Frontal brain asymmetry, childhood
maltreatment, and low-grade inflammation
at midlife
Article
Accepted Version
Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Hostinar, C. E., Davidson, R. J., Graham, E. K., Mroczek, D.
K., Lachman, M. E., Seeman, T. E., Van Reekum, C. M. and
Miller, G. E. (2017) Frontal brain asymmetry, childhood
maltreatment, and low-grade inflammation at midlife.
Psychoneuroendocrinology, 75. pp. 152-163. ISSN 1873-3360
doi: https://doi.org/10.1016/j.psyneuen.2016.10.026 Available at
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Accepted Manuscript
Title: Frontal brain asymmetry, childhood maltreatment, and
low-grade inflammation at midlife
Author: Camelia E. Hostinar Richard J. Davidson Eileen K.
Graham Daniel K. Mroczek Margie E. Lachman Teresa E.
Seeman Carien M. van Reekum Gregory E. Miller
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DOI:
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http://dx.doi.org/doi:10.1016/j.psyneuen.2016.10.026
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Frontal Brain Asymmetry, Childhood Maltreatment, and Low-grade Inflammation at Midlife
RUNNING TITLE: Frontal Brain Asymmetry, Maltreatment, and Inflammation
Camelia E. Hostinar1, Richard J. Davidson2, Eileen K. Graham3, Daniel K. Mroczek3, Margie E.
Lachman4, Teresa E. Seeman5, Carien M. van Reekum6, & Gregory E. Miller3
1. University of California, Davis, 202 Cousteau Place, Davis, CA 95618, USA.
2. University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705-2280, USA.
3. Northwestern University, 2029 Sheridan Road, Evanston, IL 60208, USA.
4. Brandeis University, 415 South Street, MS 062, Waltham, MA 02453, USA.
5. University of California, Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA 90095,
USA.
6. University of Reading, Earley Gate, Whiteknights, Reading RG6 6AL, UK.
Correspondence regarding this manuscript should be addressed to Camelia E. Hostinar,
Ph.D. Email: cehostinar@ucdavis.edu. Address: Center for Mind and Brain, University of
California, Davis, 202 Cousteau Place, Suite 254, Davis, CA 95618, USA.
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Highlights
Resting frontal EEG asymmetry was significantly associated with inflammation
Childhood maltreatment moderated frontal asymmetry’s associations
Findings support the diathesis-stress model of frontal brain asymmetry
Abstract
Frontal EEG asymmetry is thought to reflect variations in affective style, such that greater
relative right frontal activity at rest predicts enhanced emotional responding to threatening or
negative stimuli, and risk of depression and anxiety disorders. A diathesis-stress model has been
proposed to explain how this neuro-affective style might predispose to psychopathology, with
greater right frontal activity being a vulnerability factor especially under stressful conditions.
Less is known about the extent to which greater relative right frontal activity at rest might be
associated with or be a diathesis for deleterious physical health outcomes. The present study
examined the association between resting frontal EEG asymmetry and systemic, low-grade
inflammation and tested the diathesis-stress model by examining whether childhood
maltreatment exposure interacts with resting frontal asymmetry in explaining inflammation.
Resting EEG, serum inflammatory biomarkers (interleukin-6, C-reactive protein, and fibrinogen)
and self-reported psychological measures were available for 314 middle-aged adults (age M =
55.3 years, SD = 11.2, 55.7% female). Analyses supported the diathesis-stress model and
revealed that resting frontal EEG asymmetry was significantly associated with inflammation, but
only in individuals who had experienced moderate to severe levels of childhood maltreatment.
These findings suggest that, in the context of severe adversity, a trait-like tendency towards
greater relative right prefrontal activity may predispose to low-grade inflammation, a risk factor
for conditions with inflammatory underpinnings such as coronary heart disease.
Keywords: resting frontal EEG asymmetry, child maltreatment, inflammation
1. Introduction
Contemporary models of how negative psychological experiences shape long-term
human health are increasingly recognizing the role of bidirectional communication between the
brain and the immune system (Danese and McEwen, 2012; Gianaros and Hackman, 2013; Irwin
and Cole, 2011; Kop and Cohen, 2007; Miller et al., 2011; Nusslock and Miller, 2016; Raison et
al., 2006; Slavich et al., 2010). Neuro-immune transactions are thought to occur both directly and
indirectly through multiple pathways that include psychological processes such as depression or
health behaviors like sleep (Glaser and Kiecolt-Glaser, 2005; Irwin and Cole, 2011). The present
study sought to test associations between neural activity and inflammation, and to examine how
this association may be differentially shaped by early-life adversity in the form of childhood
maltreatment. We focused on functional brain asymmetry in the frontal region assessed by
resting EEG as a marker of neural diathesis, given that frontal right-hemisphere dominance has
been associated with a trait-like bias toward negative affect (Coan and Allen, 2004; Davidson,
2004; Fox, 1991) and enhanced risk for depression and anxiety disorders (Davidson, 1998a;
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Fingelkurts and Fingelkurts, 2015; Jesulola et al., 2015; Nusslock et al., 2015; Thibodeau et al.,
2006). We aimed to (1) test whether resting frontal brain asymmetry is associated with systemic,
low-grade inflammation; (2) explore whether those reporting childhood maltreatment show a
pattern of greater relative right frontal EEG activity; (3) test a diathesis-stress model of frontal
asymmetry whereby asymmetry interacts with maltreatment experiences to predict higher levels
of inflammation; and finally (4) we conducted an exploratory analysis to probe whether frontal
asymmetry’s associations with inflammation and maltreatment are independent of or overlapping
with depression, anxiety, and lifestyle indices (cigarette smoking, alcohol consumption, physical
exercise, abdominal adiposity, and sleep difficulties). We describe the theoretical rationale for
these goals next.
1.1 Associations of Frontal Brain Asymmetry with Mental and Physical Health Outcomes
Frontal EEG asymmetry is thought to reflect the activity of brain systems involved in
approach and withdrawal motivation. Relatively greater left-sided activity is associated with
approach behavior and predominantly positive affect. By contrast, relatively greater right-sided
activity is linked to avoidance behavior and negative emotions like fear or sadness (Davidson,
1998b). Most, but not all, research suggests an association between right-sided frontal
asymmetry and risk for depressive and anxiety disorders (Davidson, 1998a; Fingelkurts and
Fingelkurts, 2015; Jesulola et al., 2015; Nusslock et al., 2015; Thibodeau et al., 2006).
However, much less attention has been dedicated to examining the links between frontal
asymmetry and physical health. A handful of studies have explored frontal asymmetry in relation
to immune function, and predominantly found right-sided asymmetry to correlate with indicators
of reduced immune activity –for example, lower antibody titers in response to the influenza
vaccine (Rosenkranz et al., 2003), lower natural killer cell activity at baseline (Kang et al., 1991)
and in response to challenge (Davidson et al., 1999), as well as lower CD8 T lymphocyte counts
in HIV-positive patients (Gruzelier et al., 1996). However, it is difficult to extrapolate from these
findings to other compartments of the immune system or to broader health outcomes.
Accordingly, the present study’s goal is to examine the association between frontal asymmetry
and proteins indexing low-grade inflammation (serum interleukin-6, C-reactive protein, and
fibrinogen).
