Frontal asymmetry as a mediator and moderator of emotion: An updated review
Abstract
For over 35 years, research has examined frontal alpha EEG asymmetry, discussed in terms of relative left frontal activity (rLFA) in the present review, as a concurrent and prospective marker of affective processing and psychopathology. Because rLFA may index (a) neural correlates of frontal asymmetry, or (b) psychological constructs to which frontal asymmetry relates, rLFA can advance our understanding of both neural and psychological models of emotion and psychopathology. In order to improve such understanding, the specific role of rLFA in extending or challenging existing theory must be clear to researchers and readers alike. In particular, in 2004, Coan and Allen argued that examination of rLFA as a mediator or moderator may improve our theoretical understanding of rLFA. Despite being a commonly cited paper in the field, most rLFA research today still fails to acknowledge the statistical role of rLFA in the research. The aim of the present paper is to (a) convince the reader of the importance of distinguishing rLFA as a predictor, outcome, mediator, or moderator in order to conduct theory-driven research, and (b) highlight some of the major advances in rLFA literature since the review by Coan and Allen (2004) in the framework of mediators and moderators. We selected a broad range of search terms to capture relevant rLFA research and included only those studies utilizing established methods for rLFA measurement.
1 INTRODUCTION
A large and growing body of research has examined frontal alpha asymmetry in relation to emotion and psychopathology. In their 2004 review, Coan and Allen emphasized the trait and state nature of frontal EEG asymmetry and organized the extant literature in terms of mediator and moderator models. Over a decade later, the literature generally continues to implicate frontal asymmetry as a mediator and moderator of emotion and psychopathology, but often without explicitly examining these models. As such, the present manuscript will highlight research that suggests frontal asymmetry as a moderator or mediator and suggests that the clarification of frontal asymmetry as one of these third variables may be important for conducting theory-driven research and further understanding emotion and psychopathology through frontal asymmetry.
Frontal asymmetry as a construct may index (a) neural correlates of frontal asymmetry, or (b) psychological constructs to which frontal asymmetry relates (Allen & Kline, 2004). As such, frontal asymmetry has great relevance for improving both neural and psychological models of emotion and psychopathology. A brain metric like frontal asymmetry, however, does not in and of itself improve theory or understanding of a particular construct. In other words, the finding that a neurophysiological measure correlates with a psychological phenomenon does not validate the given psychological phenomenon but rather suggests that the neurophysiological measure may index it.
Theory-driven research guides the choice of experimental paradigm and the specific measures used in order to improve upon or clarify existing knowledge. Researchers often do not explicitly acknowledge the reasons for using frontal asymmetry in a given study. Researchers, especially those familiar with a given theory but not an EEG metric, may expand on work more easily given specific explication of frontal asymmetry as a mediator or moderator. Frontal asymmetry may advance theory by providing (a) convergent evidence for a psychological construct, (b) a nonreactive measure in a paradigm where self-report may be subject to reactivity or demand characteristics, (c) a link to neural systems that may correlate with a psychological process. Yet, lack of clear explanation as to why frontal asymmetry advances theory in any given study may lead to the flawed assumption that the addition of psychophysiological measures will always benefit the overall quality of a study. In considering theory a priori in a frontal asymmetry study, it is necessary for researchers to consider the statistical approach and proposed relationships between variables.
Frontal asymmetry is most commonly treated as either a predictor or an outcome variable related to motivation, emotion regulation, and psychopathology. Predictors are variables that are manipulated or examined to influence other variables; outcomes are variables that are affected by the predictors. Although this distinction may seem obvious, there is an important theoretical distinction between predictors and outcomes (Cacioppo, 2004) that this paper will describe.
Frontal asymmetry may also be investigated as a moderator, a variable that changes the relationship between the predictor and outcome variable (Baron & Kenny, 1986). Moderators may help identify for whom a treatment works or for whom a given relationship exists. Identifying frontal asymmetry as a moderator may provide insight into when certain relationships exist or when they may not. Frontal asymmetry may also be examined as a mediator, which is a third variable that represents or indexes a potential variable through which a main effect occurs (Baron & Kenny, 1986). Mediators may help to identify mechanisms through which an intervention works or an emotional response occurs.
The purpose of this paper is two-fold. First, we hope to convince the reader that consideration and explication of frontal asymmetry as a predictor, outcome, mediator, or moderator may improve frontal asymmetry research by enhancing the degree to which it is theory driven and thus clarifying the implications of the findings. In service of this aim, we will discuss distinctions between predictor, outcome, mediator, or moderator and how frontal asymmetry may be studied as each. In particular, we hope to convince the reader that the addition of frontal asymmetry does not inherently improve the caliber of one's study, but rather the inclusion of frontal asymmetry and the statistical role it will play must be considered carefully given the cost and potential benefit, as with any neural or psychometric measure.
Second, we hope to highlight a number of studies that have been influential in the field since the Coan and Allen review in 2004, focusing on their relevance in the mediator and moderator framework. In this way, we hope to direct readers to relevant studies upon which they may expand. In order to include a wide range of potentially influential work in the field, we searched for research since 2004 using a wide range of search terms, included but not limited to “frontal asymmetry,” “EEG asymmetry,” “frontal alpha asymmetry,” “alpha asymmetry.” The methods of these studies were examined, such that any study examining alpha activity at parietal or central rather than frontal sites was excluded. This paper does not aim to serve as a comprehensive review but rather a selection of contemporary illustrative examples that may hold relevance for relative left frontal activity (rLFA) as a mediator or moderator and impact future work. Tables will highlight such illustrative examples. Appendix of Tables from Coan & Allen (2004) are reproduced in the online supporting information.
We will begin by brief discussion of what frontal asymmetry measures and some of the methodological issues in the field for the unfamiliar reader. We will then conceptually discuss and highlight studies that examine frontal asymmetry as a predictor, outcome, mediator, or moderator. Brief mention of moderated mediation and mediated moderation and possibilities for future research will conclude the manuscript.
1.1 A brief summary of what rLFA measures
Frontal EEG asymmetry is a measure of the difference in EEG alpha power between homologous right and left frontal electrodes. Since alpha activity has an inhibitory influence on cortical network activity, lower frontal asymmetry scores (right minus left alpha) putatively reflect less left than right cortical activity (Allen, Coan, & Nazarian, 2004). Alpha activity is typically measured at 8–13 Hz, and frontal asymmetry is calculated as the natural log of the right minus left hemisphere (ln[right] − ln[left].) Activity is typically investigated at midfrontal (F4-F3) and lateral frontal (F6-F5, F8-F7) sites. Frontal pole (Fp2-Fp1) sites are less commonly examined. For research involving infants, the alpha band is often considered to be lower, around 6–9 Hz (Smith & Bell, 2010).
As a relative measure, lower frontal asymmetry scores reflect either less left than right cortical activity or more right than left cortical activity. For purposes of brevity and clarity, frontal asymmetry scores in this review will be discussed in terms of relative left frontal activity (rLFA). As such, less rLFA reflects lower frontal asymmetry scores and more rLFA reflects higher frontal asymmetry scores. Additionally, differences between groups or conditions can result from significant differences at either, neither, or both of constituent recording electrodes. As such, activity at each hemisphere must be investigated independently in order to determine which hemisphere contributes to changes in the difference score (Allen, Coan, & Nazarian, 2004; Harmon-Jones & Allen, 1997).
Most studies examine frontal asymmetry either at rest and as a trait measure of psychological phenomena (Allen & Cohen, 2010; Peltola et al., 2014; Stewart, Bismark, Towers, Coan, & Allen, 2010; Thibodeau, Jorgensen, & Kim, 2006) or during emotionally evocative tasks as a state measure of current emotion or behavior (Harmon-Jones, 2007; Killeen & Teti, 2012; Stewart, Coan, Towers, & Allen, 2011). Consequently, the literature generally focuses on two different constructs: frontal EEG “activity” and “activation.” Activity refers to recording cortical activity at a given time (e.g., at rest, or while watching a film), whereas activation refers to a change in EEG activity in response to stimuli or task (e.g., the difference from rest to the emotion). Further complicating this distinction, task-elicited rLFA is sometimes assessed as activity during the task and at other times activation reflecting change from a nontask period.
The approach-withdrawal motivational model of frontal asymmetry proposes a lateralization of affect across the right and left frontal hemispheres. Left frontal regions are primarily involved with appetitive motivation and approach-related affect such as elation, hope, happiness, and anger (Depue & Iacono, 1989; Harmon-Jones, 2003). In contrast, right frontal regions are associated with behavioral inhibition and vigilant attention that often occurs during certain negative affective states (Coan & Allen, 2004; Davidson & Irwin, 1999; Harmon-Jones & Allen, 1998). The approach system is responsible for goal-directed behavior and appetitive motivation to reward stimuli, whereas the withdrawal system responds to punishment and the termination of reward (Davidson & Irwin, 1999).
Evidence that rLFA is related to behavioral activation (BAS) and behavioral inhibition (BIS), as measured by Carver and White's (1994) scale, supports this model. A number of studies have found that rLFA is related to BAS (Alloy et al., 2008; Coan & Allen, 2003a; De Pascalis, Cozzuto, Caprara, & Alessandri, 2013; Harmon-Jones & Allen, 1997). Yet, not all studies have found that rLFA relates to BAS or agentic extraversion (Wacker, Chavanon, & Stemmler, 2010); possibly due to construct heterogeneity related to the original conceptualization of approach and withdrawal proposed by Davidson (1992). BAS involves the approach-related processes having to do with the attainment (or failed attainment) of rewards or goals. BIS, by contrast, inhibits prepotent approach or avoidant behaviors when there may be competing motivations (Gray & McNaughton, 2000). When assessed with self-report (Carver & White, 1994), BIS may more specifically reflect sensitivity to punishment and threat (Alloy, Abramson, Urosevic, Bender, & Wagner, 2010), thus failing to capture the conflict management functions of the BIS (Wacker, Chavanon, Leue, & Stemmler, 2008).
The isomorphic mapping of BIS and BAS systems to withdrawal and approach systems has been challenged (Harmon-Jones & Allen, 1997). Withdrawal defined within Davidson's model (Davidson & Irwin, 1999) includes all behavior that relates to withdrawing from stimuli but BIS involves interrupting behavior, which does not preclude increased attention or approach-oriented behavior. Additionally, BAS involves the monitoring of reward stimuli, whereas approach motivation involves appetitive behavior. As such, BAS could motivate seeking safety cues elsewhere, which could be considered a withdrawal behavior. See Coan & Allen (2003a) for a more extensive considerations of how BAS/BIS may relate to approach/withdrawal constructs.
An additional challenge to note here is that, although a number of studies reveal correlations between self-reported BAS sensitivity and rLFA, the correlations are of an insufficient magnitude to suggest that rLFA is a synonymous construct with BAS sensitivity.
1.2 Methodological issues in frontal asymmetry research
Although a substantial body of literature has linked rLFA to approach and approach-related disorders, differences in methodology between studies have led to some inconsistent findings. rLFA scores may be impacted by, among other factors, the type of task used (i.e., resting or emotionally evocative and the extent to which cognition is involved), recording montage, and site utilized in the analyses.
