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Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity disorder Caroline Skirrowa, Grainne McLoughlina, Tobias Banaschewskib, Daniel Brandeisa,b, Jonna Kuntsia, Philip Ashersona,n a

Social Genetic and Developmental Psychiatry, Institute of Psychiatry, Kings College London, London, UK Department of Child and Adolescent Psychiatry Central Institute of Mental Health, Mannheim, Germany

b

Received 16 October 2013; received in revised form 17 July 2014; accepted 21 September 2014

KEYWORDS

Abstract

Quantitative EEG; Methylphenidate; Attention-deficit/ hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is associated with cognitive performance and functional brain changes that are sensitive to task conditions, indicating a role for dynamic impairments rather than stable cognitive deficits. Prominent hypotheses consistent with this observation are a failure to optimise brain arousal or activation states. Here we investigate cortical activation during different conditions. Using a sample of 41 non-comorbid adults with ADHD and 48 controls, we examine quantitative EEG activity during a resting state, a cued continuous performance test with flankers (CPT-OX) and the sustained attention to response task (SART). We further investigate the effects of methylphenidate in a subsample of 21 ADHD cases. Control participants showed a task-related increase in theta activity when engaged in cognitive tasks, primarily in frontal and parietal regions, which was absent in participants with ADHD. Treatment with methylphenidate resulted in normalisation of the resting state to task activation pattern. These findings suggest that ADHD in adults is associated with insufficient allocation of neuronal resources required for normal cortical activation commensurate with task demands. Further work is required to clarify the causal role of the deficit in cortical activation and provide a clearer understanding of the mechanisms involved. & 2014 Elsevier B.V. and ECNP. All rights reserved.

n

Corresponding author. E-mail addresses: [email protected] (C. Skirrow), [email protected] (G. McLoughlin), [email protected] (T. Banaschewski), [email protected] (D. Brandeis), [email protected] (J. Kuntsi), [email protected] (P. Asherson).

1.

Introduction

ADHD is a common neurodevelopmental disorder affecting an average of 5% of children and 3–4% of the adult population worldwide (Fayyad et al., 2007; Polanczyk et al., 2007). The disorder is characterised by developmentally inappropriate and

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Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

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impairing levels of inattentive, hyperactive and impulsive symptoms. ADHD is often accompanied by comorbid (Kooij et al., 2010) symptoms and traits including emotional lability (Banaschewski et al., 2012; Skirrow and Asherson, 2013), cognitive performance deficits (Banaschewski et al., 2012; Johnson et al., 2009; Woods et al., 2002), neurodevelopmental impairments such as autism spectrum disorders and specific reading difficulties (Germano et al., 2010; Ronald et al., 2008) and mental health disorders such as anxiety and depression (Fayyad et al., 2007; Kessler et al., 2006). ADHD is highly heritable throughout the lifespan (Larsson et al., 2013), however, the neurobiological mechanisms that cause ADHD remain poorly understood and are likely to be heterogeneous in nature. One prominent hypothesis of the pathophysiology of ADHD argues for an important role of sub-optimal arousal or a failure to properly regulate energetic states. State regulation or cognitive-energetic models of ADHD implicate deficient arousal or neuronal activation as deficits that contribute to the symptoms and impairments of ADHD (Kuntsi and Klein, 2012; Sergeant, 2005; Todd and Botteron, 2001; van der Meere et al., 1999). A potentially related phenomenon is abnormal regulation of the default mode network (DMN). The DMN reflects a pattern of brain activity which is dominant when an individual is at rest, and attenuated when performing cognitive tasks (Broyd et al., 2009). In ADHD it has been suggested that the DMN is inadequately attenuated, leading to interference with cognitive processes required for goal-directed task performance, that are reflected in ADHD symtoms (Sonuga-Barke and Castellanos, 2007). Deficits of state regulation and the DMN have both been proposed as explanations for the observation that individuals with ADHD generate variable response times in speeded reaction time tasks. In ADHD heightened variability in reaction times (RTV) is consistently found across different tasks (Boonstra et al., 2005; Hervey et al., 2004; Klein et al., 2006; Kuntsi and Klein, 2012; Lijffijt et al., 2005). RTV indexes a large proportion of the familial/genetic influences on ADHD in both clinical and epidemiological samples (Kuntsi et al., 2010, 2014). A key characteristic of the reaction time impairments in ADHD is sensitivity to task conditions: ADHD cases compared to controls show a significantly greater improvement in the consistency of their responses under relatively stimulating (fast-rewarded) task conditions (Andreou et al., 2007; Banaschewski et al., 2012; Kuntsi et al., 2013; Uebel et al., 2010). This implicates dynamic impairments rather than stable cognitive deficits, which might be explained by difficulties in modulating arousal or the allocation of sufficient neuronal resources. One method to investigate changes in cortical activation is quantitative EEG, in which electrophysiological recordings are quantified in frequency ranges. This has revealed reduced power in fast wave (mainly beta) cortical activity and elevated power in slow (mainly theta) frequency bands in children with ADHD during resting conditions (Bresnahan et al., 1999; Clarke et al., 2003, 2006; Snyder and Hall, 2006), and mainly elevated theta in adults (Bresnahan et al., 1999; Bresnahan and Barry, 2002; Clarke et al., 2008; Koehler et al., 2009). These findings are frequently interpreted as reflecting cortical under-arousal in ADHD compared to