1.2 The Developmental Origins of Frontal Asymmetry
Despite almost four decades of research on the role of frontal asymmetry in affective
processes and psychopathology, the developmental origins of frontal EEG asymmetry are not
well understood. Twin studies reveal modest heritability estimates for this construct, ranging
from 11% to 37% of variance being attributed to genetic factors (Anokhin et al., 2006; Gao et al.,
2009; Smit et al., 2007). Additionally, there is some evidence linking prenatal conditions
including maternal depression and substance abuse to newborns’ frontal EEG activity (Field and
Diego, 2008). A recent meta-analysis has also begun revealing some of the environmental risk
factors associated with right-sided frontal asymmetry in children and adolescents (Peltola et al.,
2014). The most robust association in terms of the number of studies supporting it and the
consistency of the findings is that with parental depression, especially maternal depression
(Peltola et al., 2014). The low genetic heritability estimates suggest that some of the pathways
from parental psychopathology to offspring’s EEG phenotype might be psychosocial. Isolated
studies have supported this notion and linked frontal asymmetry to parental insensitivity (Hane
and Fox, 2006) and parental deprivation (i.e., orphanage rearing) (McLaughlin et al., 2011), but
not parental alcohol dependence (Ehlers et al., 2001). Only two studies have examined links to
childhood maltreatment, including neglect and abuse, and their findings are mixed. Miskovic et
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al. (2009) found that adolescent females exposed to maltreatment had greater right-sided frontal
EEG asymmetry compared to non-maltreated controls, whereas Curtis and Cicchetti (2007)
reported no main effect of maltreatment on frontal asymmetry and an interaction with gender
such that there was no effect in females and the opposite effect from the typical prediction in
males –i.e., greater left-sided asymmetry in maltreated males. More research is needed to clarify
the experiential precursors of frontal asymmetry, thus the present study sought to examine its
association with retrospectively-reported maltreatment experiences.
1.3 The Diathesis-Stress Model of Frontal Asymmetry
The literature on associations between resting frontal EEG asymmetry and risk for mood
and anxiety disorders also includes some mixed findings, such that not all individuals with rightsided asymmetry suffer from psychopathology (Davidson, 1998b). It has been theorized that the
individual differences in underlying prefrontal brain activity bias towards approach or
withdrawal tendencies, but are not in themselves sufficient for triggering psychopathology
(Davidson, 1998b). A diathesis-stress model of frontal asymmetry has been advanced to propose
that frontal asymmetry interacts with negative life events to precipitate psychopathology
(Davidson, 1993). Most studies of frontal asymmetry and risk for psychopathology have not
explicitly tested this hypothesis, but there is some empirical support for this idea. For instance, in
6-13-year-old children at-risk for depression, the number of negative life events experienced was
associated with proportional increases in internalizing symptoms only in children with
predominantly right-sided frontal activity (Lopez-Duran et al., 2012). It is currently unknown
whether the diathesis-stress model would also apply to outcomes related to physical health. We
sought to answer this question by examining whether the association between resting frontal
asymmetry and low-grade inflammation varies as a function of exposure to childhood
maltreatment. There is abundant evidence that maltreatment is a risk factor for affective
disorders (Teicher and Samson, 2013), inflammatory activity (Coelho et al., 2014; Danese et al.,
2007), and chronic health problems across the lifespan (Danese and McEwen, 2012; Miller et al.,
2011; Repetti et al., 2002; Wegman and Stetler, 2009).
1.4 The Role of Depression, Anxiety, and Health Behaviors
Inflammation is an adaptive response by innate immune cells to injuries and infections.
However, if this response becomes sustained and disseminated, a low-grade chronic
inflammation can develop, which has been linked to morbidity and mortality (Black, 2003;
Libby, 2012). Frontal asymmetry may foster inflammation in a number of ways. It may
predispose to depressive and anxious symptoms (Thibodeau et al., 2006), which have
bidirectional connections with inflammation (Slavich et al., 2010; Vogelzangs et al., 2013).
Additionally, frontal asymmetry is associated with positive and negative affective experiences
(Coan and Allen, 2004; Davidson, 2004), which predict engagement in restorative or
deteriorative health behaviors (e.g., sleep, physical exercise, cigarette smoking, alcohol
consumption, weight gain) (Boehm and Kubzansky, 2012), all of which can influence
inflammation (Kiecolt-Glaser and Glaser, 1988; Mullington et al., 2010; Raposa et al., 2014;
Strohacker et al., 2013). For these reasons, it is plausible that the association between frontal
asymmetry and inflammation may be accounted for by internalizing symptoms (depression,
anxiety) or health behaviors. We aimed to test this possibility in the current study.
2. The Present Study
This report is based on data from the Neuroscience Project of the Midlife in the United
States (MIDUS) study. The primary goals of the present study were to (1) examine whether
resting frontal asymmetry is associated with greater low-grade inflammation at midlife; (2) test
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whether self-reported childhood maltreatment experiences are associated with frontal EEG
asymmetry; (3) investigate if childhood maltreatment interacts with frontal asymmetry to explain
inflammation, as predicted by the diathesis-stress model; and, finally, (4) examine whether
frontal asymmetry’s associations with inflammation and maltreatment are independent of or
overlapping with depression, anxiety, and lifestyle indices (cigarette smoking, alcohol
consumption, physical exercise, abdominal adiposity, and sleep difficulties).
3. Methods
3.1 Participants
Participants were drawn from the nationally representative MIDUS study, which began in
1995-1996 with 7,108 non-institutionalized adults selected via random-digit phone dialing from
the 48 contiguous states. An average of 9 years later, 75% of surviving respondents participated
in a follow-up study, known as MIDUS 2 (see Figure 1 for visual depiction of the study’s data
collection waves). The present report used data from participants who completed the
Neuroscience Project (N = 331) during MIDUS 2 and also extracted data for these participants
from the following other MIDUS 2 assessments: the Survey Project, which included extensive
phone interview and self-administered questionnaire data; the Biomarker Project, for which
participants traveled to a General Clinical Research Center for a two-day, overnight visit and
provided fasting blood samples, among other biological specimens; and the Milwaukee Study,
which consisted of recruiting an African American subsample recruited from the Milwaukee,
Wisconsin area that completed all the measures from MIDUS 1 and MIDUS 2 at the same time.
For the analyses reported here, we included 314 participants from MIDUS 2 with
available data for the EEG recordings, the inflammation indices and questionnaire measures of
interest, as well as data on the sociodemographic and biomedical covariates. Participants
included in this analysis were on average 55.3 years old (range: 36 – 84, SD = 11.2), 55.7%
female and exhibited some diversity in terms of racial/ethnic background: 63.4% Non-Hispanic
White, 31.8% African American, and 4.8% other. The average total annual household income in
this sample was $61,537 (SD = $50,963, range $0 - $300,000). There were 35 sibling sets in the
Neuroscience Project and 31 among participants included in this report (see section 4.4 of
Results for details on how they were treated in analyses). All procedures were carried out with
the adequate understanding and informed written consent of all participants.
3.2 Procedure
3.2.1 EEG acquisition and processing. Participants visited the Laboratory for Brain
Imaging and Behavior at the University of Wisconsin-Madison. To derive measures of frontal
brain asymmetry, electrical brain activity was recorded using a 128-channel geodesic electrode
net (Electrical Geodesics, Inc. [EGI], Eugene, OR). Participants had the net placed on their head
and were then escorted into a soundproof booth where they were seated in front of a computer
screen. A computer located outside the booth recorded the data. Each participant was instructed
to rest for six 1-min periods. During three of the 1-min periods they were asked to keep their
eyes open; for the remaining three 1-min periods they were asked to keep their eyes closed. EEG
baselines were collected at the beginning and at the end of the session. The data used in this
analysis was restricted to the first set of six baselines collected at the beginning of the session. To
increase the reliability of the EEG baseline data, we collapsed across conditions and across
minutes. Processing steps were conducted according to accepted guidelines and are described
below (see Pivik et al., 1993 for additional information).
i. EEG recording. Electrical brain activity was recorded using a 128-channel geodesic
net of Ag/AgCl electrodes encased in saline-dampened sponges (EGI). Electrode impedances
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were reduced to less than 100 KΩ, and analog EEG signals were amplified and sampled at a rate
of 500 Hz (bandpass filtered from 0.1-100 Hz) with 16-bit precision using an online vertex (Cz)
reference.
ii. Data cleaning. After 60 Hz notch filtering and 0.5 Hz high-pass filtering to remove
slow frequency drift, bad channels were identified and removed. Bad sections of data were also
removed. Using EEGLAB6, the EEG data was then submitted to a PCA/ICA forcing the
identification of 20 components. PCA/ICA was conducted for each individual. The PCA/ICAs
were used to identify common artifacts in EEG, such as eye blinks and eye movements, and
cardiac signals. Based on testing performed in the laboratory with ICA and forcing the
identification of a range of PCA components, we concluded that forcing 20 components resulted
in the best decomposition of these artifacts, and with maximal time efficiency both in processing
the data and in identifying components capturing artifacts. Components containing obvious eye
blinks, eye movements and other artifacts were then removed from the data. Bad channels were
then replaced using a spherical spline interpolation. Epochs of 2 second length were then created.