First, experimental conditions may influence the measurement of frontal asymmetry in potentially substantial ways. For instance, the capability model proposes that frontal asymmetry may be an interaction between the emotional salience of a situation and an individual's emotion predisposition (Coan, Allen, & McKnight, 2006). Therefore, individual differences in frontal asymmetry may be most pronounced during an emotionally evocative task rather than at rest. In support of this model, Stewart, Coan, and colleagues (2011) found that frontal asymmetry was more strongly related to depression status during a directed facial action task than at rest (for data referenced to Cz, linked mastoids, and the averaged reference, but not for current-source-density-transformed data for which rLFA was related to depression status under both conditions).
Second, the wide range of tasks and stimuli used often involve many different emotional and cognitive processes. It is likely that cognitive processes significantly impact rLFA (Papousek & Schulter, 2004) and individuals with less rLFA may have more impaired reasoning in depression (Brzezicka, Kamiński, Kamińska, Wołyńczyk-Gmaj, & Sedek, 2016). For example, Meyer et al. (2014) found a significant difference in response to two trauma-related films that differed in their difficulty of cognitive reappraisal. Therefore, it may be important to consider the degree to which a task constrains cognition. Resting state, for example, is the least constrained task used in rLFA research. During resting state, each individual will engage in a completely individualized process that will involve both cognitive and affective components that have not been constrained by the researcher. As such, there may be greater moment-to-moment variability in resting asymmetry than during task-related asymmetry in which both cognitive and affective processing may be more constrained.
Additionally, individual differences in cognitive ability may moderate rLFA response to emotional tasks. For example, Papousek et al. (2016) found that individuals who have higher ability to cognitively reappraise potentially anger-provoking material are more likely to demonstrate more rLFA during a cognitive reappraisal task. Although moderators of rLFA and other variables will not be discussed in the present paper, it is important to note such a substantial moderator.
Gable and Harmon-Jones (2008) also found that the personal relevance of the stimuli might impact rLFA findings. In this study, participants indicated their liking of dessert before being shown dessert and neutral pictures as EEG was recorded. Participants who liked dessert more had more rLFA in response to dessert pictures. In another study, participants with greater opposition to racism had more rLFA in response to pictures depicting racism (Harmon-Jones, Lueck, Fearn, & Harmon-Jones, 2013). Steiner and Coan (2011) also examined the relationship between rLFA in both experiential reports of homesickness in college freshman and retrospective reports of freshman year homesickness. They found that rLFA predicted only the experiential but not retrospective reports, suggesting that rLFA may relate to current emotional challenge but not memory of affective state over time.
Recording montage differences between studies may lead to substantial variability across studies and thus possible inconsistencies in results. The most commonly used recording montages are an average reference (average of activity at each site), CZ (activity at vertex), and linked mastoids (activity at mastoid bones averaged) reference. The CSD transformation may be a more sensitive measure of variations in frontal asymmetry per se, rather than alpha activity originating elsewhere. CSD uses an estimate of sources and sinks on the scalp in order to minimize any effects of distal sources. If measures of frontal asymmetry are to be interpreted as a likely reflection of activity in frontal regions, then the CSD transform is preferred, as traditional reference montages may be more susceptible to volume-conducted activity. In the largest study of depressive risk and EEG asymmetry to date (n = 306), only current-source-density (CSD) transformed frontal asymmetry—but not average, linked, mastoids, nor CZ referenced frontal asymmetry—distinguished between individuals with and without a lifetime history of depression (Stewart, Coan et al., 2011). As there is only a small correlation between CSD-transformed data and data measured with traditional reference montage (Hagemann, Naumann, & Thayer, 2001), it is possible that well-established relationships between frontal asymmetry and other variables may fail to emerge when studied with CSD-transformed frontal asymmetry. Such a finding would suggest that such effects attributed to frontal asymmetry might reflect asymmetric volume-conducted activity from nonfrontal regions, an important interpretive consideration. We suggest that future research investigate reference as a moderator of frontal asymmetry relationships.
Further consideration of site and the potential differences between midfrontal and lateral frontal regions may be helpful in clarifying the role of rFLA in psychopathology. Some research has found that rLFA is related to psychopathology only at midfrontal regions (Minnix & Kline, 2004; Pössel, Lo, Fritz, & Seemann, 2008), whereas others have found relationships between rLFA and emotional responding at lateral frontal sites (Amodio, 2010). Research typically examines these sites independently, and the field lacks an a priori theory as to why different regions may be involved. Additionally, as the term rLFA encompasses both midfrontal, lateral frontal, and sometimes even frontal pole regions, researchers must take care to explicitly state the sites involved in each particular analysis. Since activity at multiple sites will be less highly correlated using CSD-transformation, CSD may have greater signal specificity. This more precise measurement at midfrontal versus lateral-frontal sites may lead to theoretical models that begin to subdivide the somewhat vague concept of “frontal” asymmetry into a more regionally specific function.
Finally, other often unmeasured factors may influence rLFA. Such effects include the time of day or year (Peterson & Harmon-Jones, 2009; Velo, Stewart, Hasler, Towers, & Allen, 2012) as well as aspects of the experimental interaction (e.g., Blackhart, Kline, Donohue, LaRowe, & Joiner, 2002; Wacker, Mueller, Pizzagalli, Hennig, & Stemmler, 2013)
More detailed reviews of methodological considerations and processing of asymmetry data are available. The reader is referred to Allen, Coan, & Nazarian (2004) and Smith, Reznik, Stewart, & Allen (2016).
1.3 rLFA as a predictor and outcome variable
Cacioppo (2004) draws an important distinction between studies in which rLFA serves as the outcome and studies in which rLFA serves as the predictor variable. When rLFA serves as the predictor variable, the results indicate the probability of a given correlate (behavioral measure) given rLFA. On the other hand, when rLFA serves as the outcome variable, the results describe the probability of rLFA given a particular behavioral measure. Some studies make no distinction and treat rLFA and behavioral measures as simple correlates.
1.3.1 rLFA as a predictor and outcome of emotion regulation
Research investigating rLFA as both a predictor and outcome suggests that rLFA may be related to both emotion regulation and psychopathology (see Table 1).
Citation | N | Age/sex | Reference | Alpha range | Predictor | Outcome | Capability? | Activity/activation? | Results summary |
---|---|---|---|---|---|---|---|---|---|
Minnix & Kline, (2004) | 142 | 98 women (M= 20.4) | LM | 8–13 Hz | Neuroticism | F8/F7 and F4/F3 | No | Activity |
P (EEG | Neuroticism) ↑ variability in rLFA at F4/F3 only ↑ neuroticism |
Harmon-Jones (2007) | 76 | 51 women, undergraduates | LM | 8–13 Hz | Trait anger | FP2/FP1, F4/F3, F8/F7, & FT7/FT8 in response to anger pictures | Yes | Activity | P (EEG to anger pictures | trait anger) ↑ trait anger ↑ rLFA in response to anger pictures |
Amodio et al. (2008) | 48 | 32 women, undergraduates | LM | 8–13 Hz | BIS/BAS Questionnaire | F4/F3 | No | Activity |
P (EEG | BAS) ↑ BAS ↑ rLFA |
Crost, Pauls, & Wacker (2008) | 106 | All men in upper or lower third on anxiety and defensiveness | LM | 8–10.5 Hz | Anxiety and defensiveness and situational context as moderator | F4/F3 | No | Activity |
P (EEG | defensiveness/anxiety & context) ↑ defensiveness ↑ rLFA, high anxiety ↑ social threat ↓ rLFA |
Shackman et al., (2009) | 51 | All women, M=19.5 | AVG | 8–10 Hz | Behavioral inhibition (BIS) | F4/F3 | No | Activity |
P (EEG | BIS) ↑ BIS ↓ rLFA |
Hannesdóttir et al. (2010) | 20 | 12 boys, ages 4.5 and 9 | AVG | 6–9 Hz | F2/F1 at 4.5 years | Physiological arousal during speech task and parent-reported emotion regulation ability at age 9 | No | Activity | P (EEG to anger pictures | trait anger) ↑ trait anger ↑ rLFA in response to anger pictures |
Papousek et al., (2011) | 86 | 42 women, ages 18–41 (M=23.5) | LM | 8–13 Hz | Emotion regulation score & other's vocal expression of anxiety | F4/F3 calculated (F4-F3)/(F4+F3) * 100 | Yes | Activation |
P (EEG | others emotional expression), ↑ emotion regulation scores ↑ return to baseline after other's vocal expression of anxiety |
Steiner & Coan (2011) | 87 | 55 women, undergraduates 18–20 years | AVG | 8–13 Hz | F2/F1, F4/F3, F6/F5, F8/F7 | Experiential reports and retrospective reports of freshman year homesickness | No | Activity |
P (Homesickness | EEG) ↓ rLFA at F8/F7 & F6/F5 ↑ experiential homesickness, no retrospective reports |
Killeen & Teti (2012) | 27 | Mothers, ages 22–45, M=30.7 | AVG | 8–13 Hz | F4/F3, change in response to infant emotional stimuli | Maternal anxiety, mother-infant emotional availability, mothers experience of sadness in response to seeing own infant in distress | Yes | Activation |
P(Maternal responding | EEG) Change to less rLFA during infant emotional stimuli predicted lower maternal anxiety, greater mother-infant emotional availability, and sadness in response to infant distress |
Nash, Inzlicht, & McGregor(2012) | 26 | 17 women, Median=19 | LM | 8–13 Hz | F8/F7 and F4/F3 | Error-related negativity (ERN) | No | Activity |
P (ERN | EEG) ↑ rLFA ↓ ERN |
Kawamoto, Nittono, & Ura, (2013) | 19 | 11 women, mean age = 18.3 | LM | 8–13 Hz | Computer-based exclusion task | F8/F7 | Yes | Activation |
P (EEG | exclusion task) Change to less rLFA after exclusion task |
Quirin et al. (2013) | 72 | 32 women, 18–33, M=22.8 | LM | 8–13 Hz | Implicit affiliation motive | F4/F3 | No | Activity |
P (EEG | affiliation motive) ↑ implicit affiliation ↓ rLFA |
Schone et al. (2015) | 17 | Men, mean age = 23.9 | AVG | 8–13 Hz | Erotic versus attractive photos of women | F4/F3 | Yes | Activation |
P (EEG | photo type) Erotic versus attractive photos, more rLFA |
- Note. Reference column indicates whether the authors used an average reference (AVG), linked-mastoid reference (LM), Cz reference, (CZ), current source density transformation (CSD). Predictor and Outcome variable columns allow the reader to see what variables serve as predictors or outcomes. Capability? indicates whether the study uses an approach in line with the capability model; i.e., whether frontal asymmetry is collected under a condition of emotional stress, which the capability model proposes should be more robust than resting measures. Activity/Activation? column indicates whether frontal EEG was recorded at a given time (activity) or reflecting a change in response to stimuli or task (activation). Results summary indicates a brief summary of the results in terms of probability of a given correlate (behavioral measure) given rLFA (predictor variable) or probability of rLFA given a particular behavioral measure (outcome variable).
