Brain and Cognition 87 (2014) 168–172

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Genetic overlap between ADHD symptoms and EEG theta power Charlotte Tye a,b,⇑, Fruhling Rijsdijk a, Gráinne McLoughlin a,c a

MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London, UK Child and Adolescent Psychiatry, Institute of Psychiatry, King’s College London, London, UK c Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, USA b

a r t i c l e

i n f o

Article history: Accepted 17 March 2014

Keywords: ADHD EEG Endophenotype Heritability Twins

a b s t r a c t Biological markers that are grounded in neuroscience may facilitate understanding of the pathophysiology of complex psychiatric disorders. One of the most consistent and robust neural abnormalities in attention deficit hyperactivity disorder (ADHD) is increased EEG power in the theta band at rest (4– 8 Hz). The present study used a twin design to estimate the extent of genetic overlap between increased theta power and risk for ADHD in order to validate theta power as a marker of genetic risk for ADHD. At rest, EEG was measured in 30 monozygotic and dizygotic adolescent twin pairs concordant or discordant for high ADHD symptom scores and 37 monozygotic and dizygotic control twin pairs with low ADHD symptom scores. Structural equation modelling was used to estimate the heritability of theta power and partition the genetic and environmental contributions to the overlap between ADHD and theta power. A significant phenotypic correlation between ADHD symptoms and elevated theta power was found. Theta power demonstrated moderate to high heritability estimates (0.77) and moderate genetic correlations with ADHD (0.35) suggesting shared genetic influences. Increased theta power is a candidate biological marker of genetic risk for ADHD, which warrants further investigation of the neurobiological mechanisms that underlie the genetic relationship. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Attention deficit hyperactivity disorder (ADHD) is a relatively common childhood-onset neurodevelopmental disorder characterised by deficits in attention and/or hyperactivity and impulsivity (American Psychiatric Association, 2000). While family and twin studies demonstrate increased rates of ADHD in closely related family members, the findings from molecular genetic studies have yielded small effect sizes (Faraone et al., 2005). This might be explained by the highly complex and heterogeneous nature of ADHD, such that a number of different underlying cognitive and neural mechanisms and multiple genes of small effect and rare genes of large effect may result in the behavioural profile (Kuntsi, McLoughlin, & Asherson, 2006). It is now widely documented that continuously distributed traits underlie the categorical diagnosis of complex disorders, such as ADHD, that may provide more power to identify quantitative trait loci (Chen et al., 2008; Levy, Hay, McStephen, Wood, & Waldman, 1997; Wood & Neale, 2010). There is increasing interest, therefore, in ⇑ Corresponding author. Address: MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, De Crespigny Park, London SE5 8AF, UK. Fax: +44 (0) 207 848 0866. E-mail addresses: [email protected] (C. Tye), [email protected] (F. Rijsdijk), [email protected] (G. McLoughlin). http://dx.doi.org/10.1016/j.bandc.2014.03.010 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.

identifying quantitative markers of disorders that are associated with the normal variation of these traits. Accordingly, one approach to refine the ADHD phenotype is the identification of quantitative cognitive or neurobiological processes that increase risk for the disorder and mediate between genes and behaviour (Castellanos & Tannock, 2002; Doyle et al., 2005). In order to be viable, the main criteria state that the candidate trait must have good metric properties, be associated with the disorder, heritable, and share genetic influences with the disorder (Gottesman & Gould, 2003; Waldman, 2005; Walters & Owen, 2008). Electroencephalographic (EEG) power represents a continuous measure with no floor or ceiling effects; lower frequency bands (4–13 Hz), in particular, show high temporal stability (Smit, Posthuma, Boomsma, & de Geus, 2005) and test–retest reliability (Williams et al., 2005). Accumulating evidence suggests that EEG is highly heritable and associated with specific genetic variants (McLoughlin, Makeig, & Tsuang, 2014; Tye, McLoughlin, Kuntsi, & Asherson, 2011). Specifically for EEG theta power, an early study of adolescent twin pairs demonstrated a heritability of 89% (van Beijsterveldt, Molenaar, De Geus, & Boomsma, 1996). More recently, heritability estimates of theta power have been estimated at a mean of 0.85 in young adults, with a range of 0.82–0.90 across scalp regions (Smit et al., 2005). In a multivariate analysis of EEG frequency bands, theta power heritability was highest in occipital

