Psychiatry Research 170 (2009) 183–191

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Psychiatry Research

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Cognitive and electroencephalographic disturbances in children with attention-deficit/hyperactivity disorder and sleep problems: New insights

Alyssa C.P. Sawyer a, C. Richard Clark a,⁎, Hannah A.D. Keage a, Kathryn A. Moores a, Simon Clarke b,c,d,Michael R. Kohn b,c,d, Evian Gordon e,f,g

a Cognitive Neuroscience Laboratory, School of Psychology, Flinders University, SA, Australiab Centre for Research into Adolescents' Health, University of Sydney and Westmead Hospital, Westmead, NSW, Australiac Department of Adolescent Medicine, Westmead Hospital, Westmead, NSW, Australiad Meridian Clinic, Bondi Junction, NSW, Australiae The Brain Resource International Database, Brain Resource Company, Ultimo, NSW, Australiaf Department of Psychological Medicine, University of Sydney, Sydney, NSW, Australiag The Brain Dynamics Centre, Westmead Hospital, Westmead, NSW, Australia

⁎ Corresponding author. School of Psychology, FlindeAdelaide, SA 5001, Australia. Tel.: +61 8 8201 2425; fax

E-mail address: Richard.Clark@flinders.edu.au (C.R. C

0165-1781/$ – see front matter © 2008 Elsevier Irelanddoi:10.1016/j.psychres.2008.10.026

a b s t r a c t

a r t i c l e i n f o

Article history:

There is overlap between t Received 28 February 2008Received in revised form 27 August 2008Accepted 23 October 2008

Keywords:AD/HDSleep problemsCognitionPsychophysiology

he behavioural symptoms and disturbances associated with Attention-Deficit/Hyperactivity Disorder (AD/HD) and sleep problems. The aim of this study was to examine the extent ofoverlap in cognitive and electrophysiological disturbances identified in children experiencing sleep problemsand children with AD/HD or both. Four groups (aged 7-18) were compared: children with combined AD/HDand sleep problems (n=32), children with AD/HD (n=52) or sleep problems (n=36) only, and childrenwith neither disorder (n=119). Electrophysiological and cognitive function measures included: absoluteEEG power during eyes open and eyes closed, event-related potential (ERP) components indexing attentionand working memory processes (P3), and a number of standard neuropsychological tests. Childrenwith symptoms of both AD/HD and sleep problems had a different profile from those of children with eitherAD/HD or sleep problems only. These findings suggest it is unlikely that disturbances in brain and cognitivefunctioning associated with sleep problems also give rise to AD/HD symptomatology and consequentdiagnosis. Furthermore, findings suggest that children with symptoms of both AD/HD and sleep problemsmay have a different underlying aetiology than children with AD/HD-only or sleep problems-only, perhapsrequiring unique treatment interventions.

© 2008 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Attention-Deficit/Hyperactivity Disorder (AD/HD) is a disruptivechildhood behavioural disorder, characterised by high levels ofinattention, and/or hyperactivity and impulsivity (American Psychia-tric Association, 2000). Prevalence of AD/HD in the general communityis common, estimated to range from 3 to 11% (American PsychiatricAssociation, 2000; Sawyer et al., 2001). AD/HD is a disorder associatedwith increased parental stress and lower health-related quality of lifefor children diagnosed with the disorder (Anastopoulos et al., 1992;Sawyer et al., 2002; Klassen et al., 2004). Children and adolescentswith AD/HD are also likely to face poorer outcomes as they enter adultlife (Brassett-Harknett and Butler, 2007).

A continuing challenge is to better understand the phenomenologyand aetiology of AD/HD. Currently, diagnosis of AD/HD is based onassessment of observable behaviour problems including inattention,

rs University, G.P.O. Box 2100,: +61 8 8201 3877.lark).

Ltd. All rights reserved.

hyperactivity and impulsivity (Oosterloo et al., 2006); however, thereis substantial intra-individual and inter-individual variability in theseverity of behavioural problems among children diagnosed withAD/HD (Castellanos et al., 2006; Klein et al., 2006). Further, a range ofdifferent factors can give rise to AD/HD, such as environmental andbiological factors including genetic risk (Bradley and Golden, 2001;Waldman and Gizer, 2006).

Of recent interest is the possibility that sleep problems may haveaetiological significance for AD/HD symptoms, with three key piecesof evidence supporting this argument. Firstly, there is a highprevalence of mild to severe sleep problems in children with AD/HDand a higher prevalence of sleeping problems among children withAD/HD than in children with other psychopathologies (Chervin et al.,1997; Corkum et al., 1999; Cortese et al., 2005; Van der Heijden et al.,2005; Oosterloo et al., 2006; Gau et al., 2007; Hiscock et al., 2007).Sung et al. (2007) reported that 73.5% reported mild to severe sleepproblems in a sample of 239 Australian school childrenwith clinician-diagnosed AD/HD, recruited through outpatient clinics and AD/HDsupport groups. Second, a relationship between sleep and behaviour isfrequently reported. For example, Fallone et al. (2005) reported that

Table 1Demographic characteristics of participants.

AD/HD Sleep Problems Combined Neither Disorder

(n=52) (n=36) (n=32) (n=119)

Age in years, M (S.D.) 12.0 (2.6) 13.6 (2.8) 12.3 (3.3) 12.6 (2.9)Males (%) 65.4 58.3 84.4 78.2Years of education,M (S.D.)

6.7 (2.6) 8.3 (2.8) 6.7 (3.2) 7.3 (2.9)

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when children had their sleep time reduced, they showed an increasein academic and behavioural problems. When sleep time wasoptimised, these problems decreased. Third, when children diagnosedwith combined AD/HD and sleep problems have their sleep problemseffectively treated, their hyperactivity, inattention and poor vigilanceimprove (Ali et al., 1996; Walters et al., 2000; Johnstone et al., 2001).