controls; although this conclusion has been questioned (Barry et al., 2004), some studies do not replicate these findings (Liechti et al., 2013; Loo et al., 2009) and the existence of electrophysiological subtypes with different EEG profiles has been proposed (Barry et al., 2003; Clarke et al., 2011). To investigate cortical activation during cognitive testing the current study examines quantitative EEG changes during a resting state and two different cognitive tasks. We compared quantitative EEG findings using a sample of noncomorbid adults with ADHD and controls, in a resting state condition with two active task conditions: first during a cued continuous performance test with flankers (CPT-OX) and second during the sustained attention to response task (SART). We further investigated the effects of methylphenidate on the behavioural, cognitive and electrophysiological phenotypes to clarify whether there are shared treatment effects across the different measures, consistent with the hypothesis that neuronal activation deficits might be closely linked to the cognitive impairments and symptoms of ADHD.

2. 2.1.

Experimental procedures Participants

2.1.1. Baseline sample Recruitment and diagnostic procedures have previously been reported (Skirrow and Asherson, 2013). In brief, 508 adult males were screened from the waiting list of the Maudsley Hospital Adult ADHD Clinic. All underwent psychiatric evaluation from a psychiatrist specialising in adult ADHD including a structured interview (Conners' Adult ADHD Diagnostic Interview for DSM-IV, CAADID (Epstein et al., 2001)). The final sample consisted of 41 men meeting DSM-IV ADHD criteria and all exclusion criteria. A control sample of 41 non-ADHD males were recruited from volunteer databases held at King's College London, and through advertising around the university and local community. Exclusion criteria were extensive: any axis I or II comorbid psychiatric diagnosis; past history of axis I psychiatric disorders (with the exception of major depressive disorder, where only those with recurrent depression or those in a depressive episode at time of contact were excluded); current or previous substance abuse; head injury or neurological conditions; IQ under 70; recent exposure to psychoactive medication (1 month minimum wash-out for stimulant medication, 6 months for other psychoactive medications). 2.1.2. Follow-up sample 38 control participants and 31 ADHD participants returned for follow-up assessments. Treatment response was investigated in an open-label design with treatment managed and implemented by community health services. Where psycho-pharmacological treatment was initiated by treating practitioners, follow-up research appointments were scheduled after treatment was maintained for a mean of 3.5 months (range 2.5–9.7 months). Control participants provided normative data for initial assessments and at a follow-up duration matched to ADHD subjects. 31 ADHD subjects returned for reassessment. Of these, 21 ADHD patients were stably treated with methylphenidate and took medication on the day of their follow-up assessment. Daily doses ranged from 30 mg to 72 mg for those taking extended release methylphenidate (Equasym XL or Concerta XL) and 15 mg to 60 mg in three divided doses for those taking immediate release methylphenidate. In the remaining ADHD participants, 2 were being treated with other ADHD medications (1 dexamphetamine, 1 atomoxetine) and 2 were inconsistently medicated (1 atomoxetine, 1 methylphenidate). Six participants were not taking any medication, 2 individuals were seeking

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

Theta activity in adult ADHD alternative treatment, either through psychological intervention or food supplementation. Due to the heterogeneity of this nonmethylphenidate treated group, they were excluded from further analysis.

2.2.

Testing sessions

IQ testing (initial session only) was followed by EEG testing: with a 3-min eyes open resting condition, followed by CPT-OX and SART in the same order for all sessions. Subjects completed rating scales for ADHD and affective lability before test sessions. All ADHD subjects were unmedicated at their first (baseline) research appointment. Stimulant response was investigated in the sub-group of 21 ADHD participants where methylphenidate treatment was maintained for a minimum of 2.5 months. Follow-up appointments were carried out a minimum of 3 months after treatment was initiated to allow adequate treatment optimisation. For individuals on methylphenidate, follow-up sessions were scheduled for EEG recordings to be carried out within 1 h of taking immediate release or within 3 h of extended release methylphenidate. All subjects were asked to refrain from drinking caffeine or smoking prior to test sessions and from consuming alcohol from the preceding evening.

