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Resting state frontal alpha asymmetry predicts emotion regulation difficulties in impulse control
Personality and Individual Differences 159 (2020) 109870
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Resting state frontal alpha asymmetry predicts emotion regulation difficulties in impulse control ⁎
Jing Zhanga, , Yan Huaa, Lichao Xiub, Tian Po Oeic, Ping Hua,
T
⁎
a
Department of Psychology, Renmin University of China, Beijing, China Lab of Cognitive Neuroscience and Communication Study, School of Journalism and Communication, Beijing Normal University, Beijing, China c School of Psychology, University of Queensland, Brisbane, Australia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Frontal alpha asymmetry Difficulties in emotion regulation scale Impulse control
Failure in emotion regulation would cause barriers to individuals’ adaptive behavior, which negatively affect a human's happy and healthy life. Recent studies indicate that decreased activity of the left frontal cortex can be treated as a neural marker of emotion disorders. The present study examined how resting state frontal alpha asymmetry could predict emotion regulation difficulties and related dimensions by resting state electroencephalogram (EEG) recordings, and a measure of the Difficulties in Emotion Regulation Scale (DERS). Eighty participants completed the resting state EEG recording and DERS ratings. Results revealed that after controlling for gender and depression scores, those participants with higher left frontal activity than the right frontal activity in the resting state have less difficulties in everyday emotion regulation, especially in the dimension of impulse control. However, there was no relation pattern with other dimensions of DERS. This study provided evidence that resting state frontal alpha asymmetry could predict emotion regulation difficulties, mainly in impulse control.
1. Introduction Successful emotion regulation is one of the main cores of human health and well-being. Emotion regulation capability has several dimensions, such as the modulation of emotional arousal, awareness, understanding, acceptance of emotions, and the ability to act in desired ways regardless of emotional state (Gratz & Roemer, 2008). Different dimensions of emotion regulation difficulties have been shown to be main risk factors to induce anxiety and depression, or to cause poor performance in emotion related activities in normal life (Campbellsills & Barlow, 2007; Markarian, Pickett, Deveson, & Kanona, 2013). Previous studies have also shown that resting state frontal alpha asymmetry (FAA) has a relationship with emotion dysfunctions, such as depression and anxiety disorder (Hannesdóttir, Doxie, Bell, Ollendick, & Wolfe, 2010; Jackson et al., 2003; Mikolajczak, Bodarwe, Laloyaux, Hansenne, & Nelis, 2010, 2008; Smith, Zambrano-Vazquez, & Allen, 2016). The present study aims to examine whether and how FAA predicts emotion regulation difficulty, especially its various dimensions. Emotion regulation difficulties comprise six dimensions, including Non-acceptance, Goals, Impulse, Awareness, Strategies, and Clarity, which are assessed by the Difficulties in Emotion Regulation Scale (DERS, Gratz & Roemer, 2004). Previous studies found that emotion
⁎
regulation difficulties had a significant relation to psychological problems reflecting emotion disorders, specifically depression, anxiety, suicidal ideation, eating disorders, alcohol use, and drug use (Bradizza et al., 2018; Gratz & Roemer, 2004; Weinberg & Klonsky, 2009). Recent studies have also found that emotion dysregulation in healthy individuals is related to emotion normative developmental processes and experiences, such as identity development, procrastination, social participation, and academic motivation and performance (Kaufman et al., 2016). Although many studies have explored the relationship of emotion regulation difficulties and mental health, there is limited research on the related neural activity that links to emotion regulation difficulties. Studies on the neural mechanisms of emotion regulation found that the frontal cortical regions are involved in the modulation of amygdala reactivity and play an important role in effective emotion regulation (Banks, Eddy, Angstadt, Nathan, & Phan, 2007; Hagemann, Waldstein, & Thayer, 2003). Successful emotion regulation is dependent on topdown control from the prefrontal cortex over subcortical regions involved in reward and emotion (Heatherton & Wagner, 2011). It has been shown that the increased connectivity between the dorsal lateral prefrontal cortex (DLPFC) and both amygdala and dorsal anterior cingulate cortex (ACC) is the mechanism of top-down regulation of
Corresponding authors. E-mail addresses: [email protected] (J. Zhang), [email protected] (P. Hu).
https://doi.org/10.1016/j.paid.2020.109870 Received 24 September 2019; Received in revised form 2 December 2019; Accepted 27 January 2020 0191-8869/ © 2020 Elsevier Ltd. All rights reserved.
Personality and Individual Differences 159 (2020) 109870
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Previous studies have indicated an association between lateralized frontal activity and behavior related to impulse control. For example, Ischebeck, Endrass, Simon, and Kathmann (2014) found that obsessivecompulsive disorder participants showed more dominance of alpha power on the left frontal cortex (more activity in the right frontal) when compared with the healthy control group. Wacker, Chavanon, Leue, and Stemmler (2009) found that participants showed more activity in right prefrontal cortex in response inhibition, especially among the high trait behavioral inhibition participants. In addition, past studies have found that the left dorsolateral prefrontal is involved in the resting state brain networks of impulse control, which is closely related to impulse control behavior (Tessitore et al., 2017; Weygandt et al., 2015). Thus, we could hypothesize that the lateralized frontal activity would be significantly correlated to the dimension of impulse control of emotion in DERS. In an attempt to further establish the role of resting state FAA in emotion regulation difficulties, the present study examined the relationship between resting state frontal EEG asymmetry and the total DERS (and its subscales) scores using the asymmetry of EEG alpha power in the frontal lobe and investigation of self-reported emotion regulation difficulties. We expected that higher FAA would be negatively associated with difficulties in controlling impulses in negative emotions in DERS and the total DERS scores.
