN 55905, United States c Sunway Medical Centre, Malaysia b

a r t i c l e

i n f o

Article history: Received 15 August 2017 Accepted 29 September 2017 Available online xxxx Keywords: Electroencephalogram (EEG) Depressive symptoms Power spectrum Alpha power

a b s t r a c t 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. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Depression is a common and impairing illness across the lifespan. It impacts 350 million people globally of all ages and is the leading cause of disability according to World Health Organization [33]. It is estimated that as many as 25% of adolescents are affected by at least mild symptoms of depression [40]. Depression typically first presents in adolescence or young adulthood. University students often have numerous psychosocial stressors such as challenging academic work, navigating independence, transitioning from home, relational stressors, and making important life decisions. These factors may contribute to a high prevalence of depression among university students [12,21,41]. Depression in adolescents and young adults contributes to academic failures, occupational impairment, interpersonal deficits, substance use, teen pregnancy, and suicidality. Early detection of depressive ⇑ Corresponding author. E-mail address: [email protected] (P.F. Lee).

symptoms would assist in timely intervention and address a substantial public health challenge. Biomarker research could further intervention development and mitigate the stigma related to the treatment of depression. Assessment with electroencephalography (EEG) may assist with diagnosis and treatment planning by characterizing the brainwaves changes in the presence of the depressive symptoms. EEG is non-invasive, widely available, and relatively cost effective [1]. EEG has been applied in numerous brain diseases including coma, brain death [22], sleep disorders [31], epilepsy [43], stroke [10] and traumatic brain injury [34] to facilitate diagnosis and treatment. Prior research has also considered EEG measures in depressive disorders [35]. Numerous studies have examined the differences in EEG frequency between healthy and depressed participants. For instance, Fingelkurts et al. reported that depressed patients demonstrated greater alpha and less distributed delta activity compared to healthy controls [14]. Other prior work demonstrated increases in EEG power in a broad range of parietal, occipital, posterior temporal and central areas in patients with new-onset

https://doi.org/10.1016/j.jocn.2017.09.030 0967-5868/Ó 2017 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

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depression [18]. Conversely, work by Begic´ and colleagues demonstrated that depressed patients had increased delta, theta, and beta but decreased alpha power, specifically in the frontal regions [2]. A recent study examined an internet addicted group of participants with depression and demonstrated increased relative theta but decreased relative alpha power in all brain regions [29]. Other studies focused on alpha power asymmetry [17,8]. EEG has also been used to predict treatment response [4] and understand associations between depression and other psychiatric co-morbidities [44]. These heterogeneous findings have limited clinical utility. The discrepancies among these studies could be explained by methodological differences, lack of standardized measures, recruitment of participants at different stages of depression or simply the involvement of different pathophysiological pathways in depression [35]. The present study examined EEG changes in individuals with newly identified depressive symptoms as compared to euthymics. Specifically, we sought to explore if any particular EEG bandwidth and localization might help discriminate depressed individuals from their euthymic peers.

2. Methods 2.1. Participants Written informed consent was obtained from all participants before study enrolment and procedures. This study was approved by University Tunku Abdul Rahman Scientific and Ethical Review Committee (SERC). A convenience sample of 125 participants between age 18 to 25 years old were recruited. On screening interview, the participants had no self-reported history of psychiatric treatment. All participants underwent an initial screening using the Patient Health Questionnaire-9 (PHQ-9) and Depression (D), Anxiety (A) and Stress (S) Scale-21 (DASS-21) at baseline. Among the 125 participants recruited, 25 reported high stress without any depressive symptoms, therefore did not qualify for either depressive group or euthymic group. The remaining 100 participants were divided into 2 groups (50 depressed and 50 healthy) based on pre-determined cut-off scores on the PHQ-9 and DASS21 as further explained below. The euthymic group included 33 males (66%), and 17 females (34%) with the mean age of 22.8 (SD: 1.45). Depressive group included a total of 29 males (58%) and 21 females (42%) with the mean age of 21.4 (SD: 1.76). Individuals with any formal history of psychiatric treatment were excluded. All participants were medication free during the study procedures.