The EEGLAB automated artifact identification routine was then run on these epoched data files,
identifying epochs containing deviations of ±100 microvolts, which were then subsequently
removed.
iii. Frequency analysis. Using LORETA-KEY, the spectral power density was then
computed for each sensor using epochs of 2 seconds duration (with 50% overlap) following
linear detrending and application of a Hanning window. Due to variability of the actual peak of
the alpha frequency across age, an alpha power band was determined on the basis of each
individual's alpha peak frequency (Klimesch, 1999). The peak frequency was identified using an
automated routine which picked the peak in a frequency window ranging from 6 to 14 Hz across
the scalp. Lower and upper alpha bands were then defined as follows: lower band of Alpha 1 was
the individual alpha peak frequency (IAP) – 30% of IAP, upper band of Alpha 1 was up to IAP;
lower band of Alpha 2 was actual IAP, whereas upper band of Alpha 2 was IAP + 30 % of IAP.
iv. Missing data. The rate of missing EEG data due to participant refusal or excluding
data having 50% or more bad EEG channels was low (3.6% total, or N = 12).
3.2.2 Biomarker collection. For the biomarker collection, participants arrived to the
clinic and were checked in for their two-day overnight stay. On the first day, they were assisted
by medical staff in completing their medical history, a physical exam, and a bone densitometry
scan. The following morning, nursing staff collected fasting blood samples from which the
inflammatory biomarker concentrations were later derived.
3.3 Measures
3.3.1 Frontal brain asymmetry. Log alpha power was averaged across multiple sites on
the scalp to create more reliable indices that approximate sites in the standard 10-20 EEG system
Log alpha power in the right frontal area was subtracted from log alpha power in the left frontal
area (left – right) to create an index of laterality. To create a single measure of relative frontal
alpha activity, the laterality indices for the FP1/FP2, F3/F4, and F7/F8 regions were averaged, as
were the Alpha 1 and Alpha 2 bands. Because greater alpha activity indicates less neural
activation, larger laterality scores indicate greater right hemisphere activation.
3.3.2 Inflammation composite. Three serum markers of low-grade inflammation derived
from fasting blood samples were used to create our composite: C-reactive protein (CRP),
interleukin-6 (IL-6), and fibrinogen1. CRP was measured using a particle enhanced
1
There were three other indices of inflammation measured in MIDUS (E-Selectin, ICAM-1, and
serum soluble IL-6 receptor), however they had zero to small correlations with the other inflammation
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immunonepholometric assay (BNII nephelometer, Dade Behring Inc., Deerfield, IL). Serum IL6
was assessed using the Quantikine® High-sensitivity ELISA kit #HS600B according to
manufacturer guidelines (R & D Systems, Minneapolis, MN). Fibrinogen antigen was measured
using the BNII nephelometer (N Antiserum to Human Fibrinogen; Dade Behring Inc., Deerfield,
IL). The laboratory intra- and inter-assay coefficients of variance (CV) for all protein assays
were in acceptable ranges (< 10%).
An inflammation composite was created by standardizing and combining the IL-6, CRP
and fibrinogen measures. According to a Principal Components Analysis, these three measures
loaded on single common factor (with loadings of .81, .83, and .84, respectively). Cronbach’s
alpha for this composite measure was .77.
3.3.4 Childhood Trauma Questionnaire (CTQ, Bernstein et al., 2003). The CTQ was
completed by participants at the biomarker collection. The CTQ is a 28-item self-report
questionnaire that assesses physical abuse, emotional abuse, sexual abuse, emotional neglect, and
physical neglect caused by a family member before the age of 18 and has high external validity,
such that self-reports on the CTQ questionnaire are consistent with information derived from
clinical interviews and Child Protective Services records (Bernstein et al., 2003). The total score
for items inquiring about the five types of maltreatment was used in analyses. The CTQ had high
internal reliability in this sample (Cronbach’s alpha = .90).
3.3.7 Depressive symptoms. The 20-item Center for Epidemiologic Studies Depression
(CES-D) Inventory was used at the time of biomarker collection to assess depressive symptoms
in the prior week. In prior studies the measure has shown high internal consistency and test-retest
reliability, as well as adequate validity assessed via correlations with other self-report measures
and clinical ratings (Radloff, 1977). In this sample the measure also had high internal
consistency (Cronbach’s alpha = .86).
3.3.8 Anxious symptoms. The 20-item Spielberger State-Trait Anxiety Inventory (STAI,
Spielberger et al., 1983) was used to extract a measure of typical levels of anxious symptoms
(only the trait measure was used here). Participants completed 4-point Likert-type items to
describe how often they were faced with thoughts such as “I worry too much over something that
doesn’t really matter.” The trait anxiety measure had a high Cronbach’s alpha in this sample
(.88).
3.3.9 Lifestyle indices. At the biomarker assessment, information regarding sleep
quality, physical exercise, cigarette smoking, alcohol consumption, and waist circumference
(measured in centimeters in the laboratory and standardized within each gender) was collected.
Sleep quality was assessed using the Pittsburgh Sleep Inventory (PSQ, Buysse et al., 1988),
which measures the following seven dimensions using a total of 19 self-rated items: subjective
sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of
sleeping medications, and daytime dysfunction. The Global Sleep score was constructed by
summing the seven sleep components for each case with complete data. Reverse-coded sleep
measures in this Neuroscience subsample of MIDUS (e.g., serum soluble IL-6 receptor had correlations
ranging from r = -.004 to -.08, p’s > .13 with four of the five other inflammatory indices and only had a
significant but small association with ICAM-1). Additionally, E-Selectin and ICAM-1 had low loadings
(.31 and .33) on a common inflammation factor extracted through Principal Components Analysis, thus
they were excluded from the inflammation composite to increase the internal consistency of the measure
(from Cronbach’s alpha = .63 to a more acceptable alpha = .77). However, results were robust with or
without E-Selectin and ICAM-1 in the inflammation composite (analyses available upon request).
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components were re-coded such that higher scores represented greater sleep difficulties across all
the scales. Global sleep scores were not computed for cases with erroneous reporting (e.g.,
Habitual Sleep Efficiency greater than 100%). Because the distributions of smoking, alcohol use,
and exercise variables were extremely skewed and could not be corrected with transformations,
they were recoded into ordinal variables. For smoking, the new variable was coded as 0 = never
smoker, 1 = former smoker, and 2 = current smoker. For alcohol, it was 0 = zero drinks per
week, 1 = less than 10 drinks per week, and 2 = 10 or more drinks per week. For physical
exercise, number of minutes of weekly strenuous activity were coded as 0 = none, 1= less than
500 minutes per week, 2 = 500-1000 minutes per week, and 3 = more than 1000 minutes per
week. These categories were chosen based on a previous MIDUS report, which significantly
linked the exercise variable coded in this fashion to inflammatory outcomes (Strohacker et al.,
2013).
3.3.10 Covariates. Basic sociodemographic, medical history, and medication usage
information was obtained during the biomarker collection and MIDUS II assessments.