emotional conflict and increased attentional control (Comte et al., 2016; Doll et al., 2016). Therefore, impaired prefrontal activities would tip the balance of the prefrontal cortex and subcortical regions, which have been shown as the main reasons for emotion regulation difficulties (Heatherton & Wagner, 2011). Resting state FAA, which is indexed by ln [right frontal alpha band (8–13 Hz) power]-ln [left frontal alpha band power], provides relative differences in the activity between the left and right frontal hemisphere (Davidson, 1995). Since alpha power has been found to be inversely related to the activity of the corresponding region of the brain, higher FAA indicates greater left-sided frontal activity (Blackhart, Minnix, & Kline, 2006). Previous studies have shown that resting state FAA is related to the ability to regulate negative emotions (Coan & Allen, 2004; Kemp et al., 2005; Mikolajczak et al., 2008, 2010). For example, Adolph, Von Glischinski, Wannemuller, and Margraf (2017) also found that lower FAA was associated with more negative subjective emotional evaluation of all stimuli and diminished emotional modulation of late positive potential in healthy college students. Jackson et al. (2003) also found that increased FAA was associated with a decreased startle reflex (indicating better emotion regulation performance) after unpleasant picture presentation to normal community participants. In addition, a developmental study revealed that a baby from a depressive mother would have less brain activity in the left frontal hemisphere than in the right frontal hemisphere (lower FAA) (Jones, Field, & Almeida, 2009). In healthy adolescents, a significant relationship between lower FAA and higher depression scores has also been shown (Grunewald et al., 2018). Based on the above, the first hypothesis would be that FAA had a negative relationship with difficulties in emotion regulation in healthy participants. However, researchers also found inconsistent results regarding the relation of FAA and emotion disorder symptoms for clinical participants (Hagemann, 2004; Vinne, Vollebregt, Van Putten, & Arns, 2017). For example, Grunewald et al. (2018) found that adolescents with major depression showed more left-sided activity than the healthy controls. Feldmann et al. (2018) revealed that when the study controlled for comorbid anxiety, depressed adolescents showed no difference in FAA compared to healthy controls. Furthermore, a meta-analysis by Der Vinne, Vollebregt, Van Putten, and Arns (2017) found that FAA is not a reliable diagnostic biomarker for major depression disorder patients. Firstly, these inconsistent findings could be explained by the mixed effects of emotional valence and approach-withdrawal motivational direction on the frontal activity asymmetry (HarmonJones, Gable, & Peterson, 2010). Specifically, possible explanations for the contrasting findings in adolescents could be the immature prefrontal cortex, heightened impulsivity, and novelty-seeking in adolescents (Feldmann et al., 2018). According to the approach-withdrawal model, heightened impulsivity and novelty-seeking were related to the approach system, which might accompany increased left frontal activity and would affect the relations of FAA and negative emotion (HarmonJones et al., 2010). Secondly, the complicated cluster criteria of emotion disorder symptoms and their multiple components would also cause inconsistent findings. Nusslock, Walden, and HarmonJones (2015) revealed that lower FAA was most strongly associated with the unipolar depressive symptom of anhedonia and higher FAA was associated with a cluster of approach-related hypomanic/manic symptoms. They also found that larger anxious arousal was related to decreased FAA while more anxious apprehension was related to increased FAA (Nusslock et al., 2015). The studies mentioned above mostly examined relationships with FAA and the scores of symptom evaluation that reflect unified behavioral aspects of emotion dysregulations. DERS and its six subscales are measurements of clinically relevant difficulties in emotion regulation that are based on a comprehensive, integrative conceptualization of emotion regulation (Gratz & Roemer, 2004). The exploration of the relationships between FAA and different dimensions of emotion regulation difficulties would provide a new explanation for inconsistent findings.
2. Methods 2.1. Participants Eighty participants (44 male, age = 21.49 ± 2.45 years, range: 17–28 years) with normal or corrected-to-normal vision were recruited from a university for the study. All participants were right-handed. The study was approved by was approved by the Ethical Committee of Department of Psychology of Renmin University of China. Informed consent was obtained from all participants. Each participant received 50 Chinese Yuan for finishing the study. 2.2. Materials Participants completed the Chinese translated version of the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2008). It is a 36-item scale measuring difficulties in the processing of six relevant domains of emotion dysregulation: nonacceptance of emotion responses (Nonacceptance, e.g., “When I'm upset, I become angry with myself for feeling that way”), lack of emotional awareness (Awareness, e.g., “I pay attention to how I feel”), limited access to emotion regulation strategies (Strategies, e.g., “When I'm upset, I believe there is nothing I can do to make myself feel better”), difficulty in engaging in goal directed behavior when emotionally aroused (Goals, e.g., “When I'm upset, I have difficulty getting work done”), impulse control difficulties (Impulse, e.g., “When I'm upset, I become out of control”), and lack of emotional clarity (Clarity, e.g., “I have difficulty making sense out of my feelings”). Participants were asked to evaluate how often statements apply to themselves on a 5-point Likert scale (ranging from 1 = almost never, 0–10% to 5 = almost always, 91–100%). Eleven items were reverse scored. The DERS scores ranged from 36 to 180. Higher scores indicated higher levels of emotional dysregulation. The DERS showed a high internal consistency in a healthy sample (α = 0.86) with a good test-retest reliability of 0.74, and was significantly correlated in the expected direction with measures of emotion regulation (Gratz & Roemer, 2004, 2008). In the present study, Cronbach's coefficient alpha was 0.82 for total DERS, 0.74 for Nonacceptance subscale, 0.65 for Awareness subscale, 0.76 for Strategies subscale, 0.80 for Goals subscale, 0.81 for Impulse subscale, and 0.70 for Clarity subscale. The Chinese version of Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996; Wang et al., 2011), a 21-item, self2
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density (PSD, μV2/Hz) was computed for alpha bands (8–13 Hz). An asymmetry score was calculated for total alpha power spectral density by subtracting the natural log transformed scores (ln[right frontal alpha power]-ln[left frontal alpha power]) for homologous left and right pairs of F3 and F4, according to a study by Smit, Posthuma, Boomsma, and De Geus (2007). Larger alpha asymmetry score values reflect relatively greater left activity (Allen, Coan, & Nazarian, 2004). Firstly, the electrodes at F3 and F4, which correspond to the dorsolateral prefrontal cortex, were selected in most of the studies (Allen et al., 2004; Beam, Borckardt, Reeves, & George, 2009). Secondly, the test-retest reliability of frontal alpha EEG of the electrodes at F3 and F4 has been shown to reach an acceptable level (Winegust, Mathewson, & Schmidt, 2014). Thirdly, a brain-activitybased feedback study used a neurofeedback protocol designed to change the frontal asymmetry based on the electrodes at F3 and F4. They found that individual frontal alpha frequency neurofeedback resulted in a change in relative frontal asymmetry at resting state in participants in the group with increased right asymmetry, and this change seemed to affect subjective stress (Quaedflieg et al., 2016).
reported, 4 point-Likert scale to measure depression during the past two weeks was used. The Cronbach's α of the Chinese version of BDI-II is 0.94. The Chinese version of State-Trait Anxiety Inventory (STAI; Li & Qian, 1995; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983), a 40-item, self-reported, 4 point-Likert scale to measure anxiety was administered. The Cronbach's α of the Chinese version of STAI-S and STAI-T is 0.91 and 0.88, respectively. The STAI-T, STAI-S and the BDI showed good internal consistency in the actual sample (STAI-T: Cronbach's α = 0.90, STAI-S: Cronbach's α = 0.86, BDI: Cronbach's α = 0.92). 2.3. Procedures The experiment was conducted individually in the private suite of the NeuroSCAN EEG Lab. Each participant sat in a chair for placement of 64 Ag-AgCl electrodes on the scalp sites, identified by the extended International 10–20 System. Following electrode placement, the participant was seated in a comfortable chair in a dimly lit testing room. To reduce muscle artifacts in the EEG signal, the participant was instructed to assume a comfortable position, to avoid movement, and unnecessary eye blinks. The presence of an adequate EEG signal was determined by visual inspection of the raw signal on the monitor screen display. The resting state EEG was recorded for six minutes with alternate 15 s eyes-closed blocks and 15 s eyes-open blocks. The order of presentation was counterbalanced between the participants. Finally, twelve eyes-closed blocks and twelve eyes-open blocks were presented to the participants. Alpha power in eye-closed blocks were included in the frontal asymmetry analysis. Previous studies have already shown a good reliability of trials of six eyes-closed blocks during alternate 30 s eyes-open and 30 s eyes-closed recording in the EEG resting state (Burnette et al., 2011). In the present study, Spearman-Brown corrected split half reliability between the first and last 90 s of the resting measurement was high (0.90) thus confirming highly reliable assessment of frontal asymmetry. In addition, in order to avoid too much attention and effort to open their eyes without blinking, to keep their eyes closed without eye movements, we used a shorter duration of 15 s in the eyesclosed block and the eyes-open block. After recording the resting state EEG data, each participant completed BDI, STAI and DERS.
