Methylphenidate does not improve interference control during a working memory task in young patients with attention-deficit hyperactivity disorder

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Methylphenidate does not improve interference control during a working memory task in young patients with attention-deficit hyperactivity disorder Alexander Prehn-Kristensen a,⁎,1 , Kerstin Krauel b,c,1 , Hermann Hinrichs c , Jochen Fischer a , Ulrike Malecki d , Hartmut Schuetze d , Stephan Wolff e , Olav Jansen e , Emrah Duezel d,f , Lioba Baving a,g a

Department of Child and Adolescent Psychiatry and Psychotherapy, Niemannsweg 147, Center for Integrative Psychiatry, 24105 Kiel, Germany b Department of Child and Adolescent Psychiatry and Psychotherapy, University of Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany c Department of Neurology and Center for Advanced Imaging, University of Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany d Department of Cognitive Neurology and Dementia Research, University of Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany e Department of Neuroradiology, Christian-Albrechts-University School of Medicine, Schittenhelmstraße 10, 24106 Kiel, Germany f Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AR, UK g Department of Child and Adolescent Psychiatry and Psychotherapy, Christian-Albrechts-University School of Medicine, Niemannsweg 147, 24105 Kiel, Germany

A R T I C LE I N FO

AB S T R A C T

Article history:

Patients with attention-deficit/hyperactivity disorder (ADHD) show deficits in working

Accepted 24 February 2011

memory (WM) which may be related to prefrontal dysfunction. Methylphenidate (MPH) can

Available online 5 March 2011

restore WM deficits in ADHD by enhancing prefrontal activity. At the same time, changes in striatal activation could cause ADHD patients to be more interference-sensitive during

Keywords: ADHD fMRI Working memory Interference Methylphenidate

working memory tasks. However, it is unclear whether MPH reduces WM distractibility in ADHD. In this fMRI study, 12 ADHD patients and 12 healthy controls participated on two separate days in a delayed-match-to-sample test. During the delay interval, a distractor stimulus was presented in half of the trials. Children and adolescents with ADHD received MPH only on one of the two sessions. Behavioral data analyses revealed that MPH normalized WM in ADHD. However, MPH did not improve WM performance when a distractor was presented during the delay interval. Functional images showed that MPH enhanced prefrontal activity during the delay in ADHD patients when no distractor was present. If the delay was interrupted by a distractor, only healthy controls showed activation

⁎ Corresponding author. Fax: + 49 431 9900 5262. E-mail addresses: [email protected] (A. Prehn-Kristensen), [email protected] (K. Krauel), [email protected] (H. Hinrichs), [email protected] (J. Fischer), [email protected] (U. Malecki), [email protected] (H. Schuetze), [email protected] (S. Wolff), [email protected] (O. Jansen), [email protected] (E. Duezel), [email protected] (L. Baving). 1 These authors contributed equally to this work. 0006-8993/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2011.02.075

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of the caudate. In patients with ADHD, however, in line with behavioral data, MPH did not enhance caudate activity. In healthy youth, caudate activity is involved in interference control allowing the successful maintenance of information in working memory even in the presence of distraction. Our findings suggest that interference control, linked to caudate activity, is not adequately enhanced by MPH in ADHD. © 2011 Elsevier B.V. All rights reserved.

1.

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is with a prevalence of 3–5% one of the most common childhood neuropsychiatric disorders. According to the Diagnostic and Statistical Manual (DSM-IV), patients are characterized by a chronic pattern of inattention, impulsivity/hyperactivity, or both (American Psychiatric Association, 2000). Neuropsychological investigations consistently show that ADHD patients display impairments in working memory (WM; Doyle, 2006; Martinussen et al., 2005; Walshaw et al., 2010). WM refers to cognitive and neural processes involved in holding information in mind for later use (Baddeley, 1992). A large body of investigations indicate that ADHD is associated with structural and functional alterations in dopaminergic frontostriatal brain circuits, critically involved in WM (Arnsten, 2009; Brennan and Arnsten, 2008; Sheridan et al., 2007). Particularly, the prefrontal cortex is believed to enable sustained representation of task-relevant information also referred to as WM maintenance (Landau et al., 2009; Ranganath, 2006; D'Esposito, 2007). Successful WM performance is not only characterized by maintenance but also by control of attention and behavior during the processing of task-irrelevant stimuli (Zanto and Gazzaley, 2009). In healthy humans, interference control during visual WM tasks is reflected by enhanced activity in frontal brain regions, visual association areas as well as in the striatum. While the former regions enable a stable representation of memorized visual stimulus material during distraction (Clapp et al., 2010; Jha et al., 2004; Yoon et al., 2006), the striatum can be associated with the filtering of relevant information and general control for motor behavior during WM performance (Postle and D'Esposito, 1999; Gazzaley et al., 2004; McNab and Klingberg, 2008). Children with ADHD show a higher distractibility in WM tasks compared to controls (Gumenyuka et al., 2005, Keage et al., 2006), and it is proposed that the intake of methylphenidate (MPH) or MPH-like stimulants may reduce WM distractibility in adult ADHD (Frank et al., 2007). MPH, the first-line treatment in ADHD, increases prefrontal dopamine and noradrenaline as well as subcortical dopamine availability by blocking the dopamine transporter (DAT; Heal et al., 2009; Wilens, 2008). While MPH-induced reduction of WM deficits in ADHD have been shown to be prefrontally mediated by catecholamine D1 receptor stimulation (Arnsten and Dudley, 2005; Berridge et al., 2006; Devilbiss and Berridge, 2008), the contribution of MPH to WM distractibility is still not sufficiently understood. Most likely, an MPH-induced reduction of impulsiveness could be achieved via a blockade of DAT on striatal level, which mainly stimulates the D2 receptor system (Camps et al., 1989; Vaidya et al., 2005). Recent data by