C. Tye et al. / Brain and Cognition 87 (2014) 168–172

regions (0.85) compared to frontal regions (0.64) (Zietsch et al., 2007). While associations between ADHD and other EEG frequency bands are more variable, the most consistent EEG abnormality in ADHD is increased low-frequency activity; predominantly high levels of absolute and relative theta in frontal and central regions (Barry, Clarke, & Johnstone, 2003; El-Sayed, Larsson, Persson, & Rydelius, 2002; Mann, Lubar, Zimmerman, Miller, & Muenchen, 1992; Monastra, Lubar, & Linden, 2001; Monastra et al., 1999; Quintana, Snyder, Purnell, Aponte, & Sita, 2007; Shi et al., 2012; Snyder & Hall, 2006; Snyder et al., 2008). Elevated theta persists into adolescence and adulthood (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Koehler et al., 2009), and sensitivity and specificity rates support theta power as a potential diagnostic tool for ADHD (Quintana et al., 2007; Snyder & Hall, 2006; Snyder et al., 2008). Theta power measured at rest is associated with drowsy states and reduced arousal compared to higher frequency bands, thus increased theta in ADHD has been interpreted as a maturational delay marked by underarousal (Barry et al., 2003; Lazzaro et al., 1999; Sergeant, 2000). Collectively, the findings suggest that theta-indexed cortical underarousal is a robust marker of ADHD. High heritability combined with consistent associations with ADHD provides rationale for investigation of theta power as a biological marker of genetic risk for ADHD. Studies to date, however, are limited. A preliminary study of affected sibling pairs with ADHD indicated sibling correlations for absolute theta power were highest in frontal regions during resting (0.57) that increased for a cognitive activation condition (0.69; Loo & Smalley, 2008), supported by, albeit lower, correlations in a larger study of multiplex families with ADHD (resting: 0.25; cognitive activation: 0.30; Loo et al., 2010). Family studies that use mean comparison analyses are not able to partition familial variance into genetic and environmental contributions and the amount of familial overlap is not quantified. Using structural equation modelling in twin samples, however, it is possible to explore whether this association is due to shared genetic influences using the different levels of genetic relatedness between monozygotic (MZ) and dizygotic (DZ) twin pairs (Neale & Cardon, 1992). The present study aimed to validate the use of theta power as a marker of genetic risk ADHD by demonstrating heritability and genetic overlap with the disorder. Theta power was measured at rest in a twin sample selected for consistently high or low ADHD symptoms. Based on previous findings, we expected (a) theta power to be moderately to highly heritable, (b) increased theta to be found in individuals with higher ADHD symptom scores, and (c) moderate genetic correlations between these theta and ADHD symptoms, to support theta power as a candidate marker of genetic risk. 2. Materials and methods 2.1. Sample The sample consisted of 67 male twin pairs aged between 12 and 15 years in groups of 22 pairs concordant for high levels of ADHD symptoms (MZ: 11; DZ: 11), 8 pairs discordant for ADHD symptoms (MZ: 2; DZ: 6) and 37 control pairs concordant for low levels of ADHD symptoms (MZ: 21; DZ: 16). Individuals with high ADHD symptoms were slightly younger (mean age = 14.00 years) than those with low ADHD symptoms (mean age = 14.51 years; p > .05). Twin pairs were selected based on an analysis of symptom development using the program MPLUS (see Supplemental Materials). Participating families gave their written informed consent and the study was approved by King’s College London Psychiatry, Nursing and Midwifery Research Ethics Sub-Committee (PNM/08/09-89).