2.6. Statistical analysis SPSS 22.0 was used to analyze the relation between FAA and DERS (and its subscale) scores. Multiple hierarchical regression tests were conducted to examine the relationship between FAA and DERS, and its subscales scores controlling for gender, and BDI. Step one included gender as the predictor. Step 2 added BDI scores and step 3 added FAA as predictors of DERS total and subscale scores. In addition, a median split was performed on FAA to stratify the participants into groups with high and low FAA. Independent samples t-tests were conducted to explore the potential differences between groups in all the variables. 3. Results Independent sample t-tests showed that the high in FAA group had lower scores in BDI, total DERS, and all subscales (see Tables 1 and 2 for the demographic information). The state-trait anxiety and depression scores indicated low levels of anxiety and depression. No participants who had anxiety or depression scores above the cut-off were included in the experiment. To examine the expected negative relationship between left asymmetry of frontal activity and difficulties in emotion regulation (DERS), correlation analyses were conducted. Pearson's r correlations (two-
2.4. EEG recording Scalp voltages were recorded by a NeuroSCAN system (according to the 10–20 system), using a 64-channel Quick cap with Ag-AgCl electrodes (Neurosoft, Inc. Sterling, USA). The horizontal electrooculography (HEOG) was recorded bipolarly from the outer canthi of both eyes, and the vertical EOG (VEOG) was recorded above and below the left eye. For reference, EEG data was continuously recorded with an on-line reference to the left mastoid and an off-line algebraic re-reference to the calculated average of the left and right mastoids (Ma, Hu, Jiang, & Meng, 2015). The electrode impedance was kept below 5 kΩ. The amplifier band-width was 0.05–100 Hz. The EEGs and EOGs were sampled with a digitization rate of 500 Hz. All EEG and EOG signals were saved on a computer hard disk for offline analysis. Offline analysis was performed using the EEGLAB 14.1.1(Brunner, Delorme, & Makeig, 2013), and blink artifacts were eliminated using independent component analysis (ICA) method. Continuous EEG signals were low-pass filtered at 30 Hz, and divided into epochs with 1024 points which was based on previous frontal EEG asymmetry studies (Amodio, 2010; Smit, Posthuma, Boomsma, & De Geus, 2005, 2007).
Table 1 FAA group comparisons on all variables. The table shows the mean and standard deviation on the resting state frontal alpha asymmetry (FAA) between high and low FAA groups. The significance of the difference between groups is also shown in the table (p < .05). FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale; BDI, Beck depression investigation. * p < .05, ** p < .01, *** p < .001.
DERS AWARE NONACC IMPULSE CLARITY GOALS STRAT BDI STAI-S STAI-T
2.5. Frontal asymmetry assessment EEG data from two participants that displayed persistent muscle artifacts or frequent blinking were excluded from the analysis. The artifact-free data were analyzed with a Fast Fourier transform (FFT) using a Hanning window of 1 s width and 50% taper. Power spectral 3
Total
High FAA
Low FAA
Comparison High vs. Low FAA group
15.64(3.23) 2.27(0.69) 2.61(0.83) 2.47(0.76) 2.41(0.65) 3.23(0.80) 2.66(0.73) 10.27(6.94) 38.18(10.04) 43.29(9.04)
14.385(2.37) 2.104(0.54) 2.421(0.72) 2.117(0.60) 2.245(0.59) 3.005(0.76) 2.493(0.70) 7.55(5.89) 37.98(10.25) 42.35(8.56)
16.931(3.49) 2.441(0.78) 2.804(0.90) 2.829(0.74) 2.575(0.68) 3.450(0.78) 2.831(0.74) 13(6.91) 38.37(9.94) 44.23(9.51)
3.816⁎⁎⁎ 2.256* 2.107* 4.735⁎⁎⁎ 2.331* 2.586* 2.097* 3.797⁎⁎⁎ 0.177 0.927
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The present study examined the relationships between FAA and particular dimensions of emotion regulation difficulties by using the technology of electroencephalogram and DERS. The findings of negative association of resting state FAA to the score of difficulties in impulse control were consistent with the results of previous studies. For example, Lake, Stanford, and Patton (2014) found that right frontal lateralization in the resting state was accompanied by a dysfunction of inhibiting aggression for impulsive aggressive individuals. A similar pattern of result was also evidenced in participants with obsessivecompulsive disorder (Ischebeck et al., 2014). Furthermore, our result was also in accordance with the findings of functional magnetic resonance imaging. For example, Tessitore et al. (2017) found that patients with Parkinson's disease with impulse control disorders showed more activity of the right dorsolateral prefrontal than patients without impulse control disorders. A possible explanation of the result would be an overactive behavioral inhibition system (BIS). According to “reinforcement sensitivity theory”, BIS is one of the two general systems to coordinate adaptive behavior (Gray, 1987). The function of BIS is to increase attention toward aversive stimuli, to interrupt ongoing behavior, and to prepare for withdrawal. The right frontal cortical activity reflects avoidance motivation which should be included in the BIS, while left frontal cortex reflects approach motivation which are the functions of the behavioral approach system (BAS) (De Pascalis, Cozzuto, Caprara, & Alessandri, 2013). Past studies have shown that high sensitivity of BIS to negative emotion is associated with an increased risk for difficulties in emotion regulation, and for anxiety and depressive disorders (Bijttebier, Beck, Claes, & Vandereycken, 2009; Muris, Merckelbach, Schmidt, Gadet, & Bogie, 2001; Tull, Gratz, Latzman, Kimbrel, & Lejuez, 2010). In the present study, larger right frontal activity suggested that some healthy participants may be more sensitive, pay more attention, and or have quick withdrawal responses to negative stimuli which might be the main cause of difficulty for them in inhibiting negative impulsive behavior. Importantly, the present study also found positive association between FAA and scores of awareness, non-acceptance, and impulse control subscales. However, when depression scores were controlled, relationships of FAA, and awareness or non-acceptance showed no significance. These interesting results can indicate several things. Firstly, the results indicated that the predication of FAA to emotion regulation difficulties relied on impulse control. Consistently, impulse control disorder has been treated as the main behavioral dysfunction in many emotion-related problematic behavior and mental diseases, such as problematic internet use, attention deficit hyperactivity disorder and Parkinson's disease (Lin, Lee, Chang, & Hong, 2014; Mazhari, 2012; Poletti & Bonuccelli, 2012; Tice, Bratslavsky, & Baumeister, 2001). Secondly, the result indicated that various relationships between FAA and dimensions of emotion regulation difficulties might be one possible reason for inconsistent findings of frontal asymmetry studies. However, this indication needs to have more evidences based on clinical patients
Table 2 Demographic data of subjects of the two frontal alpha asymmetry (FAA) groups.
