Eye-blinks in children with Attention-Deficit/Hyperactivity Disorder

(1)

Research Master Thesis

Eye-blinks in Children with

Attention-Deficit/Hyperactivity Disorder

University of Amsterdam Graduate School of Psychology

Student

Name: Lennart J. ‘t Lam Student ID number: 6345948

Supervisors

Within ResMas: Prof. Dr. H.M. Geurts External supervisor: Dr. A. Mazaheri Date: August 2014

Number of credits: 26 ECTS

(2)

1

Abstract

Objective: Attention-Deficit/Hyperactivity Disorder (ADHD) has been associated with an abnormal dopamine activity. Previous research has established a link between spontaneous Eye-Blink Rates (EBR) and dopamine level. We tested whether there is an overall difference in dopamine level and/ or a difference in its task-induced modulation. Method: Dopamine level was estimated in 7 children with ADHD and 13 typically developing controls (all ages 8 to 12 years) using EBR. EBR was extracted from electrooculography measurements that were done in rest and during a working memory task. Results: No overall difference was found between the estimated dopamine level of children with and without ADHD. The amount of task-induced dopamine modulation as estimated with EBR did differ between the two groups (M = 1.11, sd = 1.13 vs. M = 0.03, sd = .61, p = .045). Discussion: Children with ADHD show a smaller task-induced increase in dopamine than children without ADHD. Implications for future research are discussed.

Keywords

ADHD, dopamine, children, Eye-Blink Rate (EBR)

Introduction

Children with Attention Deficit/ Hyperactivity Disorder (ADHD) show great difficulty in maintaining focus on tasks at hand and exhibit impulsive and/or hyperactive behavior (DSM-5; American Psychiatric Association (APA), 2013). Almost half of these children also suffer from problems with learning and communication (Barkley, 1981). ADHD is characterized as a combination of three core symptoms: attention deficits, a high level of impulsivity and hyperactivity (APA, 2013; Barkley, 1997; Douglas, 1972). However, great differences exist in severity both within and between these symptoms (Toplak et al., 2009; Tsal, Shalev & Mevorach, 2005). Given these differences in symptom severity, there is no razor sharp behavioral distinction between children with and without ADHD. Possibly, a focus on biological measures instead of symptoms could aid in defining the distinction more clearly. In this study, we investigated whether dopamine level as estimated by the spontaneous Eye-Blink Rate (EBR) can be used as a distinctive measure on a biological level.

On a biological level, ADHD is associated with an abnormal functioning of catecholamines (Biederman, 2005; Prince, 2008). Particularly, abnormalities are found in the availability of dopamine (Faraone & Biederman, 2002; Prince, 2008; Russell, de Villiers, Sagvolden, Lamm & Taljaard, 1995; Zametkin & Rapoport, 1987). Dopamine is a neurotransmitter and plays a role in the transfer of an impulse. It is released by a presynaptic neuron into the synapse. Part of the released dopamine binds to a receptor on the postsynaptic neuron to forward the message. The remainder is deactivated by a dopamine transporter (DAT) that enables reuptake by the presynaptic neuron (Biederman, 2005).

(3)

2 The fact that abnormalities were found in the availability of dopamine led to the hypothesis that children with ADHD have learning difficulties due to a hypofunctioning dopamine system (Sagvolden, Johansen, Aase & Russell, 2005). When children learn, a neural connection is laid between task performance and receiving a reward (Luman, Scheres & Tripp, 2010). Dopamine is necessary in forming and maintaining such connections (Biederman, 2005). Therefore, dopamine shortage is known to lead to less stable connections between action and consequence. On a behavioral level this manifests in low intrinsic motivation, higher reward-seeking behavior and attention problems; all three are typically found in children with ADHD. The idea of a hypofunctioning dopamine system has been confirmed by Volkow and colleagues (2009). They reported that patients with ADHD have a higher amount of DAT, but a lower amount of dopamine receptors than controls. Not surprisingly, an important element in the treatment of ADHD is medication that targets the dopaminergic systems (Cortese et al., 2013; DelCampo, Chamberlain, Sahakian & Robbins, 2011). Medication for ADHD, such as methylphenidate, increases the availability of dopamine in the synapse by inhibiting DAT (Cortese et al., 2013; Prince, 2008). Considering all this, we aimed to investigate whether dopamine level can be used as a distinctive measure between children with and without ADHD.

