Journal of Neural Transmission

, Volume 117, Issue 3, pp 403–419 | Cite as

Cross-sectional evaluation of cognitive functioning in children, adolescents and young adults with ADHD

  • Ivo Marx
  • Thomas Hübner
  • Sabine C. Herpertz
  • Christoph Berger
  • Erik Reuter
  • Tilo Kircher
  • Beate Herpertz-Dahlmann
  • Kerstin Konrad
Biological Child and Adolescent Psychiatry - Original Article

Abstract

Attention-deficit/hyperactivity disorder (ADHD) often persists into adulthood, albeit with changes in clinical symptoms throughout the life span. Although effect sizes of neuropsychological deficits in ADHD are well established, developmental approaches have rarely been explored and little is yet known about age-dependent changes in cognitive dysfunction from childhood to adulthood. In this cross-sectional study, 20 male children (8–12 years), 20 adolescents (13–16 years), and 20 adults (18–40 years) with ADHD and a matched control group were investigated using six experimental paradigms tapping into different domains of cognitive dysfunction. Subjects with ADHD were more delay-aversive and showed deficits in time discrimination and time reproduction, but they were not impaired in working memory, interference control or time production. Independent of age, the most robust group differences were observed with respect to delay aversion and time reproduction, pointing to persistent dysfunction in the mesolimbic reward circuitry and in the frontal-striatal-cerebellar timing system in subjects with ADHD. Across all tasks, effect sizes were lowest for adolescents with ADHD compared to age-matched controls. Developmental dissociations were found only for simple stimuli comparison, which was particularly impaired in ADHD children. Thus, in line with current multiple-pathway approaches to ADHD, our data suggest that deficits in different cognitive domains are persistent across the lifespan, albeit less pronounced in adolescents with ADHD.

Keywords

ADHD Delay aversion Time perception Interference control Working memory Brain maturation 

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is characterised by developmentally inappropriate levels of activity, impulsivity and inattentive behaviour. In recent years, evidence has been accumulating showing that ADHD persists into adulthood, with a prevalence rate of about 5–10% in school-aged children (Scahill and Schwab-Stone 2000), declining to about 5% by the age of 18 (Polanczyk et al. 2007) and to 4–3% in older adults (Fayyad et al. 2007). Approximately 65% of children and adolescents with ADHD show partial remission by the age of 25, while 15% still meet full DSM-IV criteria (Faraone et al. 2006). While hyperactivity declines in the course of the disorder, the inattention and impulsivity symptoms seem to persist (Biederman et al. 2000). Although changes in clinical symptoms and neurobiological data suggest age-related maturational processes in the ADHD phenotype, the developmental changes in cognitive and motivational functions are still poorly understood.

Over the past few years, evidence has been accumulating that children with ADHD have poor executive functions (EFs; for reviews, cf. Barkley 1997; Pennington and Ozonoff 1996). However, these cognitive deficits are not present among all children with the disorder, and the importance of taking this heterogeneity into account has repeatedly been emphasised. Thus, recent studies demonstrated not only that executive deficits persist into the adolescent and adult years (McLoughlin et al. 2009), but also that inattentive-disorganised symptoms, but not hyperactive-impulsive symptoms, are associated with executive dysfunction (Nigg et al. 2005; Martel et al. 2007). Expanding theories that consider executive dysfunction as the core deficit of ADHD and current multiple pathway models of ADHD emphasise the involvement of additional circuits in the formation of the ADHD phenotype. From their perspective, the pathophysiology of the disorder bases upon deficient prefrontal control of striatal, cerebellar and limbic neural activation patterns as well as dysfunctional interconnections, which together affect cognitive control, motivation, attention, and affect regulation (Halperin and Schulz 2006; Nigg and Casey 2005; Sonuga-Barke 2002). Sonuga-Barke (2002), for example, suggested that ADHD symptoms develop along two separate and neurobiologically distinct pathways: (1) a cognitive pathway which is associated with alterations in a circuitry encompassing the dorsal striatum and dorsolateral prefrontal cortex and that may cause deficits in executive functioning which are associated with cognitive and behavioural dysregulation and therefore lead to poor task engagement and behavioural ADHD symptoms; (2) a motivational pathway which is associated with alterations in a circuitry encompassing the ventral striatum (especially the nucleus accumbens), frontal regions (including anterior cingulate and orbitofrontal cortex), and the amygdala, causing alterations in reward mechanisms which are associated with delay aversion and its manifestations on the behavioural level. The distinctiveness of both pathways was demonstrated by Solanto et al. (2001) and Sonuga-Barke et al. (2003), who showed that inhibitory deficits and delay aversion independently contributed to the prediction of ADHD symptoms. Beyond this, many studies addressed state regulation and processing of temporal information in subjects with ADHD, indicating in particular the presence of increased within-subject variability of responses as well as abnormal timing mechanisms in ADHD (Nigg 2006, for a review).