Age Gender ratio (female/male) Total number Education
High FAA
Low FAA
Comparison High vs. Low FAA group
21.89 ± 2.88 17/23
22.13 ± 2.86 19/21
NS NS
40 University student
40 University student
NS NS
Note: NS means p > .05.
tailed) were computed between the scores of DERS subscale and resting state frontal activity asymmetry. The resulting p-values were Bonferroni-corrected with a significance threshold of p < .05. Correlation results showed a significant negative relationship between FAA and BDI (r = −0.43, p < .001), and DERS total scores (r = −0.37, p < .01), in addition to the AWARE (r = −0.32, p < .01), NONACC (r = −0.26, p < .05), and IMPULSE (r = −0.48, p < .01) subscales (see Table 2). Multiple hierarchical regression models showed that after controlling for gender, and BDI scores, FAA was significantly associated with total DERS scores and IMPULSE subscale (see Table 3 for a summary of regression models and Fig. 1 for the Scatterplot of FAA and scores of Difficulties Emotion Regulation Scale and the Impulse Control subscale). Results revealed left frontal lateralization and explained total emotion regulation difficulties (R2 = 0.22, p < .05). The regression equation is Y = 14.43–4.22X (See Fig. 1). Y is score of total score of DERS in this equation (larger Y means more difficulties in emotion regulation). Results also revealed left frontal lateralization and explained impulse control difficulties (R2 = 0.25, p < .001). The regression equation is Y = 2.48–1.66X (See Fig. 1). Y is the score of Impulse control difficulties in this equation (larger Y means more difficulties in impulse control). No significant relationship was found between left lateralization of the resting state frontal activity and the other five subscales (ps < 0.05) (see Table 4). 4. Discussion The primary goal of the present study was to investigate whether and how resting state frontal alpha asymmetry (FAA) was associated with emotion regulation difficulties, indexed by DERS score and its six subscales. The results demonstrated that after controlling for the depression scores, resting state FAA would predict the total DERS scores and impulse control score. Further, the results provided evidence that those participants with larger FAA (larger alpha power of right frontal than left frontal) in the resting state have less difficulties with everyday emotion regulation, especially with impulse control. This pattern was not found for the other five dimensions of DERS.
Table 3 Correlation matrix of FAA and psychological variables. The table shows the correlations between all the variables. FAA, frontal alpha asymmetry, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale; BDI, Beck depression investigation. * p < .05, ** p < .01, *** p < .001.
FAA BDI DERS AWARE NONACC IMPULSE CLARITY GOALS STRAT
FAA
BDI
DERS
AWARE
NONACC
IMPULSE
CLARITY
GOALS
STRAT
– −0.43⁎⁎ −0.37⁎⁎ −0.32⁎⁎ −0.26* −0.48⁎⁎ −0.22 −0.17 −0.18
– 0.42⁎⁎ 0.36⁎⁎ 0.33⁎⁎ 0.34⁎⁎ 0.46⁎⁎ 0.19 0.16
– 0.66⁎⁎ 0.77⁎⁎ 0.82⁎⁎ 0.54⁎⁎ 0.75⁎⁎ 0.75⁎⁎
– 0.32⁎⁎ 0.54⁎⁎ 0.52⁎⁎ 0.26* 0.32⁎⁎
– 0.49⁎⁎ 0.33⁎⁎ 0.507⁎⁎ 0.59⁎⁎
– 0.37⁎⁎ 0.57⁎⁎ 0.56⁎⁎
– 0.13 0.13
– 0.69⁎⁎
–
4
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Fig. 1. Scatterplot of the resting state frontal alpha asymmetry (FAA) and Difficulties Emotion Regulation Scale (DERS) scores (the upper panel). Scatterplot of FAA and Impulse subscale scores (the lower panel). FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; IMPULSE, impulse control subscale.
experimental neurostimulation techniques. This can be done by repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS), which can manipulate the brain activity of the related frontal area. In addition, clinical patients should be recruited to examine the associations between FAA and DERS, especially for adolescents with major depression and patients with anxiety disorder.
in the future. One contribution of the present study is to provide the possible underlying neural mechanism of emotion regulation difficulties in healthy participants. Previous studies focused on the relation of lateralized index of alpha band of patients and their emotion disorder symptoms (Ischebeck et al., 2014); Mathersul, Williams, Hopkinson, & Kemp, 2008; Stewart, Coan, Towers, & Allen, 2014). Later studies examined how particular dimensions of the regulation difficulties in healthy participants are correlated with frontal asymmetry. The current study provided EEG evidence that emotion regulation difficulties, especially difficulties in controlling emotional impulsive behavior, could be predicted by the resting state frontal alpha asymmetry of healthy participants. There were several limitations in the present study. One limitation of the current study is that while the scores of depression was controlled, other possible covariables, such as positive and negative emotions during the EEG recording, were not measured in the experiment. Future studies that are well controlled for covariables which affect resting state frontal asymmetry are needed. Secondly, the present study showed a significant relationship of resting state frontal EEG asymmetry and impulse control difficulties in emotion regulation. The causal role of the left frontal lobe in emotion difficulties and in emotional impulse control, however, should be examined further by employing
5. Conclusion In conclusion, the present study shows a significant relationship of resting state frontal alpha asymmetry with difficulties in emotion regulation, even when depression scores are controlled. Those with higher left frontal activity than right frontal activity are associated with less difficulties in regulating their emotions, especially difficulties with impulse control during a negative emotion situation. Thus, the current study provides an indication that resting state frontal alpha asymmetry predicts self-reported difficulties in emotion regulation, mainly in the impulse control dimension. Ethical statement The study ”Resting State Frontal Alpha Asymmetry Predicts 5
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Table 4 Summary of hierarchical regression analysis for variables predicting DERS scores. This table shows standardized beta coefficients with associated significance levels and their standard errors at each step in the regression model. FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; BDI, Beck depression investigation; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale. * p < .05, ** p < .01, *** p < .001. Measure
AWARE 1 2 3 NONACC 1 2 3 IMPULSE 1 2 3 CLARITY 1 2 3 GOAL 1 2 3 START 1 2 3 TOTAL 1 2 3
Gender Beta
S.D.
BDI Beta
S.D.
0.000 0.037 0.050
0.155 0.146 0.145
0.036⁎⁎ 0.027*
0.011 0.011
0.146 0.189 0.201
0.187 0.178 0.177
0.041⁎⁎ 0.033*
0.013 0.014
−0.117 −0.078 −0.049
0.171 0.163 0.151
0.036⁎⁎ 0.017
0.012 0.012
0.028 0.074 0.076
0.147 0.132 0.133
0.043⁎⁎ 0.042⁎⁎
0.010 0.011
−0.142 −0.120 −0.112
0.179 0.178 0.179
0.021 0.015
0.013 0.014
0.037 0.055 0.065
0.166 0.165 0.165
0.017 0.011
0.012 0.013
−0.048 0.158 0.232
0.730 0.671 0.656
0.194⁎⁎⁎ 0.146⁎⁎
0.048 0.052
FAA Beta
−0.748
−0.676
−1.657⁎⁎
−0.098
−0.469
−0.569
−4.217*
Emotion Regulation Difficulties in Impulse Control” was approved by the Ethical Committee of Department of Psychology of Renmin University of China.
R2
S.D.
Constant Beta
S.D.