First, rLFA has been investigated as a predictor of emotional response. For example, Nash, Inzlicht, and McGregor (2012) found that more rLFA at rest was related to the lower amplitudes of the error-related negativity, an ERP closely associated with error processing and aversive motivation. This finding suggests that those who have more rLFA, which likely indexes increased approach motivation, may have a less pronounced response or aversive motivation to mistakes. Killeen and Teti (2012) also examined mothers' rLFA in response to emotional stimuli as a predictor of mothers' emotional responding to their infants. They found that a shift toward more rLFA during emotional stimuli for mothers predicted lower maternal anxiety, greater emotional availability of the mothers when they were at home, and mothers' more intense experience of sadness in response to seeing their own infants in distress.
A limited number of studies have examined rLFA as a prospective predictor of emotion regulation. Hannesdóttir, Doxie, Bell, Ollendick, and Wolfe (2010) examined rLFA at 4 1/2 years of age as a predictor of physiological responding and emotional responding at 9 years of age. They found that less rLFA was related to increased physiological responding and worse emotion regulation but only at F2/F1 and not F8/F7 or F4/F3. As such, further clarity may be gleaned as to whether frontal asymmetry serves as a predictor of emotional responding.
Second, rLFA has been studied as an outcome related to emotion regulation. Most of this research has focused on the trait variables that may predict rLFA. Research continues to examine rLFA in relation to BAS as measured by the Carver and White (1994) scale (Amodio, Master, Yee, & Taylor, 2008; Hewig, Hagemann, Seifert, Naumann, & Bartussek, 2004). Additionally, rLFA has been linked to BIS (Shackman, McMenamin, Maxwell, Greischar, & Davidson, 2009). rLFA has also been linked with trait measures of approach-related emotions, such as anger (Harmon-Jones, 2007). Although most of this research has examined rLFA at rest over minutes, a small number of studies have shown that rLFA can change in response to mood manipulations and even in response to brief affective-motivational stimuli. Kawamoto, Nittono, and Ura (2013) found that rLFA changed from more rLFA to less rLFA in response to a computer-based social exclusion task. Schone, Schomberg, Gruber, and Quirin (2015) found that male participants showed more rLFA at F4/F3 sites in response to erotic pictures compared to pictures of attractively dressed women in the 2,500 ms after stimulus presentation. Pönkänen, Peltola, and Hietanen (2011) also found that individuals had more rLFA in response to direct than averted gaze faces but in a follow-up study did not find any changes in rLFA in response to smiling versus neutral faces (Pönkänen & Hietanen, 2012). A recent study also used the trait affiliation motive, the tendency to approach others expecting positive interaction and affect, to predict rLFA; less trait affiliation predicted less rLFA (Quirin, Gruber, Kuhl, & Düsing, 2013).
Crost, Pauls, and Wacker (2008) also examined the relationship between anxiety and rLFA. Those with more anxiety had lower rLFA only under situations of social threat. However, it is important to note that this study utilized a lower alpha band 8–10.25 Hz rather than 8–13 Hz and did not find the same results in the traditional alpha band.
Minnix and Kline (2004) also found that trait neuroticism is associated with greater variability in rLFA. As such, this finding may significantly contribute to existing theory by suggesting that results may be inconclusive because rLFA fluctuates more in individuals with higher trait neuroticism. Other studies have not routinely examined rLFA variability across the resting period.
One study examined changes in rLFA as a function of emotion regulation ability. Participants were exposed to anxious voices of others. Those participants who were higher on emotional regulation returned to their baseline rLFA scores after exposure but those who were lower in emotional regulation had sustained changes in rLFA. As such, return to baseline in rLFA after emotional stimuli may indicate emotional flexibility and increased ability to regulate emotion (Papousek, Harald Freudenthaler, & Schulter, 2011). These results suggest that changes in rLFA related to emotional stimuli are adaptive and may reflect emotion regulation, whereas changes in rLFA during rest not in response to stimuli may indicate an inability to regulate emotion and potentially signify risk for psychopathology.
Although this review focuses on frontal asymmetry, it is also worth noting that both central alpha asymmetry scores (Curtis & Cicchetti, 2007) and parietal alpha asymmetry scores (Stewart, Towers, Coan, & Allen, 2011) have been implicated in emotion regulation. RLFA has also been linked with psychopathology as both a predictor and outcome, which will be discussed sequentially below.
1.3.2 rLFA as a predictor and outcome of psychopathology and treatment response
rLFA has been most clearly established as a potential marker of depression vulnerability (less rLFA; see Allen & Reznik, 2015, for recent review). rLFA has also been linked with internalizing and externalizing symptoms (Peltola et al., 2014), with greater rLFA in attention-deficit hyperactivity disorder (ADHD) in both children and adults (Baving, Laucht, & Schmidt, 1999; Keune, Schönenberg et al., 2011), less rLFA in anxiety (Mathersul, Williams, Hopkinson, & Kemp, 2008), and more rLFA associated with positive symptoms of schizophrenia (Jetha, Schmidt, & Goldberg, 2009). A review also found that, although posttraumatic stress disorder (PTSD) symptoms have not been linked to rLFA, some studies have found that rLFA during tasks may be related to PTSD (Meyer et al., 2015). Table 2 summarizes significant studies examining rLFA as a predictor and outcome of psychopathology and treatment response.
Citation | N | Age/sex | Reference | Alpha range | Predictor | Outcome | Capability? | Activity/activation? | Results summary |
---|---|---|---|---|---|---|---|---|---|
Blackhart et al. (2006) | 28 | 23 women, 18–25, M = 18.78 | LM | 8–13 Hz | FP2/FP1, F4/F3, & F8/F7 | Trait anxiety and depressive symptoms | No | Activity |
P (Trait anxiety/depression| EEG) Less rLFA predicted trait anxiety 1 year later but not depressive symptoms |
Diego et al. (2006) | 66 | 66 mothers and 66 infants, M = 28 | CZ | 8–13 Hz | Maternal interaction style (intrusive/withdrawn) | F4/F3 | No | Activity |
P (EEG of infants | maternal interaction) Depressed withdrawnless rLFA than intrusive |
Barnhofer et al. (2007) | 22 | 11 women (M = 48 in MBCT, M = 38.6 in TAU) | AVG | 8–13 Hz | Mindfulness-based cognitive therapy (MBCT) or treatment as usual (TAU) | F4/F3 & F8/F7 | No | Activation |
P (EEG | treatment) Decreased change in rLFA in TAU and no change in MBCT |
Mathersul et al. (2008) | 428 | 214 women, ages 16–18, M = 24.85) | AVG | 8–13 Hz | Depression, anxiety, comorbid, or healthy status | F4/F3 | No | Activity |
P (EEG | psychopathology status) Less rLFA in anxious participants, more rLFA in healthy controls, and symmetrical in depressed and comorbid |
Pössel et al. (2008) | 80 | 35 girls, ages 13–15, M = 13.92 | Nose | 8–13 Hz | F8/F7 and F4/F3 | Depression status 12 months later | No | Activity | P (Depression | EEG) Less rLFA at F4/F3 only predicted depression status |
Hale et al. (2009) | 91 | ? | LM | 8–10 Hz, 10–12 Hz | ADHD or healthy control | AF4/AF3, F4/F3, F8/F7, FT8/FT7, | Yes | Activity |
P (EEG | ADHD or healthy status) ADHD patients less rLFA than healthy controls at F8/F7 & FT8/FT7 |
Jetha et al. (2009) | 20 | Schizophrenia spectrum disorders, 7 women, M = 31.1 | CZ | 8–13 Hz | Positive symptoms | F4/F3 | No | Activity |
P (EEG | positive symptoms) ↑ positive symptoms ↑ rLFA |
Barnhofer et al. (2010) | 15 | Previously depressed, 12 women, M = 28.24 | LM | 8–13 Hz | Mindfulness breathing meditation or metta meditation | F4/F3 | No | Activation |
P (EEG | mindfulness)rLFA increased at rest after mindfulness compared to rest before |
Hale et al. (2010) | 50 | Children with ADHD, ages 7–18 | LM | 8–12 Hz | Parents with or without ADHD | AF2/AF1, F4/F3, F8/F7, FC2/FC1, FC6/FC5 | No | Activity |
P (EEG | parent status) Kids with parents with ADHD had less rLFA |
Kemp et al. (2010) | 44 | 27 women, M = 41.23 | LM | 8–13 Hz | MDD, PTSD, or healthy control status | F4/F3 | No | Activity |
P (EEG | diagnosis)rLFA differed between healthy controls and MDD but not PTSD individuals |
Smith & Bell (2010) | 48 | 22 infant girls, 10, 24, and 30 months | AVG | 6–9 Hz | Stability of F4/F3 at 10 and 24 months | Externalizing behaviors and internalizing behaviors at 30 months | No | Activation |
P (Externalizing/internalizing symptoms| EEG) Stable more rLFA predicted externalizing behaviors, stable less rLFA predicted internalizing behaviors |
Stewart et al. (2010) | 306 | 211 women, ages 17–34 (M = 19.1) | CSD, AVG, CZ, and LM | 8–13 Hz | Depression status | F2/F1, F4/F3, F6/F5, F8, F7 | No | Activity |
P (EEG | depression) Only CSD-referenced rLFA was linked to depression status |
Keune et al. (2011) | 77 | Previously depressed, 57 females, 18–65 (M = 47) | Nose, rereferenced to linked mastoid | 8–13 Hz | Mindfulness-based cognitive therapy (MBCT) or wait-list control | F8/F7, F4/F3, FC6/FC5 at rest and after negative mood induction | Yes | Activation |
P (EEG | MBCT or wait-list) No main effect of condition on change in rLFA pre- to postintervention |
Keune, Schönenberg et al. (2011) | 38 | Ages 18–65, | LM | 8–13 Hz | ADHD diagnosis or healthy control | F4/F3 | No | Activity |
P (EEG | ADHD diagnosis) Higher rLFA for those with ADHD |
Moyer et al. (2011) | 26 | 15 women, 18–73 (M = 24.52 | CZ | 8–12 Hz | Meditation training and control | Electrical Geodesics sites 10–16, 18, 19 & 2, 5, 56–60 | No | Activation |
P (EEG | meditation training) More rLFA in meditation training compared to control |
Nusslock et al. (2011) | 40 | 17 women, M = 20.32 | AVG | 8–13 Hz | Averaged metric included F8/F7 & F4/F3 | First depressive episode | No | Activity |
P (Depression | EEG) ↓ rLFA ↑ depression |
Stewart, Coan et al. (2011) | 306 | 211 women, ages 17–34 (M = 19.1) | CSD, AVG, CZ, and LM | 8–13 Hz | Depression status | F2/F1, F4/F3, F6/F5, F8, F7 during directed facial action task | Yes | Activity |
P (EEG | depression) Less rLFA in those with depression |
Mitchell & Pössel (2012) | 41 | Adolescent boys, M = 13.93 | Nose | 8.5–12.5 Hz | F8/F7 & F4/F3 | Depressive symptoms 1 year later | No | Activity |
P (Depression | EEG) ↓ rLFA ↑ depression |
Fachner et al. (2013) | 79 | 62 women, ages 18–15 (M = 35.6) | LM | 8–12 Hz | Music therapy | Fp2/Fp1, F4/F3, F8/F7 | No | Activation |
P (EEG | Music therapy) Increased rLFA after music therapy |
Keune et al. (2013) | 25 | Women, M = 43.6 | LM | 8–13 Hz | Mindfulness meditation | F8/F7, F4/F3, effects only at F4/F3 | Yes | Activation |
P (EEG | mindfulness meditation) More rLFA during meditation compared to rest, further results comparing mindfulness to rumination and negative mood condition |
Lin et al. (2013) | 24 | Women, M=20.25 | CZ | ? | PMDD status, phase of menstrual cycle, condition | F4/F3 during rest, depressive induction, depressive recall, recovery and relaxation | Yes | Activity |
P (EEG | PMDD) More rLFA during depressive induction and relaxation for those with PMDD than those without during at luteal phase |
(Moynihan et al. 2013) | 110 adults 65+ | ? | LM | 8–13 Hz | Mindfulness-based stress reduction or wait-list control | F4/F3 | No | Activation |
P (EEG | mindfulness/control)rLFA remained the same for those in mindfulness-based stress reduction and decreased for wait-list control |
Beeney et al. (2014) | 57 | 57 women, 18–60 years old (M=30.78) | AVG | 8–13 Hz | Depression, borderline personality disorder, or no psychopathology | 11 frontal electrode pairs including F4/F3, F8/F7 at 8–13 Hz baseline and postrejection | No | Activity |
P (EEG | psychopathology status) ↓ rLFA in depression ↑ rLFA in borderline personality disorder, controls in the middle postrejection |
Keune et al. (2015) | 52 | Adults with ADHD, 25 women, M=36.4 | ? | 8–13 Hz | ADHD symptoms | F7/F8, F4/F3 | No | Activity |
P (EEG | ADHD symptoms At F8/F7, higher ADHD correlated with higher rLFA |
Smith et al. (2016) | 82 | 57 women, M=18.73 | CSD | 8–10 Hz, and 10.5–12.5 Hz | GAD, OCD, elevated worry, elevated OCD or no psychopathology | F6/F5 | No | Activity |
P (EEG | psychopathology status) Elevated OCD/GAD symptoms and GAD diagnosis associated with more rLFA |
- Note. See Table 1 description for an explanation of table columns.