This leaves us with the question: how can dopamine level easily be measured in children with ADHD? Direct measurement requires invasive methods, such as Positron Emission Topography for which injection with a radioligand is necessary (Volkow et al., 2009). One non-invasive method to assess dopamine level is by quantifying EBR (Karson, 1983). Karson presented three experiments supporting EBR in his 1983 article. First, he gave primates dopamine agonists to increase the dopamine level. Next, Karson compared the EBR of patients with Parkinson on and off medication, and finally he compared the EBR of patients with schizophrenia on and off medication. In all experiments, a higher dopamine level was indicated by higher EBR. Therefore, in the current study we will use EBR as an indirect measure for dopamine level.

To our knowledge, only two previous studies have used EBR as proxy for dopamine level in the ADHD population. In the first study, the EBR of 28 children with ADHD (8 on and 20 off medication) was compared to the EBR of 47 controls during three tasks: a conversation task, a listening task and a memory task (Caplan, Guthrie & Komo, 1996). The results were mixed. Overall, the EBR of children with ADHD off medication did not differ from that of controls. However, during the listening task the EBR of the control group increased, whereas the EBR of the ADHD group off medication did not change. This could point to a problem with dopamine modulation, instead of an overall dopamine deficiency. Children with ADHD on medication displayed higher EBR than the control group during the conversation task, but also showed no performance difference between tasks. Because all EBR measurements were recorded during cognitive tasks, there was no clear

(4)

3 measurement of a baseline dopamine level. In the second study, 31 children with ADHD were compared to 26 controls and a lower EBR was found in children with ADHD (Konrad, Gauggel & Schurek, 2003). Since EBR measures were only taken after the participants performed a cognitive task, it was not clear whether this showed a general/baseline deficit or a modulation problem. To distinguish a baseline deficit from a modulation problem, we measured EBR both in rest and during a cognitive task. We chose to measure EBR with Electrooculography (EOG) as this is a reliable and non-invasive method (Colzato, Van den Wildenberg, Van der Wouwe, Pannebakker & Hommel, 2009).

Since the aim of this study was to find dopaminergic modulation, the cognitive task should induce an increase in dopamine level. Tasks that require working memory (WM) have been linked to dopaminergic functioning (Collins, Roberts, Dias, Everitt, Robbins, 1998; Diamond, 2007). WM is a system dedicated to maintaining and storing information in the short term (Baddeley, 2003). ADHD has consistently been associated with impairments in WM (Barkley, 1997; Kofler, Rapport, Bolden, Sarver & Raiker, 2010; Kuntsi, Oosterlaan, & Stevenson, 2001). Therefore, we used a WM task to induce dopaminergic modulation.

To summarize, ADHD has been linked to anomalies in the dopamine system. Based on previous research it is not clear whether this is a deficiency in baseline dopamine level or in the modulation of dopamine during cognitive load. Dopamine level can be estimated using EBR and might be a distinctive measure for ADHD on a biological level. Our main goal was to verify whether dopamine level can be used to differentiate children with and without ADHD. We hypothesize that a difference in baseline dopamine level exists; resulting in lower EBR in rest in children with ADHD compared to children from a control group. Our second hypothesis is that children with ADHD show less increase in their EBR during a WM task as compared to the control group, which would indicate a difference in dopamine modulation.

(5)

4

Method

Participants

This study included 19 boys and 1 girl in the age range of 8-12 years. The participants were 7 children with ADHD and 13 typically developing controls. All children in the ADHD group met the DSM-5 criteria for ADHD (APA, 2013). They were recruited at the Bascule, an academic child psychiatry centre in Amsterdam, where they undergo ADHD-related treatment. Children from the control group were recruited at an elementary school. In order to minimize the possibility that children from the control group had an undiagnosed form of ADHD, parents filled in the Disruptive behavior disorder scale (DBD; Pelham, Gnagy, Greenslade & Milich, 1992; Dutch version Oosterlaan, Baeyens, Scheres, Antrop, Roeyers, & Sergeant, 2008). A clinical score (score > 30) on this instrument was used as an exclusion criterion. Table 1 summarizes the descriptive variables for all participants. The IQ scores of the controls are estimated scores based on two subtests of the WISC, whereas the scores of the children in the ADHD group were taken from a previously completed WISC (no older than 2 years old). The study was approved by the ethics committee of the University of Amsterdam (http://ce.psy-uva.nl/2013-OP-2816) and conducted according to the declaration of Helsinki. Assent was obtained from the children and written informed consent from their parents.

