قدرت آلفا فرونتال EEG در دوران کودکی بعنوان پیش بینی کننده رفتار ضد اجتماعی نوجوانان: مطالعه وراثت دوقلو

Abstract High EEG frontal alpha power (FAP) is thought to represent a state of low arousal in the brain, which has been related in past research to antisocial behavior (ASB). We investigated a longitudinal sample of 900 twins in two assessments in late childhood and mid-adolescence to verify whether relationships exist between FAP and both aggressive and nonaggressive ASB. ASB was measured by the Child Behavioral Checklist, and FAP was calculated using connectivity analysis methods that used principal components analysis to derive power of the most dominant frontal activation. Significant positive predictive relationships emerged in males between childhood FAP and adolescent aggressive ASB using multilevel mixed modeling. No concurrent relationships were found. Using bivariate biometric twin modeling analysis, the relationship between childhood FAP and adolescent aggressive ASB in males was found to be entirely due to genetic factors, which were correlated r = 0.22.

. Introduction In the 1970s, a theory of antisocial behavior (ASB) emerged that is referred to as the Slow Arousal Theory. Posited by Robert Hare, a researcher of psychopathy, this theory sought to explain several findings of low arousal levels in individuals prone to crime and violence. Early research witnessed increased levels of slow-wave brain waves, including theta (4–8 Hz) and delta (1–4 Hz) in the adult brains of incarcerated psychopaths (Ellingson, 1954) and violent criminals (Hill, 1952). Since these early studies, lower prefrontal activation has been found in males with past aggressive behavior (Volkow et al., 1995), in 9 males and 1 female with repetitive violent behavior (Critchley et al., 2000), and in violent psychiatric inpatients (Kuruoglu, Arikan, Karatas, Arac, & Isik, 1996). In examining these findings, the Slow Arousal Theory suggests a ‘stimulus hunger’ in brains marked by slow-wave activity. A stimulus hungry and poorly aroused brain, it is suggested, may require risky, impulsive, or other ‘high excitement’ external stimuli to achieve the arousal levels that a normally aroused brain typically experiences. The theory that ASB often relates to difficulty in inhibiting behavior is highly prevalent and longstanding (Eysenek, 1964, Gray, 1972 and Gray, 1987), although ASB can also stem from failure to correctly estimate punishment (Gray, 1972). Impulsive and risk-taking behavior, as well as the ability to evaluate reward or punishment, are frequently thought to be related to the prefrontal cortex, which is also related to behavioral inhibition. This may explain higher propensity toward violent, delinquent or criminal activity in adolescence, when the frontal lobes are not yet fully developed. The frontal region thus appears to be a fruitful area for research on the prediction of ASB. In children, slow arousal as marked by high alpha power, has also been predictive of criminal activity. EEG alpha power, typically measured as 8–13 Hz in adults and 8–10.5 Hz in children of the age examined in the present research (Gasser, Verleger, Bacher, & Sroka, 1988), is representative of a sleepy quality. In children, increased cortical alpha power (a marker of slow arousal) has been associated with later crime (Mednick et al., 1981 and Petersén et al., 1982). In a group of 24-year-old male criminals, retrospective analysis found high alpha power at age 15 years (Raine, Venables, & Williams, 1990). Reduced frontal activation was also found in children with oppositional defiant disorder, using single-photon emission computerized topography (SPECT; Amen & Carmichael, 1997). The majority of this research, both in adults and in children, was conducted with male subjects, and an aim of the present research is to investigate these relationships in females as well. One important question that has arisen in research of ASB is that of differences between aggressive and nonaggressive ASB. Nonaggressive ASB is also referred to as delinquency or rule-breaking behavior, and is captured by the Delinquency scale of the Child Behavior Checklist (CBCL; Achenbach, 1991), which is the instrument used in this research. Aggressive and nonaggressive ASB both show heritability, with a recent metaanalysis estimating 65% heritability for aggressive ASB and 48% for nonaggressive ASB (Burt, 2009). Alpha power has also shown high heritability, ranging from 63 to 89% in children and adolescents (Van Baal et al., 1996 and van Beijsterveldt et al., 1996). In our sample, full range frontal alpha power (FAP) at 8–13 Hz was found to have 71–85% genetic influence, with the rest of the variance accounted for by non-shared environmental factors (Gao, Tuvblad, Raine, Lozano, & Baker, 2009). However, no past study has examined potential genetic correlations between FAP and any form of ASB, of either aggressive or nonaggressive nature. We hypothesized that a significant genetic correlation would emerge to explain the phenotypic relationship between the FAP and both forms of ASB, as all variables have been found to have genetic roots.