Several studies have found that rLFA is a predictor of psychopathology. Smith and Bell (2010) found that infants with less rLFA at 10 and 24 months were rated to have more internalizing behaviors at 30 months, whereas infants with more rLFA were rated to have more externalizing behaviors. Mathersul and colleagues (2008) found that less rLFA was related to anxiety, whereas comorbid anxiety and depression and depression alone had more symmetrical frontal asymmetry scores. Blackhart, Minnix, and Kline (2006) found that frontal asymmetry at rest predicted trait anxiety but not depression 1 year later.rLFA has been most strongly implicated as a prospective predictor of depression. One of the most influential studies of prospective prediction was conducted by Nusslock et al. (2011). Forty healthy undergraduates were followed prospectively, and rLFA was found to predict first depressive episode. Importantly, cognitive vulnerability was also found to predict first depressive episode. As such, the researchers used a combined model to examine whether cognitive vulnerability and rLFA were in fact independent predictors. They found that cognitive vulnerability and rLFA were common but not independent predictors. This study significantly contributes to theory of frontal asymmetry and depression by suggesting that although rLFA and cognitive vulnerability both predict first depressive episode they share variance.
Pössel and colleagues (2008) examined frontal asymmetry at rest in 80 13- to 15-year-olds without psychopathology as a predictor of depression. It was found that less rLFA predicted depression status 12 months later. This study not only allowed insight into rLFA as a potential predictor of depression status but also was important in teasing apart the theoretical relationship between frontal asymmetry and depression. Because rLFA predicted depression status later but depression status did not predict rLFA, this study suggests that frontal asymmetry may be a risk factor for depression. Mitchell and Pössel (2012) also found that, in a sample of 41 adolescent boys without history of depression, rLFA predicted depressive symptoms 1 year later. As such, even though there is a small amount of longitudinal research linking rLFA with psychopathology, the research suggests rLFA may be a promising marker of depression vulnerability.
Yet, there have been a number of inconsistent findings. Blackhart et al. (2006) found that rLFA at rest did not predict depressive symptoms 1 year later. McFarland, Shankman, Tenke, Bruder, and Klein (2006) found that rLFA was unrelated to the course of one's depression.
Additionally, rLFA has been studied as an outcome variable related to psychopathology and treatment. In the largest sample examining the relationship between rLFA and depression status to date (Stewart et al., 2010), CSD-referenced rLFA distinguished between those with and without lifetime history of depression, such that those with lifetime history (either current or past) had lower rLFA (but not using other reference montages). In a subsequent analysis of the same sample, Stewart, Coan et al. (2011) found significantly less rLFA in those with current or past depression compared to never-depressed individuals during a directed facial action task for CSD, average, and linked mastoid references. These findings are consistent with the prospective studies reviewed above, showing that less rLFA characterizes those with any history of depression regardless of current status.
Also consistent with the notion that less rLFA may index risk for depression, less rLFA characterizes infants of depressed withdrawn mothers more than infants of depressed intrusive mothers (Field & Diego, 2008; Field, Diego, & Hernandez-Reif, 2006). Goldstein et al., (2016) also found a differential change in rLFA scores for children of mothers with and without history of depression, such that children with mothers with history of depression had less rLFA from age 3 to age 6 but not children with mothers without history of depression. Additionally, children ages 5–6 with low positive affect have been found to have less rLFA (Shankman et al., 2005).
A small amount of research has also started to link rLFA with other depressive and related personality disorders. Lin, Tsai, Peper, and Yen (2013) found more rLFA in women with premenstrual dysphoric disorder (PMDD) than those without PMDD at the luteal phase during depressive induction and relaxation but not at rest. This finding suggests a potentially dynamic relationship between rLFA and PMDD status over the course of the menstrual cycle. Yet, Accortt, Stewart, Coan, Manber, and Allen (2011) found that women with PMDD had less rLFA without variation across menstrual phase. As such, further investigation as to the relationship between rLFA and other depressive disorders, such as PMDD and persistent depression, may yield further insight into our understanding of rLFA.
Additionally, one study found that, although individuals with depression have less rLFA, individuals with borderline personality disorder have more rLFA after a rejection task (Beeney, Levy, Gatzke-Kopp, & Hallquist, 2014). The authors suggested that this finding might be consistent with hostility and approach-related behavioral features that often occur after rejection for individuals with borderline personality disorder. It is also possible that cognitive response to rejection may play a role, as one study found that, for high reassurance seekers, more rLFA is related to depression, whereas for low reassurance seekers, less rLFA is related to depression (Minnix et al., 2004). Although borderline personality disorder and depression share multiple symptoms, such as mood disturbance and suicidality, this finding suggests that rLFA may distinguish between these different psychopathologies.
Although rLFA has been most consistently linked with depression, it has been examined as an outcome measure for a range of psychopathology that potentially impacts approach-related systems, such as anxiety disorders, PTSD, and ADHD. A significant amount of research has examined rLFA in anxiety disorders and comorbid anxiety and depression (for review, see Thibodeau et al., 2006). Although most of this research has examined rLFA as a predictor of anxiety (e.g., Mathersul et al., 2008), some research has found that adults with panic disorder (Wiedemann et al., 1999) and children with anxiety disorders (Baving, Laucht, & Schmidt, 2002) have lower rLFA than healthy participants. Additionally, Davidson, Marshall, Tomarken, and Henriques (2000) found that in individuals with social anxiety, a state anxiety manipulation decreases rLFA, suggesting acute anxiety is related to lower rLFA. Consistent with such findings, it was suggested that anxious arousal (e.g., panic) may be related to less rLFA, whereas anxious apprehension (e.g., worry) may be related to more rLFA (Heller, Nitschke, Etienne, & Miller, 1997). Recent research, discussed further below, is generally consistent with this notion, finding higher rLFA in those with elevated worry and generalized anxiety disorder and lower rLFA among those with high trait anxiety and low worry (Smith, Zambrano-Vazquez, & Allen, 2016). Given the complexity of findings with various aspects of anxiety, and the high overlap of anxiety and depressive symptoms, a contemporary metanalysis might profitably clarify the relative contributions to rLFA of the various forms of anxiety, when present both with and without depressive symptoms.
Furthermore, Kemp et al. (2010) found that individuals with depression had significantly lower rLFA than healthy participants, whereas individuals with PTSD did not have significantly different rLFA scores from healthy participants. Keune, Schönenberg et al. (2011) found that adults with ADHD had higher rLFA (F4/F3) and central (C4/C3) but not parietal (P4/P3) asymmetry scores than adults without ADHD. In a later study, Keune, Wiedemann, Schneidt, and Schönenberg (2015) found that, in adults with ADHD, a higher number of ADHD symptoms predicted higher rLFA at F8/F7. Additionally, a couple of studies have examined the relationship between rLFA and ADHD using another rLFA measure (R − L/R + L * 1,000), which is highly consistent with traditional measures (Allen & Kline, 2004). Hale et al. (2010) examined rLFA in children with ADHD who had parents with or without ADHD; those children with ADHD whose parents did not have ADHD had lower rLFA than those who had parents with ADHD. Another study using this alternative methodology found that lower frequency alpha asymmetry (8–10 Hz) and higher frequency alpha asymmetry (10–12 Hz) were also related to ADHD status (Hale et al., 2009).
A recent review by Nusslock, Walden, and Harmon-Jones (2015) also discussed that low rLFA may characterize depression, whereas bipolar disorder may be characterized by high rLFA. The authors also suggested that rLFA may be related to psychological domains related to psychopathology, such as anhedonia, hypomania/mania, and anxiety, in line with a Research Domain Criteria (RDoC) approach (Nusslock et al., 2015). RDoC is a research initiative proposed by the National Institute of Mental Health (NIMH) to examine domains of behavior and biology found across different psychopathology diagnoses (Cuthbert & Insel, 2013). This approach may hold particular relevance in rLFA research, as rLFA may be implicated across psychopathologies. It may be differentially implicated between disorders (i.e., depression and borderline) but also differentially implicated within one disorder as a feature of some domain (i.e., reassurance seeking in depression). As such, future rLFA research may consider the psychological and neural domains implicated in the psychopathology rather than diagnosis exclusively.
Finally, rLFA has been examined as an outcome variable for treatment research. In particular, much of this research has focused on the potential impact of mindfulness-based interventions on rLFA. At this point, there is mixed support for the impact of mindfulness-based interventions on rLFA. Differing populations, interventions, or methodologies, such as site location, could influence the various findings of these studies. As such, further research on rLFA as an outcome of treatment research is warranted, and extrapolation from the present studies remains unclear. An early and often-cited study by Davidson et al. (2003) reported that rLFA significantly increased after an 8-week mindfulness meditation intervention compared to a waitlist control, although the results reported show clear effects only at central sites. In the context of several omnibus effects with p values slightly greater than .05, the researchers followed up specifically with a comparison of treatment and wait-list groups 4 months after the intervention and found that at central leads (C4/C3) resting rLFA increased relative to baseline, and the meditation group showed significantly greater C4/C3 alpha asymmetry scores than wait-list participants both immediately following the intervention as well as 4 months later. Other sites in addition to C4/C3 were examined (F4/F3, FC8/FC7, T4/T3), but the results do not report on these sites specifically but noted in the discussion that their effects were most consistent at C4/C3. The authors also reported group differences at the p > .05 level of significance in response to emotional inductions for the T4/T3 electrodes. Travis and Arenander (2004) critiqued the authors' claim that Davidson et al. (2003) found “significant increases in left-sided anterior activation” given that they found changes as a function of group directly after the intervention (rather than 4 months later) at resting rLFA only for temporal (T4/T3) and central (C4/C3) sites. As such, further investigation of the impact of mindfulness meditation on rLFA at frontal sites remains warranted.