Frank et al. (2007) seem to support the finding that MPH reduces impulsiveness by stimulating the striatal D2 receptor system (Frank et al., 2007). Despite of the high relevance of interference control for academic achievement, to our knowledge, the impact of MPH on WM distractibility in school-aged patients with ADHD has not yet been described. The aim of the present study was therefore to investigate whether or not the intake of MPH reduces distractibility in working memory in ADHD. We hypothesized that the intake of MPH would enhance prefrontal brain activity and normalize WM performance in patients with ADHD. Moreover, we expected that in healthy controls and in ADHD patients with MPH WM interference control would lead to enhanced activity in frontal cortex, visual association cortices and in the striatum.

2.

Results

2.1.

Behavioral data

The ANOVA analysis of WM accuracy in ADHD revealed no main effect for MPH [F(1,11)= 1.28, p = .282] nor distraction [F(1,11) = 4.80, p = .051]. However, the interaction Distraction × Treatment [F(1,11) = 5.43, p = .044] reached significance. Subsequent t tests revealed that MPH enhanced WM accuracy without distraction [t(11) = 2.98, p = .013, see Table 1 and Fig. 1], but not in the presence of distraction [t(11)= .56, p = .586]. Moreover, while in untreated ADHD patients, we observed no difference between the distracted and undistracted WM task conditions [t(11)= .735, p = .478], the intake of MPH lead to better performance in the undistracted than in the distracted WM task [t(11)= 3.97, p = .002]. When ADHD patients were compared to control participants by using t tests (see Behavioral data analyses), MPH normalized WM accuracy only without distraction [MPH off vs. controls: t(22) = 2.98, p = .007; MPH on vs. controls: t(22) = .55, p = .584], but not with distraction [MPH off vs. controls: t(22) 2.06, p = .051; MPH on vs. controls: t(22) = 2.42, p = .024]. Healthy controls did not show differences in accuracy between the distracted and the undistracted WM task [t(11) = .22, p = .833]. Regarding control task performance, there were neither significant main effects nor an interaction between DISTRACTION and TREATMENT in the patient group (p > .4), nor did any comparison between ADHD and controls reach significance (p > .2). Reaction times analysis revealed that in ADHD MPH had no effect on performance speed (no main effects or interactions were significant; p > .2). When compared to controls, ADHD patients treated with MPH showed a delayed response time during the WM task without distraction [t(22) = 1.53, p = .042].

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Table 1 – Behavioral data. Task

Accuracy (%)

Distraction

− + − + − + − +

WM CONT

Reaction times (s)

WM CONT

ADHD MPH off

ADHD MPH on

Controls

Mean (±SEM)

Mean (±SEM)

Mean (±SEM)

54.2 58.3 83.3 82.3 1.62 1.61 1.73 1.66

(4.6) (5.8) (2.3) (3.4) (.14) (.11) (.13) (.14)

69.3 54.2 84.4 85.9 1.68 1.55 1.85 1.67

(5.0) (6.6) (3.9) (3.7) (.15) (.12) (.13) (.13)

72.9 73.4 78.1 80.7 1.28 1.29 1.53 1.42

(4.2) (4.5) (4.1) (3.3) (.10) (.11) (.11) (.10)

ADHD: MPH off vs. on

ADHD MPH off vs. controls

ADHD MPH on vs. controls

p

p

p

.013 .586 .767 .368 .723 .653 .394 .918

.007 .051 .283 .746 .068 .060 .259 .200

.584 .024 .281 .305 .042 .139 .076 .135

WM, working memory; CONT, control task; ADHD, attention-deficit/hyperactivity disorder; MPH, methylphenidate.

2.2.

Functional images

In response to the sample picture, ADHD patients without MPH showed broad activations in the inferior frontal cortex, insula, fusiform gyrus, cingulated gyrus, thalamus, caudate nucleus, pre- and postcentral gyrus, inferior parietal gyrus and inferior occipital gyrus when the working memory task was contrasted with the control task (Table 2). After receiving MPH, ADHD patients displayed activations in the inferior frontal cortex, insula, temporal gyrus, cingulated gyrus, precentral gyrus and medial occipital gyrus during sample encoding (Table 3). In the same contrast, healthy controls showed activations in the fusiform gyrus, the anterior cingulate gyrus, the occipital gyrus and in the precuneus (Table 4).