2.3.

Clinical measures

The Barkley Adult ADHD rating scale (BRS; Barkley, 1998) captured ADHD symptoms. The affective lability scale-short form (ALS-SF; Oliver and Simons, 2004), and the Centre for Neurologic Studylability scale (CNS-LS; Moore et al., 1997) provided measures of affective lability which are strongly associated with ADHD (Skirrow and Asherson, 2013). IQ was measured using the Wechsler Abbreviated Scale of Intelligence (Psychological Corporation, 1999).

2.4.

EEG recording and tasks

EEG was recorded using a 62 channel direct-current-coupled recording system (extended 10–20 montage), with electrode impedances below 10 kΩ. The reference electrode was positioned at FCz. Vertical and horizontal electro-oculograms were simultaneously recorded above and below the left eye and at the outer canthus of each eye. The signal was digitised at a sampling rate of 500 Hz and stored for offline analysis. Participants were seated on a height adjustable chair in a dimly lit video monitored testing cubicle. Stimuli were presented on a computer monitor at a distance of approximately 120 cm, using the Presentation software package (www.neurobs.com). Responses to tasks were made with a mouse button press with the right index finger. 2.4.1. Cued continuous performance test with flankers (CPT-OX; Doehnert et al., 2008; McLoughlin, et al., 2010): The task consisted of 400 black letters, Including cue letter ‘O’, target letter ‘X’ and distractors ‘H’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘J’ and ‘L’. Letters were presented centrally on the computer monitor, subtending approximately .51. All letters were flanked on either side by the letters ‘X’ or ‘O’, and cue and target letters (O and X) were flanked by the incompatible letter (X and O). Participants were instructed to ignore the flanking letters and respond as quickly as possible to cue-target sequences (O-X). 80 Cues (O) were followed by the target letter (X) in 40 trials (go condition), and neutral distractors in the remainder of trials (no-go condition). On 40 trials, a letter X was not preceded by a cue O and had to be ignored. Letters were presented every 1.65 s for 150 ms in a pseudo-random sequence. Ten practice trials preceded the main task blocks and were repeated, if required, to ensure participant comprehension. Task duration was 11 min.

3 2.4.2. Sustained attention to response test (SART) The SART consisted of nine digits presented in random order. Participants were instructed to withhold responses to the digit 3 (no-go trial) but to respond with a button press after all other digits (go trial). Participants completed the SART over three blocks, each lasting approximately 5 min. Individual blocks consisted of 225 digits, with each digit presented 25 times. Stimuli were presented in five digit sizes (font size 100, 120, 140, 160 and 180 in arial text), subtending approximately 1.71, 2.11, 2.41, 2.71, respectively in the vertical plane. Digits were presented .311 above a central white fixation cross on a grey background for 150 ms followed by an interstimulus interval of 1000 ms.

2.5.

Scoring performance measures

Reaction time measures were calculated for correct responses only, including mean reaction time on response trials (MRT) and standard deviation of reaction time (SD-RT). For the CPT-OX errors were broken down into commission errors (response to non-targets) and omission errors (non-response to targets). Similar measures were derived for the SART, with commission errors (response to the number 3), and omission errors (non-response on target trials). Two ADHD participants were excluded from analysis of the CPT-OX due to extreme commission (N=43) or omission errors (N=31) which were more than 3.5 SD from the mean, indicating insufficient task engagement (Tye et al., 2011).

2.6.

EEG data

Analyses were carried out in Brain Vision Analyzer 2.0 (Brain Products, Munich, Germany). EEG data were re-referenced offline to the average reference and down-sampled to 256 Hz. Data were filtered with .1–30 Hz (24 dB/oct) Butterworth filters and a 50 Hz notch filter. Ocular artifacts were removed from the data using biased infomax independent component analysis (ICA, Jung et al., 2000). Independent components were manually inspected, and all components with the exception of those which contained the ocular signal were back-projected for further analysis. All trials were inspected visually to detect additional subtle artifacts in any channel, which were removed from the data. Trials with remaining artifacts exceeding 200 μV peak-to-peak in any channel were rejected. Quantitative EEG was investigated for the three conditions (resting state, CPT-OX and SART). Data were segmented into 2-s epochs and power spectra were computed using a fast Fourier transform with a 10% Hanning window. Analyses focused on delta (.5–3.5 Hz), alpha (7.5–12.5 Hz), theta (4–7.5 Hz) and beta (12.5–30 Hz) band differences between participants with ADHD and controls. EEG power density (μV2/Hz) within each frequency bands was averaged across frontal (Fz, F1, F2, F3, F4, F5, F6, F7, F8), central (Cz, C1, C2, C3, C4, C5, C6), and parietal (Pz, P3, P4, P7, P8) scalp electrode sites. Theta–beta ratios were calculated by dividing theta power by beta power at each site.