Results Males showed higher aggressive ASB levels than females in both Wave 1 (t = 5.08, df = 887, p = 0.02) and Wave 3 (t = 2.36, df = 596, p = 0.02). Nonaggressive ASB was higher in males than in females in Wave 1 (t = 3.975, df = 887, p < 0.01), but not Wave 3 (t = 1.21, df = 596, p = 0.23). No significant sex differences emerged in FAP. Mean levels of aggressive ASB in Wave 1 were 6.12 (SD = 5.16) in males and 5.10 (SD = 4.78) in females, and in Wave 3 were 5.35 (SD = 6.00) in males and 4.32 (SD = 4.80) in females. Mean levels of nonaggressive ASB were 1.49 (SD = 1.81) in males and 1.06 (SD = 1.36) in females in Wave 1, and 1.79 (SD = 2.34) in males and 1.55 (SD = 2.52) in females in Wave 3. Mixed Model repeated measures analyses of variance revealed that aggressive ASB decreased between Waves 1 and 3 (F(1,179) = 8.16, p < 0.01), and nonaggressive ASB increased (F(1,179) = 22.12, p < 0.01), with no significant interactions with sex. Mixed model regressions of ASB on FAP conducted with both sexes did not yield significant regression coefficients in Wave 1 for either aggressive or non-aggressive ASB. In Wave 3, significant main effects of FAP were deemed not interpretable due to significant interactions of sex with FAP. When the two sexes were analyzed separately, FAP was significantly predictive of aggressive ASB in Wave 3 for males (Beta = 0.99, SE = 0.44, p = 0.03) but not females (Beta = 0.31, SE = 0.24, p = 0.26). FAP did not significantly predict Wave 3 nonaggressive ASB in males (Beta = 0.05, SE = 0.17, p = 0.76), or in females (Beta = −0.18, SE = 0.12, p = 0.09). Twin and cross-twin cross-trait correlations are presented in Table 1 for the two variables that were found to be phenotypically related – FAP and Wave 3 aggressive ASB. Twin correlations are consistently higher in the MZ than in the DZ twins, suggesting heritability. Similarly, the cross-trait correlations were slightly higher in the male MZ twins than in the male DZ twins, although no differences were found between MZ and DZ females, suggesting genetic overlap between FAP and aggressive ASB in males but not females. Table 1. Twin and cross-twin cross-trait correlations for FAP and Wave 3 aggressive ASB by zygosity. Mz male Mz female Dz male Dz female Dz opposite FAP Agg-3 FAP Agg-3 FAP Agg-3 FAP Agg-3 FAP Agg-3 FAP 0.73* – 0.64* – 0.60* – 0.54* – 0.37* – Agg-3 0.22* 0.74* −0.07 0.72* −.0.06 0.11 −0.10 0.42* 0.05 0.52* Note. Bolded values represent twin correlations, and non-bolded values cross-twin cross-trait correlations. * p < 0.05. Table options Genetic models were only explored for the relationship that demonstrated significant phenotypic relationships – namely, FAP with Wave 3 male aggressive ASB. Model fits and comparisons are shown in Table 2. Comparing models 1 and 1a demonstrates that equating the estimates on the two sexes significantly worsens model fit, and so sexes were estimated separately. Comparing Models 1 and 2 demonstrates that dropping C improved model fit, suggesting that shared environment was not significant in any of these relationships. Finally, dropping E correlations provided the best fit to the data (Models 9). Bivariate genetic analysis found that influences on FAP are 78% A (raw variance 0.882) and 22% E (raw variance 0.472) in males. Influences on Wave 3 aggressive ASB were found to be 65% A (raw variance 0.812) and 35% E (raw variance 0.592) in the males. The covariation between FAP and male Wave 3 aggressive ASB was accounted for exclusively by significant genetic correlation at Rg = 0.22 (95% CI 0.16–0.30). Table 2. Bivariate model fit indices for FAP and aggressive ASB in Wave 3. Model Likelihood values Overall fit Model comparison −2LL DF AIC BIC χ2 (Δdf) p Compared to Model Δχ2 (Δdf) p 0 Saturated model (means constrained) 3281.56 1509 263.56 −3082.07 1 ACE 3345.97 1557 231.97 −3299.10 64.41 (48) 0.06 1a. ACE M = F 3364.39 1566 232.39 −3219.06 83.83 (57) 0.02 1 7.61 (9) 0.57 2 Drop shared environmental effects (AE) 3351.39 1563 225.39 −3216.17 69.83 (54) 0.07 1 6.14 (6) 0.41 3 Drop genetic effects (CE) 3405.03 1563 279.03 −3189.35 123.47 (54) <0.01 1 60.03 (6) <0.01 4 Drop genetic and shared environmental effects (E) 3636.89 1569 498.89 −3092.20 355.33 (60) <0.01 1 371.32 (12) <0.01 5 No genetic covariance 3350.88 1559 232.88 −3203.91 69.32 (50) 0.04 1 5.25 (2) 0.07 6 No common environmental covariance 3349.59 1559 231.59 −3204.55 68.03 (50) 0.05 1 3.98 (2) 0.14 7 No A or C covariance 3355.34 1561 233.34 −3207.94 73.78 (52) 0.03 1 11.34 (4) 0.02 8 No nonshared env. covariance 3348.71 1559 230.71 −3204.99 67.15 (50) 0.05 1 2.86 (2) 0.24 9 AE, no nonshared env. covariance 3353.39 1565 223.39 −3221.43 71.83 (56) 0.08 2 6.38 (8) 0.38 Note: Best-fitting model denoted in boldface type. Table options Although the models investigated included both males and females, and estimated influences on them separately, Fig. 1 depicts only the relationships that demonstrated significant bivariate relationships between FAP and ASB, for which the 95% CI did not include zero. Female FAP was not significantly related to aggressive ASB in either wave through bivariate genetic or environmental influences. These findings are consistent with phenotypic mixed modeling results. This figure presents squared standardized estimates of the influences. Bivariate heritability model of Wave 1 EEG alpha power with Wave 3 aggressive ... Fig. 1. Bivariate heritability model of Wave 1 EEG alpha power with Wave 3 aggressive ASB in males. This figure demonstrates the small but significant genetic correlation between frontal alpha power at the age of 9–10 years and aggression at the age of 14–15 years. No shared environmental factors emerge as significant for either variable, and no environmental correlation emerges as significant to the relationship between them. The genetic correlation is significant only in the males.