Table 1 Comparison of mean PHQ-9 scores and DASS-21 scores between depressive and control group before intervention. Depressive

Control

Variables

M

M

PHQ-9 Depression Anxiety Stress

11.5 18.6 14.2 18.6

4.1 4.0 6.8 8.4

displays the mean scores of the PHQ-9 and DASS-21 for both depressive and control groups. 2.3. Self-report questionnaire Participants completed a self-report questionnaire examining their subjective conscious state during the 2-min EEG measurement. The questionnaire included whether they were 1) day dreaming, 2) sleepy, 3) relaxing or 4) ‘‘other”. This self-report questionnaire was administered to monitor for the conscious state of the participants as the different states of consciousness have different effects on brain activity measured by EEG. 2.4. EEG recordings and analysis The study was conducted with a conventional EEG registration with NCC Medical 32-channel bipolar electroencephalogram (EEG). The EEG was recorded at 32 scalp loci, referenced to vertex (Cz) complying with the international 10–20 electrodes placement system. The location of the electrode positions is shown in Fig. 1. Participants were given prior notice to clean their hair and not to apply styling products on the day of testing. Upon arrival, participants were given an information sheet, signed the consent form and completed the screening questionnaires. Each participant was seated comfortably in a controlled environment (a quiet room with white painted wall, air condition at 23 °C), and guided by a facilitator to relax for two minutes with their eyes closed. Participants initially relaxed for 2 min before starting the EEG recording and were encouraged to stay fully focused throughout the remainder of the session. EEG signals were digitized at 128 samples per

2.2. Instruments The PHQ-9 and DASS-21 measures provide an indication of the severity of depressive symptoms and assess the severity within a given period of time two weeks). The DASS-21 is widely used by clinicians in the United Kingdom and has high reliability and validity [7]. All participants completed the Patient Health Questionnaire-9 (PHQ-9) and Depression (D), Anxiety (A) and Stress (S) Scale-21 (DASS-21) before the EEG recording. The participants were masked to group assignment to avoid bias. Participants were assigned to each group, ‘‘euthymic” or ‘‘depressive” based on their scores on PHQ-9 and DASS-21. The cut-off score for euthymic participants was PHQ-9 < 10 (normal or mild level of symptoms) while DASS-21: D < 7, A < 6, and S < 10 (normal or mild level of symptoms). For depressive group, the scores were PHQ-9  10 (moderate to severe level of symptoms) and DASS-21: D  14 (moderate to extremely severe level of symptoms). Table 1

Fig. 1. Electrode localizations.

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

P.F. Lee et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx

seconds with a high-pass filter of 0.5 Hz, a low-pass filter of 40 Hz, and a notch filter of 50 Hz. The EEG recordings were inspected visually and areas contaminated by artifacts were rejected. The EEG data were then Fast Fourier Transformed FFT) to determine the EEG power spectrum of each electrode [6]. Power in mV2 was determined for standard frequency bands: delta (1–4 Hz), theta (4–8 Hz), low-alpha (8–10 Hz), high-alpha (10–12 Hz) and beta (12–30 Hz). The monitored regions included Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1 and O2. The FFT data was retrieved from the NCC Medical EEG software and then computed into MATLAB to obtain the median power of total 100 participants for each frequency band and EEG channel. 2.5. Statistical analysis The analysis tests were computed using SPSS (Statistical Package for the Social Science), ver. 11.5. (SPSS Inc., Chicago, IL, USA.) and STATA 12.1. (Stata Corp LP, Texas, USA). 2.5.1. Comparison of EEG power spectrum between groups Upon examination of the distribution of the data using the Shapiro-Wilk’s test as well as the visual inspection of the histogram, normal Q-Q plots and box plots, it was determined that

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assumptions of normality were violated. Hence, a nonparametric Mann-Whitney Test was conducted to evaluate the significant differences in EEG power spectrum between the euthymic and depressive groups for each electrode localization and EEG bandwidth. Post-hoc correction for multiple comparison was completed using the Bonferroni correction where the p-value for significance was manually determined as 0.003 for a total of 16 comparisons. 2.5.2. Simple logistic regression To explore whether EEG power can be used to discriminate between depressive and euthymic group, we performed simple logistic regression analysis for each EEG channel and bandwidth to scoop for significant models. In our regression models ‘‘group” (depressed or euthymic) was the dependent variable whereas ‘‘EEG power” was the independent variable. 2.5.3. Receiver Operating Characteristic (ROC) test The significant data from the simple logistic regression analysis were further evaluated with C-statistic, Receiver Operating Characteristic (ROC) to identify the models with good discriminative value. ROC curves with an area under the curve above 0.7 was taken into consideration and further included in our final multivariate regression models. 2.5.4. Multivariate regression analysis Finally, multivariate regression analysis was employed to further identify the significant models including all significant variables from previous steps as well as the possible confounders, age and gender. 2.6. Brain topography for high alpha Brain topography representing mean power spectrum for highalpha in euthymic and depressed groups is shown in Fig. 9. 3. Results 3.1. Self-report questionnaire during EEG acquisition

Fig. 2. Comparison of self-report measures result based on the mind states at closed eyes condition during the EEG measurement.