2.7.

Statistical analysis

Normality of data was assessed using the Shapiro–Wilk statistic and parametric and non-parametric group comparisons were performed, as appropriate. Rest to task related change in EEG activity was calculated by subtracting EEG activity during rest from task. Treatment related change in EEG activity was calculated by subtracting EEG activity at follow-up from EEG activity during the baseline assessment. All EEG data were log 10 transformed and analyses were completed using repeated measures ANOVA and Greenhouse–Geisser correction for non-sphericity, where indicated, with adjustments to

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

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degrees of freedom rounded up to the nearest whole digit for reporting. Theta–beta ratios, delta, alpha, beta and theta power were each investigated with a 2  3  3 analysis, including within (recording site and condition: rest vs. CPT-OX vs. SART) and between subjects contrasts. Bonferroni correction was implemented to correct for multiple testing in the different frequency bands: Bonferroni adjusted p-value=.01; nominally significant p-value=.05. Post-hoc analyses were carried out using independent samples t-tests for between-subjects contrasts with Bonferonni adjusted confidence intervals, and paired samples t-tests of within-individual task related differences in EEG activity. We restricted analysis of the treatment response effects to theta activity, which showed group differences in the baseline analyses. Post-hoc analysis of treatment effects were analysed using paired samples t-tests for analysis of within subject change over time and independent samples t-tests to investigate group differences in task transition effects over time.

Table 1 Group demographic data, mean (SD) and significance from Mann Whitney U for cases and controls in the baseline sample (n =41 cases and 48 controls) and follow-up sample (n =21 cases and 36 controls). ADHD

Age

28.5 (9.5) 29.0 .91 (10.4) 109.0 113.2 .14 (15.1) (13.4) 15.7 (3.8) 15.7 (2.3) .57

IQ Years in education Follow-up sample Age IQ

3. 3.1.

Results Baseline results

Years in education Follow-up duration (months)

30.0 (10.4) 108.3 (13.8) 16.2 (4.2) 8.2 (3.9)

Controls

pvalue

Baseline sample

29.4 (11.2) 112.3 (13.4) 15.9 (2.4) 8.6 (3.4)

.79 .18 .24 .72

The 88 participants with baseline data were 18–65 years of age with no significant difference between ADHD and control groups in age, IQ and years spent in education (Table 1).

3.2. 3.1.1. Task performance There were no group differences for MRT on either task. Significant differences in SD-RT, omission and commission errors were seen between ADHD and control participants during the SART. Significantly increased omission errors were seen in the ADHD group for the CPT-OX (Table 2).

3.1.2. EEG activity There were no significant main effects of group for theta power (F(1,84)=.04, p=.84). However, analyses revealed a significant effect of condition (F(2, 130)=25.81, po.001), with an increment in theta power from resting state to CPT-OX to SART (post-hoc pairwise analysis, all comparisons significant: minimum p=.01). A group-by-condition interaction emerged (F(2130)=7.97, p=.002), driven by a significantly greater increase in theta power in the control group than the ADHD group from resting state to the CPT-OX and SART (posthoc within subjects contrasts: rest to CPT-OX: (F(1,84)=4.03, p=.05; rest to SART: F(1,84)=11.04, p=.001; CPT to SART: F(1,84)=7.37), p=.008 (Figure 1)). A significant effect of recording site was identified, with significantly lower theta power in central than frontal and parietal locations (post-hoc pairwise comparisons with Bonferroni adjustment, po.001), while theta power in frontal and parietal locations did not differ (p= .57). A condition-by-site-by-group interaction was significant (F(3236) = 4.57, p= .005). Post-hoc independent samples t-tests with Bonferroni correction, showed only significant group differences in theta activity between resting state and SART were significant in frontal and parietal electrode sites (t(84) = 3.44, p= .001; and t(86) = 3.38, p= .001). Analysis of delta, alpha, and beta power, as well as theta– beta ratios identified no significant main effects or interactions with participant group.