Table 1: Descriptive variables of all participants

Participant Group Gender Age Meds (estimated) IQ Score DBD

1 Control Male 12 No 116 2 (normal)

13 Control Male 11 No 107 26 (subclinical)

14 ADHD Female 9 No 103 -

15 ADHD Male 9 Yes 82 -

16 ADHD Male 10 No 111 -

Procedure

The controls were tested at their own elementary school and children with ADHD at the institution where they also received treatment. At both locations testing took place in a room where they would not be disturbed. The typically developing controls first completed two subtests of the WISC-III (Block

(6)

5 design and word reasoning). The electroenchalography (EEG) and EOG equipment was placed according to the manufacturers specifications (more details are given in the next section). During this set-up, participants were shown a DVD of Pixar’s short movies. When set-up was complete, participants were asked to look straight ahead for a three minute interval, after which the task was explained and performed. All participants received a small gift at the end of the task.

Instruments

EEG and EOG

For the EEG and EOG measurements, we used EEG-equipment from ANT Neuro and the accompanying software (ASA). EEG recordings were not used in the current analysis, but were obtained as a courtesy to fellow researchers. Electrode placement for the EEG recordings was done using an electrode cap, and activity in 64 derivations was recorded. Separate EOG electrodes were placed directly on the skin. All electrodes were placed according to the manufacturer’s manual. The sampling rate of the recordings was 512 Hz.

Once the EEG equipment was attached, participants were asked to sit still for three minutes in order to obtain a measure of baseline EBR. This was used as an estimate of baseline dopamine level. Hereafter, participants were asked to perform a WM task. EEG and EOG equipment continued to record during this task. As estimate for the task-induced dopamine level we aimed to use the average EBR of the participants during a five-minute interval fifteen minutes into the task. The amount of modulation was defined as the difference between the task-induced and the baseline dopamine level.

Working memory task

During the EEG and EOG recordings participants had to complete a visual memory task. Figure 1 shows the different steps of the task. In the task, the participants were required to selectively remember only a few items from within one side of an array. In each trial, participants were first shown a fixation cross for 500 ms, directly followed by an indicator sign which was shown for 500 ms to inform the participants from which side of the array the information needed to be remembered. Participants were then presented with a brief (100 ms) array of 2 to 4 rectangles of different colors (red and blue) and varying orientations per side. The participants were requested to only remember the orientation of the blue rectangles (i.e. the targets) on the indicated side; the red rectangles acted as distractors. After a retention period of 1000 msec, the test array was presented. In 50% of the cases a target rectangle changed orientation. In the other 50% of the cases a distractor rectangle changed orientation or nothing changed. Participants were asked to report whether the target rectangles changed orientation or stayed the same by pressing one of two buttons during the two

(7)

6 second presentation of the test array. Between each trial was a 500 ms interval period. All participants received the same instructions via a slide show and performed the same test round consisting of 20 trials to get acquainted with the task. The experimenter repeated the instructions during the test round when needed. The test itself consisted of 15 rounds of 20 trials. The participants could start a new round themselves by pressing a button when they were ready.

Figure 1: Example of one trial of the experiment

Statistical analyses

To investigate possible confounding factors, two t-tests were performed with age and IQ as independent variables, and group dependence as dependent variable. The initial dopamine level of each participant was estimated with the average EBR during a five minute time interval at the beginning of the task. The task-induced dopamine level was estimated with the average EBR during a five minute interval at the end of the task. These results were compared using a repeated measures ANOVA. To address the first hypothesis, we used the main effect of group that would indicate an overall difference in dopamine level1. The second hypothesis was that the EBR would increase less in children with ADHD during a WM task than in the control group. This was verified by examining the interaction effect of the ANOVA. In order to conclude that controls would have a task-induced increase in dopamine level and children in the ADHD group would not, a significant interaction effect should show a bigger increase in EBR for controls as compared to children with ADHD.