100

Accuracy (%)

80

60

40

20

0 ADHD MPH off

ADHD MPH on

WM without distractor

Healthy Controls WM with distractor

Fig. 1 – Working memory accuracy (i.e., hit rate − false alarm rate in %, mean ± SEM) for tasks with and without distraction for attention-deficit/hyperactivity disorder (ADHD) patients with and without methylphenidate (MPH) medication and healthy controls; *p < .05, ***p < .001.

In the MPH-off condition, ADHD patients showed no delay activation in the contrast “WM without distraction > Control task without distraction,” while in the MPH-on condition, activations were observed in the inferior frontal gyrus and in the insula (Tables 2 and 3 and Fig. 2). Healthy controls showed activations in the fusiform gyrus, occipital gyrus, and orbitofrontal gyrus. ADHD patients did not show activation during the delay neither with nor without MPH when a distractor was presented. Healthy controls, however, displayed activations in the caudate nucleus and the fusiform gyrus (Table 4, Fig. 3). Comparisons of delay activity between controls and ADHD patients with respect to the contrast “WM with distraction > Control task with distraction” are shown in Table 5. When compared to controls, ADHD patients without MPH displayed less activity predominantly in frontal regions, in the caudate nucleus and in the occipital/temporal gyrus. Group differences were markedly reduced when ADHD patients received MPH. However, healthy controls still showed more activation of the caudatus nucleus and the temporo-parieto-occipital junction than ADHD patients (see Fig. 4). In order to calculate post hoc correlations between brain activations in the caudate nucleus and behavioral performances, beta weights were extracted from voxels that were active during the contrast “WM > control task with distraction,” using the rfxplot–toolbox (Gläscher, 2009). To obtain the maximally active voxel of the caudate nucleus captured by the standard caudate region, as suggested by TzourioMazoyer et al. ( 2002), we performed a region-of-interest (ROI) analysis. This ROI analyses revealed a maximum activation within the caudate nucleus at the coordinate x = 15, y = 0, z = 24 (z-score = 2.67). Correlation analyses were performed based on Pearson's correlation coefficient. The analyses revealed that WM accuracy during distraction was positively correlated with caudate nucleus activation in healthy controls (r = .646, p = .023), but not in ADHD patients without or with MPH (r = −.15, p = .642; r = −.102, p = .753, see also Fig. 3). Correlations between MPH dosage parameters (single dose, daily dose in mg as well as in mg/kg body weight) and behavioral data or delay activity (caudate) did not reach significance (all resp. p-values > .15) with one exception: In

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Table 2 – Regional activations in ADHD patients without MPH medication (N = 12) in the contrast working memory > controls task.

Table 3 – Regional activations in ADHD patients with MPH medication (N = 12) in the contrast working memory > controls task.

Condition

Condition

Sample

Activated region

Frontal Inferior frontal gyrus, opercular part Inferior frontal gyrus, orbital part Inferior frontal gyrus, triangular part Insula Temporal Fusiform gyrus Fusiform gyrus Limbic Medial cingulate gyrus Anterior cingulate gyrus Central Precentral gyrus/ white matter Postcentral gyrus Postcentral gyrus Subcortical Thalamus Caudate nucleus Caudate nucleus Parietal Inferior parietal gyrus Inferior parietal gyrus Occipital Inferior occipital gyrus Inferior occipital gyrus No activations

No. of Z score, voxels maximum primary peak

MNI coordinates x

y

z Sample

133

4.06

−42

13

3.66

36

23

3.62

−48

13

4.12

75 24

4.04 3.57

80

4.06

49

3.53

9

30

21 −12

30

33 −12

3

27

39 −27 −18 −27 −45 −9 0 −36

−6

9

45

27

87

4.00

−27 −21

39

14

3.53

−57

−6

36

18

3.68

36

−3

42

Delay without distractor*

Delay with distractor* 46 16

3.83 3.80

11

3.56

43

0 −15 −15 3 9

Activated region

6 27

27

0

4.66

−51 −36

45

49

3.81

−24 −51

42

44

4.88

−30 −75

33

63

4.00

−45 −60 −12

Delay without distractor* Delay with No activations distractor*

Frontal Inferior frontal gyrus, opercular part Insula Temporal Inferior temporal gyrus Fusiform gyrus Limbic Anterior/ medial cingulate gyrus Central Precentral gyrus Occipital Medial occipital gyrus Medial occipital gyrus Frontal

Inferior frontal gyrus, triangular part Insula No activations

No. of Z score, voxels maximum primary peak

MNI coordinates x

y

z

15

3.45

−51

12

18

56

4.52

−33

18

6

14

3.41

−42 −63

−9

11

3.33

30 −33 −21

32

4.55

−3

−3

30

25

3.62

−45

0

30

28

3.77

−36 −78

24

27

3.54

45 −75

24

5

3.57

36

36

3

6

2.85

−36

18

−6

p < .001; k ≥ 10; *p < .005; k ≥ 5.