Results from recent brain imaging studies suggest that both children and adults with ADHD suffer from a functional hypofrontality, and that this deficit might increase in adulthood (Ernst et al. 1998). The typical striatal abnormalities that have been reported in children with ADHD, however, have not been found in affected adolescents and young adults (Castellanos et al. 2002; Garrett et al. 2008). In a longitudinal volumetric study, Castellanos et al. (2002) found that caudate nucleus volume, which was initially reduced in children with ADHD, became equal to that of healthy controls during adolescence. In contrast, differences in cerebellar volume became more pronounced with age, a finding that was confirmed by Mackie et al. (2007). Since striatal dysfunction seems to be associated with hyperactive behaviour in particular, this result is in line with observations of declining hyperactivity across the lifespan.

Despite their differences in terms of detail, available multiple-pathway-models of ADHD show remarkable overlaps concerning the neuronal networks that potentially cause the disorder. Retaining a developmental perspective, one may assume that as the brain undergoes developmental changes, cognitive deficits associated with these networks can become attenuated or aggravated with increasing age (Halperin et al. 2008). Four of these cognitive functions, which have been extensively examined at least in children and adolescents, and in part in adults with ADHD, will now be described in further detail, as they represent the foundation of our study.

Working memory

The term working memory (WM) refers to a temporally and quantitatively limited storage mechanism in which information is actively processed (i.e., stored, monitored and manipulated) in order to provide complex goal-directed behaviour. Meta-analytic evidence points to deficits in verbal and nonverbal storage processes, as well as central executive processes, in children and adolescents with ADHD (Martinussen et al. 2005; Willcutt et al. 2005). Previous studies suggest that these deficits persist into adulthood (Boonstra et al. 2005; Dowson et al. 2004).

Interference control

Interference control denotes the ability to resist irrelevant aspects of a task while focusing attention on task-relevant content. In behavioural studies, the interference effect results in higher error rates and extended reaction times in interference trials compared to neutral trials. The results of two recently conducted meta-analyses show moderate performance deficits in interference control in subjects with ADHD, irrespective of the subjects’ age, indicating similar deficits in children, adolescents and young adults with ADHD (Homack and Riccio 2004; van Mourik et al. 2005; Hervey et al. 2004; Lijffijt et al. 2005).

Time perception

Studies on time discrimination report consistent deficits in children and adolescents with ADHD in terms of their ability to differentiate between two stimuli differing in presentation duration by several milliseconds. This was evidenced by a diminished sensitivity threshold in subjects with ADHD (Himpel et al. 2009; Smith et al. 2002), which seemed to be independent of the presentation mode (Toplak and Tannock 2005). In time estimation and time production tasks, either temporal intervals are presented, with participants being required to estimate their duration, or numbers indicating a certain number of seconds are presented, which participants have to produce by pressing a button. Time reproduction tasks require button presses in order to reproduce given temporal sequences that were previously presented in full length. The results of these studies seem to indicate that children and adolescents with ADHD show deficits in time reproduction, but not in time estimation tasks. Compared to healthy controls, they seem to overestimate short temporal intervals and underestimate long intervals (McInerney and Kerns 2003; Mullins et al. 2005). They also appear to reproduce larger deviations with increasing interval length (Barkley et al. 2001b; Bauermeister et al. 2005), which altogether results in larger deviations from the temporal intervals to be reproduced. Up to now, no time perception studies have been reported in adults with ADHD.