Treatment effects

Participants with ADHD and controls did not differ in rate of loss to follow-up (χ2 =.36, p=.55). Control participants lost to follow up did not differ significantly from those who remained in the study in terms of IQ (z= 1.25, p=.22) or age (z= .72, p=.48). ADHD participants lost to follow-up showed trends for being younger (z=1.72, p=.09), and having lower IQ (t= 1.80, p=.08). Baseline ADHD symptom scores measured by the BRS did not differ between ADHD patients who remained in the study and those lost to follow-up (inattention: t= .3, p=.98; hyperactivity–impulsivity: t=.56, p=.58). Participants with ADHD on a steady MPH regimen at follow-up (N=21) and control participants who attended follow-up (N=36) were comparable in terms of age, IQ, educational level and followup duration (Table 1). 3.2.1. Clinical symptoms The ADHD group exhibited a significant reduction in ADHD and affective lability (inattention: t=3.62, p=.002; hyperactivity– impulsivity: t=3.07, p=.006; emotional lability: ALS-SF: z=3.18, p=.001; CNS-LS: t=3.54, p=.002) after treatment. ADHD participants continued to report significantly elevated ADHD symptoms and affective lability after treatment when compared to controls (inattention: z= 5.31; hyperactivity– impulsivity: z= 5.42; ALS-SF: z= 4.34; CNS-LS: z= 3.53, all p-valueso.0001). The control group showed no change in self-reported symptoms. 3.2.2. Task performance To contrast the potential of treatment vs. practice effects on performance data, a series of repeated measures ANOVAs were carried out using both the case and control data (Table 3: see discussion for limitations of this approach). Repeated measures analysis of SD-RT revealed a marginal difference for group (F(1,53)=3.99, p=.054) and a significant effect of

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

Theta activity in adult ADHD

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Table 2 Means (SD) of task performance data on CPT-OX and the SART and statistics for group differences at baseline (n =41 ADHD and n =48 controls). Control

ADHD

Statistic and p value

MRT for correct responses (ms) CPT-OX SART

428.75 (68.1) 377.74 (66.1)

442.05 (77.2) 380.50 (64.7)

t= t=

.74, p=.46 .20, p=.85

SD-RT for correct responses (ms) CPT-OX SART

91.53 (45.7) 102.51 (33.6)

101.23 (47.5) 125.3 (43.8)

t= t=

.83, p=41 2.57, p=.01

CV CPT-OX SART

.21 (.09) .27 (.07)

.22 (.09) .34 (.12)

z= z=

.99, p=.32 3.20, p =.005a

Commission errors CPT-OX SART

2.36 (4.0) 16.09 (8.2)

2.47 (4.2) 24.64 (15.0)

z= t=

.55, p=.59 3.37a, p=.002

Omission errors CPT-OX SART

.91 (2.0) 4.94 (7.0)

1.67 (2.5) 15.41 (18.3)

Z= z=

2.24, p =.03 3.11a, p=.002

Note: MRT, mean reaction time in milliseconds; SD-RT, within-subject variability in RTs in milliseconds; CV: Coefficient of variation (SD-RT/MRT). a Differences robust to Bonferroni correction (adjusted p=.005).

(t(20)=4.49, po.001), with no significant change in the control group (p=.32). Data from omission errors revealed a significant effect of group (F(1,57)=10.344, p=.002) and a significant group-by-time interaction (F(1,57)=7.20, p=.01). Post-hoc analyses revealed that these effects were driven by significantly higher rates of omission errors at baseline in participants with ADHD (t(57)=3.60, p=.001, at follow-up t(57=1.57), p=.12), and a reduction in omission errors in the ADHD group only (paired samples t-test: t(20)=2.15, p=.04), with no change in the control group (p=.80). Figure 1 Frontal theta (mean, SE) across three conditions (resting state, CPT-OX and SART).

assessment time, with a reduction in SD-RT at time 2 shown in both groups (F(1,53)=9.54, p=.003), but no time-by-group interaction (F(1,53)=.11, p=.74); indicating that changes in SD-RT did not differ between groups. Analysis of MRT revealed no significant group effect (F(1,57)=.001 p=.98), but did reveal a significant time-by-group interaction (F(1,57)=10.26, p=.002), this was driven by a decrease in MRT in control participants at time 2 (t(37)=2.47, p=.02) and a trending increase in MRT in ADHD participants at the second assessment (t(20)= 1.96, p=.06). Analysis of commission errors found a significant group effect (F(1,57)=6.55, p=.01), driven by higher rates of commission errors in participants with ADHD than controls, and a time-by-group interaction effect (F(1,57) =27.67, po.001). Post-hoc testing revealed that group differences were due to higher commission rates in participants with ADHD at baseline only (t(57)=4.46, po.001), at followup group differences were non-significant (p=.71). Moreover the interaction effect was driven additionally by a significant reduction in commission errors in the ADHD group at follow-up