1

The initial goal was to look for a baseline difference. However, it proved infeasible to obtain a valid baseline measure as we described in the results section. Therefore, we investigated whether an overall difference in dopamine levels could be found.

(8)

7 All analyses were done using IBM SPSS Statistics 19® (IBM, Armonk, NY, USA). All tests were performed with a significance level of α = .05. A partial eta-squared measure was used for the effect size. The interpretation of the effect size was based on the recommendations by Cohen (1998): partial squared > .01 (small effect), partial squared > .13 (medium effect), and partial eta-squared > .26 (large effect).

(9)

8

Results

The EOG data obtained during the first three minutes that participants were asked to just look straight ahead proved unreliable because it contained too many artifacts caused by excessive movements from the participants. Two five-minute time intervals during the task were chosen in order to determine modulation in dopamine level. These intervals were minutes 1-6 for the initial EBR and minutes 15-20 for the task-induced EBR. A repeated measures ANOVA was performed with initial EBR and task-induced EBR as within subject factors; group dependence was used as between subject factor. We first investigated group differences other than ADHD diagnosis to control for possible confounding effects.

Group differences

No age differences were found between the control group (M = 10.38, sd = 1.39) and the ADHD group (M = 10.67, sd = 1.21; CI 95% [-1.68, 1.11], t(17) = -0.43, p > .05). Furthermore, no differences in IQ were found between the control group (M = 103.54, sd = 16.03) and the ADHD group (M = 101.67, sd = 7.79; CI 95% [-12.83, 16.57], t(17) = 0.27, p > .05).

Repeated measures ANOVA

We found a significant difference for the overall group between initial EBR (M = 9.50, sd = 1.97) and task-induced EBR (M = 10.26, sd = 2.60; CI 95% [-1.09, -.05], F(1,17) = 5.28, p = .035, η2 = .24). This shows that, in general, our manipulation caused an increase in EBR. This gives credence to the viability of our manipulation.

Our first hypothesis was that children with ADHD show a lower estimated dopamine level than children with ADHD. In contrast with this hypothesis, we found no difference between the overall EBR in controls (M = 10.40, sd = 1.6) and the overall EBR in children with ADHD (M = 8.75, sd = 3.1; CI 95% [-.60, 3.90], F(1,17) = 2.40, p > .05, η2 = .12).

Our second hypothesis was that the EBR would increase less in children with ADHD during a WM task than in the control group. In correspondence to this hypothesis, we found a significant interaction effect in which the control group (M = 1.11, sd = 1.13) showed a bigger increase in EBR than the ADHD group (M = 0.03, sd = .61; CI 95% [.03, 2.12], F(1,17) = 4.68, p = .045, η2 = .22). This effect is visualized in Figure 2.

(10)

9 Figure 2: Controls show a bigger task-induced increase in dopamine level as estimated with EBR compared to children with ADHD

6,0 7,0 8,0 9,0 10,0 11,0 12,0

Beginning of task End of task

EB R per m inut e Control ADHD

(11)

10

Discussion

The purpose of the present study was to examine whether EBR as an estimate of dopamine level can differentiate children with and without ADHD. We sought to extend on past research by establishing a clear measure of dopamine modulation. We found no clear difference between the overall dopamine level as estimated by EBR for children with and without ADHD. However, there was a difference in the amount of task-induced dopamine modulation as estimated with EBR.

The findings of this study are consistent with previous research that investigated the differences in EBR between children with and without ADHD (Caplan, Guthrie and Komo, 1996; Konrad, Gauggel & Schurek, 2003). However, it extends beyond this research by showing that the deficiency in dopaminergic functioning in children with ADHD could be the lack of task-induced modulation of dopamine.