Delay aversion

Delay aversion denotes a motivational style of avoiding delays in the course of time, with the individual instead preferring immediate consequences of his or her own actions in order to reduce waiting situations that are lacking in stimulation. Sonuga-Barke et al. (1992) showed that children with ADHD preferred smaller, immediate rewards over larger, more temporally delayed rewards when this choice resulted in a shorter overall experimental duration. When delay is unavoidable, children with ADHD choose an immediate reward with a subsequent delay (post-reward delay) rather than a delay that is followed by the reward (pre-reward delay), a finding that underlines the preference for immediacy (Tripp and Alsop 2001).

Study objectives

Although there are neurobiological data demonstrating persisting hypofrontality, increasing cerebellar dysfunction and striatal normalisation (Castellanos et al. 2002; Ernst et al. 1998; Mackie et al. 2007), as well as clinical data showing age-dependent changes in the behavioural phenotype (Biederman et al. 2000), barely any neuropsychological studies have examined the course of cognitive functioning in subjects with ADHD longitudinally, using the same experimental paradigms. One of these few studies was conducted by Biederman et al. (2007), and showed that a high proportion of male subjects (69%) who were diagnosed as having ADHD at the age of 6–18 and who displayed deficits in executive functions at that time continued to show impaired executive functioning 7 years later, suggesting a comparably high stability of deficits associated with frontal brain structures. In their study, executive dysfunction was defined as two or more tests 1.5 standard deviations from the mean of the controls in tasks measuring sustained attention, interference control, selective attention and visual scanning, planning and organisation, set shifting and categorisation, verbal and visual learning, and memory. Likewise, Halperin et al. (2008) found, in a predominantly male sample of subjects, that those who were diagnosed as having ADHD at age 7–11 and whose symptoms were not remitted 10 years later continued to show deficits in executive control functions in terms of impaired working memory and deficits in a continuous performance task. The authors further noted that both ADHD persisters and remitters differed from controls in response variability and perceptual sensitivity (the ability to discriminate signal plus noise from noise only) associated with arousal deficits based on subcortical dysfunction (Halperin and Schulz 2006; Sergeant et al. 1999).

In the present cross-sectional study, we aimed to investigate neurocognitive deficits in subjects with ADHD across different age groups within a cross-sectional study design. So far, the few longitudinal studies with ADHD patients have been restricted to executive and attention functions (Biederman et al. 2007; Drechsler et al. 2005; Halperin et al. 2008). In our study, we selected experimental tasks that tap into different cognitive domains associated with different neural circuitries (prefrontal cortex: working memory (Bunge et al. 2001; Valera et al. 2005; Wager and Smith 2003); frontal cortical: interference control (Peterson et al. 2002; Blasi et al. 2006); fronto-striatal-cerebellar: time perception (Ivry 1996; Neufang et al. 2008; Smith et al. 2003); striatal-limbic: delay aversion (McClure et al. 2004; Scheres et al. 2007; Ströhle et al. 2008). Although these tasks have already been applied to school-aged children with ADHD, hardly any study investigated adolescents and adults with ADHD; this is particularly true for delay aversion and time perception using intervals in the range of milliseconds (Plichta et al. 2009; Smith et al. 2008). Application of identical paradigms that do not suffer from ceiling or floor effects across the different age groups within a cross-sectional study allows direct comparison of task performance of children, adolescents and adults with ADHD and thus enables us to identify age-dependent changes in the neurocognitive phenotype. Compared to studies which examined specific cognitive functions in single age groups, this approach has the advantage that task-specific variance in the results can be ruled out. We decided to investigate three age groups of ADHD patients, since these age groups have been typically included in previous studies (Crone et al. 2006; Rubia et al. 2006; Scheres et al. 2006), enabling an easier comparison of results from the present study with studies that exclusively focused on one age group.