In studies using polysomnography, the gold-standard diagnostictool for sleep problems, childrenwith AD/HD are also reported to havean increased number of sleep problems (such as sleep-disorderedbreathing and sleep fragmentation) in comparison to childrenwithoutsleep problems (Kirov et al., 2004; Surman et al., 2006). In the studyby Kirov et al. (2004), children with AD/HD were found to haveabnormal sleep architecture in comparison to children without AD/HD. For the purposes of the present study, which aimed to recruit alarge sample of participants, it was not feasible to investigate sleepproblems using polysomnography, and instead we used a self-reportquestionnaire to identify sleep problems.

A number of studies have independently examined cognitivefunctioning and electrophysiology in children with sleep problems orchildren with AD/HD using electroencephalography (EEG) and event-related potentials (ERPs; e.g. Johnstone and Barry, 1996; Barry et al.,2003). However, we know of no previous study that has directlycompared the characteristics of children with AD/HD and childrenwith sleep problems. Further, no study has examined the extent towhich the cognitive functioning andelectrophysiologyof childrenwithboth AD/HD and sleep problems are more similar to children withsleep problems-only or those with AD/HD-only. An investigation ofthis nature would provide information about a possible aetiologicalrelationship between AD/HD and sleep problems. In order toinvestigate this relationship, the present study employed a 2 (AD/HD: yes, no) by2 (sleepproblems: yes, no) design that has beenused inrecent research of comorbidity in AD/HD (Kirov et al., 2007; Roessneret al., 2007). The study measured cognitive functioning and electro-physiology in four groups of children with AD/HD-only, children withsleep problems-only, children with combined AD/HD and sleepproblems, and children with neither of these disorders (i.e., healthycontrols). By using these electrophysiological techniques, in combina-tion with standard neuropsychological tests of cognitive function, wewere able to investigate abnormalities associated with AD/HD andsleep problems.

As discussed, AD/HD is diagnosed through the observation ofbehaviour, particularly inattention, hyperactivity and impulsivity.However, these behaviours are also apparent in children followingsleep restriction (Fallone et al., 2005). In order to understand therelationship between sleep problems and AD/HD symptomatology,the present study used a profiling technique to investigate endophe-notypes associated with the two conditions in isolation (i.e., AD/HD orsleep problems) and combination (i.e., AD/HD and sleep problems).Psychometric and psychophysiological variables were used forprofiling to obtain an integrative picture of brain and behaviouralfunctioning. This profiling approach allowed examination of whetherthe profile of abnormalities associated with comorbid AD/HD andsleep problems was primarily associated with sleep problems, AD/HDor neither (i.e., separate endophenotype).

Initially, we compared the cognitive and electrophysiologicalprofiles of children with AD/HD and sleep problems only. We used arange of cognitive and electrophysiological measures known todiscriminate abnormalities associated with AD/HD from thoseassociated with sleep problems (Bohnen and Gaillard, 1994; Allowayet al., 1997; Klimesch, 1999; Gumenyuk et al., 2005; Salmi et al., 2005;Beebe, 2006; Clark et al., 2006; Keage et al., 2006), including absoluteEEG power for alpha, beta, theta, and delta frequencies, P3 ERPcomponents from a working memory and oddball task, as well as fiveneuropsychological tests of cognitive functioning. EEG measures havebeen shown to be abnormal in children with AD/HD, with increasedtheta and delta power (Hermens et al., 2005; Hobbs et al., 2007), and

decreased beta power (Barry et al., 2003). In contrast, studies of sleepdeprivation suggest that children with sleep problems may showincreased theta power and decreased alpha power (Smith et al.,2002). Abnormal P3 components are the most consistent finding inERP studies of children with AD/HD (Johnstone and Barry, 1996;Jonkman et al., 1997), reflecting the evaluation of stimulus signifi-cance, and/or the updating of stimulus information within workingmemory depending on task requirements (Kok, 2001; Gumenyuket al., 2005; Keage et al., 2006). The few studies examining thiscomponent in children with sleep problems indicate that the P3 isaffected (Walsleben et al., 1989; Johnstone et al., 2001). There are fivecognitive tests that previous work suggests may discriminate betweenchildren with AD/HD and children with sleep problems, includingVerbal Interference, Switching of Attention, Time Estimation, VerbalMemory Recall and Verbal Fluency or FAS tasks (Bohnen and Gaillard,1994; Kerns et al., 2001; Frazier et al., 2004; Beebe, 2006; Clark et al.,2006; Keage et al., 2006; Wong et al., 2006).

Secondly, the study investigated whether the cognitive andelectrophysiological profile of children with combined AD/HD andsleep problems was more comparable to that of children with sleepproblems or children with AD/HD. We investigated whetherthe cognitive and electrophysiological disturbances associated withsleep problems may also exist in children with combined AD/HD andsleep problems. Such a finding would provide support for theargument that abnormalities underlying sleep problems may alsocontribute to AD/HD-type symptoms in children with both disorders.The research into the cognitive and electrophysiological functioning ofchildren with combined AD/HD and sleep problems is limited;therefore, the present study took an exploratory approach toinvestigate the profile of deficits that may arise when these disordersoccur independently and comorbidly.

2. Methods

2.1. Participants

Participants were 239 male and female children and adolescents, aged between 7and 18 years, divided into four groups labelled “AD/HD”, “Sleep-Problems”,“Combined” (i.e. children with combined AD/HD and sleep problems), and “NeitherDisorder” (i.e. children with neither AD/HD nor sleep problems). Participants withAD/HD were not divided into groups of diagnostic subtypes (inattentive, hyper-active–impulsive and combined subtypes), as this was beyond the scope of the presentstudy. Table 1 provides demographic information for each of the four groups. Participants'age and years of education did not differ significantly between groups, but there weresignificantly (χ2 (3)=9.2, P=0.03) more males in the Combined group than in othergroups. Measurements from all children form part of the Brain Resource InternationalDatabase (BRID; Gordon, 2003).