We did not find an overall difference in EBR, although the biological abnormalities found in children with ADHD by Volkow and colleagues (2009) would predict that such a difference would exist. Similar to our findings, Caplan, Guthrie and Komo (1996) also did not find overall differences in dopamine level in children with ADHD. We did find a difference in dopamine level during a WM task, which is known to be related to dopaminergic functioning (Collins, Roberts, Dias, Everitt, Robbins, 1998; Diamond, 2007). This could indicate that, in line with the behavioral symptoms seen in ADHD, the biological differences present themselves more clearly when the situation calls for a higher dopamine level. This amplifies the need for a clear understanding of what the dopamine deficiency in children with ADHD entails and how it presents itself. It should be noted that our null finding could also stem from limitations in our study.

Limitations

Although we found a significant effect of our manipulation, i.e. the amount of EBR increased because of the task, it should still be noted that EBR is just an estimate of dopamine level. The amount of blinking can also be influenced by external factors. In our experiment the focus on an object for an extended period of time is the main external factor influencing EBR. However, we did find a clear increase in EBR during the task, whereas focusing on an object has been found to decrease EBR (Bentivoglio et al, 1997).

The small number of the ADHD group makes the results of this study less reliable because individual variety has a bigger influence in a small sample size. The standard deviation in the ADHD group was larger than that for controls, which implies that more individual variety existed in de smaller group. No outliers were detected, however not all participant characteristics are accounted for. In future research one should try to obtain a bigger sample size.

(12)

11 Besides the size of the experimental group, the heterogeneity should also be considered. Even though the groups did not differ with respect to age and estimated IQ, some of the participants had taken dopamine agonistic medication prior to completing the task. In future studies, this possible confounding variable should be kept to a minimum. Furthermore, the children in the control group performed two subtests of the WISC-III before set-up of the EEG-equipment. This could have lead to an increase in dopamine level. However, set-up usually takes up to half an hour which allows time for the dopamine level to return to baseline (George, Le Moal & Koob, 2012). Nevertheless, in future research it would be better if the procedure was exactly the same for both the control group and the experimental group.

Future research

Since this study has several limitations, replication should be the first goal for future research. Once the deficiency in modulation has been confirmed, this gives rise to further research possibilities that can be incorporated in two existing theoretical models.

First, on a cognitive level, motivational problems are seen as a key problem in ADHD (Sergeant, Oosterlaan & Van der Meere, 1999). These motivational problems have already been linked to a hypofunctioning dopamine system, causing a less stable connection between task performance and reward (Luman, Scheres & Tripp, 2010; Sagvolden, Johansen, Aase & Russell, 2005; Volkow et al., 2009). Not surprisingly, Dovis et al. (2012) have found that motivational deficits in task persistence and performance in children with ADHD can be overcome by offering reinforcements. Expanding on these studies, future research might incorporate a measure of EBR to investigate how reinforcements affect both dopamine level and task performance. Perhaps, this will make it possible to quantify the modulation deficit in dopamine level as estimated by EBR by comparing baseline EBR, EBR during a task without reinforcements and EBR during a task with reinforcements.

Second, on an empirical level, ADHD is described by the cognitive-energetic model (Sergeant, 2000). In this model, ADHD is approached at three distinct levels: a lower set of cognitive processes (encoding, central processing and response organization), the energetic pools (arousal, activation and effort) and the management or executive function system (e.g., behavioral inhibition and sustained attention). The activation and effort energetic pools appear most relevant to ADHD and show a modulation problem that impairs response organization, and can explain, at least partly, the inhibition deficits in ADHD (Sergeant, 2005). It is striking that both our study and the cognitive-energetic model have found modulation problems. Future research could investigate how the modulation problem in the energetic pools corresponds with and correlates to the modulation problem in dopamine level.

(13)

12 The results from this study can also guide future research on medication effects in children with ADHD. The medications for ADHD increase the availability of dopamine (Cortese et al., 2013; Prince, 2008). However, to our knowledge, less is known of its effect on the modulation process. Therefore, it is possible that medicated children have a higher baseline level, but that the modulation deficiency is unaffected. This is in accordance to the finding of Caplan, Guthrie and Komo (1996) that medicated children with ADHD have a higher baseline dopamine level compared to non-medicated children with ADHD; while neither group showed modulation of dopamine level. Our results show that the modulation deficiency might be the main problem in dopaminergic functioning, and raising the baseline level might, therefore, not be enough to alleviate all symptoms associated with ADHD. One can compare this with increasing an athlete’s heart rate to a fixed 120 beats per minute: too high in rest while at the same time not high enough when pushed to the limit. Therefore, it is unlikely that medication alone will ‘cure’ children with ADHD; offering external guidance, motivation and assistance to children with ADHD is still essential.