In such a follow-up, Barnhofer et al. (2007) examined the effects of mindfulness-based cognitive therapy (MBCT) and treatment as usual, the care of a physician, on frontal asymmetry in previously suicidal individuals. Change in rLFA from pre- to posttreatment was examined. Although there was no change in rLFA for those who received MBCT, rLFA actually decreased for those in treatment as usual. This study suggests that MBCT may maintain current rLFA, whereas treatment as usual likely does not beneficially impact rLFA. Additionally, Barnhofer, Chittka, Nightingale, Visser, and Crane (2010) found that rLFA increased after 15 min of either breathing meditation or loving-kindness meditation. In another study, Moyer et al. (2011) found that after 5 weeks of meditation training, healthy individuals in the meditation condition had a significant increase in rLFA compared to those in the control condition. The researchers used a montage of right frontal (10–16, 18, 19; Electrical Geodesics) and left frontal sites (51–60, 2, 5; Electrical Geodesics). According to the Electrical Geodesics (Eugene, OR) website, these sites correspond to FP2/FP1, F4/F3, F8/F7, and 12 other sites in between. As such, there were two potential methodological irregularities in this study: FP2/FP1 is not typically included in measures of rLFA, and alpha was measured at 8–12 rather than 8–13 Hz.
Moynihan et al. (2013) examined the impact of an 8-week mindfulness-based stress reduction (MBSR) intervention compared to wait-list control on rLFA, depressive symptoms, perceived stress, antibody response, and a measure of executive function in adults over 65. MBSR in this population significantly improved executive function and antibody response 24 weeks later compared to wait-list control. Those in the wait-list control group had a decrease in rLFA scores over the course of the intervention, whereas those in the intervention group did not have a significant change, suggesting that mindfulness intervention may prevent natural decrease in rLFA score over time.
However, Keune, Bostanov, Hautzinger, and Kotchoubey (2011) found that mindfulness-based cognitive therapy (MBCT) reduced depressive symptoms and trait rumination but did not alter rLFA compared to a wait-list control group in previously depressed participants. In fact, in both the MBCT and wait-list control groups, rLFA decreased over time, suggesting that an 8-week MBCT intervention may not alter rLFA significantly in previously depressed participants.
A small number of studies have also examined the impact of one mindfulness period on rLFA. Keune, Bostanov, Hautzinger, and Kotchoubey (2013) found a significant increase in rLFA in previously depressed women during mindfulness meditation compared to a rest period beforehand. Barnhofer et al. (2010) also found that rLFA increased at rest after both mindfulness breathing meditation and mindfulness meditation focused on positive affect and loving kindness in a small sample of 15 previously depressed individuals.
Additionally, Fachner, Gold, and Erkkilä (2013) found that music therapy over the course of 3 months increased rLFA from baseline in adults who were currently depressed. However, none of these studies have linked these changes in rLFA with positive affect or psychopathology symptoms. As such, further work might profitably explore whether rLFA serves as a mediator between mindfulness interventions and symptomology (discussed in detail in the mediator section below).
It is worth noting that a small amount of research has also examined rLFA as a moderator within the context of treatment response (e.g., Gollan et al., 2014; Moscovitch et al., 2011). This research will be discussed in later sections of the manuscript (see rLFA as a moderator of emotion and psychopathology).
1.4 Frontal asymmetry as a moderator of emotion
A moderator is a third variable that alters the relationship between the predictor and the outcome (Baron & Kenny, 1986). Most commonly, moderators emerge when the relationship between two variables is different for different groups of people or for those with different scores on a continuous measure of individual differences. rLFA is a moderator when some relationship differs as a function of whether one has a high or low rLFA score. For example, Bruder et al (2001) found that those who responded to antidepressant treatment had more rLFA at rest. Both rLFA (frontal asymmetry activity) and change in rLFA (frontal asymmetry activation) may serve as potential moderators, as described below (Dennis & Solomon, 2010).
Moderators can be enhancing, buffering, or antagonistic. When a moderator is enhancing, a higher level of the moderator variable increases the effect of the predictor on the outcome. When a moderator is buffering, a higher level of the moderator variable decreases the effect of the predictor on the outcome. When a moderator is antagonistic, the effect of the predictor on the outcome is reversed at different levels of the moderator variable (Breitborde, Srihari, Pollard, Addington, & Woods, 2010). Figure 1 illustrates the conceptual relationship between a moderator and the predictor and outcome variable.

Moderation model. Relationships between a moderator, predictor, and outcome depicted conceptually (a) as well as statistically (b). The statistical representation is patterned after Baron and Kenny (1986). A moderator is a third variable that changes the relationship between the predictor and the outcome. Statistically, both the predictor and the third variable may predict the outcome, but main effects are not needed to identify a moderator. A moderator only needs the presence of an interaction effect between the predictor and the third variable that predicts the outcome. This interaction represents that the relationship between the predictor and the outcome differ at different levels of the third variable
Statistically, moderators emerge as interactions between the third variable and the predictor variable (see Figure 1). In other words, the effect of one variable on another depends upon the level of a third variable. Main effects of the predictor on the outcome and the third variable on the outcome may or may not be present (MacKinnon, Lockhart, Baraldi, & Gelfand, 2013). In fact, moderators often emerge when it appears that there is no relationship between the predictor and the outcome. For example, imagine that a researcher does not find a relationship between a therapeutic intervention and self-reported positive mood. It is possible that treatment does not impact any group. However, it may also be possible that the treatment greatly improved mood for people with more rLFA and did not change mood for people with less rLFA. In this hypothetical example, an important relationship emerges because of the addition of a moderator. However, it is important that the addition of moderators is theoretically driven. Without theory, it may be difficult to infer whether a moderator is spurious. In this particular example, one could suppose that those with more rLFA had greater approach motivation and as such had higher motivation and engagement with the intervention. It would be important to examine that both those with more and less rLFA had equivalent mood symptoms at the outset of the study for this theory to be pragmatic. As such, consideration of theoretically driven potential moderators at the outset of a study may provide significant insight into for whom a treatment works or under what conditions a particular relationship exists.
Moderators should be distinguished from covariates, which are third variables statistically related to the outcome variable. Adjusting for a covariate clarifies the relationship between the predictor and the outcome by reducing the variance attributed to the relationship between the predictor and the outcome. As such, there is an important conceptual distinction between moderators and covariates in that moderators change the relationship, whereas covariates clarify it by adjusting for another variable (see Smith, Reznik et al., 2016 for illustration of the conceptual distinction between moderator and covariate). In the example above, the relationship between treatment and outcome changed based on whether one had less or more rLFA. If there was no interaction between rLFA and the predictor on the outcome but rather rLFA was related only to the outcome, it may be reasonable to adjust for rLFA as a covariate. True covariates explain variance in the outcome but not predictor variables. If a third variable explains variance in both the outcome and predictor, including such a variable as a covariate may lead to ambiguous results (Kraemer, 2015; Miller & Chapman, 2001). For example, if examining the effect of a treatment on depressive symptoms, rLFA may only be treated as a covariate in the case that it relates to the depressive symptoms but not the treatment itself. Statistically, covariates may be identified when there is a statistically significant main effect of the third variable but no interaction between the predictor and third variable on the main effect. Moderators, on the other hand, must have a significant interaction between them and the predictor variable.
1.5 Evidence supporting frontal EEG asymmetry as moderator of emotion
Although the vast majority of frontal asymmetry research examines rLFA as a predictor or outcome, there is some evidence that suggests rLFA may serve as a moderator of emotional response in both infants and adults. See Table 3 for a summary of rLFA as a moderator of emotion. Additionally, there is some evidence that rLFA in infancy may moderate the relationship between emotional responses and behavior. Infants with less rLFA are more likely to cry in response to separation from their mothers (Davidson & Fox, 1989; Fox, Bell, & Jones, 1992). Infants with less rLFA at 9 months had negative emotionality scores that predicted their social wariness 4 years later. On the other hand, this predictive relationship did not exist in infants with more rLFA (Henderson, Fox, & Rubin, 2001).