2.3.

Supplemental correlation analyses

In the CBCL, parents of ADHD patients reported more internalizing problems than parents of healthy controls. In order to control for possible effects of internalizing problems on WM performance in ADHD, we calculated correlations between CBCL internalizing scores and behavioral data respectively delay activity. The analyses revealed that no corresponding correlation reached significance in the MPH-off condition (all resp. p-values > .1).

3.

Discussion

p < .001; k ≥ 10; *p < .005; k ≥ 5.

ADHD, there was a negative correlation between MPH daily dose mg/kg and beta-values in the caudate nucleus during the delay in the WM task with distraction (r = −.603, p = .038).

Here, we investigated the effect of MPH on a distracted and undistracted WM task in ADHD. We found that the intake of MPH in ADHD normalized WM performance when WM was not distracted. Contrary to our hypothesis, MPH did not improve WM performance in ADHD in case of additional interference. While in healthy controls interference control was associated with caudate activity, no such relations were found in ADHD patients. These data provide evidence that the

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Table 4 – Regional activations in healthy controls (N = 12) in the contrast working memory > controls task. Condition

Sample

Delay without distractor*

Delay with distractor*

Activated region

Temporal Fusiform gyrus Limbic Anterior cingulate gyrus Parietal Precuneus Occipital Medial occipital gyrus Frontal

Inferior frontal gyrus, orbital part Temporal Fusiform gyrus Occipital Medial occipital gyrus Temporal Fusiform gyrus Subcortical Caudate nucleus

No. of Z score, voxels maximum primary peak 129

4.33

25

3.88

17

MNI coordinates x

y

z

−33 −39 −18 3

6

24

3.58

15 −51

12

12

3.61

−36 −81

21

13

2.97

−36

22

3.19

−36 −42 −12

10

3.10

−33 −72

9

2.97

27 −36 −18

7

3.22

12

21 −12

−3

6

27

p < .001; k ≥ 10; *p < .005; k ≥ 5.

intake of MPH has a differential impact on WM performance in ADHD, which could be related to striatal dysfunctions. During the delay period without distraction, there was more frontal cortex activation in the working memory task compared to the control task in healthy controls as well as in MPH treated ADHD patients. Frontal activity was not seen during the delay in ADHD patients, when MPH was omitted. These data fit well with the assumption that the ventral PFC enables successful WM maintenance (D'Esposito, 2007; Landau et al., 2009; Ranganath, 2006). With respect to behavioral performance, our data are in

line with other studies, showing that MPH can restore deficits in visual-spatial WM performance in ADHD by enhancing PFC activity (Bedard et al., 2007; Kobel et al., 2008). However, in the WM task with distraction, MPH had no effect on WM performance in ADHD. A comparable lack of effect of MPH has been described in other WM paradigms with distraction in older monkeys (Prendergast et al., 1998) and humans: Stimulating MPH-sensitive D2 receptor activity by D2 receptor agonist cabergoline lead to an enhancement of WM maintenance but increased distractibility to interference (Frank and O'Reilly, 2006). The intake of the D2 antagonist sulpiride impaired WM maintenance but protected against task-irrelevant distraction (Mehta et al., 2004). However, one study reported conflicting results: The intake of MPH or MPHlike stimulants (mainly amphetamine–dextroamphetamine) decreased distractibility in a WM task in adult ADHD (Frank et al., 2007). Since dextroamphetamine and MPH have different dopamine releasing actions (Cadoni et al., 1995; Wall et al., 1995), the later results should be interpreted with some care. In healthy controls, caudate activity occurring during the delay in the WM task with distractor correlated positively with WM performance. These results are in line with other studies, reporting that the caudate nucleus is involved in control for unexpected events requiring attention inhibition (Casey et al., 2004) and is associated with WM capacity (Landau et al., 2009) as well as motor control during WM maintenance (Postle and D'Esposito, 1999; Gazzaley et al., 2004). In contrast, caudate activity was not seen during the delay period in the WM task with distraction in ADHD patients, neither with nor without MPH. Comparable effects for unmedicated ADHD patients were observed in a further functional imaging study: in young ADHD patients, the failure to suppress interfering stimuli was associated with an inability to activate the caudatus (Vaidya et al., 2005). More recently it was shown that a 10-repeat homozygosity of DAT1 gene, as a candidate gene for ADHD (Bellgrove and Mattingley, 2008) is related to a lack of increase in caudate activity involvement under higher working memory demands (Stollstorff et al., 2010). ADHD is characterized by frontostriatal dysfunctions (Arnsten, 2009; Bush et al., 2005; Dickstein et al., 2006; Paloyelis et al., 2007) and it is proposed that interference control during WM requires an optimal level of prefrontal/striatal dopamine level (Clatworthy et al., 2009; Cools and Robbins, 2004). Given that MPH enhances striatal D2 receptor activity by blocking DAT1 (Volkow et al., 2001, 2005)

Fig. 2 – Delay activation in the contrast “Working memory > Control task without distraction” in patients with ADHD with methylphenidate (MPH on). Activation has its local maximum in the inferior frontal gyrus, triangular part.

