In the current study, the heritability of frontal asymmetry and alpha power was examined in male and female twins in late childhood. Key findings are as follows: (1) frontal asymmetry showed a modest degree of heritability at both the mid-frontal and lateral-frontal locations, with 11–27% of the total variance being attributable to genetic factors and 72–89% attributable to non-shared environmental factors, (2) alpha power are highly heritable at all four frontal sites, with 70–85% of the variance accounted for by the genetic factors, (3) there were no shared environmental influences on any EEG measure, and (4) the genetic architecture of frontal asymmetry and alpha power is similar for boys and girls. To the authors’ knowledge, this constitutes the first twin study to investigate the heritability of frontal asymmetry in children.
Consistent with the prior literature, EEG alpha power at all four frontal sites are highly heritable in children and there are no sex differences in heritability (except F4 at rest 2). This high heritability is comparable with the heritability found in other studies. Together with the findings of high heritability in 5-year-olds (van Baal et al., 1996
) and 16-year-olds (van Beijsterveldt et al., 1996
), it is suggested that alpha power is a highly stable and heritable trait across childhood and adolescence. Findings support the argument that alpha activation is associated with a spectral fingerprint of the individual brain (Nunez, 1981
Regarding frontal asymmetry, in line with prior findings in adult populations (Anokhin et al., 2006
; Coan, 2003
; Smit et al., 2007
), non-shared environmental influences were high, suggesting possibly similar genetic mechanisms in frontal asymmetry in late childhood and adulthood. Genetic influences were low, and no shared environmental influences were observed for most of the measures. The genetic mechanisms underlying frontal asymmetry are similar in males and females. Environmental influences were less in evidence for alpha power, but still accounted for 16–36% of the variance (see ). The predominance of environmental influences in our child sample may come from a number of sources. One source may be the influence of the environment on individual differences in brain development.
Research has shown that the development of frontal areas extends into late adolescence and even early adulthood, which makes this brain region more sensitive to environmental influences compared to other brain areas, and there are substantial individual differences in the rate of development (Casey, Giedd, & Thomas, 2000.
; Hudspeth & Pribram, 1990
; Thatcher, Walker, Giudice, 1987
). In this context, environmental differences may partly account for the large individual differences in the rate of maturation of frontal cortex, especially in the development of inter-hemispheric relationships in this age range, which in turn may contribute to the small twin correlations and hence low heritability in frontal asymmetry. In addition, in the current study participants were asked to keep their eyes open during the rest sessions. The high non-shared environmental influences may be partly due to the variation induced by eye movements. Furthermore, the eyes open condition could lead to greater environmental variability in visual stimulation across subjects.
Although the age range of the current sample is narrow (9–10 years), we nevertheless examined the effect of age on EEG measures. Results showed that age expressed in months was not significantly correlated with frontal asymmetry. In contrast, age was associated with alpha power, with older children showing decreasing power scores. This finding is in line with the prior literature showing maturation reductions in EEG power with increasing age (e.g., McGuire, Katsanis, Iacono, & McGue, 1998
There have been very few experimental studies in children that identify specific components of the environment that alter EEG. One longitudinal study demonstrated that children randomly assigned to an environmental enrichment group from ages 3–5 years (better nutrition, more physical exercise, and cognitive stimulation) showed a significant reduction in slow-wave EEG both at rest and during a sustained attention task at age 11 compared to matched controls (Raine et al., 2001
). On the other hand, studies on posttraumatic stress disorder (PTSD) have failed to find frontal asymmetry differences between individuals with PTSD and normal controls (Rabe, Beauducel, Zollner, Maercker, & Karl, 2006
; Shankman et al., 2008
), suggesting that trauma may not be an environmental factor affecting EEG asymmetries. At the same time, Rabe, Zoellner, Beauducel, Maercker, and Karl (2008)
recently found that cognitive behavioral therapy can change frontal asymmetries (reduced right frontal activation) in individuals with PTSD. Identifying which specific environmental influences permanently shape EEG power and asymmetries reflects a significant gap in our knowledge, and remains an important next step for future studies.