Citation | N | Age/sex | Reference | Alpha range | Predictor | Outcome | Capability? | Activity/activation? | Results summary |
---|---|---|---|---|---|---|---|---|---|
Bruder et al. (2001) | 64 | 28 women with depression, ages 18–65 | Nose | 7.8–12.5Hz | F8/F7 & F4/F3 at rest | Relationship between fluoxetine and depressive symptoms | No | Activity | Those with less rLFA less likely to respond to fluoxetine |
Forbes et al. (2008) | 74 | 32 girls, ages 3–9 (M = 5) | AVG | 7–10 Hz for 3–5-year-olds, 8–11 Hz for 6–9-year-olds | F4/F3 at rest | Mothers depressive symptoms and child negative affect | No | Activity | Depressive symptoms in mother associated with high child negative affect only for children with less rLFA |
Kline & Allen (2008) | 71 | 50 women, ages 17–25 (M = 18.9) | AVG | 8–13 Hz | F8/F7 at rest | Defensiveness and depressive symptoms | No | Activity | Those with less rLFA had a positive relationship between defensiveness and depressive symptoms, whereas those with more rLFA had a negative relationship |
Schmidt & Hanslmayr(2009) | 16 | 8 women, M age = 22 | AVG | 8.29–11.71 Hz |
E1/E2, E3/E4, E11/E12, etc. Split scores greater than 0 (“left-active”) and less than 0 (“right-active”) at rest |
Musical stimuli and individual rating | No | Activity | Those with less rLFA rated music less positively |
Amodio (2010) | 46 | White undergraduates | AVG | 8–13 Hz | F8/F7 during intertrial intervals during task completion | P2 response to black and white faces | Yes | Activity | Less rLFA during intertribal intervals led to larger P2 responses to race, and P2 also predicts action control for low-prejudice participants |
Hall, et al. (2010) | 30 | 14 women who are physically fit, M age = 21.2 | LM | 8–13 Hz | F4/F3 & F8/F7 during rest | Emotional response (energetic arousal) to exercise | No | Activity | Less rLFA predicted lower energetic arousal to exercise |
Moscovitch et al. (2011) | 23 | 11 female, ages 19–73 (M = 35.74) | CZ | 10–13 Hz & nonsignificant at 8–10 Hz & 10–13 Hz | F4/F3 | Difference in social anxiety scores in response to CBT treatment | No | Activity | More rLFA at rest before treatment predicted more change in social anxiety scores from pre- to posttreatment |
Swingler et al. (2014) | 466 (233 infants and their moms) | 125 female infants, 233 mothers | AVG | 6–9 Hz | F4/F3 at rest | Maternal sensitivity and infant regulating behavior (orienting and distraction) and emotion response during an emotional challenge | Yes | Activity | For infants with more rLFA, maternal sensitivity predicted distraction, whereas for those with less rLFA, maternal sensitivity predicted negative affect during emotional challenge. |
Lopez-Duran et al. (2012) | 90 | 44 girls designated high-risk, ages 6–13 (M = 7.36) | AVG | 7.5–11 Hz | F8/F7 & F3/F4 during sad film | Relationship between stressful life events and internalizing symptoms | Yes | Activity (during sad films) | As rLFA increases, effect of stressful life events on internalizing symptoms decreases |
Tullett et al. (2012) | 32 | Undergraduates, M age = 19.34 (23 women) | AVG | 8–12 Hz | F4/F3 at rest | Images associated with charity and empathic concern | No | Activity | Those with less rLFA had more empathic concern in response to charity images |
Goodman et al. (2013) | 30 | 12 women, M age = 20.6 | LM | 8–13 Hz | F4/F3 during threat of shock | Threat of shock and eye blink startle magnitudes | Yes | Activity | Less rLFA corresponded with attenuation of eyeblink startle magnitudes in response to shock threat |
Gatzke-Kopp et al. (2014) | 209 | Kindergarteners, M age = 6.03 | AVG | 7–12 Hz | AF4/AF3, F4/F3, & F8/F7 at rest | Reactivity to sad condition and externalizing/internalizing symptoms | No | Activity | For those with more rLFA, increased arousal in response to sad clip was associated with more externalizing symptoms, and for those with less rLFA, increased arousal was associated with more internalizing symptoms |
Gollan et al. (2014) | 72 | 45 women, ages 18–65, M = 35.65 | LM | 8–12 Hz | F4/F3 & F8/F7 | Behavioral activation treatment | No | Activity | rLFA did not predict clinical response to behavioral activation, rLFA correlated with negative emotion and inhibition |
Papousek et al. (2014) | 148 | All women, ages 18–42 (M = 22.5) | LM | 8–12 Hz | F4/F3 & F8/F7 during of injury and death | Cardiac response, mood, film-related intrusive memories | Yes | Activation | Those with more decreased rLFA during films had a weaker decelerative cardiac response, greater mood deterioration, and more film-related intrusive memories and avoidance over the next week |
Arns et al. (2015) | 1008 | 571 women with depression, M age = 37.84 | LM & eLORETA | 8–13 Hz | F4/F3 | Clinical response to SSRI, escitalopram, sertraline, venlafaxine-extended release | No | Activity | More rLFA in women only associated with clinical response to SSRI, escitalopram, and sertraline |
- Note. See Table 1 description for an explanation of table columns.
Citation | N | Age/sex | Reference | Alpha range | Predictor | Outcome | Capability? | Activity/activation? | Results summary |
---|---|---|---|---|---|---|---|---|---|
Peterson et al. (2007) | 24 | All women undergraduates | AVG | 8–13 Hz | F3/F4 & F7/F8 | Right or left hand contraction affects aggression | No | Activation | Hand contraction that increased rLFA, increased behavioral aggression |
Lewis et al. (2007) | 49 | 24 women, ages 18–22 | AVG | 8–13 Hz | F3/F4 & F7/F8 | Change in rLFA from low to high stress periods (examination stress) affects negative health outcome | No | Activation | Change to less rLFA (rather than more) was associated with both change from low to high stress period and negative health outcomes |
Verona et al. (2009) | 135 | 61 women, ages 18–40 (M = 24.6) | AVG | 8–13 Hz | F8/F7 & nonsignificant at F4/F3 | Laboratory stress task and aggression | No | Activity | More rLFA in response to stress task predicted more aggressive behavior |
Peeters et al. (2014) | 40 | All women, ages 18–34 (M = 22.6) | LM | 8–13 Hz | F3/F4 | Neurofeedback and changes in mood | No | Activity | rLFA changed in response to neurofeedback but no changes in mood were observed |
- Note. See Table 1 description for an explanation of table columns.
There is also some evidence that rLFA may serve as a moderator of the relationship between infant and mother affect. Theoretically, this research suggests that both less rLFA and having a mother with depression represent risk factors for developing depression. However, one is most likely to develop depression if one has both low rLFA and a mother with depression. Forbes et al. (2008) examined the relationship between depressive symptoms in mothers and negative affect in their 3- to 9-year-old children. They found that for children with less rLFA at rest, mothers' depressive symptoms predicted negative affect in their children. This finding suggests that rLFA is a moderator of the relationship between mother and child's depression; that is, a mother's depressive symptoms predict a child's depressive symptoms only for those children who have less rLFA, with greater rLFA indexing a potential buffering mechanism between maternal depression and negative affect in their children.
Swingler, Perry, Calkins, and Ann (2014) examined rLFA at rest in 233 5-month-old infants as a moderator of the relationship between maternal sensitivity and infant response to an emotional challenge task. During the emotional challenge task, mothers were asked to hold their infants' arms by their sides to restrict their movement, while maintaining a neutral facial expression. Infants' regulatory behavior (i.e., orienting to mother or distracting attention to other things in the room) and affect (as rated by observer) were recorded. For infants with more rLFA at rest, maternal sensitivity predicted distraction during the emotional challenge task. For infants with less rLFA, maternal sensitivity predicted negative affect during the emotional challenge. These results suggest that infant rLFA may serve as a moderator between maternal sensitivity and infant emotional response. These findings suggest that rLFA may play a role in moderating emotional response or the relationship between emotional response and other trait variables such as maternal sensitivity. Similar findings exist in the adult literature.
In particular, Tomarken, Davidson, and Henriques (1990) found that those with less rLFA at rest had greater self-reported negative affect in response to negative emotional film clips than those with more rLFA. As such, rLFA serves as a moderator of the relationship between film clips and self-reported emotional response, such that those with less rLFA report more negative affect in response to negative stimuli. A subsequent study found that those with more rLFA had greater self-reported positive affect in response to positive emotional films than those with less rLFA (Wheeler, Davidson, & Tomarken, 1993). Recently, Schmidt and Hanslmayr (2009) examined ratings of mood and enjoyment in response to musical stimuli. They found that participants with more rLFA rated the musical stimuli more positively than those with less rLFA. Additionally, self-reported enjoyment of the stimuli differed as a function of rLFA for negative musical stimuli. This work suggests that rLFA at rest changes the relationship between the emotional stimuli (predictor) and self-rated affect and enjoyment (outcome). More generally, these studies indicate that frontal asymmetry is implicated as a potential moderator of emotional responding for both approach- and withdrawal-related emotions. Having less rLFA predicts increased attention to negative affect in response to withdrawal-related stimuli, which may be less adaptive and pose a potential risk factor for disorders characterized by deficits in approach-related motivation and affect, such as depression. More rLFA may predict increased positive affect to approach-related stimuli and indicate less vulnerability to such disorders.
Gatzke-Kopp, Jetha, and Segalowitz (2014) examined rLFA as a potential moderator of emotional response to sad stimuli and internalizing and externalizing symptoms in kindergarten children. This study was theory-driven in exploring whether rLFA served as an “afferent” or “efferent” filter. The afferent model proposes that rLFA is a filter of emotional stimuli that influences the behavioral response via affective arousal. The efferent model of rLFA proposes that rLFA alters the behavioral response to affective arousal without changing affective arousal. They found that rLFA served as a moderator; those who had increased arousal in response to the sad clip with more rLFA had more externalizing symptoms, whereas those with less rLFA had more internalizing symptoms. As such, these results suggest that rLFA is moderating the relationship between affective arousal and behavioral response rather than the relationship between emotional stimulus and affective arousal. This finding supports the efferent model.
Papousek et al. (2014) also found that those who had a higher decrease in rLFA in response to videos of injury and death had greater mood deterioration and film-related intrusive memories and avoidance over the next week. This study suggests that greater change in rLFA may be a vulnerability factor for negative affect and related symptoms. Given the research discussed earlier that suggests changes in rLFA related to emotional stimuli are adaptive (Papousek & Schulter, 2004), future research may examine under what conditions changes in rLFA may be adaptive or maladaptive.
Despite the implication of rLFA as a potential moderator of emotional response, results are somewhat inconsistent and should be further explored. Hagemann, Naumann, Becker, Maier, and Bartussek (1998) were able to replicate the results of Tomarken et al. (1990) with a Cz reference but not with a linked mastoid reference. Coan and Allen (2003b) explicitly tested rLFA as a moderator of emotional response by asking participants to make disgust, anger, joy, sadness, and fear faces. They found that rLFA at rest moderated the relationship between directed faces and self-reported emotional response; such that more rLFA predicted increased experience of anger, joy, and disgust. More rLFA has been associated with anger and joy in the literature more broadly (Carver & Harmon-Jones, 2009; Harmon-Jones & Allen, 1998; Killeen & Teti, 2012), whereas disgust has been associated with less rLFA (Jones & Fox, 1992).
Some evidence suggests that, in line with the capability model, rLFA may be a more consistent moderator of emotional response when measured during emotional stress task rather than at rest. For example, Goodman, Rietschel, Lo, Costanzo, and Hatfield (2013) studied the relationship between eyeblink startle magnitude and threat of shock with both rLFA at rest and during an emotional challenge task. rLFA at rest did not serve as a moderator of this relationship, but rLFA during the threat of shock did serve as a moderator of this relationship. Results suggest that rLFA measured during stress situations may predict emotion regulation abilities. Additionally, these results suggest that the relationship between rLFA and emotion regulation may not be as robust at rest as during emotional challenge tasks, as suggested by the capability model.
In addition to emotional response, a few studies have also examined rLFA as a moderator of attention processes. Amodio (2010) explored rLFA as a predictor of attention to black versus white faces. The researchers measured rLFA during a goal-directed state in line with the capability model. They found that those with more rLFA during these periods had larger P2 responses to black versus white faces, suggesting rLFA as a moderator of attention response to race. Interestingly, they also conducted a more in-depth moderated mediation model by examining the relationship between P2 and action control. Whereas frontal asymmetry predicts P2 amplitude for all participants, P2 amplitude predicts action control only for low-prejudice participants. In this latter analysis, frontal asymmetry is the predictor, P2 amplitude is the third variable, and action control is the outcome.
More recently, Tullett, Harmon-Jones, and Inzlicht (2012) found that individuals with less rLFA responded with more empathic concern to images related to a charity than those with more rLFA. This finding suggests that rLFA may not only be a moderator of emotional response but also empathic responding.
Hall, Ekkekakis, and Petruzzello (2010) also examined emotional responses to exercise using rLFA. They found that less rLFA at rest predicted lower energetic arousal at multiple points after exercise. This finding suggests that lower rLFA may predict less energetic arousal in response to approach-related tasks such as exercise.