Self-report measures of subjective conscious state during EEG acquisition are shown in Fig. 2. No statistical test was conducted

Fig. 3. Delta power of normal and depressive group during closed eyes condition. *p < 0.05, **p < 0.01,

***

p < 0.00: significant different from normal group.

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

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P.F. Lee et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx

Fig. 4. Theta power of normal and depressive group during closed eyes condition. *p < 0.05, **p < 0.01,

***

Fig. 5. Alpha 1 power of normal and depressive group during closed eyes condition. *p < 0.05, **p < 0.01,

for comparison of different conscious states but the self-report measures indicated approximately half of the control group participants (52%) felt relaxed during EEG recording, whereas only 26% of the depressive group was able to relax. The depressive group endorsed more drowsiness (sleepy) and day dreaming compared to control group.

3.2. Comparison of power spectrum – Mann-Whitney test Comparison of depressive and euthymic groups revealed higher delta power but decreased theta, low-alpha and high-alpha power in majority of brain regions in depressed participants (Figs. 3–7). Following post hoc comparison with Bonferroni correction, where the p-value was set to 0.003 for determining significance, only high-alpha power over C3 (left central area, U = 661.5, P < 0.001) and over T3 (left temporal area, U = 903.5, P < 0.002); as well as

p < 0.001: significant different from normal group.

***

p < 0.001: significant different from normal group.

beta power over C3 (left central area, U = 671.5, p < 0.001) and F3 (left frontal area, U = 762.5, p = 0.001) remained significant (Table 2). 3.3. Simple logistic regression The significant variables are shown in bold in Table 3. Significant differences were found for high-alpha and beta power especially in the frontal and central areas. Significant variables were further tested with C-statistic to identify the discriminative value of the selected variables. 3.4. Receiver Operating Characteristic (ROC) test, c-statistic Further analysis of the significant variables with C-statistics revealed that only high-alpha and beta power over C3 had area

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

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Fig. 6. Alpha 2 power of normal and depressive group during closed eyes condition. *p < 0.05, **p < 0.01,

Fig. 7. Beta power of normal and depressive group during closed eyes condition. *p < 0.05, **p < 0.01,

***

***

p < 0.001: significant different from normal group.

p < 0.001: significant different from normal group.

Table 2 Post-hoc comparison with Bonferroni correction for Mann-Whitney test. Note that p-value for significance determined as p < 0.003 for a total of 16 comparisons.

*

Channels/Frequency bands

Delta

Theta

Low Alpha

High Alpha

Beta

Fp1 Fp2 F3 F4 F7 F8 C3 C4 P3 P4 O1 O2 T3 T4 T5 T6

0.909 0.568 0.285 0.357 0.669 0.962 0.400 0.576 0.454 0.569 0.025 0.179 0.504 0.603 0.078 0.569

0.086 0.145 0.006 0.165 0.054 0.054 0.032 0.315 0.341 0.278 0.577 0.296 0.052 0.198 0.537 0.078

0.061 0.069 0.071 0.095 0.072 0.078 0.006 0.066 0.448 0.572 0.940 0.647 0.167 0.202 0.920 0.168

0.008 0.007 0.005 0.007 0.004 0.005 0.000* 0.040 0.128 0.024 0.058 0.030 0.002* 0.010 0.063 0.008

0.013 0.009 0.001* 0.036 0.004 0.014 0.000* 0.191 0.038 0.040 0.178 0.112 0.096 0.197 0.133 0.018

Bold values for p < 0.003.

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

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Table 3 Single logistic regression analysis with ‘‘group” (being depressed or not) as the dependent variable and power spectrum as the independent variable. Channels

Fp1 Fp2 F3 F4 F7 F8 C3 C4 O1 O2 P3 P4 T3 T4 T5 T6 *

P>x Low Alpha

High Alpha

Beta

Delta

Theta

0.491 0.409 0.438 0.220 0.409 0.269 0.398 0.651 0.498 0.690 0.968 0.700 0.386 0.477 0.466 0.975

0.012* 0.011* 0.011* 0.018* 0.008* 0.010* 0.001* 0.028* 0.672 0.558 0.917 0.106 0.008* 0.028* 0.799 0.663