Compared to alpha power, frontal asymmetry measures showed lower stability across the two rest sessions in the current study. This is consistent with prior studies showing relatively low to modest stability of frontal asymmetry and high stability of alpha power (Vuga, Fox, Cohn, Kovacs, & George, 2008
). Specifically, Hagemann et al. (2002)
recorded EEG data from 59 individuals on four occasions of measurements each separated by 4 weeks and in each occasion six baselines were collected. Using latent state-trait change model, they found that 40% of the variance of the asymmetry measure was due to state-dependent fluctuations whereas the rest 60% of the variance was due to a temporally-stable latent trait. It has been argued that the observed frontal asymmetry represents a superimposition of a trait-like activation asymmetry with substantial state-dependent fluctuations (Davidson, 1992
; Hagemann et al., 2002
; Tomarken et al., 1992
Although aggregation across the two rest sessions in the current study was conducted to derive a more reliable measure of EEG asymmetry, it has been argued that this procedure cannot entirely eliminate state-dependent fluctuations (Steyer & Schmitt, 1990
). Indeed, Davidson (1998)
has argued that repeated testing across several weeks is required to derive a reliable asymmetry measure. Despite this, most studies derive asymmetry scores from either one rest period or two rest periods in a same-day assessment (Hagemann et al., 2002
; Shankman et al., 2008
). The relatively low heritability of frontal asymmetry measures observed in the current study may be partly due to the situational effects or person-situation interactions (Davidson, 1992
). However, when frontal asymmetry indices were averaged across the two rest sessions to increase measurement reliability, results similar to those presented in and were obtained. These findings suggest that the relatively high non-shared environmental effects may not be entirely due to short-term effects, such as measurement error. It is also worth noting that specific conditions of experiment, time-of-day, mood state, and temperature may contribute to non-shared environmental influences. Consequently it cannot be concluded from current findings that trait
frontal asymmetry is not highly heritable, since state-dependent specificity rather than trait specificity was more likely measured in the current study. In future studies, genetic modeling would benefit from assessing resting asymmetry at multiple time-points in which measurement error, state-dependent fluctuations, and trait specificity are statistically separated (Hagemann et al., 2002
), so that the heritability of the frontal asymmetry as a trait can be more reliably examined.
A few potential limitations of the current study should be noted. First, only mid-frontal and lateral-frontal regions were included in the current study. The analyses were restricted to the alpha band at frontal locations because it is the dominant frequency in resting subjects and a replication of Anokhin et al.’s study (2006)
was intended. Future research could examine the genetic origins of frontal asymmetry acquired from other anterior regions, e.g., frontal-temporal-central, anterior temporal, and frontal-central regions.
Secondly, asymmetry indices were calculated as L-R in the current study. Given the fact that difference scores between correlated measures are inherently less reliable than the individual measures contributing to them, it may be possible that environmental effects on asymmetry are artifactually inflated, as reliability constrains heritability, and the sum of genetic and environmental influences must equal 100% of the phenotypic variance in AE models. Furthermore, the current study is not strictly comparable to other laterality studies which use a log transformation (e.g., Anokhin et al., 2006
; Smit et al., 2007
). To the authors’ knowledge there has been at least one prior failure to fit genetic models to frontal asymmetry data using the traditionally log transformation (Coan, 2003
). Although for comparability purposes model-fitting based on the traditional asymmetry measure would have been preferable, the current study documents an alternative approach for asymmetry data modeling in future studies when log transformations fail to fit.
A third limitation concerns the low intraclass correlations in male DZ twins at F3 and F4 at rest 2. It is possible that this is due to dominant genetic influences. However, a model including dominant genetic effects did not describe the data better than a model with additive genetic influences. Although the low DZ male intraclass correlation might suggest dominant genetic influences in the boys, the moderate resemblance among DZ opposite sex pairs would suggest otherwise. When examining the data for outliers, no influential points were found.
Finally, there are several factors that may affect psychophysiological recording, such as temperature, humidity, experimenter characteristics, and season of testing (Bouscein, 1992
), which may contribute to part of the shared environmental components. Therefore, it is possible that the impact of genetic influences on the etiology of all EEG measures, especially mid-frontal asymmetry, is underestimated in our univariate analyses.
In conclusion, the present study extends the previous findings on adult populations and has shown no or only modest genetic influences on frontal asymmetry measured at mid-frontal and lateral-frontal locations in 9- to 10- year-old children. In contrast, EEG alpha power is highly heritable in children, comparable to what is found in older adolescents and adults. Males and females show similar genetic architecture in the majority of these EEG measures. It is suggested that the high non-shared environmental influences on frontal asymmetry in children may be partly due to both state influences and also environmental influences on individual differences in rate of maturation of the frontal cortex.