3.2.3. Quantitative EEG Treatment with methylphenidate led to a normalised pattern of rest to task transition in theta activity in individuals with ADHD, when compared to the controls (Figure 2). The results for theta power are listed in Table 4. No significant main effect of group was seen (F(1,55)=5.77, p=.64), however, a task-by-group interaction (F(1,55)=5.77, p=.02), a threeway interactive effect between assessment time, task and group (F(1,55)=5.31, p=.04), and an interaction between time, condition, site and group was seen (F(2,91)=3.39, p=.05). Post-hoc testing confirmed no group differences in theta activity for any electrode site during any task (independent samples t-test, minimum p=.36). Post-hoc testing revealed that change task-related increased in theta activity was seen for control participants at baseline and follow-up (baseline: t(37)= 6.60, po.001; follow-up: (t(37) = 6.35), po.001). Similar task-related enhancement of theta activity was seen in participants with ADHD at followup only (baseline: t(2)= .65, p=.53; follow-up: t(20) =4.93, po.001). Significant interactions were driven primarily by group differences in rest to task transition of theta activity, which were present in frontal and parietal

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

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Table 3 Means (SDs) of overall performance data, reaction times and number of errors on the SART at time 1 and time 2, with statistics for group differences. MRT: mean reaction time in milliseconds; SD-RT: within-subject variability in RTs in milliseconds. Time 1

Time 2

Control

ADHD

Statistic and p-value

Control

ADHD

Statistic and p-value

MRT for correct responses (ms)

373.28 (64.8)

350.33 (48.0)

t= 1.36 p =.18

354.24 (66.75)

387.06 (86.52)

t = 1.56 p =.12

SD-RT for correct responses (ms)

95.74 (32.65)

114.83 (38.7)

t= 1.95 p =.06

87.83 (31.08)

103.31 (34.5)

t = 1.78 p =.08

CV

0.25 (0.06) 15.44 (8.4)

0.34 (0.13) 28.37 (16.8)

t= 2.77 p =.010 z = 2.79a p =.005

0.25 (0.09) 16.89 (11.6)

0.27 (0.08) 15.79 (13.0)

t =.48 p =.64 z = .42 p =.68

4.19 (6.4)

15.57 (19.8)

z = 2.91a p =.004

4.14 (5.9)

6.68 (12.5)

z = .40 p =.69

Commission errors Omission errors

Note: MRT, mean reaction time in milliseconds; SD-RT, within-subject variabilty in RTs in milliseconds; CV, Cofficient of variation (SD-RT/MRT). a Differences robust to Bonferroni correction (adjusted p =.005) (participants returning for follow-up only: ADHD n=21, controls n=38).

electrodes at baseline (frontal: t(55)=2.45, p=.02; parietal: t(55)=2.34, p=.02, uncorrected), which were no longer seen at follow-up (frontal: t(55)=.53, p=.60; parietal: t(55)=.31, p=.76), indicating of a normalised theta activity pattern in individuals with ADHD. To investigate whether changed rest-to-task transition in theta activity was associated with medication, rather than an artefact of regression to the mean, correlational analysis were carried out whether the effects seen frontally and parietally were associated with medication dose. Frontal rest-to-task change in theta activity was moderately correlated with medication dose (r= .53, p= .02), whilst the relationship for parietal electrodes was non-significant (r= .31, p= .18).

4.

Discussion

This study investigated the relationship of EEG indices of cortical activity in a sample of non-comorbid and drug free male adults with ADHD and matched controls. We identified significant attenuation of the normal cortical activation associated with engagement in cognitive tasks; control participants showed a task-related increase in theta activity when engaged in cognitive tasks, primarily in frontal and parietal regions, which was significantly reduced in participants with ADHD. The EEG resting state to task transition effects for theta were greatest with the SART, which also showed the greatest case-control performance differences. This suggests that the SART demands greater neuronal resource allocation than the CPT-OX. The reason for these differences in the two tasks have not been investigated, but could, for example, reflect the presence of the cue in the CPT-OX leading to an easier task with ceiling effects; or the frequency of responses which is greater for SART than CPT-OX. However, previous studies have shown case-control differences for

reaction time variability in the CPT-OX (McLoughlin et al., 2010) which we do not see here, suggesting that our ADHD sample might reflect a less impaired group, perhaps because of the strong selection for non-comorbid and medication free cases. In addition, we cannot exclude time on task effects leading to fatigue and poor performance during the SART, since the SART was always the last of the three tasks administered. In the follow-up subset of ADHD cases treated with methylphenidate, we saw normalisation of the pattern of EEG activity for resting state to SART theta band transition among the ADHD cases. Although we show that transitional change in rest-to-task theta after medication is correlated with dose of medication, it is of interest that neither medication related change in rest-totask theta transition nor dose of medication correlate with change in clinical symptoms of ADHD (maximum r=.13, minimum p=.33). This suggests that although the mechanisms underlying normalisation of task-dependent cortical theta activity are likely to be shared with the effects of methylphenidate treatment, they are not clearly related to medicationrelated improvements in symptoms of inattention and hyperactivity–impulsivity. Overall the finding of associations with both case-control differences and treatment effects of methylphenidate for theta, suggest that changes in theta activity may reflect processes associated with ADHD and its response to medication. In contrast to the positive findings for task transition effects for theta, we did not see any differences between groups in resting state quantitative EEG for the other wavebands, including the increase in resting state beta, theta and the theta/beta ratio previously reported in child and adult ADHD samples. However, as outlined in the introduction, several datasets have failed to replicate these findings, raising uncertainties about the robustness of these effects. One possibility is that resting state conditions are not all equivalent across the various studies. Alternatively the cross-study differences might

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

Theta power density

Theta activity in adult ADHD

0.405

7 Table 4 Follow-up sample: Theta mean power density (μV2/Hz) across groups for time 1 and time 2 (raw, untransformed data). F =frontal site, C =central site, P=parietal site.