Acknowledgements

I would like to thank mw. prof. dr. H.M. (Hilde) Geurts for her supervising role in this project. Special thanks go out to A. Mazaheri and R. van Diepen for their help in collecting the data and their teachings in equipment management.

Declaration of conflicting interests

(14)

13

References

American Psychiatric Association (2013). Diagnostic

and statistical manual of mental disorders (5th ed.).

Washington, DC: American Psychiatric Association.

Baddeley, A. (2003). Working Memory: Looking Forward and Looking Back. Neuroscience, 4, 829-839.

Barkley, R. A. (1981). Hyperactive children: A handbook

for diagnosis and treatment. New York: Guilford.

Barkley, R.A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin,

121, 65-94.

Bentivoglio, A.R., Bressman, S.B., Cassetta, E., Carretta, D., Tonali, P., & Albanese, A. (1997). Analysis of Blink Rate Patterns in Normal Subjects. Movement Disorders, 12, 1028-1034. Biederman, J. (2005). Attention-Deficit/

Hyperactivity Disorder: a selective overview.

Biological Psychiatry, 57, 1215-1220.

Caplan, R., Guthrie, D., & Komo, S. (1996). Blink Rate in Children with Attention-Deficit-Hyperactivity Disorder. Biological Psychiatry, 39, 1032-1038.

Cohen J, (1998). Statistical power and analysis for the

behavioral sciences. 2 edition. Hillsdale: Lawrence.

Collins, P., Roberts, A.C., Dias, R., Everitt, B.J., Robbins, T.W. (1998). Perseveration and strategy in a novel spatial self-ordered task for nonhuman primates: effect of excitotoxic lesions and dopamine depletions of the prefrontal cortex.

Journal of Cognitive Neuroscience, 10, 332-354.

Cortese, S., Holtmann, M., Banaschewski, T., Buitelaar, J., Coghill, D., Danckaerts, M., Dittmann, R.W., Graham, J., Taylor, E., & Sergeant, J. (2013). Practitioner Review: Current best practice in the management of adverse events during treatment with ADHD medications in children and adolescents. The Journal of Child

Psychology and Psychiatry, 54, 227-246.

Colzato, L.S., Van den Wildenberg, W.P.M., Van der Wouwe, N.C., Pannebakker, M.M., & Hommel, B. (2009). Dopamine and inhibitory action control: evidence from spontaneous eye blink rates. Experimental Brain Research, 196, 467-474. Del Campo, N., Chamberlain, S.R., Sahakian, B.J., &

Robbins, T.W. (2011). The Roles of Dopamine and Noradrenaline in the Pathophysiology and Treatment of Attention-Deficit/ Hyperactivity Disorder. Biological Psychiatry, 69, 145-157.

Diamond, A. (2007). Consequences of Variations in Genes that affect Dopamine in Prefrontal Cortex.

Cerebral Cortex, 17, 161-170.

Douglas, V. I. (1972). Stop, look, and listen: The problem of sustained attention and impulse control in hyperactive and normal children.

Canadian Journal of Behavioural Science, 4, 259-282.

Dovis, S., Van der Oord, S., Wiers, R.W., & Prins, P.J.M. (2012). Can Motivation Normalize Working Memory and Task Persistance in Children With Attention-Deficit/Hyperactivity Disorder? The Effects of Money and Computer-Gaming. Journal of Abnormal Child Psychology, 40, 669-681.

Faraone, S.V., Biederman, J. (2002): Pathophysiology of

Attention Deficit Hyperactivity Disorder. In: Davis, K.,

Charney, D., Coyle, J.T., Nemeroff, C. (eds), ACNP’s Fifth Generation of Progress Version 2. New York: Lipponcott, Williams, and Wilkens George, O., Le Moal, M. & Koob, G.F. (2012).

Allostasis and addiction: Role of the dopamine and corticotrophin-releasing factor systems.

Physiology and Behavior, 106, 58-64.