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Fig. 3 – Delay activation in the contrast “Working memory with distraction > Control task with distraction” in healthy controls. The activation has its local maximum in the caudate nucleus; correlations between beta values of caudate activity in the delay activity during WM with distraction in healthy controls and patients with ADHD without (MPH off) and with methylphenidate (MPH on).

and that the caudate nucleus as part of the striatum prevents distractibility, it could be expected that MPH improves WM interference control by enhancing caudate activity in ADHD. However, PET studies revealed that, although the density of D2 receptors in the striatum is high (Camps et al., 1989), the acute intake of MPH in ADHD has only little impact on caudate activity (Volkow et al., 2007) and prolonged intake of MPH (3 weeks) even decreased blood flow in the caudate nucleus (Schweitzer et al., 2003). On the other hand, the putamen as the second prominent striatal structure is also sensitive for MPH (Volkow et al., 2007). D2 receptor agonist bromocriptine, by mediating putamen activity, enhances flexibility in the processing and control of distracting events during WM in high-impulsive participants, but not in low-impulsive participants (Cools et al., 2007). Since we did not observe any activation in the putamen, further imaging (e.g., PET) studies are required in order to assess dissociable effects of MPH on striatum associated WM interference control in ADHD. Based on the rather low MPH dose in our ADHD patients, it could be expected that a higher MPH dose could lead to positive effects on WM distractor interference. However, it is proposed that enhancement of cognitive behavior by MPH follows a U-inverted shape (Arnsten, 2009) and that even low MPH doses can lead to the most positive effect in cognitive performance (Arnsten and Dudley, 2005; Berridge et al., 2006; Devilbiss and Berridge, 2008). Therefore, an application of higher MPH dose might not necessarily foster, but could even impair WM maintenance. To control for possible effects of MPH dosage on WM performance, supplemental correlation analyses were performed. The only result that reached significance was a negative correlation between daily dose in

mg/kg body weight and delay activity in the caudate nucleus in ADHD. This result supports our finding of a reduced MPH responsiveness of caudate in the context of WM interference control. However, we cannot exclude that higher MPH doses would have lead to better performances in the more demanding WM task (i.e., with interference). Group comparisons further revealed that MPH indeed normalized frontal activation but did not lead to an increase of activation in the parieto-occipital junction during the delay with distraction. It has been shown that the parieto-occipital area is involved in the processing of scenes (Nakamura et al., 2000) and more important in the maintenance and updating of visual working memory (Roth and Courtney, 2007). Possibly, the incomplete restoration of fronto-parietal activation during the WM delay contributed to the increased vulnerability to interference in the patient group. Our data not only suggest a dysfunctional WM maintenance and interference control in ADHD, but also a disturbance in early WM processes during encoding, which can be reduced by MPH: During sample presentation in untreated ADHD patients, broad activations in the frontal cortex, visual association areas, and parietal cortex were observed. These areas are reported to be activated during WM encoding (Bledowski et al., 2006; Clapp et al., 2010; Motes and Rypma, 2010; Ranganath, 2006), reflecting a high effort to encode WM samples in untreated ADHD patients. While the intake of MPH in the same ADHD patients reduced activations particularly in the inferior frontal gyrus, other activations, e.g., in the parietal cortex even vanished. In healthy controls, activations during sample presentation comprised only the cingulated gyrus and the visual association areas. Activations in the inferior frontal

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Table 5 – Comparisons of delay activity between controls vs. ADHD in the contrast working memory with distractor > control task with distractor. Comparison

Controls > ADHD MPH off

ADHD MPH off > Controls Controls > ADHD MPH on

ADHD MPH on > Controls

Activated region

Frontal Inferior frontal gyrus, opercular part Medial frontal gyrus Inferior frontal gyrus, triangular part Inferior frontal gyrus, opercular part Limbic Medial cingulate gyurs Central Precentral gyrus Precentral gyrus/ white matter Subcortical Caudate nucleus Caudate nucleus Caudate nucleus Parietal Supra marginal gyrus/ white matter Supra marginal gyrus Occipital Medial occipital/temporal gyrus Medial occipital gyrus Medial occipital gyrus No activations Occipital Medial occipital/temporal gyrus Subcortical Caudate nucleus Insula

No. of voxels

Z score, maximum primary peak

MNI coordinates x

y

z

56 10 16 11

3.19 2.98 2.85 2.77

54 −24 −42 36

6 39 30 12

21 15 24 27

13

2.97

−6

21

39

18 12

3.19 3.00

−48 33

0 −6

27 33

295 76 8

4.45 3.58 2.83

12 −12 −15

6 −3 18

27 30 −9

16 21

3.03 3.00

−39 51

−39 −18

30 30

25 5 17

3.55 3.17 3.09

−45 36 −30

−72 −75 −72

24 30 39

23

3.49

−48

−75

18

5 6

2.97 3.49

−21 30

6 18

18 15

p < .005; k ≥ 5.