Children with a primary diagnosis of AD/HD according to DSM-TR-IV criteria(American Psychiatric Association, 2000) were referred by paediatricians andpsychologists in Sydney and Adelaide, Australia. All participants were naïve tostimulant and non-stimulant medications, commonly used to treat AD/HD. Childrenwith AD/HD and comorbid disorders including Oppositional Defiant Disorder, ConductDisorder and learning disorder (American Psychiatric Association, 2000) were includedin the study. Comorbidities were assessed at diagnosis using the Diagnostic Instrumentfor Children and Adolescents (DICA), completed by parents. AD/HD is a commonlycomorbid disorder, and we included children with such comorbidities to ensurerepresentativeness of the sample and generalisability of the results (Brassett-Harknettand Butler, 2007). Two children in the Combined group also had comorbid depression,which can be associated with sleep problems, so in order to control for this effect, these

185A.C.P. Sawyer et al. / Psychiatry Research 170 (2009) 183–191

children were matched to two children with comorbid depression in the AD/HD group.Further, in order to confirm that comorbid depression in these children did notinfluence the results, analyses were also performed with these children excluded,which demonstrated that excluding these children had a trivial effect on the results.Therefore, we report results with these children included in the analyses.

Children and adolescents without AD/HD were recruited through advertisements inschools and community groups. The children without AD/HD also reported no familialhistory of AD/HD. For purposes of this study, polysomnography was not used to diagnosesleep problems, children with sleep problems were identified through the web-based,BRID Sleep History Questionnaire, developed from an apnea risk index (Maislin et al.,1995). The questionnaire uses a Likert-type response format, with respondents indicatingthe number of times a week they experience each sleep problem described in thechecklist, e.g.: “In the lastmonth, have you experienced or have you been told about any ofthe following sleep symptoms — difficulty in falling asleep at night; frequent nightawakenings; breathing difficulties, snorting, gasping or loud snoring?”

All children reported English as their primary language. All participants werescreened for psychological illnesses other than those investigated in this study, andwere excluded from study on this basis. In addition, participants with any history ofneurological problems, physical brain injury, or loss of consciousness in the past 5 yearswere excluded from the study to prevent contamination of electrophysiologicalmeasures, as were children with any muscular or sensory impairment, which wouldprevent adequate task performance. All participants also reported normal hearing andnormal or corrected to normal vision, as was verified with a standard test of vision andhearing. Ethics approval was granted by the Flinders Social and Behavioural ResearchEthics Committee.

2.2. Procedure

All participants attended a laboratory recording session in either Sydney orAdelaide. Formal consent was given by parents or guardians, and children gave theirassent to participate. All participants completed a battery of tests including a web-based questionnaire (parents or guardians assisted children under the age of 12 withthe web-questionnaire only), an EEG recording session during which participantsperformed various tasks presented via a computer screen and headphones, and aneuropsychological test battery, administered with a touch screen computer usingstandardised instructions, with manual and verbal responses recorded digitally by thissystem.

2.3. EEG data collection and pre-processing

A 32-electrode EEG cap (QuickCap, Neuroscan) was used to record EEG fromelectrodes positioned according to the International 10–10 System (AmericanElectroencephalographic Society, 1991). Scalp EEG electrodes were referenced to theaverage of A1 and A2 (mastoid) electrode sites. Horizontal eye movements wererecorded (electro-oculogram, EOG) by electrodes 1.5 cm from the outer canthus of eacheye, and vertical eyemovements by electrodes placed 3mm above and 1.5 cm below theleft eye. EEG and EOG activity was digitized and amplified by NuAmps (Compumedics,Inc.). Impedances were kept below 5 kΩ at each electrode.

Activity at midline sites (Fz, Cz and Pz) was analysed in this study. For spectralanalyses, absolute EEG power was computed using a fast-Fourier transformation(Andreassi, 2007), and computed for four frequency bands: delta (1.5–3.5 Hz), theta(4–7.5 Hz), alpha (8–13 Hz) and beta (14.5–30 Hz) at each site. Averaged ERPs werecomputed from stimulus-locked EEG data for each participant. The P3 ERP componentsof interest (see task descriptions below) were defined as the maximum value between400 and 600 ms post-stimulus for both targets and non-targets; and 300–600 ms post-stimulus for distracters which have been found to occur earlier than the P3 in responseto target and non target stimuli (Daffner et al., 2000). These components wereidentified for each participant, using an automated algorithm which was cross-validated by experienced ERP scorers.

The reliability of these electrophysiological measures has been established. In aninvestigation of results from laboratories in Europe, the United States and Australia, nosignificant differences were reported for these measures (Paul et al., 2007). Thus, thesemeasures appear to have high reliability even when collected in different laboratoriesaround the world.

2.4. Psychophysiological tasks

2.4.1. Eyes-open/eyes-closedThe Eyes-open task required the participants to relax and fixate on a red dot in the

centre of the computer monitor for 3 min, while EEG was recorded. This task wasrepeated with Eyes-closed for the same duration.

2.4.2. Auditory OddballAn Auditory Oddball task, consisting of two tones — one high (1000 Hz) and one

low (500 Hz), were presented binaurally through headphones at 75 dB for 50 ms, withan inter-stimulus interval of 1 s. Rare high tones were designated as targets and lowtones as non-targets, with participants instructed via headphones to respond to targettones with a button press using the index fingers of both hands. During the task, 280non-target and 60 target tones were presented over 6 min, with no targets occurringconsecutively. Target P3 ERP amplitude and latency were analysed. The behavioural

measures examined were the mean reaction time (RT) and the standard deviation ofreaction time (SDRT) for responses to target stimuli. False positives (errors ofcommission, FP) and false negatives (errors of omission, FN) were also recorded.

2.4.2.1. Working memory task. The Working Memory Task consisted of a series of 125letters (B, C, D, or G) presented for 200 ms on a computer screen, with an inter-stimulus-interval of 2.5 s. Of the 125 letter stimuli, 20 were target letters and 85 werenon-target letters. A target letter was defined as any letter that was repeatedconsecutively (twice in a row), whereas non-targets were non-repeated letters.Standardised task instructions delivered by headphones indicated that participantsshould respond to target letters using a button press with the index fingers of bothhands. P3 ERP amplitudes and latencies to target, non-target and distracter stimuli wereanalysed. Again, behavioural data were recorded during performance of this task (RT,SDRT, FP, and FN).