A number of studies have also investigated the relationship between depressive symptoms and other trait variables, with rLFA as a moderator. For example, Kline and Allen (2008) examined the relationship between defensiveness and depressive symptoms. They found that rLFA and defensiveness interacted to predict depressive symptoms. Those individuals with less rLFA had a positive relationship between defensiveness and depressive symptoms, whereas those individuals with more rLFA had a negative relationship between them. As such, rLFA may in this case serve as an antagonistic moderator, as the relationship between defensiveness and depressive symptoms is reversed as a function of frontal asymmetry. Lopez-Duran, Nusslock, George, and Kovacs (2012) examined the relationship between stressful life events and internalizing symptoms in children at risk for depression based on their mother's depression. They found that rLFA serves as a moderator of the relationship between stressful life events and internalizing symptoms. Specifically, the less rLFA one has, the more an increase in stressful life events leads to an increase in internalizing symptoms.
Given the relationship between rLFA and psychopathology discussed above (Allen & Reznik, 2015; Mathersul et al., 2008), a few studies have examined rLFA as a potential moderator of treatment response. Exploration of rLFA as a moderator of treatment response would theoretically suggest that treatments work only for those who have certain levels of rLFA. Bruder et al. (2001) found that rLFA moderated response to fluoxetine in individuals with depressions. Those participants with less rLFA did not respond to treatment, whereas those with more rLFA responded to treatment. However, Gollan et al., (2014) examined rLFA as a potential moderator of behavioral activation treatment. As behavioral activation aims to increase approach-related behavior, it is likely that it would impact systems of approach/withdrawal motivation (Davidson, 2000). Yet, no relationship between rLFA and treatment response was found. As this study examined rLFA at rest, future research may examine rLFA as a moderator of treatment response during an emotionally evocative task. It also may be possible that rLFA will only serve as a moderator of treatment response for certain populations.
In a multicenter, randomized trial, Arns et al. (2015) examined 2 min of baseline resting EEG as a predictor of treatment response to escitalopram, sertraline, or venlafaxine-extended release. They found that more rLFA in women predicted response to escitalopram and sertraline but not to venlafaxine. However, the authors failed to find a relationship between rLFA and depression status (1,008 depressed participants compared to 336 healthy controls) possibly due to the short length of baseline recording (2 min) and using the average reference. Future research may examine whether sex or gender plays a role in identifying treatment moderators.
In another study, Moscovitch et al. (2011) examined rLFA at 8–10 Hz, 10–13 Hz, and the typical 8–13 Hz as potential predictors of treatment response for individuals with social anxiety disorder receiving cognitive behavioral therapy. In this study, rLFA moderated treatment response, such that those with more rLFA at rest at 10–13 Hz before treatment had greater reduction in social anxiety from pre- to posttreatment. Although these results are promising, further research may examine potentially differential effects using low, high, and full alpha bands as well as traditional rLFA versus other EEG profiles as moderators of treatment response.
Additionally, some research that has addressed alpha activity as a moderator of treatment response has tended to utilize alpha asymmetry at nonfrontal sites, or using other frequency bands. These studies are mentioned here only because they are often cited as studies examining frontal alpha asymmetry specifically. For example, Bruder et al. (2008) found that measures of alpha asymmetry at occipital, rather than frontal, sites predicted response to selective serotonin reuptake inhibitors (SSRIs) in individuals with depression. Another study found that baseline theta power and relative theta power during the first week predicted treatment response to SSRIs or venlafaxine but frontal asymmetry did not (Iosifescu et al., 2009). It was also found that change in combined theta and alpha power after SSRI treatment significantly predicted change in suicidal ideation from baseline (Iosifescu et al., 2008). Additionally, Quraan et al. (2014) examined alpha activity as a predictor of treatment response for individuals with depression undergoing deep-brain stimulation surgery. Both theta at frontal sites and alpha activity at parietal sites predicted treatment response, revealing that theta and alpha activity may serve as moderators in predicting treatment response.
1.6 Frontal asymmetry as a mediator of emotion
A mediator is a third variable through which (or partially through which) the predictor influences the outcome variable (Baron & Kenny, 1986). Whereas moderators allow insight into when or for whom a relationship holds, mediators provide insight into how or why a process occurs. For example, rLFA would serve as a mediator in a case where a given treatment alters rLFA, which in turn alters depressive symptoms. For example, Allen, Harmon-Jones, and Cavender (2001) used biofeedback to alter rLFA, which in turn altered affect. Frontal asymmetry serves as a mediator in this case because it is related to both biofeedback and outcome; it is able to explain variance in the relationship between the predictor and outcome. Mediator analyses may occur at any level (psychological, social, neural, etc.) and can improve our theoretical understanding at any of them. A mediator may or may not serve as a direct index of the mechanism by which actual change occurs. As such, it may be important to consider the distinction between mediators and mechanisms of change (Kazdin, 2007). In examining rLFA as a potential mediator, it is important to note that rLFA may be conceptualized as indexing either a psychological construct or the activity of specific neural systems or both. As a mediator, rLFA may provide a theoretical frame to examine how the predictor impacts the outcome, but should not be held inherently as the mechanism by which change occurs. Figure 2 illustrates the conceptual and statistical relationships in the case of mediation.

Mediation model. This figure represents one conceptual example of mediation. In this example, the third variable serves as a mediator between the outcome and the predictor
According to Baron and Kenny (1986), mediation occurs when (a) variations in the predictor account for variations in the third variable, (b) variations in the third variable account for variations in the outcome, and (c) when the relationship between predictor and third variable as well as third variable and outcome are adjusted for, the relationship between predictor and outcome should disappear. In other words, frontal asymmetry would serve as a mediator when some change in frontal asymmetry is necessary for the predictor to affect the outcome. One relationship may include multiple mediators. Baron and Kenny (1986) propose using multiple regression models to identify mediators. Although this technique has been widely used, Preacher and Hayes (2004) suggest use of the Sobel test. The Sobel test is a t test that compares the null hypothesis that mediator has no effect on the effect of the mediator. More recently, Preacher and Hayes have developed a computation to conduct mediation using path analysis called PROCESS (Hayes, 2012, 2013). Researchers may also explore specialized techniques for longitudinal data, such as panel models and latent growth curve models (Preacher, 2015).
While mediators and confounds share the same statistical relationships, they can be distinguished using theory to guide analysis. Mediators are theorized to be related to the cause by which a process likely occurs and are of significant theoretical interest or importance. On the other hand, confounders are either variables that are related by chance and cannot reasonably be the cause by which the predictor affects the outcome or fail to have significant theoretical interest or importance a priori. For example, imagine a novel psychosocial intervention proposed to impact rLFA. The researchers hypothesize that increasing rLFA will decrease depressive symptoms. In the case where the intervention (vs. control condition) increases rLFA and that change in rLFA accounts for decrease in depressive symptoms, rLFA would serve as a mediator. The findings would support the researchers' hypothesis that rLFA may be changed though this intervention to decrease depressive symptoms. However, one can imagine the case where the intervention does not alter rLFA, yet depressive symptoms are decreased. The perplexed researchers may then search for unexpected mechanisms that caused this change in depressive symptoms. Because the researchers collected a host of other potentially confounding variables, they compare the impact of intervention versus control on all of these variables. They find that the intervention significantly increased participants' time out of the house compared to the control condition. The time out of the house variable also accounted for decrease in depressive symptoms. In this latter case, the proposed mediator (rLFA) does not exist, and the researchers have identified a confounder (amount of time out of the house). Many researchers may predict that increasing time out of the house would decrease depressive symptoms, and this discovery provides important novel information for the researchers; yet, in this particular study, this variable would serve as a confound due to its lack of significant theoretical relevance to the intervention's impact on rLFA. In this way, researchers can distinguish cases where rLFA is spuriously related and causally related to the outcome and predictor (MacKinnon et al., 2013).
1.7 Evidence supporting rLFA as mediator of emotion
Studies designed to examine rLFA as a statistical mediator can lead to the identification of mechanisms by which emotional manipulations, interventions, or individual differences cause changes in mood and emotion. Because rLFA can be both an index of psychological and neural mechanisms, examining rLFA as a mediator offers substantial potential to broaden theories of emotion regulation and psychopathology by identifying potential psychological, social, or neural mechanisms of change. See Table 4 for studies examining rLFA as a mediator of emotion and psychopathology.
In order to establish rLFA as a mediator, it is necessary to determine that the rLFA score is both related to the predictor variable and the outcome variable. The extent to which the predictor variable alters rLFA should correspond to the extent of change in the outcome variable. For example, imagine that a researcher aims to examine rLFA as a mediator of depression treatment response. In the case that rLFA serves as a true mediator, rLFA scores should change based on the amount of intervention, and decrease in depressive symptoms should also be related to change in rLFA. As such, although a number of studies have elicited changes in rLFA (Coan & Allen, 2003b; Harmon-Jones, Gable, & Price, 2011), few studies have examined the impact of such changes on subsequent outcome measures like self-reported mood or behavior (Allen et al., 2001). Further research examining outcomes remains needed. In particular, researchers may examine whether changes in rLFA correspond to changes in clinical self-report.
In studies without measured outcomes, frontal asymmetry must be conceptually considered an outcome rather than a mediator variable. For example, Barnhofer et al. (2007), discussed in detail above, found that rLFA changed in response to treatment type for previously suicidal individuals but did not link these changes to any psychological phenomena. It is possible that rLFA serves as a mediator in this case between intervention and depressive symptoms or positive affect. As there is no outcome variable explicitly measured in this study, rLFA functions as the outcome variable. Additionally, one study examined rLFA as a potential mediator of depression using cortisol (Tops et al., 2005). It was found that administering cortisol to 11 healthy male volunteers decreased rLFA. Because less rLFA is associated with depression and increased cortisol is associated with depression, this research may suggest that rLFA is a mediating pathway in the onset of depression. However, this study did not conduct a true mediation analysis, as an outcome variable that would likely be related to the change in rLFA was not examined. Evidence to date supports the general stability of rLFA across episodes of major depressive disorder (MDD), both within subjects over time for those with history of MDD (Allen, Urry, Hitt, & Coan, 2004; Vuga et al., 2006) and between subjects differing in clinical status (Stewart et al., 2010). Yet, identifying that a treatment impacted depression via rLFA (as a mediator of depression treatment response) would have significant implications. Such treatments may be most promising for providing lasting remission and reducing risk for future episodes.
Additionally, Allen, Mcknight, Moreno, Demaree, and Delgado (2009) examined rLFA in response to tryptophan depletion. They found that change in rLFA because of tryptophan depletion predicted the development of depression 6 to 12 months out. Although there was no main effect of tryptophan depletion on depression status, this study suggests a potential causal role of change in rLFA to depression.
At the time of publication of Coan and Allen (2004), they reported that there had been no full meditational analyses (i.e., each path tested) involving rLFA. That paper summarized the results of a then recent biofeedback study that altered frontal asymmetry (Allen et al., 2001), in which biofeedback altered rLFA as well as self-reported affect and facial muscle activity in response to emotionally evocative films. There was no formal mediation analysis, however, and rLFA may be implicated as a potential mediator if the extent to which rLFA changed corresponds to the extent to which self-reported mood changed. They found that mood and electromyography (EMG) effects were stronger in those whose EEG asymmetry scores changed the most in response to the biofeedback training providing evidence for rLFA mediation. Since that time, Peeters, Ronner, Bodar, van Os, and Lousberg (2014) used a single session of neurofeedback to alter frontal asymmetry scores in 49 women and conducted a mediation analysis. Although rLFA was altered, there were no self-reported changes in mood. Therefore, it is unclear under what conditions changes in rLFA may influence mood changes. Additionally, although neurofeedback may alter rLFA, it is unclear from this research what impacts it may have on mood or motivation. An important distinction may be whether unprovoked mood (as in Peeters et al., 2014) or whether emotional reactions to evocative stimuli is assessed (as in Allen et al., 2001). Further research may attempt to provide additional theoretical clarity as to the conditions under which altering rLFA changes mood or other clinical outcomes.