gyrus were not observed, suggesting a low cognitive effort during WM encoding in healthy controls (Motes and Rypma, 2010). Taking into account that during WM encoding suppression of parietal brain areas predicts successful WM performance (Anticevic et al., 2010), our data indicates that MPH in ADHD optimizes WM capacity during encoding by focussing neuronal activations. One limitation of the study concerns the fact that ADHD patients did not receive a placebo during the MPH-off condition. It cannot be excluded that ADHD patients, when receiving MPH, felt extrinsically or intrinsically motivated to show better performances. However, a higher motivation may explain at least only a part of the performance enhancement during the MPH-on condition. If awareness of being medicated in ADHD improved motivation in general, this should have lead to an overall performance improvement. Since we observed only a specific performance enhancement in the MPH-on condition, possible motivational processes cannot explain the lack of performance improvement during the WM task with distraction. Another limitation concerns possible effects of MPH dosage and MPH history. While ADHD patients received the MPH dosage determined most effective during clinical treatment by a child and adolescent psychiatrist, we did not obtain an objective measure of treatment response which would have been helpful to confirm that patients received an optimal MPH dose. Based on the proposal that

MPH may cause long-term changes in brain function even after discontinuation of 2 months (Akay et al., 2006; Konrad et al., 2007; Pliszka et al., 2006), conclusions about the impact of single MPH doses on WM performance can be more comprehensively drawn in drug-naïve ADHD patients rather than in ADHD patients with a MPH history. Recently, Schecklmann and colleagues suggested that prefrontal brain functions are more reduced in ADHD patients with no MPH history compared to those patients who took MPH chronically (Schecklmann et al., 2010). On the other hand, it could also be seen as an advantage that ADHD patients received a clinically adjusted dose and were familiar with the drug effect. Additionally, it is suggested that MPH only exerts clinically meaningful therapeutic effects when administered chronically (Coghill et al., 2007). Further studies should address comparisons between drug-naïve and chronically treated ADHD patients with respect to prefrontal brain functions. Besides MPH dosage, it needs to be addressed whether comorbid ODD or co-occurring internalizing problems in ADHD patients might have influenced our results. Some studies indicated that comorbid anxiety can modulate working memory performance in ADHD (Bedard and Tannock, 2008). However, several investigations concluded that comorbid ODD or internalizing problems as anxiety or depression have no substantial impact on executive functions in ADHD, and most research has shown that neuropsychological deficits

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Fig. 4 – Delay activation in the contrast “Working memory with distraction > Control task with distraction” for the group comparisons “Healthy controls vs. ADHD/MPH off” (red) and “Healthy controls vs. ADHD/MPH on” (green). Activation overlap (yellow) between both group comparisons is evident in the left caudate nucleus and left temporo-parieto-occipital junction.

are not found in children with ODD, anxiety, or depression, unless ADHD is also present (see Mayes et al., 2009). In line with this, supplemental correlation analyses revealed no impact of internalizing problems on WM performance, neither with respect to behavioral nor functional data. Although we found that, in general, a distractor leads to a decline in WM performance (see also Lavie, 2005), we did not find performance differences between undistracted and distracted WM task in unmedicated ADHD patients. However, data are in line with other studies, reporting that a higher WM distractibility in ADHD was detectable in psycho-physiological rather than in behavioral data (Gumenyuka et al., 2005; Keage et al., 2006; van Mourik et al., 2007). In summary, our data provide evidence that MPH enhances WM encoding and maintenance, but does not reduce WM distractibility in ADHD. These results indicate that WM deficits in ADHD cannot be corrected only by MPH medication. Everyday life in school is characterized by a high degree of interference (Anderson, 2004), and particularly children with ADHD are disadvantaged in usual school class environments. It is therefore important to find comprehensive therapeutic treatments to reduce distractibility in ADHD patients.

4.

Experimental procedures

4.1.

Participants

Patients with ADHD were initially contacted through the outpatient clinic of the Department of Child and Adolescent

Psychiatry at the University of Kiel. All patients were diagnosed by a senior psychologist or child and adolescent psychiatrist according to the ICD-10. Only if patients fulfilled the criteria of a hyperkinetic disorder (F90.0, F90.1, F98.8), they were asked to participate in the study. Healthy controls were recruited through advertisements from the local newspapers. All participants (normal or corrected-to-normal vision) and their parents were interviewed with a German translation of the Revised Schedule for Affective Disorders and Schizophrenia for School-Age Children: Present and Lifetime Version (Delmo et al., 2000; Kaufman et al., 1997). The interview was conducted by a psychologist or child and adolescent psychiatrist. Evaluation of the interview was supervised by a senior child and adolescent psychiatrist. Only patients treated with MPH were included in the study. Exclusion criteria for all subjects were the existence of any past or present neurological disorder or substance abuse. Control subjects were excluded from the study if they had any psychiatric disorder in the past or present. Besides the clinical interview, participants and their parents were requested to fill in the Child Behavior Checklist (CBCL, Achenbach, 1991b) or the Youth Self-Report (YSR, Achenbach, 1991a) in order to assess self reported psychiatric symptoms. Participants with a sub-average intelligence quotient (cut off IQ-score < 85 on the Culture Fair Intelligence Test Revised Vision; CFT-R, Weiß, 2006), or with visual–spatial memory impairments (cut off: mean score < 16 percentile of the reference sample on a standardized visual memory screening; Diagnosticum für Cerebralschädigung; DCS, Lamberti and Weidlich, 1999) were excluded from the study.