2.4.3. Neuropsychological tasksThe neuropsychological test battery included a series of standardised and compu-

terised tests assessing attention (Switching of Attention Task), executive function (VerbalInterference, Time Estimation and Verbal Fluency Tasks) and auditory verbal learning andmemory (VerbalMemory Task)with establishedvalidity, reliability and sensitivity (Paul etal., 2005;Willliams et al., 2005; Clark et al., 2006). Thesemeasures have also been found tobe reliable across laboratories (Paul et al., 2007).

2.4.3.1. Switching of attention task. The Switching of AttentionTask (similar to the TrailMaking Test; Lezak, 2004) contains two subtests (administered after a practice trial),with Trial 1 requiring participants to link together 25 numbers in sequential order, thusinvolving a single sort criterion. Trial 2 required participants to link a series of letters andnumbers in sequential order, alternating between numbers and letters (e.g., 1-a-2-b-3-c), and therefore involving switching between sort criteria. The difference between trials1 and 2 in completion time and number of errors has been suggested to be a measure ofdistraction (Keage et al., 2006). In this study, we analysed completion time and numberof errors in trials 1 and 2, and the difference between trials in completion time and thenumber of errors.

2.4.3.2. Verbal interference test. The Verbal Interference Test has been proposed toinvolve inhibition of automatic, prepotent responses (Paul et al., 2005; Willliams et al.,2005). Similar to the conventional Stroop task (Golden, 1978; Lezak, 2004), participantswere presented with coloured words (red, yellow, green, blue) with incongruencybetween the name of the colour and the colour in which the word was shown (e.g. theword “red” presented in blue letters). Part 1 of this task (proposed to not involveinhibitory processes) required participants to identify the name of each word as quicklyas possible, using a response pad with the four possible words displayed in black andwhite. In Part 2, participants were instead required to identify the colour of each word,instead of reading the colour name of the word, thus a task proposed to requireinhibition of a prepotent response. The present study used the followingmeasures fromthis task: number of errors and correct responses from parts 1 and 2, reaction time inparts 1 and 2, and the difference between parts 1 and 2 in reaction time, number oferrors and correct responses.

2.4.3.3. Time estimation task. For the Time Estimation Task, a circle appeared on thescreen for an interval from 1 to 12 s, and participants were then required to estimatehow long they believed the interval lasted (Lezak, 2004). The task is proposed tomeasure self-monitoring and vigilance skills (Clark et al., 2006). The outcome measurefrom this task was the average of the difference between the duration of the period andthe participants' estimate of the period length measured in seconds (Clark et al., 2006),termed the proportional bias in time estimation.

2.4.3.4. Verbal memory task. The Verbal Memory Task, a form of the Auditory–VerbalLearning Test (Schmidt, 1997), involved the aural presentation of a list of 12 concretenouns which the participant was asked to remember. The list was presented four times,and after each presentation, the participant was asked to recall as many words aspossible. Memory recall was defined as the total number of words recalled on trials 1–4(Paul et al., 2005). The number of words recalled on trials 1–4 that were not included inthe original list served as a measure of intrusion or poor verbal memory (Keage et al.,2006).

2.4.3.5. Verbal fluency task. The Verbal Fluency Task (also termed FAS) required theparticipant to generate as many words as possible in 1 min that start with a given letter(F, A, or S) or fit a semantic category (animals; Lezak, 2004). The participant wasinstructed not to use proper nouns or to simply vary to prefix, suffix or tense of a word(e.g. run, running, ran). The total score for the FAS for the letter condition was theaverage number of generated words for the three letters. For the semantic condition, itwas the total number of animal names generated (Paul et al., 2005).

2.4.4. Data analysisData were initially screened to assess which variable scores significantly differed

between the four groups and were therefore capable of distinguishing a cognitive andelectrophysiological profile specific to each group. These t-tests were used to identifymeasures of best separation between groups, in preparation for the discriminant functionanalysis (DFA), and therefore no alpha adjustment was applied, as would usually be

Table 2Mean scores on cognitive and electrophysiological variables for AD/HD, Sleep Problems, Combined and Neither Disorder groups.

Groups

AD/HD Sleep problems Combined Neither Disorder

(n=52) (n=36) (n=32) (n=119)

Variables M S.D. M S.D. M S.D. M S.D. F(3, 235)