0.430
2.273⁎⁎⁎ 1.887⁎⁎⁎ 2.038⁎⁎⁎
0.115 0.157 0.178
0.000 0.129 0.162
0.527
2.532⁎⁎⁎ 2.087⁎⁎⁎ 2.224⁎⁎⁎
0.139 0.191 0.218
0.008 0.124 0.143
0.451
2.537 2.145 2.480
0.127 0.176 0.187
0.006 0.115 0.248
0.396
2.394⁎⁎⁎ 1.925⁎⁎⁎ 1.945⁎⁎⁎
0.109 0.142 0.164
0.000 0.212 0.213
0.532
3.306⁎⁎⁎ 3.081⁎⁎⁎ 3.176⁎⁎⁎
0.133 0.192 0.220
0.008 0.040 0.050
0.492
2.642⁎⁎⁎ 2.454⁎⁎⁎ 2.569⁎⁎⁎
0.123 0.178 0.204
0.001 0.027 0.044
1.954
15.685⁎⁎⁎ 13.578⁎⁎⁎ 14.433⁎⁎⁎
0.542 0.722 0.809
0.000 0.173 0.221
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Resting state frontal alpha asymmetry predicts emotion regulation difficulties in impulse control ⁎
Jing Zhanga, , Yan Huaa, Lichao Xiub, Tian Po Oeic, Ping Hua,
T
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a
Department of Psychology, Renmin University of China, Beijing, China Lab of Cognitive Neuroscience and Communication Study, School of Journalism and Communication, Beijing Normal University, Beijing, China c School of Psychology, University of Queensland, Brisbane, Australia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Frontal alpha asymmetry Difficulties in emotion regulation scale Impulse control
Failure in emotion regulation would cause barriers to individuals’ adaptive behavior, which negatively affect a human's happy and healthy life. Recent studies indicate that decreased activity of the left frontal cortex can be treated as a neural marker of emotion disorders. The present study examined how resting state frontal alpha asymmetry could predict emotion regulation difficulties and related dimensions by resting state electroencephalogram (EEG) recordings, and a measure of the Difficulties in Emotion Regulation Scale (DERS). Eighty participants completed the resting state EEG recording and DERS ratings. Results revealed that after controlling for gender and depression scores, those participants with higher left frontal activity than the right frontal activity in the resting state have less difficulties in everyday emotion regulation, especially in the dimension of impulse control. However, there was no relation pattern with other dimensions of DERS. This study provided evidence that resting state frontal alpha asymmetry could predict emotion regulation difficulties, mainly in impulse control.
1. Introduction Successful emotion regulation is one of the main cores of human health and well-being. Emotion regulation capability has several dimensions, such as the modulation of emotional arousal, awareness, understanding, acceptance of emotions, and the ability to act in desired ways regardless of emotional state (Gratz & Roemer, 2008). Different dimensions of emotion regulation difficulties have been shown to be main risk factors to induce anxiety and depression, or to cause poor performance in emotion related activities in normal life (Campbellsills & Barlow, 2007; Markarian, Pickett, Deveson, & Kanona, 2013). Previous studies have also shown that resting state frontal alpha asymmetry (FAA) has a relationship with emotion dysfunctions, such as depression and anxiety disorder (Hannesdóttir, Doxie, Bell, Ollendick, & Wolfe, 2010; Jackson et al., 2003; Mikolajczak, Bodarwe, Laloyaux, Hansenne, & Nelis, 2010, 2008; Smith, Zambrano-Vazquez, & Allen, 2016). The present study aims to examine whether and how FAA predicts emotion regulation difficulty, especially its various dimensions. Emotion regulation difficulties comprise six dimensions, including Non-acceptance, Goals, Impulse, Awareness, Strategies, and Clarity, which are assessed by the Difficulties in Emotion Regulation Scale (DERS, Gratz & Roemer, 2004). Previous studies found that emotion
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regulation difficulties had a significant relation to psychological problems reflecting emotion disorders, specifically depression, anxiety, suicidal ideation, eating disorders, alcohol use, and drug use (Bradizza et al., 2018; Gratz & Roemer, 2004; Weinberg & Klonsky, 2009). Recent studies have also found that emotion dysregulation in healthy individuals is related to emotion normative developmental processes and experiences, such as identity development, procrastination, social participation, and academic motivation and performance (Kaufman et al., 2016). Although many studies have explored the relationship of emotion regulation difficulties and mental health, there is limited research on the related neural activity that links to emotion regulation difficulties. Studies on the neural mechanisms of emotion regulation found that the frontal cortical regions are involved in the modulation of amygdala reactivity and play an important role in effective emotion regulation (Banks, Eddy, Angstadt, Nathan, & Phan, 2007; Hagemann, Waldstein, & Thayer, 2003). Successful emotion regulation is dependent on topdown control from the prefrontal cortex over subcortical regions involved in reward and emotion (Heatherton & Wagner, 2011). It has been shown that the increased connectivity between the dorsal lateral prefrontal cortex (DLPFC) and both amygdala and dorsal anterior cingulate cortex (ACC) is the mechanism of top-down regulation of
Corresponding authors. E-mail addresses: [email protected] (J. Zhang), [email protected] (P. Hu).
https://doi.org/10.1016/j.paid.2020.109870 Received 24 September 2019; Received in revised form 2 December 2019; Accepted 27 January 2020 0191-8869/ © 2020 Elsevier Ltd. All rights reserved.
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Previous studies have indicated an association between lateralized frontal activity and behavior related to impulse control. For example, Ischebeck, Endrass, Simon, and Kathmann (2014) found that obsessivecompulsive disorder participants showed more dominance of alpha power on the left frontal cortex (more activity in the right frontal) when compared with the healthy control group. Wacker, Chavanon, Leue, and Stemmler (2009) found that participants showed more activity in right prefrontal cortex in response inhibition, especially among the high trait behavioral inhibition participants. In addition, past studies have found that the left dorsolateral prefrontal is involved in the resting state brain networks of impulse control, which is closely related to impulse control behavior (Tessitore et al., 2017; Weygandt et al., 2015). Thus, we could hypothesize that the lateralized frontal activity would be significantly correlated to the dimension of impulse control of emotion in DERS. In an attempt to further establish the role of resting state FAA in emotion regulation difficulties, the present study examined the relationship between resting state frontal EEG asymmetry and the total DERS (and its subscales) scores using the asymmetry of EEG alpha power in the frontal lobe and investigation of self-reported emotion regulation difficulties. We expected that higher FAA would be negatively associated with difficulties in controlling impulses in negative emotions in DERS and the total DERS scores.
emotional conflict and increased attentional control (Comte et al., 2016; Doll et al., 2016). Therefore, impaired prefrontal activities would tip the balance of the prefrontal cortex and subcortical regions, which have been shown as the main reasons for emotion regulation difficulties (Heatherton & Wagner, 2011). Resting state FAA, which is indexed by ln [right frontal alpha band (8–13 Hz) power]-ln [left frontal alpha band power], provides relative differences in the activity between the left and right frontal hemisphere (Davidson, 1995). Since alpha power has been found to be inversely related to the activity of the corresponding region of the brain, higher FAA indicates greater left-sided frontal activity (Blackhart, Minnix, & Kline, 2006). Previous studies have shown that resting state FAA is related to the ability to regulate negative emotions (Coan & Allen, 2004; Kemp et al., 2005; Mikolajczak et al., 2008, 2010). For example, Adolph, Von Glischinski, Wannemuller, and Margraf (2017) also found that lower FAA was associated with more negative subjective emotional evaluation of all stimuli and diminished emotional modulation of late positive potential in healthy college students. Jackson et al. (2003) also found that increased FAA was associated with a decreased startle reflex (indicating better emotion regulation performance) after unpleasant picture presentation to normal community participants. In addition, a developmental study revealed that a baby from a depressive mother would have less brain activity in the left frontal hemisphere than in the right frontal hemisphere (lower FAA) (Jones, Field, & Almeida, 2009). In healthy adolescents, a significant relationship between lower FAA and higher depression scores has also been shown (Grunewald et al., 2018). Based on the above, the first hypothesis would be that FAA had a negative relationship with difficulties in emotion regulation in healthy participants. However, researchers also found inconsistent results regarding the relation of FAA and emotion disorder symptoms for clinical participants (Hagemann, 2004; Vinne, Vollebregt, Van Putten, & Arns, 2017). For example, Grunewald et al. (2018) found that adolescents with major depression showed more left-sided activity than the healthy controls. Feldmann et al. (2018) revealed that when the study controlled for comorbid anxiety, depressed adolescents showed no difference in FAA compared to healthy controls. Furthermore, a meta-analysis by Der Vinne, Vollebregt, Van Putten, and Arns (2017) found that FAA is not a reliable diagnostic biomarker for major depression disorder patients. Firstly, these inconsistent findings could be explained by the mixed effects of emotional valence and approach-withdrawal motivational direction on the frontal activity asymmetry (HarmonJones, Gable, & Peterson, 2010). Specifically, possible explanations for the contrasting findings in adolescents could be the immature prefrontal cortex, heightened impulsivity, and novelty-seeking in adolescents (Feldmann et al., 2018). According to the approach-withdrawal model, heightened impulsivity and novelty-seeking were related to the approach system, which might accompany increased left frontal activity and would affect the relations of FAA and negative emotion (HarmonJones et al., 2010). Secondly, the complicated cluster criteria of emotion disorder symptoms and their multiple components would also cause inconsistent findings. Nusslock, Walden, and HarmonJones (2015) revealed that lower FAA was most strongly associated with the unipolar depressive symptom of anhedonia and higher FAA was associated with a cluster of approach-related hypomanic/manic symptoms. They also found that larger anxious arousal was related to decreased FAA while more anxious apprehension was related to increased FAA (Nusslock et al., 2015). The studies mentioned above mostly examined relationships with FAA and the scores of symptom evaluation that reflect unified behavioral aspects of emotion dysregulations. DERS and its six subscales are measurements of clinically relevant difficulties in emotion regulation that are based on a comprehensive, integrative conceptualization of emotion regulation (Gratz & Roemer, 2004). The exploration of the relationships between FAA and different dimensions of emotion regulation difficulties would provide a new explanation for inconsistent findings.