According to our neurodevelopmental hypothesis derived from the above-mentioned findings regarding altered brain maturation processes and morphometric and functional distinctions within altered neuronal networks in subjects with ADHD, impairments in working memory and interference control should persist into adulthood due to enduring frontal dysfunction. Moreover, deficits in time perception should even deteriorate in adulthood due to increasing cerebellar dysfunction. We further assume that deficits in delay aversion may be more pronounced in childhood and adolescence, with normalisation of striatal functioning in later years at least partially balancing out these impairments.

Methods

Participants

We examined male subjects with ADHD in three different age categories (children 8–12 years; adolescents 13–16 years; adults 18–40 years), along with their age-matched controls. The age classes were defined considering neurobiological findings of age-related structural changes in the brain that suggest substantial developmental changes before the age of 12 and after puberty (Giedd et al. 1999; Sowell et al. 2003; Thompson et al. 2000).

Patients with ADHD were recruited either from the outpatient clinic of the Department of Child and Adolescent Psychiatry, Aachen University, or from the outpatient clinics of the Department of Psychiatry and Psychotherapy, Rostock and Aachen University. Healthy children, adolescents and adults were recruited via announcements in primary and secondary schools and in supermarkets in a broad area around Aachen or Rostock (Germany).

For all children and adolescents, the diagnostic procedure included the German version of the Kiddie-Sads-Present and Lifetime Version (K-SADS-PL, Kaufman et al. 1997), which is a semi-structured interview to assess lifetime and current psychiatric diagnoses based on DSM-IV criteria; it was administered by a senior child and adolescent psychiatrist. To be diagnosed as having ADHD, children and adolescents had to currently fulfil the relevant number of diagnostic criteria, including those related to age of onset. As the K-SADS-PL does not yield severity ratings for clinically significant symptoms, the interviewer additionally rated a diagnostic checklist regarding the DSM-IV symptoms of ADHD based on the answers and comments given by the parent (Diagnostic System for Psychiatric Disorders in Childhood and Adolescence DISYPS: DCL-HKS, Doepfner and Lehmkuhl 1998). This assessment resulted in a severity score for each item, ranging from 0 (not appropriate) to 3 (particularly appropriate).

Following DSM-IV criteria, a senior psychiatrist assessed adult ADHD based on a diagnostic interview and German versions of the following questionnaires. Within the clinical examination, adults underwent the Barkley Interview (Barkley 1998). Additionally, they completed a short version of the Wender Utah Rating Scale (WURS-K, Retz-Junginger et al. 2002, 2003), Conners’ Adult ADHD Rating Scales (CAARS-S-L, Conners et al. 1998) and a short self-rating behavioural questionnaire, the ADHS-SB (Rosler et al. 2004), based on DSM-IV criteria for the assessment of ADHD symptoms. Except for one item, the ADHS-SB is equivalent to the FBB-HKS. Adults also underwent extensive psychiatric examination using the SCID, a structured clinical interview for axis I and II disorders based on DSM-IV criteria (Wittchen et al. 1997). Criteria for ADHD diagnosis in adults, according to the DSM-IV, were both a WURS-k sum score ≥30 points (Retz-Junginger et al. 2003) and an age- and gender-adjusted total ADHD symptom subscale score of ≥1.5 SD above the mean in the CAARS-S:L, as well as substantial impairment in more than one setting and clinically relevant psychological strain.

Exclusion criteria for all participants included IQ below 85. In children and adolescents, IQ was measured through German adaptations of Cattell’s Culture Fair Intelligence Tests, including two age-related subscales (Weiss and Osterland 1997; Weiss 1998). Adults completed a short version of the revised Wechsler Intelligence Scale (HAWIE-R, Tewes 1994). Patients who fulfilled the criteria for the following mental disorders were excluded from the study: pervasive developmental disorder, dyslexia, psychosis, Tourette syndrome, anxiety or mood disorders and current psychotropic substance abuse disorder. Although we are aware of the fact that ADHD is accompanied by a high rate of comorbid disorders (Biederman et al. 1993; Gillberg et al. 2004; Pliszka 1998; Spencer et al. 1999; Steinhausen et al. 1998), we excluded comorbidities that might significantly affect test performance, in order to attain ADHD-specific results.