Participants’ age, sex, and educational level were included in our models. Additionally,
race/ethnicity was dummy-coded for analyses, with the most numerous group (non-Hispanic
Whites) serving as the reference and binary codes being used to denote African American race
and Other race/ethnicity (sample sizes were too small to account for any other racial/ethnic group
–e.g., there were only n = 5 participants of Hispanic origin in this sample). Medical diagnoses
and medications with potential associations with inflammation were also selected for inclusion –
namely, history of heart disease or diabetes; use of anti-hypertensive, cholesterol-lowering,
corticosteroid, or non-steroidal anti-inflammatory medications.
3.4 Data Analysis Plan
3.4.1 Data preparation. Variables were examined for outliers and for their
approximation of the normal distribution before analyses. Values that exceeded four standard
deviations from the mean were Winsorized and replaced with the value at the 99.9 th percentile
(CRP: n = 5; IL-6: n = 7; frontal asymmetry scores: FP1/FP2 alpha 1 band, n = 3; FP1/FP2 alpha
2 band, n = 4; F7/F8 alpha 1 band, n = 3; F7/F8 alpha 2 band, n = 3; F3/F4 alpha 1 band, n = 2;
F3/F4 alpha 2 band, n = 2). A logarithmic transformation was also applied to normalize the
distributions of skewed variables (CRP, IL-6, CTQ total, and CES-D scores; all had a right skew
prior to log transformation).
3.4.2 Missing data. The rate of missing data for the variables used in our analyses was
low, ranging from 0% to 8.5% (e.g., 8.5% out of the 331 participants were missing data on sleep
difficulties). Data were missing completely at random (MCAR) according to Little’s MCAR test:
χ2 = 137.31, df = 119, p = .12. Multiple imputation was used to verify that results are robust when
including all the participants in the models. We generated 40 imputed datasets based on
recommendations by Graham (2009) and re-conducted the primary study analyses on the pooled
data from these imputations using IBM SPSS Statistics 23 software. Our primary results were
replicated in the analyses using the multiply-imputed pooled dataset (see Supplemental Table 1
for these results).
3.4.3 Statistical analyses. We used multiple linear regression models and analyses of
covariance (ANCOVAs) to examine our four hypotheses. All the analyses adjusted for the set of
sociodemographic and biomedical covariates described above, but unadjusted associations
among the primary study variables are also presented in Table 1.
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(1) For our first question regarding the association between frontal asymmetry and
inflammation, we regressed the inflammation composite onto frontal asymmetry and the panel of
covariates.
(2) For the second question regarding maltreatment as a potential predictor of asymmetry,
we regressed frontal asymmetry onto maltreatment and the covariates. To further characterize
differences between maltreated and non-maltreated individuals, we created a binary variable
where 1 indicated meeting or exceeding the CTQ cutoff score for experiencing any one of the
five possible maltreatment subtypes (physical abuse, sexual abuse, emotional abuse, emotional
neglect, physical neglect) based on the respective subscale, whereas zero indicated being under
the threshold for all five subscales; we then compared these two groups on their frontal
asymmetry scores using ANCOVAs. Given gender differences in the association between
maltreatment and asymmetry in the previous literature, we then re-conducted these analyses
while also entering an interaction term between gender and maltreatment status.
(3) To test whether the interaction of frontal asymmetry and childhood maltreatment
exposure in predicting inflammation best fits a diathesis-stress model, we used the criteria
recommended by Roisman and colleagues (Roisman et al., 2012). Specifically, this included the
following steps: a) showing a statistically significant interaction between frontal asymmetry and
maltreatment; b) testing the significance of simple slopes at high and low values of the
moderator (we chose +1SD and -1SD of the moderator; note that we tested simple slopes for
both maltreatment and for frontal asymmetry, which were in turn considered the moderator); c)
computing regions of significance (RoS) using the Johnson-Neyman technique (Johnson and
Neyman, 1936) implemented using the SPSS process macro (Hayes, 2013), which is a technique
that provides the full range of values of the moderator for which the independent and dependent
variable are significantly associated, rather than testing single values through simple slopes
analysis; Roisman et al. recommend identifying RoS on both the predictor and the moderator by
reversing the role of the predictor and the moderator after the initial moderation analysis. Thus,
we report both the RoS on maltreatment, which shows the range of maltreatment experiences for
which asymmetry and inflammation are associated, and the RoS on frontal asymmetry, which
reveals the range of frontal EEG asymmetry for which maltreatment is significantly associated
with inflammation; d) plotting the interaction and these RoS in graphs that display values
ranging from -2SD to +2SD of the predictor; to obtain the figures, we used the web-based
program recommended by Roisman, Fraley and colleagues (Roisman et al., 2012), available at
http://www.yourpersonality.net/interaction/; e) to test how well the data fit a diathesis-stress
model where maltreatment is the stressor and frontal asymmetry is the diathesis, we computed
the proportion of the interaction (PoI) index, a measure of how much a crossover interaction is
“for better” versus “for worse” –i.e., how much the data fit a diathesis-stress model (values for
PoI closer to zero) versus a differential susceptibility model (values of PoI are closer to 0.50; see
Roisman et al., 2012 for a detailed explanation of how this index is derived); f) we further
computed a proportion affected (PA) index, which captures the proportion of the sample that is
affected by the statistical interaction; and finally, g) we ruled out the possibility of the diathesisstress effects being due to a nonlinear interaction by adding terms for the predictor-squared and
predictor-squared multiplied by the moderator. This final step is intended to demonstrate that the
significant linear interaction of the predictor and the moderator is not an artifact of a nonlinear
effect of one of the predictors.
(4) Finally, to examine whether frontal asymmetry’s associations with inflammation and
maltreatment are independent of or overlapping with depression, anxiety, and lifestyle indices,
P. 10
we aimed to conduct linear regression analyses (inflammation regressed onto frontal asymmetry
and maltreatment regressed onto frontal asymmetry) that also entered one of the following
covariates in separate analyses: depressive symptoms, anxiety symptoms, cigarette smoking,
alcohol consumption, physical exercise, abdominal adiposity, and sleep difficulties. These
analyses were conducted on the pooled multiply-imputed dataset (N = 331) to account for
varying amounts of missing data on each of these additional measures. To equalize degrees of
freedom across these analyses, listwise deletion would have resulted in a 14.5% loss of sample
size (N = 283) and in these instances multiple imputation is recommended as it guards against
loss of statistical power and possible bias of estimates (Bennett, 2001).
4. Results
Bivariate correlations and descriptive statistics for the main study variables are shown in
Table 1. A total of 106 participants (33.8% of the sample) reported at least one abuse subtype
according to clinical cut-off criteria for the CTQ subscales, as follows: 11.8% of the full sample
endorsed physical abuse, 13.7% emotional abuse, 15% sexual abuse, 17.2% emotional neglect,
and 15.9% physical neglect. These percentages add up to more than 33.8% of the sample due to
comorbidity of maltreatment subtypes. Of the 106 of participants who experienced maltreatment,
45 reported one maltreatment subtype (14.3% of full sample), 25 reported two subtypes (8% of
sample), 18 reported three subtypes (5.7% of sample), 8 reported four subtypes (2.5% of sample)
and 10 participants endorsed all five subtypes (3.2% of sample). The average maltreatment
severity score on the CTQ scale in this sample was M = 37.7 (SD = 13.9, range = 25 – 106).
4.1 Frontal Brain Asymmetry and Systemic Low-grade Inflammation
The regression analyses indicated that frontal brain asymmetry was significantly
associated with low-grade inflammation (b = .13, SE = .06, p = .02) such that more right activity
covaried with higher inflammation composite scores. This association remained significant after
adjusting for sociodemographic and medical history covariates (b = .11, SE = .05, p = .04, see
Model 2 in Table 2).