Karson, C. N. (1983). Spontaneous eye-blink rates and dopaminergic systems. Brain, 106, 643–653. Kofler, M.J., Rapport, M.D., Bolden, J., Sarver, D.E.,

& Raiker, J.S. (2010). ADHD and working memory: the impact of central executive deficits and exceeding storage/rehearsal. Journal of

Abnormal Child Psychology, 38, 149-161.

Konrad, K., Gauggel, S., & Schurek, J. (2003). Catecholamine functioning in children with traumatic brain injuries and children with attention-deficit/hyperactivity disorder. Cognitive

Brain Research, 16, 425-433.

Kuntsi, J., Oosterlaan, J., & Stevenson, J. (2001). Psychological mechanisms in hyperactivity: Response inhibition deficit, working memory impairment, delay aversion, or something else?

Journal of Child Psychology and Psychiatry, 42, 199-210.

Luman, M., Tripp, G., & Scheres, A. (2010). Identifying the neurobiology of altered reinforcement sensitivity in ADGD. A review and research agenda. Neuroscience and Biobehavioral

Reviews, 34, 744-754.

Oosterlaan, J., Baeyens, D., Scheres, A., Antrop, I., Roeyers, H. & Sergeant, J.A. (2008). Vragenlijst

voor gedragsproblemen bij kinderen 6-16 jaar, Handleiding. Amsterdam: Harcourt Publishers.

Pelham, W.E., Gnagy, E.M., Greenslade, K.E., & Milich, R. (1992). Teacher ratings of DSM–III–R symptoms of the disruptive behavior disorders.

Journal of the American Academy of Child & Adolescent Psychiatry, 31, 210-218.

Prince, J. (2008). Catecholamine Dysfunction in Attention-Deficit/Hyperactivity Disorder: An

(15)

14 Update. Journal of Clinical Psychopharmacology, 28,

s39-s45.

Russell, V, de Villiers, A., Sagvolden, T., Lamm, M., Taljaard J. (1995): Altered dopaminergic function in the prefrontal cortex, nucleus accumbens and caudate-putamen of an animal model of attention-deficit hyperactivity disorder: the spontaneously hypertensive rat. Brain Research, 676, 343–351. Sagvolden, T., Johansen, E.B., Aase, H., & Russell,

V.A. (2005). A dynamic developmental theory of attention-deficit/ hyperactivity disorder (ADHD) predominantly hyperactive/ compulsive and combined subtypes. Behavioral and Brain Sciences,

28, 397-468.

Sergeant, J.A. (2000). The cognitive-energetic model: an empirical approach to Attention-Deficit/ Hyperactivity Disorder, Neuroscience and Biobehavioral Reviews, 24, 7-12.

Sergeant, J.A., Oosterlaan, J., & Van der Meere, J. (1999). Information processing and energetic factors in Attention-Deficit/Hyperactivity Disorder. In H.C. Quay & A.E. Hogan (Eds.),

Handbook of disruptive behavior disorders (pp 75-104).

New York: Kluwer Academic/Plenum Publishers. Sergeant, J.A. (2005). Modeling Attention-Deficit/

Hyperactivity Disorder: A Critical Appraisal of the Cognitive-Energetic Model. Biological Psychiatry,

57, 1248-1255.

Toplak, M.E., Pitch, A., Flora, D.B., Iwenofu, L., Ghelani, K., Jain, U., & Tannock, R. (2009). The Unity and Diversity of Inattention and Hyperactivity/Impulsivity in ADHD: Evidence for a General Factor with Separable Dimensions.

Journal of Abnormal Child Psychology, 37, 1137-1150.

Tsal, Y., Shalev, L., & Mevorach, C. (2005). The Diversity of Attention Deficits in ADHD: The Prevalence of Four Cognitive Factors in ADHD Versus Controls. Journal of Learning Disabilities, 38, 142-157.

Volkow, N.D., Wang, G.J., Kollins, S.H., Wigal, T.L., Newcorn, J.H., Telang, F., Fowler, J.S., Zhu, W., Logan, J., Ma, Y., Pradhan, K., Wong, C., & Swanson, J.M. (2009). Evaluating Dopamine Reward Pathway in ADHD. Journal of the American