Auditory Oddball S.D. of reaction time 101.9 36.8 79.4 26.7 102.2 28.3 78.7 26.4 11.4⁎⁎⁎Auditory Oddball false positive 5.7 8.9 1.67 1.9 3.2 2.8 1.4 1.6 11.0⁎⁎⁎Working Memory S.D. of reaction time 284.3 153.9 194.6 102.2 292.8 118.2 205.7 94.0 10.1⁎⁎⁎Switching of Attention duration trial 1 32.5 13.1 23.1 61.2 35.31 12.1 27.2 10.6 9.9⁎⁎⁎Auditory Oddball false negative 2.3 4.5 0.8 1.6 1.1 1.4 0.3 0.7 9.7⁎⁎Working Memory false negative 3.6 2.9 1.6 2.1 3.16 2.6 1.9 1.9 9.1⁎⁎⁎Working Memory false positive 6.7 8.2 2.9 4.1 8.44 9.9 3.4 3.7 8.9⁎⁎⁎Switching of Attention error trial 2a 8.2 12.1 2.6 3.4 8.34 13.3 2.7 4.0 8.7⁎⁎Working Memory reaction time 647.5 161.4 548.6 128.5 656.1 149.9 563.0 127.5 7.9⁎⁎⁎Verbal Memory recall total score trials 1–4 27.3 6.9 32.9 5.3 28.3 7.0 31.2 5.9 7.9⁎⁎⁎FAS animal condition 16.8 5.6 22.6 5.9 17.2 6.9 19.5 6.4 7.4⁎⁎⁎Working Memory distracter P3 amplitude (Pz) 20.8 8.9 28.7 9.7 20.2 8.9 24.5 9.0 7.3⁎⁎⁎Working Memory distracter P3 amplitude (Cz) 15.5 10.8 23.5 11.1 12.8 10.3 19.2 10.7 6.9⁎⁎⁎Time Estimation proportional bias 0.2 0.3 0.0 0.2 0.3 0.4 0.1 0.2 6.9⁎⁎⁎Switching of Attention error trial 1a 3.4 5.4 1.2 1.2 3.19 4.7 1.4 1.7 6.7⁎⁎FAS letter condition 8.3 3.7 11.0 3.6 8.32 4.2 10.3 3.5 6.6⁎⁎⁎Verbal Interference errors trial 1 0.7 1.4 0.7 0.9 1.06 1.4 0.3 0.5 6.2⁎⁎⁎Switching of Attention duration trial 2 70.9 28.6 51.4 15.1 74.30 30.9 61.9 25.1 6.1⁎⁎Working Memory background P3 latency (Cz) 459.8 51.6 431.3 41.4 464.9 46.1 436.9 46.5 5.9⁎⁎Working Memory target P3 amplitude (Pz) 20.2 7.6 19.8 8.4 17.8 7.8 23.3 7.1 5.9⁎⁎Auditory Oddball reaction time 409.7 79.8 372.8 59.4 414.3 67.4 375.8 60.1 5.6⁎⁎⁎Working Memory background P3 latency (Pz) 453.5 51.5 431.1 43.8 469.5 40.6 438.4 46.7 5.4⁎⁎Switching of Attention error (trial 2–1) 4.8 10.5 1.4 3.2 5.16 10.6 1.4 3.7 4.9⁎⁎Verbal Interference errors trial 2 1.8 1.6 1.3 1.4 1.84 1.9 1.1 1.2 3.6⁎Working Memory distracter P3 latency (Pz) 395.9 40.4 368.8 37.5 392.5 45.5 381.2 42.2 3.6⁎Working Memory background P3 latency (Fz) 443.8 51.8 427.3 45.8 452.3 49.2 426.9 45.3 3.4⁎Working Memory distracter P3 amplitude (Fz) 10.1 9.1 14.5 9.4 7.9 7.1 11.9 9.5 3.4⁎⁎Eyes open theta (Cz) 50.9 27.9 38.5 21.4 38.4 18.5 40.2 24.0 3.2⁎Eyes open delta (Fz) 55.9 27.6 44.8 23.9 54.3 26.1 45.6 21.5 3.1⁎Verbal Memory recall intrusions trials 1–4 3.3 3.7 1.9 2.6 3.3 3.7 2.15 2.6 3.0⁎Working Memory background P3 amplitude (Cz) 5.5 4.9 6.9 5.9 4.3 4.9 7.1 5.4 3.0⁎Eyes closed delta (Fz) 56.4 26.4 44.3 20.2 57.0 28.6 48.5 23.0 2.9⁎Working Memory target P3 latency (Fz) 403.5 59.2 410.7 46.9 421.8 52.5 394.5 49.8 2.7⁎

⁎Pb0.05. ⁎⁎Pb0.01. ⁎⁎⁎Pb0.001.a As Switching of Attention errors (trial 2–1) reached a level below Pb0.05, the scores for the individual trials 1 and 2 were not included as predictors in the discriminant function analysis.

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performed in a hypothesis-testing study. This analysis identified 31 variables (see Table 2)that differentiated the groups.

Data analysis was then performed in two phases. Phase 1 involved using DFA forthree groups (AD/HD, Sleep Problems, Neither Disorder), using data from these 31variables. The purpose of the three-group DFA was to enable classification analysis ofthe Combined group, which was performed in order to determine whether thesechildren (i.e. children with combined AD/HD and sleep problems) had a cognitive andelectrophysiological profile similar to children in the sleep problems-only group. Phase2 involved an additional DFA including all four groups (i.e. Combined, AD/HD, SleepProblems and Neither Disorder groups) using data from the same 31 variables, toidentify whether children in the Combined group had a cognitive and electrophysio-logical profile that was distinct from the other three groups.

3. Results

3.1. Phase 1

Thefirst DFA (see Fig.1) identified two significant Functions (χ2 (62)=112.01, Pb0.001, Wilk's Lambda=0.55), which in combinationsignificantly discriminated between the AD/HD, Sleep Problems andNeither Disorder groups. When Function 1 was excluded, Function 2continued to significantly discriminate between the three groups (χ2

(30)=44.13, P=0.046, Wilk's Lambda=0.79). Function 1 accountedfor 62.2% of the between-groups variability, while Function 2 accountedfor 37.8%. The canonical r2 values for Functions 1 and 2 were 0.30 and0.21, respectively, indicating a moderate amount of variance (30%, 21%)shared between groups and predictors for each Function. Function 1clearly discriminated between children in the AD/HD group and SleepProblems group, with group centroid scores of 1.081 and −0.729,respectively. For Function 1, the centroid (−0.246) for children in theNeither Disorder group was located between the centroids for the AD/HD and Sleep Problems groups, and was therefore not as clearlydiscriminated by the analysis. However, Function 2 best discriminatedbetween children in the Neither Disorder group (centroid−0.394) andthe Sleep Problems group (centroid 0.943).

The classification analysis of children in the Combined group foundthat 25%were classified into the AD/HDgroup,15.6%were classified intothe Sleep Problems group and 59.4% were classified into the NeitherDisorder group. Thus, individual cases of children in the Combinedgroup did not cluster around any particular centroid of the other groups,as can be seen in Fig. 1. This finding suggests that the cognitive andelectrophysiological profile of the Combined group is not similar to thatof either the Sleep Problems or AD/HD groups.

3.2. Phase 2

The second DFA identified three significant Functions (χ2 (93)=178.53, Pb0.001, Wilk's Lambda=0.45), which in combinationsignificantly discriminated between the Combined, AD/HD, SleepProblems and Neither Disorder groups. When Function 1 was ex-cluded, Functions 2 and 3 continued to significantly discriminate (χ2

(60)=97.94, P=0.001, Wilk's Lambda=0.64) between the fourgroups. Further, when Functions 1 and 2 were excluded, Function 3continued to significantly discriminate between the groups (χ2 (29)=42.89, P=0.047, Wilk's Lambda=0.82).