4.2 Frontal Brain Asymmetry and Self-reported Childhood Maltreatment
Frontal brain asymmetry was not significantly associated with CTQ maltreatment scores
(r = -.009, p = .87). This association remained non-significant (b = .005, SE = .06, p = .93) when
regressing frontal asymmetry on child maltreatment and including our panel of covariates. These
results were consistent with those of an ANCOVA showing no significant main effect of
maltreatment status on frontal asymmetry, F (1,301) = .49, p = .49 such that mean asymmetry for
maltreated participants (M = -.05, SD = 1.04) did not differ from that of non-maltreated
individuals (M = .03, SD = .98). This analysis adjusted for our full panel of covariates, including
gender, which was not a significant predictor of asymmetry, F (1,301) = .002, p = .96. Given
prior literature regarding gender effects, we repeated this analysis to include an interaction term
between maltreatment and gender, however this interaction was also not significant, F (1,301) =
.16, p = .69. Additionally, associations between maltreatment scores and frontal asymmetry were
also non-significant within each of the genders (p’s > .34).
4.3 Testing the Diathesis-Stress Model
Regression analysis revealed a significant interaction between frontal asymmetry and
childhood maltreatment exposure in predicting inflammation (b = .12, SE = .05, p = .02), and
this remained significant after adjustment for sociodemographic and biomedical covariates (b =
.10, SE =.05, p = .03, see Model 4 in Table 2). We followed up on this analysis by first
considering maltreatment to be the moderator, and frontal asymmetry the predictor. Simple
slopes analysis revealed that frontal asymmetry was significantly associated with inflammation
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at high (+1SD) levels of CTQ maltreatment scores (slope: β = .21, p = .003), but not at low (1SD) levels of maltreatment (slope: β = -.001, p = .99). The Johnson-Neyman technique
identified the region of significance for the association between frontal asymmetry and
inflammation as including individuals scoring above .013 on the Z-scored CTQ scale (i.e., the
inflection point was close to the mean of the scale), which was equivalent to being in the top
40.8% scores for the CTQ in this sample. Figure 2 illustrates this interaction and the regions of
significance described.
When considering frontal asymmetry to be the moderator and maltreatment the predictor,
we found that maltreatment was significantly associated with inflammation only for those with
high asymmetry scores (i.e., with right-sided dominance). Specifically, simple slopes analysis
revealed that maltreatment was marginally related to inflammation at +1SD levels on asymmetry
(β = .14, p = .06), but not related to inflammation at -1SD on asymmetry (β = -.07, p = .35, see
Figure 3). The region of significance for the association between maltreatment and inflammation
included values of 1.15 or higher on asymmetry (Z-scored), which was equivalent to the top
10.8% of asymmetry scores in this sample.
We then computed the indices recommended by Roisman and colleagues to test whether
our results best resemble a diathesis-stress pattern. Frontal asymmetry was considered the
diathesis, and maltreatment the stressor (see Figure 3 for the graph corresponding to this
analysis). The PoI index was 0.10, suggesting that 10% of the interaction occurred left of the
crossover point (“for better”), whereas 90% was right of the crossover point (“for worse”). The
fact that the PoI value was closer to 0 than 0.50 is evidence supportive of a diathesis-stress model
interpretation (in contrast, PoI values closer to 0.50, where the crossover point would be close to
the middle of a graph spanning from -2SD to +2SD, would support a differential susceptibility
model). Further supporting our diathesis-stress interpretation, the PA index was .13, suggesting
that only 13% of individuals were affected by the interaction “for better”, whereas 87% were
affected “for worse”. This result supported the diathesis-stress interpretation of our results given
recommendations that at least 16% of the sample needs to be affected “for better” before a
differential susceptibility interpretation would be preferred to a diathesis-stress interpretation
(Roisman et al., 2012). There was also no evidence of nonlinear effects, as terms for the
predictor-squared and predictor-squared multiplied by the moderator were not significant (p =
.30 and p = .13 respectively). This suggested that our diathesis-stress results were not an artifact
of nonlinear associations between the predictor and the outcome.
4.4 Exploring the Role of Depression, Anxiety and Lifestyle Indices
Given that frontal asymmetry was not associated with our measure of childhood
maltreatment, we focused next on examining the role of depression, anxiety and lifestyle indices
in potentially explaining some of the association between frontal asymmetry and inflammation.
Multiple regression analyses revealed that the interaction between frontal asymmetry and
inflammation remained significant when entering depression, anxiety, cigarette smoking, alcohol
use, or physical exercise as covariates one at a time (Table 3). Furthermore, these were not
significant predictors of inflammation in this sample (Table 3). In contrast, abdominal adiposity
and sleep difficulties were each a significant predictor of inflammation independently of all other
variables in the model (b = .44, SE =.05, p < .001, and b =.05, SE =.02, p = .003, respectively).
Furthermore, the interaction between frontal asymmetry and inflammation no longer
significantly predicted inflammation when either of these two variables were added to the
multiple regression models (Table 3), suggesting that they explain shared variance in the
outcome measure.
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4.4. Sensitivity Analyses
To rule out the potentially confounding role of handedness, we re-conducted the primary
analyses with right-handed participants only (N = 293). All of the primary results were robust in
this subsample. Furthermore, the frontal asymmetry scores of right-handed participants (M = .03, SD = .11) did not differ significantly from those of left-handed participants (M = -.015, SD =
.10, N = 21), t(312) = .68, p = .50. Nevertheless, we present our primary results in the subsample
composed exclusively of right-handed participants in Supplemental Table 2.
In this study we included frontal asymmetry scores aggregated across FP1/FP2, F3/F4
and F7/F8 electrode sites to reduce the number of statistical tests conducted. This was also
supported by prior literature supporting associations of asymmetries in these regions with
measures of affective processes (for a review, see Coan and Allen, 2004). Given our significant
results involving the frontal asymmetry composite, we further probed which of these locations
were primarily responsible for the association with inflammation. As shown in Supplemental
Table 3, our findings were driven by lateral frontal sites F7/F8, which were the only ones
significantly associated with inflammation after partialing out the effect of sociodemographic
and biomedical covariates. Childhood maltreatment was not associated with asymmetry scores at
any of the frontal sites.
As measures of potential self-report biases, the CTQ Minimization/Denial Scale and the
Neuroticism scale from the Midlife Development Inventory-Personality Scales were tested as
covariates in sensitivity analyses to assess the role of under-reporting or over-reporting
childhood maltreatment experiences, respectively. Our primary results reported above were
robust when statistically adjusting for these measures of self-report bias and also when excluding
participants whose scores were in the top 5% for these measures.
There were 31 sibling sets in this sample. Because their data are likely to be correlated
and violate the assumption of independent and identically distributed observations, we repeated
all our analyses including only one sibling from each family (selected using a random number
generator) and all significant results were unchanged, thus results are reported on the full sample.
5. Discussion
Right-sided frontal EEG asymmetry has been proposed as a diathesis for experiencing
negative affect when confronted with environmental challenges, and has been linked to an
increased risk of depression and anxiety disorders (Davidson, 1998a; Fingelkurts and
Fingelkurts, 2015; Jesulola et al., 2015; Nusslock et al., 2015; Thibodeau et al., 2006). However,
much less is known about frontal asymmetry’s link with physical health, or its experiential
correlates. The present study targeted these questions.
Our primary finding was a positive association between right-sided frontal EEG
asymmetry and low-grade inflammation. This association was qualified by an interaction with
childhood maltreatment, such that the association was only present for individuals with moderate
to high levels of self-reported childhood maltreatment indices. This finding suggests that, in the
context of major stressors, a trait-like tendency towards greater relative right prefrontal activity
may not only be a vulnerability factor for affective disorders (Lopez-Duran et al., 2012), but also
for low-grade inflammation. If sustained, that inflammation could have repercussions for
physical health problems that have inflammatory underpinnings, such as coronary heart disease,
diabetes, and metabolic syndrome (Black, 2003; Libby, 2012). Our findings also provided
support for the diathesis-stress model of frontal brain asymmetry (Davidson, 1993). As already
noted, that model posits that relatively greater right frontal activity creates vulnerability for
individuals confronted with emotionally challenging major environmental stressors. Consistent
P. 13
with this view, our findings demonstrate that, in individuals exposed to maltreatment, frontal
EEG asymmetry is a marker of risk for inflammation, and potentially also its long-term health
consequences. Future studies should use natural or laboratory-based experiments to explicitly
test the mechanisms hypothesized here. Namely, it will be important to test whether individuals
who have greater right-sided frontal EEG activity respond to a randomly occurring or
standardized laboratory stressor with greater inflammatory activity compared to those who show
greater left-sided EEG activity. It would then also be informative to know whether this pattern is
related to long-term patterns of chronic low-grade inflammation and cardiovascular risk.