0.118 0.032* 0.011* 0.509 0.017* 0.039* 0.001* 0.214 0.066 0.078 0.166 0.183 0.090 0.237 0.134 0.046*

0.673 0.974 0.393 0.119 0.852 0.964 0.143 0.098 0.415 0.503 0.306 0.377 0.681 0.356 0.195 0.995

0.635 0.304 0.104 0.564 0.140 0.165 0.172 0.848 0.460 0.203 0.284 0.241 0.094 0.192 0.354 0.150

Bold values for p < 0.05.

under the curve above the pre-determined cut-off of 0.7 (70%) (Fig. 8(a) and (b)). 3.5. Multivariate regression analysis Lastly, multivariate regression analysis further narrowed down the significant predictor to high-alpha power at C3 (p = 0.03, b = 0.15) where age and gender were included as confounders for the dependent variable ‘‘group” (depressive vs. euthymic) (Table 4). 3.6. Brain topography for high alpha Brain topography in Fig. 9 shows mean power spectrum for high alpha suggesting higher power in euthymic as compared to depressive group. 4. Discussion There is a dearth of prior EEG work and biomarker studies in general focused on young adults. Young adults have considerable neurodevelopmental changes and psychological stressors. An enhanced understanding of the underlying neurobiology of depression and optimized translational tools for this age group would address an emerging global public health problem. In this study we investigated the potential utility of EEG power as a biomarker in identifying depressive symptoms in young adults. Our results suggest that high-alpha power originating from the central areas of the left hemisphere can help discriminate participants with depressive symptoms from their euthymic peers. Prior findings regarding alpha power in depressed individuals are heterogeneous [35]. While some studies showed global decreased alpha power in depressed participants as compared to healthy participants [36,24] other studies showed either increased alpha power [39,25,2,23] or no differences [15,5]. Supporting the relation between EEG alpha power and depression, Zoon et al. showed that decreased alpha power is related to brain-derived neutrotrophic factor (BDNF) Met/Met polymorphism [46], a polymorphism which has been shown to be associated with trait depression identified by the DASS-21 in healthy participants [16]. It was suggested that EEG alpha power mediates the association between depression and BDNF polymorphism [16,46]. Even though no such assumption can be made from the present study, our main finding of decreased alpha power in the non-clinical

Fig. 8. (a) ROC curve for beta power band at C3. (b) ROC curve for high-alpha (highalpha) power band at C3.

group of depressive young adults supports the potential role of alpha power in depression. It is possible that EEG alpha power reflects changes in excitatory and inhibitory pathways in depression through the generalized effects of BDNF on cortical excitability processes [20,3,38]. Indeed, dysfunction in excitatory and inhibitory pathways in depression has also been implied in other studies

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P.F. Lee et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx Table 4 Multivariate logistic regression model with age and gender controlled. Group

Coefficient *

High alpha Beta Gender Age *

0.1504 0.2416 0.4743 0.2701

Std. Err. *

0.694 0.192 0.463 0.145

x

P>x *

2.16 1.26 1.02 1.86

0.030 0.209 0.306 0.063

95% Conf. Interval *

0.2866* 0.6186 0.5547 0.5644

Bolded numbers for p < 0.05.

Fig. 9. Brain topography plotting with mean power spectrum of the euthymic and depressive groups for alpha-2.

[37,30]. In addition to cortical excitability, alpha power could also represent changes in the functional connectivity within the default mode network, a network model that has been associated with depression [32,11] where the alpha power is identified as the main regulator [28]. Yet, scalp EEG recordings are known to lack spatial resolution and therefore may not necessarily represent the subcortical network activity accurately. Notably EEG power is highly sensitive to other states of consciousness, including cognitive processes [27]. In this context, EEG can be combined with other techniques, such as transcranial magnetic stimulation (TMS), to better delineate the neurophysiological markers associated with depression. While EEG can help elucidate more temporal associations, TMS can be used assess intra and inter-cortical connections as well as the cortical excitability. For example, previous studies integrated EEG and TMS to identify neurophysiological correlates of post-stroke mood and motor recovery [42,10]. A relative strength of our current approach was the quantification of conscious state during EEG testing. Even the present final multivariate model did not reach significance, we found that beta power was significantly reduced over left central areas in the depressive group as compared to the euthymic group. Beta power is reported to be higher during attention tasks [13] sensorimotor activation [26], language processing [45] and memory retrieving [19]. Our results contradict the findings of prior studies which reported an increase in beta power in depression [18,2]. The difference between prior literature and our study results might be explained the fact that our participants were not known to be clinically depressed. Alternatively, poorly understood compensatory mechanisms could be involved in the early stages of depression. For example, according to the cognitive