Function 1 accounted for 47.0% of the between-groups variability,Function 2 accounted for 30.2%, and Function 3 accounted for 22.9%.The canonical r2 values for Functions 1, 2 and 3 were 0.30, 0.22 and0.17, respectively, indicating a small (17%) to moderate (30%) amountof variance shared between groups and predictors for each Function.

Function 1 from the phase two DFA (not shown) best discriminatedbetween children with AD/HD and without AD/HD. Group centroidsfor the AD/HD and Combined groups were similar, at 0.758 and 1.081,respectively, whereas group centroids for the Sleep Problems andNeither Disorder groups clustered together at −0.628 and −0.432,respectively. Function 2 best discriminated between childrenwith sleepproblems and childrenwithout sleepproblems,with group centroids forthe Combined (0.910) and Sleep Problems (0.633) groups clustering

together, comparedwith centroids for the AD/HD(−0.636) andNeitherDisorder (−0.167) groups clustering together. Table 3 shows variablesthat were associated with each Function.

Fig. 2 shows the group centroids for Functions 2 and 3. While thegroup centroid for the Neither Disorder group falls in the same spaceon Function 3 as the Combined group, Functions 1 and 2 have alreadyestablished these profiles are significantly different. Thus, Fig. 2 showsthat Function 3 best discriminated between children in the Combinedgroup (−0.414) versus children in the AD/HD (0.416) and SleepProblems (0.816) groups. These results indicate that children in theCombined group were classified as distinct from those in the AD/HDand Sleep Problems groups, with a unique cognitive and electro-physiological profile.

For each Function, factor loadings (0.2 and above) for thepredictor cognitive and electrophysiological variables (see Table 3)were used to drive post-hoc t-tests, comparing scores on thesevariables, between the groups discriminated by each Function. Alphaadjustments were not performed for these analyses (Rothman et al.,2008). In order to account for the possibility of type I errorsassociated with this process, effect sizes are provided foreach subsequent analysis. For the purpose of calculating the effectsizes, the sample standard deviation estimate was used. As a guide,Cohen (1988) defines the strength of effect sizes as: small=0.2,medium=0.5 and large=0.8.

For Function 1, data from children with AD/HD (acrossCombined and AD/HD groups) were compared with those withoutAD/HD (across Sleep problems and Neither Disorder groups; seeTable 4). Similarly, for Function 2, children with sleep problems(across Sleep problems and Combined groups) were compared withthose without sleep problems (across AD/HD and Neither Disordergroups; see Table 5). Lastly, for Function 3, results from children inthe Combined group were compared with those from children inthe AD/HD and Sleep problems only groups (see Table 6).

In instances where variables were not normally distributed, anonparametric test (Mann Whitney-U Test) was also used to testthese differences. Non-parametric statistical testing did not yielddifferent results, with the following exceptions: (1) for Function 2,the difference between scores for the Auditory Oddball FNs wassignificant (Z=−2.49, P=0.013), (2) for Function 3, the differencebetween the scores on the Working Memory distracter P3 ampli-tude (Pz) was significant (Z=−2.04, P=0.041) and (3) thedifference between scores on the Switching of Attention durationtrial was not significant (Z=−1.85, P=0.065).

4. Discussion

We investigated the extent to which underlying disturbances incognitive functioning and electrophysiology associated with sleepproblems are also associated with symptoms of AD/HD. Findingsindicated that children with only AD/HD or sleep problems showedsignificantly different cognitive and electrophysiological profiles.Further, results showed that children with symptoms of both AD/HDand sleep problems showed a significantly different profile from thoseof children with either AD/HD or sleep problems.

These results suggested that abnormalities in the cognitiveand electrophysiological profile of children with symptoms of bothAD/HD and sleep problems are not simply an additive function ofabnormalities observed in children with either problem. Recent work(Fallone et al., 2005) has suggested that sleep problems may havesome influence on AD/HD symptoms in children without AD/HD.Similarly, studies have shown that treating sleep problems inchildren with co-occurring symptoms of AD/HD results in decreasedAD/HD symptoms (Ali et al., 1996; Walters et al., 2000; Johnstoneet al., 2001; Fallone et al., 2005), also suggesting a relationshipbetween sleep problems and AD/HD symptomatology. However,results from this study show that children with symptoms of both

Fig. 2. Plot of group centroids (squares) from the phase two DFA for Combined, AD/HD,Sleep Problems and Neither Disorder groups on Functions 2 and 3. This figure illustratesthe discrimination of the Combined group from the AD/HD and Sleep Problems groups.

Fig. 1. Plot of group centroids (squares) from the phase one DFA for AD/HD, SleepProblems and Neither Disorder groups, with scores of individual children in theCombined group overlaid (circles). This figure illustrates the absence of clustering ofchildren from the Combined group around any of the centroids of the other 3 groups.

188 A.C.P. Sawyer et al. / Psychiatry Research 170 (2009) 183–191

AD/HD and sleep problems differ significantly from children withsymptoms of AD/HD or sleep problems only. The implication of thisfinding is that amongst children with symptoms of both AD/HD andsleep problems, the cognitive and electrophysiological disturbancesassociated with sleep problems may not have aetiological signifi-

Table 3Factor loadings of the predictor variables for each discriminant function from thesecond phase DFA.