A number of studies have elicited changes in rLFA and examined the effects on a meaningful outcome variable. For example, Peterson, Shackman, and Harmon-Jones (2008) found that hand contraction elicited more rLFA and increased behavioral aggression on a subsequent task. In this study, rLFA serves a mediator of the relationship between hand contraction and aggression. This study supports the finding that increases in rLFA may play a causal role in aggression. A more recent follow-up study also found that rLFA was a partial mediator of the relationship between hand contractions and anger induced by an ostracism task (Peterson, Gravens, & Harmon-Jones, 2011). Another study examined rLFA as a mediator of the relationship between stress and aggression (Verona, Sadeh, & Curtin, 2009). It found that stress induction in the laboratory increased rLFA. Frontal asymmetry scores after the stress induction also predicted aggression in the laboratory, using a task in which participants are asked to shock an employee (no shocks are actually delivered).
Amodio, Devine, and Harmon-Jones (2007) also explored the relationship between guilt and rLFA. After a multiracial facial viewing task, they provided participants with false performance feedback that they had made anti-Black responses. They found that to the extent that guilt increased, rLFA decreased.
A number of recent studies have also expanded theory by examining rLFA as a longitudinal predictor. Lewis, Weekes, and Wang (2007) studied the relationships between rLFA, psychological stress, hormonal stress, and negative health during both high and low stress times for undergraduates. They found that rLFA shifted from more rLFA during periods of low academic stress to less rLFA during periods of high academic stress. Importantly, greater change in rLFA was related to increases in negative health from the low to high stress periods. These results suggest that rLFA may mediate the relationship between environmental stress and negative health outcomes; the more an individual's rLFA is altered by periods of stress, the more negative health outcomes one is likely to experience. This study is innovative in its ability to link rLFA as a mediator of the relationship between emotional and physical health in a natural environment for the first time.
1.8 The importance of moderated mediation and mediated moderation
1.8.1 Moderated mediation
In addition to mediation and moderation effects, more complex models of mediated moderation and moderated mediation may be relevant to frontal asymmetry. Moderated mediation occurs when the mediation process of a given relationship differs as a function of a moderator (e.g., group or condition). The moderator may influence the relationship between the predictor and the mediator or the relationship between the mediator and the outcome. For example, imagine that moderated mediation exists such that gender serves as a moderator for a cognitive behavioral therapy (CBT) that reduces cognitive biases and, therefore, depressive symptoms. Gender may influence the link between CBT and cognitive biases, such that women have further reduction in cognitive biases after CBT than men, or gender may influence the link between cognitive biases and depressive symptoms, such that women have further reduction in depressive symptoms due to reduction in cognitive biases. There does not need to be an overall moderation effect for a mediated moderation effect to occur (Muller, Judd, & Yzerbyt, 2005). For example, imagine that a novel treatment improves depression scores. The novel treatment includes behavioral activation, mindfulness, and cognitive bias modification. For those with less rLFA, behavioral activation serves as the mediator. For those with more rLFA, mindfulness serves as the mediator. As such, the mediating process differs as a function of one's rLFA score (the moderator). Figure 3 shows an example of such a moderated mediation, which represents just one of many ways that a moderator may impact mediation. Importantly, there should be theoretical backing for such a model (e.g., depression in those with less rLFA is primarily an approach-related deficiency that may be improved with behavioral activation) but different mediating processes for different groups may emerge unexpectedly. Amodio et al. (2007) recently found moderated mediation in the relationship between frontal asymmetry and action control. P2 was found to be a mediator between rLFA and action control but only for participants with positive racial attitudes.

Moderated mediation. One conceptual example of moderated mediation. This example shows the case where the meditational process actually differs as a function of group (the moderator). As such, this figure shows one of the many ways in which a moderator may impact mediation (moderator has different meditational processes). Moderators may impact meditational processes in many ways, such as impacting the link between predictor and third variable or third variable and outcome
1.8.2 Mediated moderation
Mediated moderation occurs when there is a mediated process through which moderation occurs (Muller et al., 2005). In contrast to moderated mediation, mediated moderation occurs only when moderation occurs. For example, imagine again that behavioral activation improves depression scores only for individuals with lower BAS scores. A moderated mediation would occur if behavioral activation therapy only reduces depressive symptoms in individuals with low BAS scores because those with low BAS scores have less rLFA. As those with low BAS scores have less rLFA, rLFA mediates the relationship between treatment and outcome but also is moderated by BAS score (see Figure 4 for conceptual representation of mediated moderation). Importantly, recent research has combined mediated moderation and moderated mediation under the term conditional process analysis, and such models can be evaluated using PROCESS software (Hayes, 2012). Hayes (2013) argues that the semantic distinction between moderated mediation and mediated moderation does not provide conceptual benefit to modern mediation and moderation research. In particular, he argues that reframing interpretation in terms of “moderated mediation” will lend to clearer results than “mediated moderation.” Although the ability and knowledge to distinguish these two types of models may always be relevant to interpreting prior research, we argue that clear, interpretable explication of models with reference to variables that moderate and mediate, when done with great care and effort, may supersede the terms moderated mediation and mediated moderation without explication. Hayes (2016) may provide the reader with a more thorough understanding of conditional process models and ability to run and clearly explicate such models.

Mediated moderation. One conceptual example of mediated moderation. In this example, there is a moderation effect, and a mediated process through which this moderation effect occurs
2 FUTURE DIRECTIONS AND CONCLUSION
The present review aimed to highlight some of the influential findings of rLFA as a mediator and a moderator since 2004 and provide readers with a conceptual understanding of the distinction between predictors, outcomes, mediators, and moderators. We hope that this review suggests the importance of focusing on theory-driven research in the field. Although a number of significant empirical and theoretical advances have been made, future research would benefit from clarifying how rLFA may serve as an index of both psychological and neural processes.
Most rLFA research has focused on resting activity or sustained activity during a task (measures of activity), yet less research has focused on change or variability in rLFA as a predictor or outcome (measures of activation). Although some evidence suggests that change in rLFA in response to emotional tasks may relate to emotional flexibility (Papousek & Schulter, 2004), the conditions under which change in rLFA is adaptive or maladaptive remain poorly delineated. As such, we will provide a number of suggestions for future research that we hope may spark the readers' interest.
First, future research may examine how changes in rLFA, rather than rLFA at a given time, relates to emotional flexibility or indicates vulnerability for psychopathology. In particular, research may examine whether ability to shift rLFA in certain emotional contexts may relate to psychology flexibility and improved mental health outcomes.
Second, only a small number of studies have examined rLFA as a prospective predictor (Mitchell & Pössel, 2012; Nusslock, Young, & Damme, 2014), and future research may seek to investigate rLFA as a prospective predictor of depression or related psychopathology.
Third, only a few studies have examined rLFA as a mediator or moderator of treatment response. Although a number of interventions, such as behavioral activation, specifically aim to alter approach/withdrawal processes indexed by rLFA (Davidson, 2000), few studies have examined rLFA as a potential mediator or moderator of these interventions (Gollan et al., 2014).
Fourth, future research may focus on clarifying inconsistencies between research groups likely caused by differences in methodological approach. As summarized above, different tasks and recording montages have led to inconsistencies in rLFA findings. To remedy these inconsistencies, in line with the capability model, rLFA should be assessed under a variety of emotionally or motivationally relevant tasks rather than only during resting state (Coan, Allen, & McKnight, 2006). Moreover, to the extent that theories posit that the neural systems tapped by rLFA are frontal systems, a montage that most effectively isolates local activity and attenuates distal volume-conducted activity should be used (e.g., CSD, Stewart et al., 2010). Finally, within the resting state, there are dynamic shifts in asymmetry that are obscured by a single metric summarizing the entire resting period; future work might profitably explore the neural dynamics within the resting state (Allen & Cohen, 2010).
Fifth, future research may include other neuroimaging techniques in conjunction with rLFA. Such approaches have been previously used to compare theories of positive versus negative valence or approach versus withdrawal processes (Berkman & Lieberman, 2010). For further discussion of how neuroimaging may be used in conjunction with rLFA see Allen and Reznik (2015). Genetic and behavioral genetic research may also help improve understanding of biological mechanisms correlated with rLFA (e.g., Smit, Posthuma, Boomsma, & De Geus, 2007).
Sixth, the present review does not address potential moderators of the relationship between rLFA and psychopathology, which may be an aim of future reviews. This line of research may be important for understanding under which circumstances rLFA predicts psychopathology. For example, Thibodeau et al. (2006) proposed that medication and age of participant may significantly impact the relationship between rLFA and psychopathology status. Wacker et al. (2013) found that the dopamine-D2-receptor moderates the relationship between rLFA and trait approach motivation. Additionally, moderators may impact rLFA in response to emotional stimuli, as Cole, Zapp, Katherine Nelson, and Perez-Edgar (2012) found that rLFA increased after anxiety-producing video only for socially withdrawn individuals.
Seventh, rLFA research may benefit from examining symptom clusters rather than diagnoses, given their heterogeneity (Nusslock et al., 2015). In particular, rLFA could serve as a potential target within RDoC domains such as positive and negative valence systems that crosscut different psychological disorders.
Future research may examine rLFA as a mediator, moderator, predictor, and outcome in order to lead to further theoretical clarity. Additionally, more complex moderated mediation and mediated moderation models may lead to increased insight in the role of rLFA. As a neurophysiological metric, rLFA's inclusion in a study does not inherently validate or improve theoretical understanding. Rather, rLFA may increase theoretical understanding when studies are designed with the aim of improving existing models. As such, it is important that the reason for including rLFA is clearly explicated and that its role as mediator, moderator, outcome, or predictor is carefully chosen (Cacioppo, 2004).
Much of rLFA research has focused on distinguishing between groups (e.g., depressed versus nondepressed; Thibodeau et al., 2006). Examining rLFA as a mediator and moderator does not preclude such analysis, which may lead to further understanding of the etiology and maintenance of psychopathology. However, in order to propel our understanding of psychopathology and emotion regulation, these studies may occur in conjunction with mediation and moderation analysis.
Such theory-driven research may improve models of rLFA, emotion regulation, and psychopathology. In basic research, consideration of rLFA as a mediator and moderator may elucidate for whom certain relationships hold (e.g., only those with more rLFA have a certain pattern of emotional response) or how a process occurs (e.g., only when rLFA changes does some emotional response occur). In treatment research, examination of rLFA as a mediator and moderator may improve understanding of how change occurs or identify for whom a treatment might work. Such goals are in line with the initiatives by the NIMH to understand psychological and neural domains that crosscut psychopathology and processes by which change occurs in these domains (Insel, 2015; Nusslock et al., 2015).