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Twelve ADHD patients (mean age: 13.0; SD = 1.8; range: 11.2–16.5 years) and 12 healthy controls (mean age: 13.6; SD = 2; range = 10.9–17.1 years) participated in the experiment. Groups did not differ in age, intelligence or DCS score (see Table 6). None of the participants had a sub-average IQ (range: 90–120) or visual-spatial memory impairments (DCS score range: 16– 95). All patients met diagnostic criteria for ADHD according to the DSM-IV-TR (10 with combined and 2 with inattentive type). Five patients were additionally diagnosed with oppositional defiant disorder (ODD). As expected, ADHD patients showed more attention problems than the controls [CBCL: t(22) = 8.74, p < .001; YSR: t(22) = 3.51, p < .01]. CBCL and YSR scores for internalizing [CBCL: t(22) = 5.69, p < .001; YSR: t(22) = 3.22, p < .01] and externalizing symptoms [CBCL: t(22) = 6.13, p < .001; YSR: t (22) = 3.15, p < .01] were both higher in ADHD patients. Clinically relevant amounts of internalizing and externalizing symptoms were only reported in the parents' but not in the childrens' view. It is important to note, however, that the clinical interviews did not reveal comorbid anxiety or mood disorders in the patient group. All participants and their parents gave written informed assent/consent. Participants were paid for their participation with a voucher (6 €/h). The study was approved by the ethical committee of the medical faculty of the University of Kiel.

4.2.

Stimulus material

Stimuli were 232 pictures of places (116 indoor and 116 outdoor) and 64 pictures of faces (32 male and 32 female; no emotional expression; hairs were retouched). All pictures were black-and-white and were normalized with respect to their luminance. Two types of tasks were carried out (see Fig. 5). The working memory task consisted of a delayed-match-tosample (DMS) paradigm. The picture of a place (sample) was initially presented for 1 s with the subtitle “identical!” indicating that this picture had to be memorized. After the

Table 6 – Characteristics of participants. ADHD Controls (n = 12) (n = 12) mean (± SD) mean (± SD) Age IQ Figural memory CBCL Attention problems Delinquent rule-breaking behavior Aggressive behavior Internalizing Externalizing YSR Attention problems Delinquent rule-breaking behavior Aggressive behavior Internalizing Externalizing

p

13.0 (1.8) 105.2 (9.3) 46.2 (32.2)

13.6 (2.0) 106.5 (10.3) 46.9 (32.5)

.453 .743 .959

72.2 (7.4) 64.4 (7.9)

51.7 (3.2) 50.6 (1.5)

>.001 >.001

67.5 (10.8) 66.2 (9.6) 66.1 (9.8)

51.1 (1.8) 45.8 (7.9) 46.0 (5.6)

>.001 >.001 >.001

58.2 (5.6) 56.2 (7.3)

51.4 (3.5) 51.7 (3.3)

.002 .064

56.3 (5.7) 55.6 (6.6) 55.9 (6.7)

51.8 (2.7) 45.7 (8.2) 47.7 (6.1)

.023 .004 .005

ADHD, attention-deficit/hyperactivity disorder; CBCL, Child Behavior Checklist; YSR, youth self report.

following fixed delay of 8 s showing a gray square with a centered fixation asterisk a second picture of a place (probe; subtitle: “identical?”) was presented. Participants had to indicate whether or not the second picture was identical to the first by pressing one of two response buttons. In half of the trials, 3.5 s after presentation of the sample a picture of a face (distractor) occurred for 1 s. In the control task, the trial course was identical to the DMS paradigm with the following exceptions: the first picture was subtitled with “attention!” indicating that participants were asked to pay attention to the second place picture, which was subtitled with “indoor?” When the second place picture was presented, participants were requested to indicate whether or not the second picture with a place was an indoor scene, by pressing one of two response buttons. Again, in half of the trials, a face was presented between the first and second scene picture. Participants were informed that an additional picture could occur in the delay interval of both tasks but were instructed to ignore those pictures. To keep participants oriented to the current type of task the subtitle were shown during the whole delay. Independently of the task, a new trial started after 10 s (jitter: ± 2 s). Each set consisted of 64 WM trials and 64 control task trials. Trials were presented in pseudo randomized order. Stimulus presentation and response recording were conducted with E-Prime (Psychology Software Tools, Inc., Pittsburgh, PA; http://www.pstnet.com). Stimuli were projected onto a screen viewed by participants through a mirror mounted on the sense head-coil.

4.3.