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2.2. IQ measurement General cognitive ability was assessed at age 14 as part of ongoing TEDS web-based data collection (Haworth et al., 2010). The twins were tested on the Wechsler Intelligence Scale for Children as a Process Instrument (WISC-III-PI) vocabulary multiple choice subtests (Wechsler, 1992) and Raven’s standard and advanced progressive matrices (Raven, Raven, & Court, 1996). Further information on the calculation of IQ is in Supplemental Materials. 2.3. EEG recording Resting EEG (eyes open) was collected for three minutes at the beginning of the testing session. Participants were instructed to restrict movement and look at a fixation point in front of them. EEG was recorded using a 62 active electrode recording system (ActiCap, Brain Products, Munich, Germany; extended 10–20 montage). The reference electrode was positioned at FCz. Vertical and horizontal electrooculograms (EOGs) were simultaneously recorded from electrodes above and below the left eye and at the outer canthi. The signal was digitized at 500 Hz sampling rate, stored and analysed offline. 2.4. EEG analysis EEG data were analysed in Brain Vision Analyzer (2.0) (Brain Products, Munich, Germany). The signal was re-referenced offline to the average reference, and downsampled to 256 Hz. We applied 0.1–30 Hz (24 dB/Oct) Butterworth filters. Ocular artefacts were removed from the data using semi-automatic Independent Component Analysis (Jung et al., 2000). The extracted independent components were manually inspected, and ocular artefacts were removed by back-projection of all but those components. Continuous EEG was segmented into 2s epochs. Segments with artefacts exceeding 200 lV peak-to-peak in any channel were rejected. Fast-Fourier Transform analysis was performed on the data from each of the electrodes for each participant. Two second Hanning windows were used, and absolute power was calculated for the theta band (4–8 Hz). To reduce the number of comparisons, and in order to allow comparison with other studies investigating familial effects in ADHD (Loo et al., 2010), electrodes were grouped by region in the following way: frontal (F1, F2, F3, F4, F5, F6, F7, F8 and Fz), central (C1, C2, C3, C4, C5, C6 and Cz) and parietal (P3, P4, P7, P8 and Pz) scalp electrode locations. 2.5. Statistical analysis Two participants were excluded from analysis due to excessive artifacts (1 DZ ADHD and 1 DZ control). Since age and IQ significantly differed between ADHD and control groups and were significantly associated with theta power (r = 0.18 to 0.30) and (r = 0.09 to 0.19), their effects were regressed out. Each EEG parameter had skewed distributions as defined by sktest in Stata (Stata Corp, College Station, Texas) and therefore were log-transformed (optimised minimal skew through the lnskew0 command in Stata). We analysed group differences in theta power by means of a Stata regression command that corrects for non-independent observations (e.g., in twin pairs) using the robust clustering command (vce cluster) to estimate standard errors. 2.6. Preparation of data prior to model fitting Simultaneous analyses of dichotomous and continuous data could not be performed in the Mx program so both ADHD, which was scored as a dichotomous attribute, and the EEG measure, which was scored as a continuous variable, were modelled as

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threshold traits. Accordingly, each EEG measure was ordinalised into equal classes in terms of proportions, which should capture most of the information in the continuous data. Polychoric correlations and genetic model parameters were estimated using full information maximum likelihood, while correcting for the selected nature of the sample (Hall et al., 2007); the threshold for ADHD was fixed to give a population prevalence of 5% (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007); and the MZ and DZ cross-twin correlations were fixed according to the point estimates for the heritability of ADHD based on a meta-analysis (h2 = 76%, c2 = 0%, e2 = 24%, consistent with rMZ = 0.76, rDZ = 0.38 (Faraone et al., 2005). 2.7. Polychoric correlations Correlations between theta power and ADHD were estimated by fitting a constrained correlational model to the observed MZ and DZ data to produce estimates of (i) the within-subject across-trait correlation regardless of zygosity, i.e. between ADHD and EEG, (ii) the MZ and DZ cross-twin within-trait correlation for theta, and (iii) the MZ and DZ cross-twin cross-trait correlation between one twin’s ADHD score and the co-twin’s EEG score. 2.8. Genetic model fitting Twin model-fitting can be used to estimate (1) the heritability of EEG activity and (2) genetic and environmental correlations of ADHD with EEG activity, through examination of the differences in correlations between MZ (who share 100% of their genetic influences) and DZ twin pairs (who share 50% of their genetic influences). The applied genetic bivariate liability-threshold model (illustrated in Fig. 1) uses the MZ:DZ ratio of the cross-twin

within-trait correlations to decompose the variance of EEG activity can be decomposed into additive genetic (referred to as A) and unique environmental (E) components including measurement error. The parameter estimates from the model can be used to estimate the correlation between the genetic factors for ADHD and EEG activity (rg), which is an index of the extent to which the same genetic factors influence the two traits, and similarly for correlations of unique environmental factors (re). As the rg and re correlations do not take into account the heritability of either trait, it is possible for a large genetic correlation to actually explain a very small portion of the observed covariation between these two traits. Combining the information from the rg and re with the heritabilities of each trait, we can establish the genetic (rph-a) and unique environmental (rph-e) contributions to the total phenotypic correlation (rph) between ADHD and EEG activity (Neale, 1997).