2. Methods 2.1. Participants Eighty participants (44 male, age = 21.49 ± 2.45 years, range: 17–28 years) with normal or corrected-to-normal vision were recruited from a university for the study. All participants were right-handed. The study was approved by was approved by the Ethical Committee of Department of Psychology of Renmin University of China. Informed consent was obtained from all participants. Each participant received 50 Chinese Yuan for finishing the study. 2.2. Materials Participants completed the Chinese translated version of the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2008). It is a 36-item scale measuring difficulties in the processing of six relevant domains of emotion dysregulation: nonacceptance of emotion responses (Nonacceptance, e.g., “When I'm upset, I become angry with myself for feeling that way”), lack of emotional awareness (Awareness, e.g., “I pay attention to how I feel”), limited access to emotion regulation strategies (Strategies, e.g., “When I'm upset, I believe there is nothing I can do to make myself feel better”), difficulty in engaging in goal directed behavior when emotionally aroused (Goals, e.g., “When I'm upset, I have difficulty getting work done”), impulse control difficulties (Impulse, e.g., “When I'm upset, I become out of control”), and lack of emotional clarity (Clarity, e.g., “I have difficulty making sense out of my feelings”). Participants were asked to evaluate how often statements apply to themselves on a 5-point Likert scale (ranging from 1 = almost never, 0–10% to 5 = almost always, 91–100%). Eleven items were reverse scored. The DERS scores ranged from 36 to 180. Higher scores indicated higher levels of emotional dysregulation. The DERS showed a high internal consistency in a healthy sample (α = 0.86) with a good test-retest reliability of 0.74, and was significantly correlated in the expected direction with measures of emotion regulation (Gratz & Roemer, 2004, 2008). In the present study, Cronbach's coefficient alpha was 0.82 for total DERS, 0.74 for Nonacceptance subscale, 0.65 for Awareness subscale, 0.76 for Strategies subscale, 0.80 for Goals subscale, 0.81 for Impulse subscale, and 0.70 for Clarity subscale. The Chinese version of Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996; Wang et al., 2011), a 21-item, self2
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density (PSD, μV2/Hz) was computed for alpha bands (8–13 Hz). An asymmetry score was calculated for total alpha power spectral density by subtracting the natural log transformed scores (ln[right frontal alpha power]-ln[left frontal alpha power]) for homologous left and right pairs of F3 and F4, according to a study by Smit, Posthuma, Boomsma, and De Geus (2007). Larger alpha asymmetry score values reflect relatively greater left activity (Allen, Coan, & Nazarian, 2004). Firstly, the electrodes at F3 and F4, which correspond to the dorsolateral prefrontal cortex, were selected in most of the studies (Allen et al., 2004; Beam, Borckardt, Reeves, & George, 2009). Secondly, the test-retest reliability of frontal alpha EEG of the electrodes at F3 and F4 has been shown to reach an acceptable level (Winegust, Mathewson, & Schmidt, 2014). Thirdly, a brain-activitybased feedback study used a neurofeedback protocol designed to change the frontal asymmetry based on the electrodes at F3 and F4. They found that individual frontal alpha frequency neurofeedback resulted in a change in relative frontal asymmetry at resting state in participants in the group with increased right asymmetry, and this change seemed to affect subjective stress (Quaedflieg et al., 2016).
reported, 4 point-Likert scale to measure depression during the past two weeks was used. The Cronbach's α of the Chinese version of BDI-II is 0.94. The Chinese version of State-Trait Anxiety Inventory (STAI; Li & Qian, 1995; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983), a 40-item, self-reported, 4 point-Likert scale to measure anxiety was administered. The Cronbach's α of the Chinese version of STAI-S and STAI-T is 0.91 and 0.88, respectively. The STAI-T, STAI-S and the BDI showed good internal consistency in the actual sample (STAI-T: Cronbach's α = 0.90, STAI-S: Cronbach's α = 0.86, BDI: Cronbach's α = 0.92). 2.3. Procedures The experiment was conducted individually in the private suite of the NeuroSCAN EEG Lab. Each participant sat in a chair for placement of 64 Ag-AgCl electrodes on the scalp sites, identified by the extended International 10–20 System. Following electrode placement, the participant was seated in a comfortable chair in a dimly lit testing room. To reduce muscle artifacts in the EEG signal, the participant was instructed to assume a comfortable position, to avoid movement, and unnecessary eye blinks. The presence of an adequate EEG signal was determined by visual inspection of the raw signal on the monitor screen display. The resting state EEG was recorded for six minutes with alternate 15 s eyes-closed blocks and 15 s eyes-open blocks. The order of presentation was counterbalanced between the participants. Finally, twelve eyes-closed blocks and twelve eyes-open blocks were presented to the participants. Alpha power in eye-closed blocks were included in the frontal asymmetry analysis. Previous studies have already shown a good reliability of trials of six eyes-closed blocks during alternate 30 s eyes-open and 30 s eyes-closed recording in the EEG resting state (Burnette et al., 2011). In the present study, Spearman-Brown corrected split half reliability between the first and last 90 s of the resting measurement was high (0.90) thus confirming highly reliable assessment of frontal asymmetry. In addition, in order to avoid too much attention and effort to open their eyes without blinking, to keep their eyes closed without eye movements, we used a shorter duration of 15 s in the eyesclosed block and the eyes-open block. After recording the resting state EEG data, each participant completed BDI, STAI and DERS.
