Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD)
Introduction
Attention-deficit/hyperactivity disorder (ADHD) is a common psychiatric disorder affecting children, adolescents, and adults. The prevalence of ADHD in children is around 5% (Polanczyk and Rohde, 2007), and varies from 3% to 16% in adults depending on the diagnostic criteria (Faraone and Biederman, 2005). ADHD is associated with serious socio-economical consequences, resulting from difficulties with social interactions, inattention at school or work, and a higher risk of psychological maladjustment (Faraone et al., 2000), leading to a loss in productivity and increased societal healthcare costs (Biederman et al., 2006).
Clinical diagnosis of ADHD is based on structured interviews and questionnaires assessing behavioral symptoms according to standardized diagnostic manuals (DSM-IV and DSM-5, (American Psychiatric Association, 2000, American Psychiatric Association, 2013)), although research diagnostics increasingly focus on the neural systems affected (Cuthbert and Insel, 2013). The observed heterogeneity of ADHD highlights the need for more objective and precise diagnostic and prognostic approaches such as advanced pattern classification analyses (Clarke et al., 2011, Poil et al., 2013). Biomarkers based on electroencephalography (EEG) measurements are both inexpensive and non-invasive, making them suitable for screening purposes or for following treatment outcomes (Clarke et al., 2002a). These biomarkers may add an additional dimension to the current diagnostic criteria of ADHD, e.g., with respect to identification of relevant sub-groups. In addition, spectral markers of EEG slowing reflect effects of maturational lag, inattention, hypoarousal, and control deficits found in ADHD. As a result, EEG biomarkers have been explored extensively in ADHD, but have not yet achieved clinical acceptance.
The most common effect reported in the literature is an increase in theta (4–7 Hz) power or theta/beta ratio, measured during rest over fronto-central regions in subjects with ADHD (Arns et al., 2013, Barry et al., 2003, Barry et al., 2009, Bresnahan et al., 1999, Clarke et al., 2001a, Magee et al., 2005, Snyder and Hall, 2006). The validity of this effect presumed to represent a form of EEG slowing related to maturational lag and hypoarousal has, however, been questioned by recent studies which failed to observed theta or beta effects (in both theta/beta ratio and power) (Liechti et al., 2013, Loo et al., 2009, Loo et al., 2013, Van Dongen-Boomsma et al., 2010). The lack of theta effect may be caused by differences in methodology or by the general heterogeneity of ADHD and control groups (Arns et al., 2013, Clarke et al., 2011, Liechti et al., 2013, Loo et al., 2013). Combining EEG with skin conductance measures of arousal has further challenged the proposed link between theta/beta levels and hypoarousal, and instead suggests that increased alpha levels reflect reduced arousal in both ADHD and control children (Barry et al., 2009). Similarly, slower alpha peak or central frequencies have been implicated in ADHD (Arns et al., 2008). In sum, several thorough recent studies (Liechti et al., 2013) and reviews (Arns et al., 2013, Clarke et al., 2013) suggest that findings of elevated theta and theta/beta ratios in ADHD exhibit considerable more heterogeneity, relate less clearly to hypoarousal, and yield less consistent results across the life span in recent work than claimed previously.
This study aims to clarify the potential diagnostic or prognostic value of EEG spectral biomarkers through a better understanding of age-related changes in these biomarkers. Evidence from previous studies indicates that the developmental changes in ADHD subjects are characterized by a delayed maturation of spectral biomarkers at rest (Clarke et al., 2002b), inhibition related activation deficits (Doehnert et al., 2010, Rubia et al., 1999), and structural changes in, for example, frontal cortex (Shaw et al., 2007). While we could not confirm such delays for spectral theta and beta power of ADHD children and adults in an earlier study (Liechti et al., 2013), combining a wider range of frequency bands and measures with multidimensional classification could provide more sensitivity. Therefore, we hypothesized that ADHD patients would show a different developmental trajectory compared with healthy subjects in EEG power and central frequency biomarkers (Arns et al., 2013, Clarke et al., 2001b).
Since traditional log-linear regression analysis has previously been shown to characterize EEG developmental trajectories closely (Wackermann and Matousek, 1998), we expect this analysis to reveal different developmental trajectories between ADHD and control subjects based on the delayed maturation theory. We further employ a binary multi-dimensional classification method, i.e., a support vector machine (SVM), to investigate whether the combined spectral EEG biomarkers (power and central frequency) could provide additional information that may give better separations between groups (ADHD versus healthy and “young” versus “adult” subjects) than classification based on single biomarkers alone. Classification analysis during rest or task has previously shown good accuracy in classifying several disorders, such as schizophrenia, depression, Alzheimer’s disease (Lehmann et al., 2007, Orrù et al., 2012, Poil et al., 2013, Übeyli, 2010), and ADHD (Abibullaev and An, 2012, Cheng et al., 2012, Dai et al., 2012, Eloyan et al., 2012, Hart et al., 2013, Mueller et al., 2011, Tenev et al., 2013). A recent pattern classification competition of resting state functional MRI data from a large number of ADHD and control subjects (>200 children and adolescents with ADHD) yielded cross-validated group discrimination from 55% (compared to 33% obtained by chance, Colby et al. 2012) to 78% (Eloyan et al. 2012; reduced to 61% for an external data set), and exceeded classification based on single markers (Dai et al., 2012). We thus hypothesize that whole-brain pattern analysis using resting state EEG markers will similarly improve individual classification of ADHD patients.
Section snippets
Subjects
The study involved a large sample of 116 subjects, including 48 ADHD patients and 68 control subjects. We studied 19 children with ADHD and 22 control children, 7 adolescents with ADHD, and 19 control adolescents. The adult group consisted of 22 adults with ADHD, and 27 control adults (Table 1, Table 2, Table 3). The control group was recruited from nearby regular schools, personal contacts, and through advertisement at public science presentations, while patients were mostly recruited from our
The power spectrum of the resting-state electroencephalography is sensitive to development and ADHD
In keeping with the known developmental trajectory of the power spectrum of the resting-state electroencephalogram (Gasser et al., 1988, Wackermann and Matousek, 1998), we observed the typical developmental pattern; such as, low frequency band (delta–theta) power decreases with development in both control and ADHD subjects (Fig. 1). In the children we observe higher delta and alpha-2 power in ADHD in mainly the frontal region compared with control children (binomial correction, p < 0.05) (Figs. 1
Discussion
This study investigated a group of ADHD and control subjects across a broad age range using EEG during an eyes-closed resting state with spectral biomarkers. We observed that adult ADHD subjects have higher beta power and lower alpha central frequency than adult control subjects, whereas ADHD children have higher delta power and higher alpha-2 power than control children. We show that a log-linear model of the EEG spectral changes can explain the developmental changes in most frequency bands in
Conclusion
This study reveals strong atypical development of resting EEG power in theta, alpha-1, beta, and alpha central frequency in ADHD patients. The effects in theta and alpha-1 were obscured by normal developmental changes, but were revealed by linear regression modeling of the developmental effects. Based on classification analysis, we conclude that ADHD is not characterized by a maturational lag, but rather by an atypical developmental trajectory in the adults with ADHD, possibly related to
Acknowledgement
This work was supported by the by the University Research Priority Program “Integrative Human Physiology” (“Linking the major system markers for typical and atypical brain development: a multimodal imaging and spectroscopy study” from the Zurich Center for Integrative Human Physiology, University of Zurich). We thank Ernst Martin and Karin Kucian for valuable comments on previous versions of the manuscript.
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