Allowing for exclusion criteria, the final ADHD sample consisted of 21 children, 22 adolescents and 20 adults. One child and two adolescents failed to discontinue medication and were thus excluded from the study. The resulting analysed ADHD sample consisted of 20 subjects in each age group. Within this sample, two children and two adolescents suffered from an early-onset conduct disorder, and two adolescents were diagnosed with oppositional defiant disorder. The following comorbidities were found in adults: two narcissistic, one antisocial, one borderline and one passive-aggressive personality disorder, and one combined with dependent, avoidant and borderline traits. Forty per cent of the subjects were drug-naïve (seven children, four adolescents, and 13 adults); the others had taken methylphenidate previously, but had been free of any medication for a minimum of 24 (immediate-release MPH) or 48 h (retarded MPH) prior to testing, with a median of 72 h (SD = 21).

The control sample of 20 subjects per age group was recruited according to an age- and IQ-matching procedure. Diagnostics were carried out in the same manner as for the ADHD subjects. No psychiatric diseases or personality disorders were found within the resulting control sample. A severity score for patients and controls was computed for the dimensions of inattentiveness, hyperactivity and impulsivity, based on parental ratings on the DCL-HKS items for children and adolescents and on the psychiatrist’s ratings on the ADHS-DC items for adults.

Overall, ADHD subjects and controls did not differ in terms of age and IQ, but differences in IQ became evident in the sub-sample of adolescents. It should be noted here that the relatively higher IQ in the adult group could be due to use of the short version of WAIS, which depends on relatively old German norms. Rather than examining a group of only well-educated adults, educational and occupational levels were mixed in both adults with ADHD and healthy controls. Children, adolescents, and adults with ADHD differed neither in their overall symptom severity nor in inattentive, hyperactive, or impulsive behaviours. Demographic characteristics, ADHD subtype profile and severity scores are presented in Tables 1 and 2.
Table 1

Descriptive statistics

 

ADHD

Control

  

M

SD

M

SD

F(1, 38)

p

Age

 Children

9.75

1.84

9.76

1.59

0.00

0.98

 Adolescents

14.25

1.19

14.12

1.11

0.14

0.71

 Adults

24.22

5.62

25.26

5.91

0.33

0.57

 All

16.07

6.99

16.38

7.47

0.54

0.82

IQ

 Children

104.26

10.34

107.15

9.05

0.86

0.36

 Adolescents

98.70

7.74

107.35

10.20

9.13**

0.004

 Adults

119.20

13.02

117.05

19.60

0.17

0.69

 All

107.44

13.63

110.52

14.33

1.44

0.23

p < 0.05; ** p < 0.01

Table 2

ADHD subtypes (N) and severity scores for inattention, hyperactivity and impulsivity

 

Subtypes

     

Children

Adolescents

Adults

Inattentive

4

4

1

     

Hyperactive/impulsive

5

6

2

     

Combined

11

10

17

     
 

Severity scores

M

SD

M

SD

M

SD

F(2, 58)a

p

Inattention

 ADHD

16.45

6.00

15.16

5.83

15.47

5.31

0.27

0.77

 Control

5.45

3.03

5.15

4.40

3.65

3.15

1.45

0.24

 F(1, 38), pb

53.60**

<0.001

36.81**

<0.001

70.35**

<0.001

  

Hyperactivity

 ADHD

8.40

3.55

7.16

6.13

8.06

4.67

0.33

0.72

 Control

1.50

1.50

1.20

1.70

2.60

3.12

2.19

0.12

 F(1, 38), p

64.20**

<0.001

17.49**

<0.001

17.96**

<0.001

  