A corollary of the statistical interaction we discovered was that childhood maltreatment
was only related to inflammation in this sample in those with high asymmetry scores (i.e., rightsided dominance, at least 1.15 standard deviations above the mean). This finding is reminiscent
of some reports in which maltreatment is more strongly coupled with inflammation in those who
are also depressed (Danese et al., 2011; Miller and Cole, 2012), thus it is possible that intense
negative affect may moderate the association between maltreatment and inflammation. The
moderating role of affective style may explain why some studies find main effects of childhood
maltreatment on inflammation, whereas a minority of studies do not (Coelho et al., 2014).
Another aim of this study was to examine the association between self-reported child
maltreatment experiences and frontal asymmetry. The developmental origins of frontal brain
asymmetry are not fully understood, with prior research suggesting a large contribution for
environmental factors, including prenatal conditions (Field and Diego, 2008), and modest genetic
heritability estimates (Anokhin et al., 2006; Gao et al., 2009; Smit et al., 2007). We found that
self-reported childhood maltreatment experiences in middle age were not associated with frontal
asymmetry, consistent with another study of 6-12-year-old children which did not find a main
effect of objectively-documented maltreatment on frontal EEG asymmetry (Curtis and Cicchetti,
2007). Maltreatment and asymmetry were also not correlated within each gender, contrary to one
previous study showing greater right-sided asymmetry in 38 maltreated adolescent females
compared to 25 non-maltreated female peers (Miskovic et al., 2009). One possible interpretation
of the fact that maltreatment is not reliably associated with frontal asymmetry across these
studies but appears to moderate its association with negative outcomes is that exposure to
stressors may not be the root cause of resting frontal brain asymmetry. Consistent with this
interpretation, Lopez-Duran and colleagues found that life events were not associated with
frontal asymmetry scores at rest in 6-13-year-olds (Lopez-Duran et al., 2012), but asymmetrical
patterns of frontal brain activity while watching sad and happy films were correlated with
stressful life events (Lopez-Duran et al., 2012). Emotion-eliciting conditions or events might be
required to reveal these associations. With respect to the developmental origins of this
vulnerability, parental depression (especially in mothers) is robustly associated with a right-sided
bias in resting frontal EEG activity in the offspring, an effect that has been documented as early
as infancy in multiple studies (Field and Diego, 2008; Peltola et al., 2014). It is possible that
parent-child interactions during infancy may be shaped by withdrawn/depressed parent behavior
and may establish a stable tendency towards avoidance/withdrawal in infants, which in the
context of later adverse events like maltreatment or other life events may lead to persistent
negative affect or excessive stress reactions. The same pathway might explain increased risk of
low-grade inflammation.
In our exploratory analysis of the role of depression, anxiety and health behaviors in
potentially explaining the links between frontal asymmetry and inflammation, sleep difficulties
and waist circumference emerged as potential candidates that might be worth pursuing as
P. 14
mediators in future analyses. First, these were both significant predictors of inflammation
independently of all other predictors in the model. Second, the interaction of frontal asymmetry
and maltreatment was no longer significant in predicting inflammation when accounting for the
role of either sleep or abdominal adiposity. We discuss each of these findings in turn.
With respect to the role of sleep difficulties, individuals exposed to trauma can
experience disruptive nocturnal behaviors such as nightmares, sleep terrors, nocturnal panic
attacks and dream enactment behaviors for decades after the trauma (Cecil et al., 2015).
Controlled experimental studies in humans have also convincingly established that sleep
disruption can alter mediators of inflammation by activating components of the active phase
response (Mullington et al., 2010). These associations between maltreatment and sleep
difficulties, as well as between sleep difficulties and inflammation were also observed in this
study (Table 1). Additionally, we found that the interaction between frontal asymmetry and
childhood maltreatment was no longer significant after partialing out the effect of sleep. This
pattern is suggestive of a pathway mediated by sleep, though the cross-sectional design in the
present report was not optimal for testing mediation or moderated mediation models. We
speculate that, in the context of maltreatment exposure, right-prefrontal activity may index a
pattern of ruminative cognitions about past trauma that may be disruptive to sleep and conducive
to inflammation, but the mediating role of sleep disruption and rumination will need to be
explicitly tested in future studies that longitudinally track these processes as they unfold. Studies
that shift patterns of EEG activity through interventions such as cognitive-behavioral therapy
(Moscovitch et al., 2011) could test whether sleep improvements and decreases in systemic lowgrade inflammation occur in trauma-exposed patients undergoing these treatments.
The role of abdominal adiposity in predicting inflammation is not surprising, given the
role of adipose tissue in releasing pro-inflammatory cytokines like IL-6. These cytokines recruit
macrophages to the abdomen, where they attempt to clear necrotic adipocytes, and in doing so
further potentiate inflammation (Hotamisligil, 2006). The novel finding in this study is that
abdominal adiposity may explain some of the association between frontal brain asymmetry and
inflammation in maltreated individuals. Stress eating may be the behavior that explains this
association, given prior evidence that it mediates links between waist circumference and health
(Tsenkova et al., 2013). Stress-evoked eating can stimulate endogenous opioid release and
thereby improve mood (Adam and Epel, 2007), thus it is possible that individuals with rightsided frontal EEG asymmetry are using stress eating as a coping mechanism. Future studies
should test this scenario more thoroughly.
Associations of depressive and anxious symptoms with inflammation were nonsignificant after stringent adjustment for our panel of covariates, but given the extensive
literature linking depression with inflammation (Slavich et al., 2010) and some emerging
evidence on possible connections between anxiety and inflammation (Vogelzangs et al., 2013),
this pathway deserves further scrutiny in future studies. Nevertheless, these null findings suggest
that the presence of psychopathology is not required for frontal asymmetry to be linked to
deleterious physical health outcomes like inflammation. The significant and independent
explanatory roles of sleep difficulties and abdominal adiposity inform us that other behavioral
pathways may be at play in the realm of physical health outcomes. Furthermore, the diathesisstress model may also explain the lack of direct associations of frontal asymmetry with
depression and anxiety, which has been found some prior studies (Thibodeau et al., 2006). Based
on the average effect sizes for the association between asymmetry and depression (r = .26) and
asymmetry and anxiety (r = .17) reported in a prior meta-analysis (Thibodeau et al., 2006), we
P. 15
conducted power analyses to examine the sample size required to detect such effects with α = .05
and power of .90 in this study. To detect the effect for depression, we needed at least 120
participants, whereas the anxiety effect size required at least 290 participants. Thus, our nonsignificant bivariate associations are not due to low statistical power. Instead, the discrepant
findings across this literature suggest the presence of moderators that need further exploration
(Thibodeau et al., 2006). Our study and the diathesis-stress model suggest that assessing stressful
life events (e.g., maltreatment), which have only rarely been measured in studies of frontal
asymmetry, might be fruitful.