Time 1 (pre-treatment)

0.4 0.395 0.39

Group

Controls

0.385

ADHD

Time Task Site Mean Std. error

Linear (Controls)

0.38 0.375 0.37

Rest

Control 1

Sart

Time 2 (post-treatment) Theta power density

0.405 0.4 0.395

2

0.39 0.385

Controls

0.38

ADHD

0.375 0.37

Rest

SART

ADHD

1

Figure 2 Task-related change in absolute power density in frontal theta, across rest and SART conditions, plotted separately for time 1 (pre-treatment) and time 2 (post-treatment). 2

reflect heterogeneity of the samples, although the lack of replication remains surprising given the very large effect sizes reported in some studies (Barry et al., 2003). Possible sources of sample heterogeneity relevant to this study could include age group, comorbidity and drug effects, since we used a unique sample of adults with ADHD with a mean age of 29 years, in which we excluded all axis I and II comorbidities, and the concomitant use of drugs, alcohol or psychotropic medications. The wide age range in our sample is a consideration since EEG power tends to decline with age, potentially reducing power for the detection of case-control differences. As expected, we also saw case-control differences and treatment effects in some of the cognitive performance measures, as well as on the clinical symptoms of inattention, hyperactivity–impulsivity and affective lability. We included affective lability alongside core ADHD symptoms, since recent research has demonstrated the very close links between ADHD and emotional lability during the treatment response (Reimherr et al., 2007; Rosler et al., 2010). For the cognitive performance measures, significant findings were mostly seen during the SART where SD-RT, commission and omission errors all showed case-control differences; and treatment effects were seen for commission and omission errors. These findings suggest broad effects of medication on aspects of executive control (commission and omission errors) during a sustained attention task. The observed pattern of findings, in which task-dependent modulation of theta activity is seen in controls, but not ADHD cases, could be explained by existing theories of ADHD, including default mode network and state regulation deficit hyotheses. Research on the DMN has identified a pattern of brain activity which is dominant when an individual is at rest,

95% Confidence interval Lower bound

Upper bound

Rest F C P SART F C P Rest F C P SART F C P

.382 .354 .377 .401 .367 .399 .380 .354 .374 .395 .365 .392

.006 .004 .007 .008 .005 .008 .005 .004 .006 .006 .004 .006

.369 .346 .364 .386 .358 .384 .370 .346 .362 .383 .357 .379

.394 .362 .390 .417 .376 .414 .390 .362 .386 .406 .373 .404

Rest F C P SART F C P Rest F C P SART F C P

.384 .356 .385 .385 .361 .387 .375 .348 .378 .386 .356 .389

.009 .005 .009 .010 .006 .010 .007 .005 .008 .008 .006 .009

.367 .345 .367 .364 .348 .366 .362 .338 .362 .370 .345 .372

.402 .367 .403 .406 .373 .407 .389 .358 .393 .402 .368 .406

and is attenuated during task performance (Broyd et al., 2009). In ADHD, the DMN has been found to be inadequately attenuated when individuals with ADHD engage with tasks, which then has the potential to interfere with cognitive processes required for goal-directed task performance (Cortese et al., 2012; Fassbender et al., 2009; Sonuga-Barke and Castellanos, 2007). Therefore the lack of task related increases in theta activity in adults with ADHD might reflect poor attenuation of the DMN. Supporting this view, several studies using simultenous functional magnetic resonance imaging and EEG have identified a negative correlation between frontal theta and default mode network deactivation, leading to the suggestion that theta oscillations are key to the attenuating processes required for normal cognitive functioning (Michels et al., 2012; Scheeringa et al., 2008; White et al., 2012). However, since the direction of effects has not been evaluated, it could be the case that frontal theta represents synchronous firing that is specifically related to cognitive function, for example the task related activity during the SART. Another prominent hypothesis of the pathophysiology of ADHD argues for a failure to optimise energetic state (Kuntsi and Klein, 2012; Russell et al., 2006; Sergeant, 2005; Todd and Botteron, 2001). In the context of the cognitive–energetic

Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015

8

C. Skirrow et al.

model (Sergeant, 2005) our findings may indicate deficient activation, reflecting impaired task-related mobilisation of energetic resources required for task performance. There are limitations of interpretation because we have not specifically tested causal models, or the direction of effect of the observed associations. An alternative explanation is that our findings reflect the consequence of a lack of task engagement. This is perhaps unlikely since all participants were video monitored during task performance and the few who overtly displayed a drift away from the task were quickly prompted. However, this would not account for a covert drift off task, where individuals with ADHD may appear to remain on task, but nevertheless experience a drift of attention. Such an effect could result in decreased cortical activity as a consequence (rather than a cause) of such attentional drift. As discussed, the present data suggest that the heightened difference between the SART and resting state, as opposed to comparisons with the CPT-OX, reflect greater cognitive demands for the SART seen on both the performance level and EEG differences. However, because of the fixed order of the tasks in this study, we are not able to disentangle task difficulty from time on task (fatigue) effects. Future research should therefore consider balancing the order of tasks and consider the factors that influence task transition abnormalities in ADHD. Nevertheless, our data suggests the presence of a core dysfunction in adults with ADHD that prevents allocation of sufficient neuronal resources commensurate with task demands. Although previous studies have investigated treatment response on measures of cognitive performance and functional brain changes (Hart et al., 2013; Swanson et al., 2011), most are single dose studies and only a few (Coghill et al., 2007; Schulz et al., 2012) have investigated potential shared treatment effects on indices of brain function and cognitive performance during the clinical response of ADHD patients to stimulant medications. Such designs provide an important method for identifying those indices of brain function that not only share treatment effects, but also identify those processes mediate treatment effects on symptoms and are candidates for a direct causal role in the generation of ADHD symptoms (Kendler and Neale, 2010). However, several limitations meant that we could not explore such mediating hypotheses here: first, because our follow-up sample was relatively small; and second, because we did not include a placebo control condition we cannot be certain that the observed changes are linked directly to the use of methylphenidate. Non-drug related effects of the treatment process on symptoms and impairments, are seen in all ADHD drug treatment trials of methylphenidate in adult ADHD (Koesters et al., 2009). Our use of a control group at baseline and follow-up provides some traction on the likelihood that the observed treatment effects are due to medication (Hood et al., 2005). Moreover, our analyses revealing change in theta rest-to-task transition effects to be moderately correlated with dose of methylphenidate provides further support of our assertions that this is likely to reflect an electrophysiological marker of response to treatment. In conclusion, our data point towards a key role for theta activity as a marker of core neurobiological processes linked to ADHD. However, further large scale studies, using

appropriately controlled designs, are required to clarify whether these effects mediate changes in cognitive performance and clinical symptoms. As discussed by Kendler and Neale (2010), such studies are critical as one of several methods required to identify the processes that play a direct causal role in pathogenesis of ADHD.

Role of the funding source Funding for this work was from the UK National Institute of Health Research (NIHR Project RC-PG-0308-10245) to Philip Asherson and Caroline Skirrow. NIHR had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Contributors Asherson, Skirrow, Kuntsi and McLoughlin were involved in conceiving the protocol and study design. All authors were involved in the interpretation, review and drafting of the paper. Analysis and collection of data was performed by KS and supervised by PA. KS wrote the first draft for her PhD thesis. DB, TB and GM provided expert guidance on the selection of tasks and interpretation of the neurophysiological data.

Conflict of interests Philip Asherson, working on behalf of Kings College London, was a consultant or advisor for Janssen-Cilag, Eli-Lilly, Shire, Novartis and PCM Scientific. He received educational or research grants from Shire, Vifor Pharma, Janssen-Cilag, Eli-Lilly and QBTech Limited; and spoke at sponsored talks from the same companies. All funds were used to support research activities within KCL. Tobias Banaschewski served in an advisory or consultancy role for Hexal Pharma, Lilly, Medice, Novartis, PCM Scientific, Shore and Vifor Pharma. He received conference attendance support and conference support or received speaker fees from Lilly, Janssen McNeil, Medice, Novartis and Shire. He has been involved in clinical trial conducted by Lilly, Shire and Vifor Pharma. There are no conflicts of interest from the other co-authors.

Acknowledgements Funding for this work was from the UK National Institute of Health Research (NIHR Project RC-PG-0308-10245) to Philip Asherson and Caroline Skirrow. We thank all the participants who made this research possible. We thank Jadwiga Mika, Tessa Mellow, Sarah Bates and Peter Reid for their assistance on the project.

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Please cite this article as: Skirrow, C., et al., Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/hyperactivity.... European Neuropsychopharmacology (2014), http://dx.doi.org/10.1016/j.euroneuro.2014.09.015