2.6. Statistical analysis SPSS 22.0 was used to analyze the relation between FAA and DERS (and its subscale) scores. Multiple hierarchical regression tests were conducted to examine the relationship between FAA and DERS, and its subscales scores controlling for gender, and BDI. Step one included gender as the predictor. Step 2 added BDI scores and step 3 added FAA as predictors of DERS total and subscale scores. In addition, a median split was performed on FAA to stratify the participants into groups with high and low FAA. Independent samples t-tests were conducted to explore the potential differences between groups in all the variables. 3. Results Independent sample t-tests showed that the high in FAA group had lower scores in BDI, total DERS, and all subscales (see Tables 1 and 2 for the demographic information). The state-trait anxiety and depression scores indicated low levels of anxiety and depression. No participants who had anxiety or depression scores above the cut-off were included in the experiment. To examine the expected negative relationship between left asymmetry of frontal activity and difficulties in emotion regulation (DERS), correlation analyses were conducted. Pearson's r correlations (two-
2.4. EEG recording Scalp voltages were recorded by a NeuroSCAN system (according to the 10–20 system), using a 64-channel Quick cap with Ag-AgCl electrodes (Neurosoft, Inc. Sterling, USA). The horizontal electrooculography (HEOG) was recorded bipolarly from the outer canthi of both eyes, and the vertical EOG (VEOG) was recorded above and below the left eye. For reference, EEG data was continuously recorded with an on-line reference to the left mastoid and an off-line algebraic re-reference to the calculated average of the left and right mastoids (Ma, Hu, Jiang, & Meng, 2015). The electrode impedance was kept below 5 kΩ. The amplifier band-width was 0.05–100 Hz. The EEGs and EOGs were sampled with a digitization rate of 500 Hz. All EEG and EOG signals were saved on a computer hard disk for offline analysis. Offline analysis was performed using the EEGLAB 14.1.1(Brunner, Delorme, & Makeig, 2013), and blink artifacts were eliminated using independent component analysis (ICA) method. Continuous EEG signals were low-pass filtered at 30 Hz, and divided into epochs with 1024 points which was based on previous frontal EEG asymmetry studies (Amodio, 2010; Smit, Posthuma, Boomsma, & De Geus, 2005, 2007).
Table 1 FAA group comparisons on all variables. The table shows the mean and standard deviation on the resting state frontal alpha asymmetry (FAA) between high and low FAA groups. The significance of the difference between groups is also shown in the table (p < .05). FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale; BDI, Beck depression investigation. * p < .05, ** p < .01, *** p < .001.
DERS AWARE NONACC IMPULSE CLARITY GOALS STRAT BDI STAI-S STAI-T
2.5. Frontal asymmetry assessment EEG data from two participants that displayed persistent muscle artifacts or frequent blinking were excluded from the analysis. The artifact-free data were analyzed with a Fast Fourier transform (FFT) using a Hanning window of 1 s width and 50% taper. Power spectral 3
Total
High FAA
Low FAA
Comparison High vs. Low FAA group
15.64(3.23) 2.27(0.69) 2.61(0.83) 2.47(0.76) 2.41(0.65) 3.23(0.80) 2.66(0.73) 10.27(6.94) 38.18(10.04) 43.29(9.04)
14.385(2.37) 2.104(0.54) 2.421(0.72) 2.117(0.60) 2.245(0.59) 3.005(0.76) 2.493(0.70) 7.55(5.89) 37.98(10.25) 42.35(8.56)
16.931(3.49) 2.441(0.78) 2.804(0.90) 2.829(0.74) 2.575(0.68) 3.450(0.78) 2.831(0.74) 13(6.91) 38.37(9.94) 44.23(9.51)
3.816⁎⁎⁎ 2.256* 2.107* 4.735⁎⁎⁎ 2.331* 2.586* 2.097* 3.797⁎⁎⁎ 0.177 0.927
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The present study examined the relationships between FAA and particular dimensions of emotion regulation difficulties by using the technology of electroencephalogram and DERS. The findings of negative association of resting state FAA to the score of difficulties in impulse control were consistent with the results of previous studies. For example, Lake, Stanford, and Patton (2014) found that right frontal lateralization in the resting state was accompanied by a dysfunction of inhibiting aggression for impulsive aggressive individuals. A similar pattern of result was also evidenced in participants with obsessivecompulsive disorder (Ischebeck et al., 2014). Furthermore, our result was also in accordance with the findings of functional magnetic resonance imaging. For example, Tessitore et al. (2017) found that patients with Parkinson's disease with impulse control disorders showed more activity of the right dorsolateral prefrontal than patients without impulse control disorders. A possible explanation of the result would be an overactive behavioral inhibition system (BIS). According to “reinforcement sensitivity theory”, BIS is one of the two general systems to coordinate adaptive behavior (Gray, 1987). The function of BIS is to increase attention toward aversive stimuli, to interrupt ongoing behavior, and to prepare for withdrawal. The right frontal cortical activity reflects avoidance motivation which should be included in the BIS, while left frontal cortex reflects approach motivation which are the functions of the behavioral approach system (BAS) (De Pascalis, Cozzuto, Caprara, & Alessandri, 2013). Past studies have shown that high sensitivity of BIS to negative emotion is associated with an increased risk for difficulties in emotion regulation, and for anxiety and depressive disorders (Bijttebier, Beck, Claes, & Vandereycken, 2009; Muris, Merckelbach, Schmidt, Gadet, & Bogie, 2001; Tull, Gratz, Latzman, Kimbrel, & Lejuez, 2010). In the present study, larger right frontal activity suggested that some healthy participants may be more sensitive, pay more attention, and or have quick withdrawal responses to negative stimuli which might be the main cause of difficulty for them in inhibiting negative impulsive behavior. Importantly, the present study also found positive association between FAA and scores of awareness, non-acceptance, and impulse control subscales. However, when depression scores were controlled, relationships of FAA, and awareness or non-acceptance showed no significance. These interesting results can indicate several things. Firstly, the results indicated that the predication of FAA to emotion regulation difficulties relied on impulse control. Consistently, impulse control disorder has been treated as the main behavioral dysfunction in many emotion-related problematic behavior and mental diseases, such as problematic internet use, attention deficit hyperactivity disorder and Parkinson's disease (Lin, Lee, Chang, & Hong, 2014; Mazhari, 2012; Poletti & Bonuccelli, 2012; Tice, Bratslavsky, & Baumeister, 2001). Secondly, the result indicated that various relationships between FAA and dimensions of emotion regulation difficulties might be one possible reason for inconsistent findings of frontal asymmetry studies. However, this indication needs to have more evidences based on clinical patients
Table 2 Demographic data of subjects of the two frontal alpha asymmetry (FAA) groups.
Age Gender ratio (female/male) Total number Education
High FAA
Low FAA
Comparison High vs. Low FAA group
21.89 ± 2.88 17/23
22.13 ± 2.86 19/21
NS NS
40 University student
40 University student
NS NS
Note: NS means p > .05.
tailed) were computed between the scores of DERS subscale and resting state frontal activity asymmetry. The resulting p-values were Bonferroni-corrected with a significance threshold of p < .05. Correlation results showed a significant negative relationship between FAA and BDI (r = −0.43, p < .001), and DERS total scores (r = −0.37, p < .01), in addition to the AWARE (r = −0.32, p < .01), NONACC (r = −0.26, p < .05), and IMPULSE (r = −0.48, p < .01) subscales (see Table 2). Multiple hierarchical regression models showed that after controlling for gender, and BDI scores, FAA was significantly associated with total DERS scores and IMPULSE subscale (see Table 3 for a summary of regression models and Fig. 1 for the Scatterplot of FAA and scores of Difficulties Emotion Regulation Scale and the Impulse Control subscale). Results revealed left frontal lateralization and explained total emotion regulation difficulties (R2 = 0.22, p < .05). The regression equation is Y = 14.43–4.22X (See Fig. 1). Y is score of total score of DERS in this equation (larger Y means more difficulties in emotion regulation). Results also revealed left frontal lateralization and explained impulse control difficulties (R2 = 0.25, p < .001). The regression equation is Y = 2.48–1.66X (See Fig. 1). Y is the score of Impulse control difficulties in this equation (larger Y means more difficulties in impulse control). No significant relationship was found between left lateralization of the resting state frontal activity and the other five subscales (ps < 0.05) (see Table 4). 4. Discussion The primary goal of the present study was to investigate whether and how resting state frontal alpha asymmetry (FAA) was associated with emotion regulation difficulties, indexed by DERS score and its six subscales. The results demonstrated that after controlling for the depression scores, resting state FAA would predict the total DERS scores and impulse control score. Further, the results provided evidence that those participants with larger FAA (larger alpha power of right frontal than left frontal) in the resting state have less difficulties with everyday emotion regulation, especially with impulse control. This pattern was not found for the other five dimensions of DERS.