3. Results Theta power was highest in frontal regions and was significantly increased in frontal locations in the ADHD group compared to the control group (Table 1). There were trends towards increased theta power in central and parietal regions also. Theta power in frontal regions was subsequently used in twin modelling analyses. The MZ cross-twin within-trait correlation for theta power (r = 0.79; 95% CI, 0.60 to 0.89) was greater than the DZ cross-twin within-trait correlation for theta power (r = 0.13; 95% CI, 0.23 to 0.46) which suggests that genetic effects contribute to theta power. As the MZ correlation for theta power deviated from 1, this suggests that unique environmental influences (and measurement error) contribute to theta power. Furthermore, the DZ cross-twin within-trait correlation for theta power was less than half the MZ correlations, suggesting that nonadditive genetic dominance effects may contribute to this, but due to a lack of power we will look at the broad-sense heritable effects (Neale & Cardon, 1992). The MZ cross-twin cross-trait correlation between ADHD and theta power (r = 0.26; 95% CI, 0.01 to 0.49) was greater than the DZ cross-twin cross-trait correlation (r = 0.13; 95% CI, 0.13 to 0.39), suggesting that genetic effects contribute to the association between theta power and ADHD. Structural equation modelling indicated that genetic factors accounted substantially for the total variation in theta power during resting (h2 = 0.77; 95% CI, 0.43–0.89), whereas unique environmental effects (incorporating also measurement error) accounted for a modest part of the variance in theta power (e2 = 0.23; 95% CI, 0.11–0.45).

Table 1 Summary statistics and mean comparisons adjusted for genetic relatedness for theta power based on transformed age- and IQ-regressed scores. All ADHD versus all controls

Fig. 1. Constrained correlated factors bivariate model for ADHD and EEG power. Circles represent latent additive genetic (A) and non-shared environmental (E) factors. a1, a2, e1, e2, c2 represent genetic and environmental path estimates. rg and re represent genetic and environmental correlations between ADHD and EEG activity. Parameters for ADHD (heritability and unique environmental estimates) are fixed values according to meta-analysis (Faraone et al., 2005).

Mean

SD

t

p

Theta frontal

MZ control DZ control MZ ADHD DZ ADHD

2.31 2.32 2.34 2.34

.05 .05 .05 .04

2.58

.012

Theta central

MZ control DZ control MZ ADHD DZ ADHD

2.04 2.03 2.05 2.06

.05 .05 .06 .05

1.81

.076

Theta parietal

MZ control DZ control MZ ADHD DZ ADHD

2.14 2.14 2.15 2.18

.05 .05 .05 .06

1.97

.053

C. Tye et al. / Brain and Cognition 87 (2014) 168–172

The extent to which the genetic and environmental factors of ADHD and theta activity overlap is given by the correlations rg and re, respectively. The significant genetic correlation (rg = 0.35; 95% CI, 0.04 to 0.68) indicated substantial overlap in genetic influences on ADHD and theta power during resting state. The environmental correlation was nonsignificant (re = 0.28; 95% CI, 0.90 to 0.65). The phenotypic correlation (rph) suggested that increased liability to ADHD was associated with increased theta power (0.21; 95% CI, 0.01 to 0.39). Due to the high heritability and a moderate genetic correlation, the phenotypic correlation between ADHD and theta power appears to be largely attributable to genetic effects (rph-a = 0.27; 95% CI, 0.03 to 0.48). Contribution of the unique environment to the phenotypic correlation was nonsignificant (rph-e = 0.07; 95% CI, 0.25 to 0.18).

4. Discussion In this study, we investigated the association between ADHD and EEG theta power during a resting condition as one of the most consistent neural markers of the disorder, and its potential as a marker of genetic risk in a selected sample of twins concordant and discordant for ADHD symptoms. Genetic analyses showed that theta power at rest was highly heritable. In addition, adolescents with consistently high ADHD symptom scores displayed elevated theta power at rest. Further, genetic factors were the main source of these effects, and shared genetic influences between theta power and ADHD were moderate to large. These findings converge to support theta power as an electrophysiological marker of genetic risk in ADHD. Substantial heritability of frontal theta power at rest is consistent with previous reports in twin samples (Smit et al., 2005; van Beijsterveldt et al., 1996; Zietsch et al., 2007). The estimate reported here is slightly reduced, which may reflect differences in age (adult versus adolescent), sample (typically developing participants versus selection on the basis of ADHD symptoms) or reflect topographical differences in heritability estimates (Zietsch et al., 2007), but also suggests the role of unique environmental effects. There was no evidence of shared environmental effects, in line with previous research (Smit et al., 2005), which suggests that environmental factors that make family members similar are not the same as those that influence theta power. The phenotypic associations in this study are largely consistent with previous studies of EEG correlates of ADHD, showing elevated theta in the ADHD group (Barry et al., 2003; Monastra et al., 1999, 2001; Shi et al., 2012; Snyder & Hall, 2006; Snyder et al., 2008). Although findings were only significant for frontal scalp regions (in line with previous research), there were trends toward elevated theta in central and parietal scalp regions, which may indicate a broad effect of arousal as measured on the scalp (VaezMousavi, Barry, Rushby, & Clarke, 2007). As theta activity during rest conditions is proposed to be an index of underarousal (Barry et al., 2003), this may reflect suboptimal energetic state at rest in ADHD which is not sufficiently stimulating or rewarding for the subject (Borger & Van der Meere, 2000; Kuntsi, Andreou, Ma, Borger, & van der Meere, 2005; Scheres, Oosterlaan, & Sergeant, 2001; Sergeant, 2000; Slusarek, Velling, Bunk, & Eggers, 2001; Van der Meere, Stemerdink, & Gunning, 1995), and/or the action of a topdown attention control network that regulates the arousal level (Sergeant, Geurts, Huijbregts, Scheres, & Oosterlaan, 2003). Importantly, significant genetic correlations between ADHD and theta power at rest suggest there is overlap in their genetic influences. Establishing a correlated liability between ADHD and increased theta power validates its use as a candidate marker of genetic risk in order to further our understanding of the pathophysiological mechanisms underlying ADHD (Gottesman &