Functions

Predictor variable 1 2 3

Auditory Oddball standard deviation of reaction time 0.566 – –

Working Memory standard deviation of reaction time 0.538 – –

Switching of Attention duration trial 1 0.513 – −0.207Working Memory false positive 0.503 – –

Working Memory false negative 0.487 – –

Working Memory reaction time 0.478 – –

Auditory Oddball false positive 0.468 −0.274 0.327Verbal Memory recall total score trials 1–4 −0.449 0.208 –

FAS letter condition −0.428 – –

Time Estimation proportional bias 0.424 – –

Working Memory distracter P3 amplitude (Pz) −0.416 – 0.220Working Memory distracter P3 amplitude (Cz) −0.415 – 0.240Auditory Oddball false negative 0.413 −0.204 0.411Working Memory background P3 latency (Cz) 0.413 – –

FAS animal condition −0.400 0.233 0.201Auditory Oddball reaction time 0.400 – –

Switching of Attention duration trial 2 0.382 – −0.218Working Memory background P3 latency (Pz) 0.380 – –

Switching of Attention error (trial 2–1) 0.374 – –

Verbal Interference errors trial 1 0.333 0.267 0.209Verbal Interference errors trial 2 0.314 – –

Working Memory background P3 latency (Fz) 0.312 – –

Working Memory target P3 amplitude (Pz) −0.295 −0.284 −0.255Eyes open delta (Fz) 0.293 – –

Verbal Memory recall intrusions trials 1–4 0.292 – –

Working Memory distracter P3 latency (Pz) 0.290 – –

Working Memory background P3 amplitude (Cz) −0.287 – –

Working Memory distracter P3 amplitude (Fz) −0.282 – –

Eyes closed delta (Fz) 0.279 – –

Eyes open theta (Cz) – −0.281 –

Working Memory target P3 latency (Fz) – 0.255 –

Note: Factor loadings±0.2 and above are reported.

cance for AD/HD-related symptoms. Inversely, the cognitive andelectrophysiological disturbances associated with AD/HD may notinfluence sleep problems.

One explanation for this discrepancy is that previous work hasexamined the relationship between observable behaviours associatedwith sleep problems and AD/HD. In contrast, the present studyexamined the underlying cognitive and electrophysiological distur-bances associated with each condition, in addition to measuringobservable behaviours. It appears that while there may be a relation-ship between AD/HD and sleep problems at a symptom level, this isnot reflected at an underlying cognitive and electrophysiological level.

In contrast, findings from this study suggest that when symptomsof AD/HD and sleep problems co-occur in the same child, a uniquepattern of abnormalities underlies these symptoms that is distinctfrom the pattern of abnormalities in a child with a single disorder. Thisunique profile of abnormalities in childrenwith symptoms of both AD/HD and sleep problems may have aetiological significance, perhapssuggesting a distinct underlying cause.

Childrenwith AD/HDwere characterised by a profile of cognitive andelectrophysiological deficits such as inattention, impulsivity, distractionand reaction time variability, a pattern that is typically associated withthe disorder. The main feature of the profile of children with sleepproblems was increased levels of inattention, also not surprising con-sidering a large body of research that has shown such effects associatedwith reduced sleep time (e.g. Fallone et al., 2005). The characteristicfeature of the cognitive and electrophysiological profile of childrenwithsymptoms of both AD/HD and sleep problems was increased levels ofdistractibility, demonstrated bymeasures of both cognitive function andelectrophysiology. Notably, childrenwith symptoms of both AD/HD andsleep problemswere significantlymore distractible than thosewith onlya single disorder, suggesting the main problem experienced by childrenwith both AD/HD and sleep problems may be difficulty in effectivelymanaging distracting stimuli in the environment.

We argue that the disproportionate number ofmales in the Combinedgroup is not a limitation of the study. Previous work using a largerepresentative sample of Australian childrenwith AD/HD by Graetz et al.(2005) found that, although girls had a higher level of somatic complaintsand boys had poorer school functioning, boys and girls did not differ onthe core symptoms of the disorder, number of comorbidities or level ofimpairment. It has also been shown that males and females with AD/HD

Table 4Mean scores for children with AD/HD (across AD/HD and Combined groups) versus children without AD/HD (across Sleep problems and Neither Disorder groups) for Function 1.

AD/HD No AD/HD

(n=84) (n=155)

Predictors loading on Function 1 M S.D. M S.D. t (df)a Cohen's d

Auditory Oddball standard deviation of reaction time 102.0 33.6 78.8 26.4 5.5 (139.3)⁎⁎⁎ 0.76Working Memory standard deviation of reaction time 287.5 146.7 203.1 95.7 4.9 (125.6)⁎⁎⁎ 0.66Switching of Attention duration trial 1 33.5 12.7 26.3 9.9 4.6 (137.7)⁎⁎⁎ 0.62Working Memory false negative 3.4 2.8 1.9 1.9 4.6 (128.8)⁎⁎⁎ 0.58Working Memory reaction time 650.7 156.3 559.7 127.5 4.6 (143.6)⁎⁎⁎ 0.63Verbal Memory recall total score trials 1–4 27.7 6.9 31.6 5.9 −4.6 (237)⁎⁎⁎ 0.61FAS letter condition 8.3 3.9 10.5 3.5 −4.3 (237)⁎⁎⁎ 0.60Working Memory background P3 latency (Cz) 461.7 49.4 435.4 45.3 4.2 (237)⁎⁎⁎ 0.55Auditory Oddball reaction time 411.4 74.9 375.1 60.1 4.1 (237)⁎⁎⁎ 0.53Working Memory false positive 7.4 8.9 3.3 3.8 4.0 (99.6)⁎⁎⁎ 0.52Auditory Oddball false positive 4.7 7.5 1.5 1.6 3.9 (87.3)⁎⁎⁎ 0.45Working Memory distracter P3 amplitude (Pz) 20.6 8.8 25.5 9.3 −3.9 (237)⁎⁎⁎ 0.54Working Memory distracter P3 amplitude (Cz) 14.5 10.6 20.2 10.9 −3.9 (237)⁎⁎⁎ 0.53FAS animal condition 16.9 6.1 20.3 6.4 −3.9 (237)⁎⁎⁎ 0.54Auditory Oddball false negative 1.9 3.7 0.4 1.0 3.6 (89.8)⁎⁎ 0.24Time Estimation proportional bias 0.2 0.3 0.1 0.2 3.6 (128.9)⁎⁎⁎ 0.40Working Memory background P3 latency (Pz) 459.6 48.0 436.7 46.0 3.6 (237)⁎⁎⁎ 0.49Switching of Attention duration trial 2 72.2 29.4 59.5 23.6 3.4 (141.8)⁎⁎ 0.47Working Memory background P3 latency (Fz) 447.0 50.7 427.0 45.3 3.1 (237)⁎⁎ 0.42Working Memory target P3 amplitude (Pz) 19.3 7.7 22.5 7.5 −3.1 (237)⁎⁎ 0.42Switching of Attention error (trial 2–1) 4.9 10.5 1.4 3.6 3.0 (93.5)⁎⁎ 0.35Working Memory distracter P3 latency (Pz) 394.6 42.2 378.3 41.4 2.9 (237)⁎⁎ 0.39Working Memory background P3 amplitude (Cz) 5.0 4.9 7.1 5.5 −2.9 (237)⁎⁎ 0.40Verbal Interference errors trial 2 1.8 1.7 1.2 1.2 2.9 (128.7)⁎⁎ 0.37Eyes open delta (Fz) 55.3 26.9 45.4 21.9 2.9 (143.8)⁎⁎ 0.40Verbal Interference errors trial 1 0.9 1.4 0.4 0.7 2.8 (106.2)⁎⁎ 0.51Eyes closed delta (Fz) 56.6 27.1 47.5 22.8 2.8 (237)⁎⁎ 0.36Verbal Memory recall intrusions trials 1–4 3.3 3.7 2.1 2.6 2.7 (130.9)⁎⁎ 0.35Working Memory distracter P3 amplitude (Fz) 9.3 8.4 12.5 9.5 −2.6 (237)⁎ 0.35