Table 3 Correlation matrix of FAA and psychological variables. The table shows the correlations between all the variables. FAA, frontal alpha asymmetry, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale; BDI, Beck depression investigation. * p < .05, ** p < .01, *** p < .001.
FAA BDI DERS AWARE NONACC IMPULSE CLARITY GOALS STRAT
FAA
BDI
DERS
AWARE
NONACC
IMPULSE
CLARITY
GOALS
STRAT
– −0.43⁎⁎ −0.37⁎⁎ −0.32⁎⁎ −0.26* −0.48⁎⁎ −0.22 −0.17 −0.18
– 0.42⁎⁎ 0.36⁎⁎ 0.33⁎⁎ 0.34⁎⁎ 0.46⁎⁎ 0.19 0.16
– 0.66⁎⁎ 0.77⁎⁎ 0.82⁎⁎ 0.54⁎⁎ 0.75⁎⁎ 0.75⁎⁎
– 0.32⁎⁎ 0.54⁎⁎ 0.52⁎⁎ 0.26* 0.32⁎⁎
– 0.49⁎⁎ 0.33⁎⁎ 0.507⁎⁎ 0.59⁎⁎
– 0.37⁎⁎ 0.57⁎⁎ 0.56⁎⁎
– 0.13 0.13
– 0.69⁎⁎
–
4
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Fig. 1. Scatterplot of the resting state frontal alpha asymmetry (FAA) and Difficulties Emotion Regulation Scale (DERS) scores (the upper panel). Scatterplot of FAA and Impulse subscale scores (the lower panel). FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; IMPULSE, impulse control subscale.
experimental neurostimulation techniques. This can be done by repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS), which can manipulate the brain activity of the related frontal area. In addition, clinical patients should be recruited to examine the associations between FAA and DERS, especially for adolescents with major depression and patients with anxiety disorder.
in the future. One contribution of the present study is to provide the possible underlying neural mechanism of emotion regulation difficulties in healthy participants. Previous studies focused on the relation of lateralized index of alpha band of patients and their emotion disorder symptoms (Ischebeck et al., 2014); Mathersul, Williams, Hopkinson, & Kemp, 2008; Stewart, Coan, Towers, & Allen, 2014). Later studies examined how particular dimensions of the regulation difficulties in healthy participants are correlated with frontal asymmetry. The current study provided EEG evidence that emotion regulation difficulties, especially difficulties in controlling emotional impulsive behavior, could be predicted by the resting state frontal alpha asymmetry of healthy participants. There were several limitations in the present study. One limitation of the current study is that while the scores of depression was controlled, other possible covariables, such as positive and negative emotions during the EEG recording, were not measured in the experiment. Future studies that are well controlled for covariables which affect resting state frontal asymmetry are needed. Secondly, the present study showed a significant relationship of resting state frontal EEG asymmetry and impulse control difficulties in emotion regulation. The causal role of the left frontal lobe in emotion difficulties and in emotional impulse control, however, should be examined further by employing
5. Conclusion In conclusion, the present study shows a significant relationship of resting state frontal alpha asymmetry with difficulties in emotion regulation, even when depression scores are controlled. Those with higher left frontal activity than right frontal activity are associated with less difficulties in regulating their emotions, especially difficulties with impulse control during a negative emotion situation. Thus, the current study provides an indication that resting state frontal alpha asymmetry predicts self-reported difficulties in emotion regulation, mainly in the impulse control dimension. Ethical statement The study ”Resting State Frontal Alpha Asymmetry Predicts 5
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Table 4 Summary of hierarchical regression analysis for variables predicting DERS scores. This table shows standardized beta coefficients with associated significance levels and their standard errors at each step in the regression model. FAA, ln[right frontal alpha power]-ln[left frontal alpha power]; DERS, difficulties in emotion regulation scale; BDI, Beck depression investigation; AWARE, emotional awareness subscale; NONACC, non-acceptance subscale; IMPULSE, impulse control subscale; CLARITY, emotional clarity subscale; GOALS, goals subscale; STRAT, emotional regulation strategies subscale. * p < .05, ** p < .01, *** p < .001. Measure
AWARE 1 2 3 NONACC 1 2 3 IMPULSE 1 2 3 CLARITY 1 2 3 GOAL 1 2 3 START 1 2 3 TOTAL 1 2 3
Gender Beta
S.D.
BDI Beta
S.D.
0.000 0.037 0.050
0.155 0.146 0.145
0.036⁎⁎ 0.027*
0.011 0.011
0.146 0.189 0.201
0.187 0.178 0.177
0.041⁎⁎ 0.033*
0.013 0.014
−0.117 −0.078 −0.049
0.171 0.163 0.151
0.036⁎⁎ 0.017
0.012 0.012
0.028 0.074 0.076
0.147 0.132 0.133
0.043⁎⁎ 0.042⁎⁎
0.010 0.011
−0.142 −0.120 −0.112
0.179 0.178 0.179
0.021 0.015
0.013 0.014
0.037 0.055 0.065
0.166 0.165 0.165
0.017 0.011
0.012 0.013
−0.048 0.158 0.232
0.730 0.671 0.656
0.194⁎⁎⁎ 0.146⁎⁎
0.048 0.052
FAA Beta
−0.748
−0.676
−1.657⁎⁎
−0.098
−0.469
−0.569
−4.217*
Emotion Regulation Difficulties in Impulse Control” was approved by the Ethical Committee of Department of Psychology of Renmin University of China.
R2
S.D.
Constant Beta
S.D.
0.430
2.273⁎⁎⁎ 1.887⁎⁎⁎ 2.038⁎⁎⁎
0.115 0.157 0.178
0.000 0.129 0.162
0.527
2.532⁎⁎⁎ 2.087⁎⁎⁎ 2.224⁎⁎⁎
0.139 0.191 0.218
0.008 0.124 0.143
0.451
2.537 2.145 2.480
0.127 0.176 0.187
0.006 0.115 0.248
0.396
2.394⁎⁎⁎ 1.925⁎⁎⁎ 1.945⁎⁎⁎
0.109 0.142 0.164
0.000 0.212 0.213
0.532
3.306⁎⁎⁎ 3.081⁎⁎⁎ 3.176⁎⁎⁎
0.133 0.192 0.220
0.008 0.040 0.050
0.492
2.642⁎⁎⁎ 2.454⁎⁎⁎ 2.569⁎⁎⁎
0.123 0.178 0.204
0.001 0.027 0.044
1.954
15.685⁎⁎⁎ 13.578⁎⁎⁎ 14.433⁎⁎⁎
0.542 0.722 0.809
0.000 0.173 0.221
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