Impulsiveness

 ADHD

7.90

3.52

7.53

5.07

6.00

3.28

1.11

0.34

 Control

3.00

2.71

0.95

1.47

1.45

1.54

5.77**

0.005

 F(1, 38), p

24.28**

<0.001

30.96**

<0.001

30.69**

<0.001

  

Sum score

 ADHD

32.75

10.69

29.84

15.46

29.53

10.95

0.38

0.68

 Control

9.95

5.27

7.30

6.21

7.70

5.72

1.24

0.30

 F(1, 38), p

73.20**

<0.001

36.40**

<0.001

60.33**

<0.001

  

Severity scores are sum scores for the dimensions of inattention, hyperactivity, impulsiveness and the total score of DCL-HKS (children and adolescents) and ADHS-DC (adults)

N = Number of subjects

p < 0.05; ** p < 0.01

aComparison of age groups within the diagnostic groups

bComparison of ADHD versus control subjects per age group

Procedure

After receiving a comprehensive description of the study, informed consent was obtained from the parents, children and adolescents, as well as from the adult participants. All tasks consisted of computerised paradigms, which were conducted on three different computers with comparable performance features. The order of tasks was randomised, and the total duration of testing was approximately 90 min, including one break. The study was carried out in accordance with the latest version of the Declaration of Helsinki, and the study design was reviewed by the local ethics committee. Subsequent to task completion, each subject received a 20.00 € honorarium for participating.

Tasks

All tasks were executed on Pentium 3 desktop computers with 850 MHz, 512 MB RAM and were presented with a resolution of 1024 × 768 × 32 (100 Hz). In all tasks, a cross served as the central fixation point. The background was black, and the font size for stimuli was 48, except for the interference task, in which we chose a font size of 22. The stimuli were printed in white. For all experiments, the answer buttons were either the left (yes) and right (no) mouse buttons, or the buttons D, F, J, and K for the number of stimuli presented on the screen (1–4) or the position of a stimulus on the screen. For all tasks, the instructions were to respond as quickly and as accurately as possible. All tasks were preceded by a short practice trial with error feedback. During task completion, the participants received visual feedback about their choice, but no error feedback.

Working memory

The WM task was a classic n-back task with the conditions 1-back and 2-back. The participants had to track letters that were presented consecutively on the computer screen and had to decide for each successive letter whether it had already been presented one (1-back) or two (2-back) positions before. In each condition, 60 stimuli were presented with a duration of 500 ms and a fixed inter-stimulus interval of 3,000 ms. Each condition contained ten target stimuli, and the reaction time window was 3,000 ms. A proportional measure for performance accuracy called discriminability served as a dependent measure. Discriminability was calculated by the number of correctly identified targets (true positives/number of targets) minus the number of wrongly selected distractors (false positives/number of distractors). The values ranged from 0 = low accuracy to 1 = high accuracy (Ragland et al. 2002).

Interference control

A number-counting Stroop task (Bush et al. 1999) was used to assess interference control. Subjects were shown one to four identical words, which were presented on top of each other on the screen, with the instruction to press the button corresponding to the number of words being presented. The experiment consisted of 90 trials for each condition, whereby control stimuli (fish, house, door, arm) and interference stimuli (one, two, three, four) were presented randomly. In the interference condition, subjects had to suppress the automatic tendency to press the answer button according to the semantic content of the stimulus rather than according to its physical number shown on the screen. The stimuli were presented for 1,200 ms, preceded by a 500 ms fixation cross. The reaction time window was 1,200 ms. Again, trial length was fixed, meaning that the experiment could not be shortened by faster reaction times. Interference trial median reaction time in milliseconds and the number of false responses in the interference condition, both corrected for neutral baseline (RTInterference − RTNeutral); (ErrorsInterference − ErrorsNeutral), served as dependent variables.