⁎Pb0.05. ⁎⁎Pb0.01. ⁎⁎⁎Pb0.001.a Independent samples t-tests.

189A.C.P. Sawyer et al. / Psychiatry Research 170 (2009) 183–191

do not differ in their neuropsychological or cognitive functioning (Arciaand Conners,1998), suggesting that the increased number ofmales in theCombined groupmost likely did not influence the results. Identificationofchildrenwith sleep problems using a self- or parent-report questionnairemay be a limitation. Although the questionnaire used has been shown tobe reliable in assessing those likely tohave or not have sleepproblems in acommunity sample (Maislin et al., 1995), it would have been valuable tohave also included more objective measures of sleep disorders such as

Table 5Mean scores for children with sleep problems (across Combined and Sleep Problemsgroups) versus children without sleep problems (across AD/HD and Neither Disordergroups) for Function 2.

Sleep problems No sleep problems

(n=68) (n=171)

Predictors loadingonFunction2 M S.D. M S.D. t(df)a Cohen's d

Working Memory targetP3 amplitude (Pz)

18.9 8.1 22.4 7.4 −3.2 (237)⁎⁎ 0.45

Verbal Interferenceerrors trial 1

0.9 1.2 0.4 0.9 2.7 (98.2)⁎⁎ 0.53

Working Memory targetP3 latency (Fz)

415.9 49.6 397.2 52.8 2.5 (237)⁎ 0.37

Eyes open theta (Cz) 38.5 19.9 43.5 25.7 −1.4 (237)FAS animal condition 20.1 6.9 18.7 6.3 1.5 (237)Verbal Memory recalltotal score trials 1–4

30.9 6.5 30.0 6.5 0.8 (237)

Auditory Oddballfalse positive

2.4 2.9 2.7 5.5 −0.5 (237)

Auditory Oddballfalse negative

0.9 1.5 0.9 2.7 0.1 (237)⁎ 0.01

⁎ Pb0.05. ⁎⁎Pb0.01. ⁎⁎⁎Pb0.001.a Independent samples t-tests.

polysomnography, in addition to the cognitive and electrophysiologicalmeasures.

In conclusion, this study demonstrated a unique profile of abnorm-alities that distinguishes children with co-occurring AD/HD and sleepproblems from those children with AD/HD or sleep problems only,indicating that symptoms associated with these conditions do notcombine in an additive way. This distinct profile may have aetiologicalsignificance for children with both problems, suggesting that children

Table 6Mean scores for children with combined AD/HD and sleep problems versus childrenwith only AD/HD or sleep problems (across AD/HD and Sleep Problems groups) forFunction 3.

Combined AD/HD and Sleep problems

(n=32) (n=88)

Predictors loading on Function 3 M S.D. M S.D. t(df)a Cohen's d

Switching of Attentionduration trial 1

35.3 12.1 28.6 11.7 2.7 (118)⁎⁎ 0.56

Working Memory distracterP3 amplitude (Cz)

12.8 10.3 18.8 11.5 −2.6 (118)⁎ 0.54

Working Memory distracterP3 amplitude (Pz)

20.2 8.9 24.0 9.9 −1.9 (118)⁎ 0.40

Switching of Attentionduration trial 2

74.3 30.9 62.9 25.8 2.0 (118) ⁎ 0.40

FAS animal condition 17.2 6.9 19.2 6.4 −1.5 (118)Working Memory targetP3 amplitude (Pz)

17.8 7.8 20.1 7.9 −1.4 (118)

Verbal Interference errors trial 1 1.1 1.4 0.7 1.2 1.3 (118)Auditory Oddball false negative 1.1 1.7 1.7 3.7 −1.2 (117.9)Auditory Oddball false positive 3.2 3.8 4.0 7.3 − .63 (118)

⁎ Pb0.05. ⁎⁎Pb0.01. ⁎⁎⁎Pb0.001.a Independent samples t-tests.

190 A.C.P. Sawyer et al. / Psychiatry Research 170 (2009) 183–191

who have symptoms of both AD/HD and sleep problems may have adifferentunderlying cause for their problems than childrenwhohaveonlyAD/HD or only sleep problems. Consequently, it is possible that differenttreatment approaches may be needed for childrenwith combined symp-toms of AD/HD and sleep problems.

Acknowledgments

This studywas funded by an Australian Research Council (ARC) LinkageGrantwith theBrain Resource Company (Grant no. LP0349079).We acknowledge the support of the BrainResource InternationalDatabase (BRID), under the auspicesof TheBrainResourceCompany(www.brainresource.com) for the use of the psychophysiological and cognitive data. Wealso thank the individuals who gave their time to take part in the study.

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