As for the other lifestyle indices (cigarette smoking, alcohol consumption, and physical
exercise), their lack of an association with frontal asymmetry in this study may be due to
complex, non-linear associations between approach/avoidance brain systems and these lifestyle
indices. For example, the appetitive/approach system (left-prefrontal) may promote a physically
active lifestyle, whereas the avoidance/withdrawal system (right-prefrontal) might lead to higher
levels of exercise as individuals use exercise to cope with prolonged stress reactions. Similarly,
cigarette smoking and alcohol use may be driven by a motivational pull towards rewards (leftprefrontal) or the need to self-medicate negative affect (right-prefrontal). There is a paucity of
studies on links between frontal asymmetry and health behaviors such as these, thus future
studies should examine these possibilities in greater detail.
Finally, it must be noted that our frontal asymmetry findings were primarily driven by
lateral frontal electrode sites (F7/F8), consistent with other studies that only find significant
associations with psychopathology at these lateral frontal sites but not mid-frontal ones (Jacobs
and Snyder, 1996; Lopez-Duran et al., 2012), though some reports detect stronger effects at
F3/F4 sites (Coan and Allen, 2003). More research is needed to understand the neuroanatomical
basis for these findings, and whether they are due to methodological differences, characteristics
of the individuals, or the nature of the outcome that frontal asymmetry is being correlated with.
In conclusion, the present study had a number of strengths, including a large sample for
psychophysiological research, which was drawn from a nationally representative study.
Additionally, the in-depth assessment of inflammation using multiple biomarkers strengthens the
reliability of our composite inflammation measure. Nevertheless, the study also had a number of
limitations. Primarily, the correlational and cross-sectional nature of these analyses precludes
any conclusions regarding causality, timing of effects, or mediating pathways. The patterns
emerging from our analyses will need to be corroborated by longitudinal research, and by
experimental studies that try to alter patterns of frontal EEG asymmetry (e.g., cognitivebehavioral therapy, Moscovitch et al., 2011). Future studies should explore whether interventions
that can shift patterns of frontal EEG activity might also mitigate the risk of systemic, low-grade
inflammation. It will be especially important to conduct such intervention studies with
individuals exposed to past trauma.
Contributors
RJD, MEL, and TES designed the study. RJD and CMvR collected, processed, and
prepared the data for analysis. CEH and GEM analyzed the data and interpreted the results. EKG
and DKM provided statistical guidance. CEH wrote the first draft of the manuscript. CEH, RJD,
EKG, DKM, MEL, TES, CMvR and GEM contributed to and have approved the final
manuscript.
P. 16
Role of the Funding Source
The funding agencies had no role in the collection, analysis, and interpretation of data, in
the writing of the report, or in the decision to submit the paper for publication.
Conflicts of Interest: None.
Acknowledgements
Data used for this research was provided by the longitudinal study titled “Midlife in the
United States” (MIDUS) managed by the Institute on Aging, University of Wisconsin, and
supported by a grant from the National Institute on Aging (P01-AG020166). The authors’ efforts
on this manuscript were supported by grants from the National Institute of Child Health and
Human Development (F32HD078048 and R01 HD058502), the National Institute on Aging (R01
AG018436) and the National Institute on Drug Abuse (P30 DA027827).
P. 17
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P. 22
Figures
Figure 1. Data collection waves in the MIDUS (Midlife in the United States) study.
P. 23
Figure 2. Right-sided frontal asymmetry (i.e., having a higher asymmetry score) was associated
with more inflammation in those reporting high levels of maltreatment (top 40.8% of CTQ
scores). The gray shaded area represents the region where the two lines differ significantly from
each other. All variables were standardized, thus values represent Z-scores. Statistics for simple
slopes are displayed next to each line.
P. 24
Figure 3. Maltreatment was associated with higher levels of inflammation in those with high
asymmetry scores (indicating right-sided dominance), roughly 1.15 SD above the mean on
asymmetry or higher. All variables were standardized, thus values represent Z-scores. The gray
shaded area represents the region where the two lines significantly differ.
P. 25
Tables
Table 1. Bivariate correlations and descriptive statistics for primary study variables. *p < .05. **p < .01.
1
2
3
*
.13 .07
-.01
4
.13*
.04
.31**
5
.05
.002
.28**
.65**
6
.27**
.03
.29**
.41**
.32**
7
-.15**
.03
-.06
-.11
-.10
-.05
8
.12*
.05
.19**
.18**
.08
.18**
.05
9
-.13*
-.10
.05
.05
.02
.06
.14*
.19**
10
.47**
.10
.12*
.08
.003
.11
-.11
-.02
-.11*
11
.08
.09
-.12*
-.12*
-.12*
-.13*
-.07
-.05
-.19**
.06
12
.11
.004
.06
-.05
-.02
.01
-.10
-.16**
-.24**
.01
.02
13
.25**
.03
.09
.23**
.18**
.24**
-.05
.22**
-.09
.11
-.11
.05
14
-.16**
.06
-.15**
-.19**
-.19**
-.16**
-.01
-.28**
-.05
-.10
-.04
.02
-.31**
1. Inflammation
2. Frontal asymmetry
3. Maltreatment (log 10 CTQ)
4. Depression (log 10 CESD)
5. Anxiety (STAI)
6. Sleep difficulties (PSQ)
7. Physical exercise
8. Smoking cigarettes
9. Alcohol consumption
10. Abdominal adiposity±
11. AgeΔ
12. Sex (1=female)
13. African American ethnicity
14. Educational level
Means
.00 .00 1.55 .81
1.74 6.39 1.03
.64
.72
.00
55.28 .56
.32
7.66
SDs
.83 1.00 .13
.38
.42
3.75 .87
.74
.63
1.00 11.15 .50
.47
2.57
±
Waist circumference was standardized within gender to account for significant gender differences.
Δ
Given possible cohort, developmental or survival effects such that older middle-aged participants had lower maltreatment scores and
exhibited less depression, anxiety, sleep difficulties and alcohol consumption compared to younger middle-aged participants, we
tested whether age moderated any effects of frontal asymmetry, maltreatment, or their interaction. None of these effects were
significant (p’s >.30).
P. 26
Table 2. Multiple linear regression results with the inflammation composite as the dependent variable. Analyses were conducted on N
= 314. *p < .05.
Predictors
Constant
Age
Gender (1=female)
African American
Other ethnicity
Educational level
History of heart disease
History of diabetes
Anti-hypertensive medications
Cholesterol-lowering medications
Corticosteroids
NSAID medications
Frontal asymmetry (FA)
Childhood maltreatment (CM)
FA x CM
R2
R2 change
Model 1
b
SE
.00 .05
-.04 .06
.10 .05
.19 .06
-.02 .05
-.09 .06
.17 .06
.08 .06
.13 .06
.02 .06
.00 .05
.02 .06
t
.00
-.65
1.92
3.20
-.38
-1.63
3.00
1.49
2.04
.35
.01
.29
p
1.00
.52
.06
.002*
.70
.10
.003*
.14
.04*
.72
.99
.77
.152
.152*
Model 2
b
SE
.00 .05
-.05 .06
.10 .05
.18 .06
-.02 .05
-.10 .06
.17 .06
.09 .06
.13 .06
.01 .06
.00 .05
.01 .06
.11 .05
.164
.012*
Model 3
t
p
b
SE
.00
1.00 .00 .05
-.74 .46
-.04 .06
1.94 .054 .10 .05
3.08 .002* .18 .06
-.29 .77
-.02 .05
-1.80 .07
-.10 .06
2.98 .003* .17 .06
1.55 .12
.09 .06
2.01 .046* .13 .06
.22
.83
.01 .06
-.05 .96
.00 .05
.24
.81
.01 .06
2.05 .04* .11 .05
.03 .06
.165
.001
Model 4
t
p
b
SE
.00
1.00 .001 .05
-.64 .52
-.05 .06
1.89 .06
.11 .05
3.05 .003* .17 .06
-.36 .72
-.01 .05
-1.72 .09
-.10 .06
2.96 .003* .16 .06
1.54 .12
.09 .06
2.03 .04* .13 .06
.19
.85
.01 .06
-.06 .95
-.01 .05
.17
.87
.02 .06
2.04 .04* .10 .05
.59
.56
.04 .05
.10 .05
.178
.013*
t
.02
-.78
2.04
2.90
-.22
-1.74
2.81
1.55
2.05
.24
-.22
.28
1.93
.66
2.17
p
.98
.44
.04*
.00*
.83
.08
.005*
.12
.04*
.81
.83
.78
.055
.51
.03*
27
Table 3. Results of multiple linear regression analyses probing whether frontal asymmetry’s association with inflammation is
independent of psychopathology and health behaviors. Sociodemographic and biomedical covariates were included in all the models
(coefficients not shown for simplicity but available upon request). Models were conducted on N = 331 and results are pooled across 40
multiple imputations. FA = frontal asymmetry; CM = childhood maltreatment. *p < .05.