model of depression, symptoms are preceded by biased attention, processing and memory [9]. Our study provides preliminary information on potential neurophysiological markers of depression in young adults with no previous psychiatric diagnosis and therefore has important implications. First, it can help develop diagnostic tools for early detection of depressive symptoms leading to early intervention and therefore prevention of disease progression. It can also help identify the presence of trait depression which in turn could help determine the need of close follow-up for those who are at risk of developing severe depression and other psychiatric co-morbidities. Also, EEG markers of depression can be used in combination with other diagnostic and treatment modalities, such as neuromodulation techniques and biofeedback methods, to better delineate the surrogate markers and individualize treatment plans. Future studies are needed to better understand the neurophysiological markers associated with the sub-groups of depressive disorders which could help explain the inconsistent results in biomarker studies in depressive disorders. Also larger studies enabling strict confounder analysis may help clarify the discrepancies in the literature.

5. Conclusions The present study demonstrated that high-alpha power originating from central regions of the left hemisphere can be used to discriminate between euthymic and depressive participants. Electroencephalogram (EEG) is an important tool that can yield biomarkers for depressive disorders guiding identification, diagnosis, and treatment.

Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030

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Conflict of interest statement None of the authors have potential conflicts of interest to be disclosed. Acknowledgments This research was funded by research funding by University of Tunku Abdul Rahman (UTAR) under vote account number 6200/ LB1. We would like to thank all the participants who involved in this study. This publication was supported by Grant Number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jocn.2017.09.030. References [1] Baskaran A, Milev R, McIntyre RS. The neurobiology of the EEG biomarker as a predictor of treatment response in depression. Neuropharmacology 2012;63:507–13. [2] Begic D, Popovic-Knapic V, Grubisin J, Kosanovic-Rajacic B, Filipcic I, Telarovic I, et al. Quantitative electroencephalography in schizophrenia and depression. Psychiatr Danub 2011;23:355–62. [3] Bolton MM, Pittman AJ, Lo DC. Brain-derived neurotrophic factor differentially regulates excitatory and inhibitory synaptic transmission in hippocampal cultures. J Neurosci 2000;20:3221–32. [4] Bruder GE, Sedoruk JP, Stewart JW, McGrath PJ, Quitkin FM, Tenke CE. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: pre- and post-treatment findings. Biol Psychiatry 2008;63:1171–7. [5] Carvalho A, Moraes H, Silveira H, Ribeiro P, Piedade RA, Deslandes AC, 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;129:143–8. [6] Cooley JW, Tukey JW. An algorithm for the machine calculation of complex Fourier series. Math Comput 1965;19:297–301. [7] Crawford JR, Henry JD. The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br J Clin Psychol 2003;42:111–31. [8] Debener S, Beauducel A, Nessler D, Brocke B, Heilemann H, Kayser J. Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology 2000;41:31–7. [9] Disner SG, Beevers CG, Haigh EA, Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci 2011;12:467–77. [10] Doruk D, Simis M, Imamura M, Brunoni AR, Morales-Quezada L, Anghinah R, et al. Neurophysiologic correlates of post-stroke mood and emotional control. Front Hum Neurosci 2016;10:428. [11] Dutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res 2014;224:139–51. [12] Dyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad Med 2006;81:354–73. [13] Fan J, Byrne J, Worden MS, Guise KG, McCandliss BD, Fossella J, et al. The relation of brain oscillations to attentional networks. J Neurosci 2007;27:6197–206. [14] Fingelkurts AA, Fingelkurts AA, Rytsala H, Suominen K, Isometsa E, Kahkonen S. Composition of brain oscillations in ongoing EEG during major depression disorder. Neurosci Res 2006;56:133–44. [15] Flor-Henry P, Lind JC, Koles ZJ. A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated males with depression. Psychiatry Res 2004;130:191–207. [16] Gatt JM, Kuan SA, Dobson-Stone C, Paul RH, Joffe RT, Kemp AH, et al. Association between BDNF Val66Met polymorphism and trait depression is mediated via resting EEG alpha band activity. Biol Psychol 2008;79:275–84. [17] Gotlib IH. EEG alpha asymmetry, depression, and cognitive functioning. Cogn Emotion 1998;12:449–78.

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Please cite this article in press as: Lee PF et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci (2017), https://doi.org/10.1016/j.jocn.2017.09.030