Experimental protocol

All participants attended two sessions. Patients participated either under the influence of MPH (therapeutic dose) or discontinued their medication at least 48 h prior to the experiment. On average, single MPH dose was 16.7 mg (range: 10–36; SD = 7.7; in mg/kg body weight: mean = .40; range: .17—1.06; SD = .23) with an average daily dose of 28 mg MHP (range: 20–40; SD = 6.7; in mg/kg body weight: mean = .60; range: .49–1.06; SD = .17). The order medication on/off was counterbalanced across ADHD patients. To control for session effects, healthy controls had to complete two sessions, too. The sessions were at least 1 week apart. For further analyses in the healthy control group, the first or the second session was chosen randomly and included into the analyses, guaranteeing that the distribution of first and second sessions was counterbalanced across controls.

4.4.

fMRI data acquisition and analysis

Images were acquired using a 3 T Intera Achieva (Phillips, NL) with a sense head-coil. A T1-weighted TFE-3D sequence was used for structural MRI of the whole brain [repetition time (TR)=7.6 ms, echo time (TE)=3.5 ms, flip-angle=8°, 150 slices, slice thickness = 1 mm, gap: 0.1 mm, matrix: 224 × 224]. For functional imaging a single-shot T2*-weighted gradient echo-planar imaging sequence (EPI) was performed with 29 transversal slices covering the whole brain (TR=2000 ms, TE=35 ms, flip angle=90°, slice thickness=2.7 mm, gap: 0.3 mm, matrix: 64×64 voxels, in-plane resolution=3×3 mm).

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65

Fig. 5 – Trial time course. Two task conditions were carried out: in the working memory task participants had to determine whether or not the first (sample) and the second place picture (probe) were identical. In 50% of all trials, a picture of a face occurred during the delay (distractor), and participants were requested to ignore this picture. The control task was conducted similar to the working memory task, with the two exceptions that participants (a) did not have to memorize the first picture and (b) were asked to judge whether or not the second picture was an indoor scene. To keep attention high during the first 9 s in the control task, the first picture was subtitled with “Attention!”.

For the pre-processing and statistical analyses, the statistical parametric mapping software package (SPM5, Wellcome Department of Cognitive Neurology, London; www.fil.ion.ucl.ac.uk/spm) was used and implemented in Matlab (Mathworks, Inc., Natick, MA, USA release 14). Slice timing correction was performed and head motions across time were corrected by realigning all scans to the first volume. Participants’ T1-weighted images were coregistered to the corresponding mean EPI images and subsequently normalized to Montreal Neurological Institute standard space during the segmentation procedure, thus taking maximal advantage of the structural information in high-resolution T1weighted images. EPI images were then normalized using the normalization-parameters written during segmentation of coregistered T1-weighted images (Ashburner and Friston, 2005) and spatially smoothed using an isotropic Gaussian kernel at 8-mm full width at half maximum. For the individual subject analysis (first level), we specified the task phases “sample,” “distractor,” and “probe” each task and condition as regressors. Residual movement effects were then modeled by including the 6 rigid-body

motion parameters as regressors in the design matrix of a GLM (Friston et al., 1996), resulting in a total number of 18 regressors. Each regressor was convolved with a canonical hemodynamic response function (HRF). Due to the small amount of trials (n = 32) per condition, we refrained from separating correct/incorrect trials. Further, we calculated contrasts “working memory > control task” for the regressors “sample” (collapsed over the conditions with and without distraction, n = 64), as well as for “delay without distraction” and “delay with distraction” (n = 32 each). Random effects analyses in regard to the relevant contrasts were performed for each group separately (one-sample t test). For group comparisons (two-sample t test), we focused on the contrast “Working memory with distraction vs. Control task with distraction.” For results of regional activations during encoding (“sample”) error probability was set to p<.001. Due to the relative small amount of trials for the conditions “delay with distractor” and “delay without distractor,” here, error probability was set to p<.005. For report of peak activations, Montreal Neurological Institute (MNI) coordinates were used.

66 4.5.

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Behavioral data analysis

Working memory accuracy was calculated by subtracting the false-alarm rate from the hit rate (Snodgrass and Corwin, 1988). The impact of distraction and MPH on WM accuracy and reaction times in ADHD was analyzed based on analyses of variances (ANOVA) with two within-factors [DISTRACTION: with vs. without distractor; and TREATMENT: MPH on vs. MPH off]. All single comparisons were analyzed using two-tailed paired Student's t tests. Differences in accuracy and reaction times between ADHD patients and healthy control participants were analyzed via single comparisons based on two-tailed unpaired Student's t tests: since in case of ADHD patients data of two sessions (with and without MPH) had to be entered into the analyses, whereas in healthy controls data from only one session (random selection) were included. Statistical analyses were performed using SPSS 17.0.

Disclosure statement All authors declare no conflicts of interest.

Acknowledgment We thank Christian D. Wiesner for fruitful discussions as well as Susanne Kell and Petra Schneckenburger for technical assistance. This study was supported by a grant from the German Research Foundation (KFO 163).

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