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Gould, 2003). Future work should aim to identify specific genes that influence ADHD and theta power. For example, dopaminergic alterations are consistently reported in ADHD and have been linked with EEG power across frequency bands (Tye et al., 2011). More specifically, associations between theta power and ADHD may vary on the basis of specific genetic variants that are themselves associated with ADHD (Gizer, Ficks, & Waldman, 2009; Li, Sham, Owen, & He, 2006); the DRD4 7-repeat allele is associated with increased frontal theta power (Loo et al., 2010), and methylphenidate treatment reduces theta activity in children with the 10-repeat allele of DAT1 in multiplex ADHD families (Loo et al., 2003). Therefore, this finding warrants the continued investigation of biological mechanisms and genes underlying theta activity. The integration of these approaches is likely to provide substantial insight into the pathophysiology of ADHD. While these findings are promising, the relatively small sample size warrants replication in larger twin samples The limited power restricts the detection of shared environmental and non-additive genetic effects that, according to the twin correlations, may represent a proportion of the variance in theta power. In addition, these and other electroencephalographic abnormalities are not necessarily homogenous within, and specific to, ADHD. Some studies demonstrate subtypes of EEG indices within ADHD (Clarke, Barry, McCarthy, & Selikowitz, 1998, 2001) and elevated theta power is also reported in ADHD with autistic traits (Clarke, Barry, Irving, McCarthy, & Selikowitz, 2011). While we have focused on a consistent neural marker of ADHD, other parameters in EEG, as well as event-related potentials (ERPs), may identify ADHD with greater accuracy, either alone or in combination. Computational modelling of EEG data could improve the spatial and temporal characterisation of cortical circuits and systems underlying theta activity in ADHD (McLoughlin, Makeig, & Tsuang, 2014; McLoughlin, Palmer, Rijsdijk, & Makeig, 2014). Further work that builds on these findings is required to identify the causal pathways to ADHD and comorbid disorders. Such an approach moves towards a classification of disorders based on identifiable neural circuits and is in line with recent initiatives that aim to close the gap in understanding between the symptoms and causes of psychopathology (Insel et al., 2010). In conclusion, this study demonstrated that elevated theta power at rest is associated with ADHD symptoms, heritable and shares genetic influences with the disorder, thereby validating theta as a potential marker of genetic risk for ADHD. These findings provide improved understanding of underlying pathophysiology through convergence with biological mechanisms, which support the use of biological markers, such as theta power, as complementary tools in the assessment and treatment of ADHD and in the search for causal factors. This warrants the investigation of potential causal relationships and the biological underpinnings of the genetic interplay between ADHD and theta power.

Acknowledgments The authors gratefully acknowledge the participating families and all staff involved in this study, in particular, Chloe Booth, Sarah Lewis, Stuart Newman, the TEDS research team and the Director of TEDS, Robert Plomin. The PI(GM) was supported by a fellowship from the National Institute for Health Research.

Appendix A. Supplementary material Supplementary information associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bandc. 2014.03.010.

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Genetic overlap between ADHD symptoms and EEG theta power.

Biological markers that are grounded in neuroscience may facilitate understanding of the pathophysiology of complex psychiatric disorders. One of the ...
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