Predictors
Model 1
b SE p
.10 .05 .07
.03 .05 .53
.10 .05 .03*
FA
CM
FA x CM
Depression
Anxiety
Cigarette smoking
Alcohol consumption
Physical exercise
Abdominal adiposity
Sleep difficulties
Average R2
.176
Model 2
b SE p
.10 .05 .07
.03 .06 .60
.10 .05 .04*
.04 .16 .78
Model 3
b
SE
.10 .05
.05 .06
.11 .05
p
.07
.39
.02*
Model 4
b SE p
.10 .05 .07
.03 .06 .59
.10 .05 .03*
Model 5
b
SE
.10 .05
.04 .05
.10 .05
p
.08
.51
.04*
Model 6
b
SE
.10 .05
.03 .05
.10 .05
p
.05
.60
.04*
Model 7
b SE p
.06 .05 .19
.00 .05 .92
.07 .04 .12
Model 8
b
SE
.10 .05
-.01 .06
.08 .05
p
.07
.93
.12
-.14 .14 .32
.05 .08 .54
-.07 .09 .47
-.12 .06 .06
.44 .05 <.001*
.177
.179
.178
.178
.186
.344
.05 .02 .003*
.203
28
Supplemental Table 1. Results of primary analyses re-conducted by pooling estimates from data created through multiple imputation
(40 imputed datasets), N = 331. *p < .05.
Predictors
Constant
Age
Gender (1=female)
African American
Other ethnicity
Educational level
History of heart disease
History of diabetes
Anti-hypertensive medications
Cholesterol-lowering medications
Corticosteroids
NSAID medications
Frontal asymmetry (FA)
Childhood maltreatment (CM)
FA x CM
Average R2 across imputations
Model 1
b
SE
-.42 .10
-.05 .06
.21 .11
.36 .13
-.09 .26
-.10 .06
.49 .18
.24 .16
.26 .13
.03 .14
-.05 .25
.02 .06
t
-4.11
-.76
1.94
2.86
-.33
-1.74
2.76
1.53
2.00
.24
-.19
.31
p
.00*
.45
.05
.002*
.74
.08
.003*
.13
.048*
.81
.85
.76
.152
Model 2
b
SE
-.42 .10
-.04 .06
.19 .11
.38 .13
-.10 .26
-.10 .06
.52 .18
.24 .16
.26 .13
.03 .14
-.01 .25
.02 .06
.10 .05
.163
Model 3
t
p
b
SE
-4.12 .00* -.42 .10
-.72 .47
-.04 .06
1.83 .07
.19 .11
3.03 .002* .38 .13
-.41 .68
-.12 .26
-1.80 .07
-.10 .06
2.94 .003* .52 .18
1.53 .13
.24 .16
1.96 .05
.26 .13
.21
.84
.03 .14
-.04 .97
-.01 .25
.28
.78
.01 .06
1.93 .053 .10 .05
.03 .05
.164
Model 4
t
p
b
SE
-4.06 .00* -.42 .10
-.63 .53
-.05 .06
1.79 .07
.21 .11
3.01 .003* .36 .13
-.46 .64
-.09 .26
-1.72 .09
-.10 .06
2.92 .004* .49 .18
1.51 .13
.24 .16
1.98 .048* .26 .13
.18
.86
.03 .14
-.05 .96
-.05 .25
.20
.84
.02 .06
1.93 .05
.10 .05
.57
.57
.03 .05
.10 .05
.177
t
-4.11
-.76
1.94
2.86
-.33
-1.74
2.76
1.53
2.00
.24
-.19
.31
1.82
.63
2.13
p
.00*
.45
.05
.004*
.74
.08
.01*
.13
.045*
.81
.85
.76
.07
.53
.03*
29
Supplemental Table 2. Results of primary analyses re-conducted by excluding left-handed participants (N = 293 right-handed
individuals had available data on measures of interest and were included in this analysis). *p < .05.
Predictors
Constant
Age
Gender (1=female)
African American
Other ethnicity
Educational level
History of heart disease
History of diabetes
Anti-hypertensive medications
Cholesterol-lowering medications
Corticosteroids
NSAID medications
Frontal asymmetry (FA)
Childhood maltreatment (CM)
FA x CM
R2
R2 change
Model 1
b
SE
-.01 .06
-.03 .07
.11 .06
.18 .06
-.05 .06
-.09 .06
.18 .06
.10 .06
.12 .07
.04 .06
.00 .06
.02 .06
t
-.12
-.53
1.96
2.87
-.78
-1.53
3.09
1.67
1.77
.65
-.02
.29
p
.91
.60
.05
.004*
.43
.13
.002*
.10
.08
.52
.99
.77
Model 2
b
SE
-.01 .06
-.04 .07
.11 .06
.17 .06
-.04 .06
-.10 .06
.18 .06
.10 .06
.12 .07
.03 .06
.00 .06
.02 .06
.10 .06
.157
.157*
.167
.01*
Model 3
t
p
b
SE
-.14 .89
-.01 .06
-.62 .54
-.04 .07
2.02 .04* .11 .06
2.76 .006* .17 .06
-.69 .49
-.04 .06
-1.68 .09
-.10 .06
3.07 .002* .18 .06
1.75 .08
.10 .06
1.73 .08
.12 .07
.53
.60
.03 .06
-.07 .94
.00 .06
.24
.81
.01 .06
1.81 .07
.10 .06
.03 .06
.167
.001
Model 4
t
p
b
SE
-.14 .89
-.01 .05
-.55 .59
-.05 .07
1.97 .049* .12 .06
2.72 .007* .16 .06
-.74 .46
-.03 .06
-1.61 .11
-.10 .06
3.06 .002* .17 .06
1.74 .08
.10 .06
1.75 .08
.12 .07
.50
.62
.03 .06
-.08 .93
-.01 .06
.18
.86
.02 .06
1.80 .07
.09 .06
.49
.63
.03 .06
.11 .05
.182
.015*
t
-.15
-.68
2.14
2.57
-.58
-1.65
2.93
1.77
1.76
.55
-.26
.28
1.66
.56
2.25
p
.88
.49
.03*
.01*
.57
.10
.004*
.08
.08
.59
.80
.78
.10
.58
.03*
30
Supplemental Table 3. Partial correlations of inflammation with frontal asymmetry scores at specific electrode sites after adjusting for
our standard panel of covariates included in previous analyses. Asymmetry scores were computed such that higher values indicate
greater right activity than left. *p <. 05; **p < .01.
1
1. Inflammation
2. Childhood maltreatment (log10 CTQ score)
3. Asymmetry score FP1/FP2 alpha band 1
4. Asymmetry score FP1/FP2 alpha band 2
5. Asymmetry score F3/F4 alpha band 1
6. Asymmetry score F3/F4 alpha band 2
7. Asymmetry score F7/F8 alpha band 1
8. Asymmetry score F7/F8 alpha band 2
2
3
4
5
6
7
8
.03
.06
.07
.05
.02
.13*
.15**
.00
.01
-.02
-.03
.05
.03
.91**
.49**
.44**
.27**
.26**
.49**
.49**
.24**
.33**
.92**
.36**
.35**
.34**
.39**
.90**