Time discrimination

Participants were shown a red and a green circle in quick succession, which hardly differed in the duration of their presentation. They were then required to decide which of the circles was presented for a longer duration (Smith et al. 2002). One of the circles was always presented with a duration of 1,000 ms; the other one was initially presented with a duration of 1,300 ms, but was successively shortened by 15-ms intervals. In each successive trial, colours and positions were randomly interchanged in order to rule out guessing strategies. The two circles were separated by a fixation cross, which was shown for 800 ms. Subsequent to the presentation of the second circle, a delay of 500 ms was introduced, followed by the instruction to choose one of the circles by responding with either the left or the right mouse button. Correct answers were followed by a reduction in the presentation duration of the longer circle by 15 ms, while incorrect answers were followed by an increase of 15 ms. This staircase method was introduced by Levitt (1971). The point of subjective equality between the circles, i.e., the point at which subjects failed to discriminate the presentation duration of the circles adequately and assessed them as being equal, served as a dependent variable. The sensitivity threshold was computed according to Smith et al. (2002).

Time production and time reproduction

Time estimation in the range of several seconds was assessed by a time production and a time reproduction paradigm (Meaux and Chelonis 2003). In the time production task, subjects saw a number on the screen, which was a time in seconds, and were asked to press the left mouse button for the duration of the time interval being displayed. In the time reproduction task, yellow “smiley faces” were presented for a certain time interval. Participants then had to infer the duration for which the smileys were shown on the screen and, again, were asked to press the left mouse button to indicate this time interval. During the button press phase, a green smiley was displayed on the screen. In both the time production (production phase) and the time reproduction (inference and production phase) task, subjects were explicitly instructed to count the seconds in their heads. The time intervals were 2, 6, 12, 24, 36 and 48 s. In both tasks, the time intervals were presented twice, in two successive blocks and were randomised within the blocks. In the time reproduction task, the presentation of the smiley was signalled by a 3-s countdown. To rule out the possibility that overestimations and underestimations would average each other out, the absolute value of the deviation between the specified and the produced time interval, as a measure of accuracy, served as a dependent variable, reflecting the overall magnitude of error regardless of its direction.

Delay aversion

To evaluate delay aversion, participants could decide between two alternative stimuli (e.g., red or green circle), each of which was linked to different consequences. One of the circles was associated with a 2-s delay, followed by a reward of one Euro; the other circle was associated with a 30-s delay and was rewarded with two Euros. Effects of colour preference were controlled by balancing the combination of colour and reward across subjects (Sonuga-Barke et al. 1992). In five preceding trials, subjects were able to test which colour was connected with which consequence. In the main experiment, subjects were asked to make their decisions under two different conditions: in the time constraint condition, the participants had 5 min to make their decisions, while in the trial constraint condition, they could make exactly 20 decisions, independent of time restrictions. When the pre-reward delay had elapsed, the circles disappeared, and the cumulative earnings were shown in the centre of the screen. The best strategy to maximise the profit in the time constraint condition was to press the button associated with the smaller but more immediate reward as often as possible, whereas the best strategy in the trial constraint condition was to prefer the button associated with the larger reward. The number of decisions for the more delayed reward in the trial constraint condition was used as a dependent measure. Importantly, only datasets from those subjects who recognised the best strategies, at least for the trial constraint condition, were analysed, irrespective of their response behaviour.

Statistical analysis

Testing assumptions

The data were analysed using the SPSS statistical package. Prior to analysis, all variables were screened for violations of the assumptions associated with univariate and multivariate tests. The Kolmogorov–Smirnov test showed a normal distribution of all but three variables (number of decisions for the longer delayed reward, Z = 2.00, p = 0.001, 1-back discriminability, Z = 1.52, p = 0.02, and time reproduction absolute error, Z = 1.68, p = 0.01). The Levene test yielded homogeneity of variance for six of the eight variables, with the exceptions being number of errors in the interference trials, F(5, 104) = 3.66, p = 0.004, and time reproduction absolute error, F(5, 104) = 5.33, p < 0.001. The assumption of homogeneity of variance–covariance matrices was violated, Box-M: F = 1.87, p < 0.001.

Missing data and outliers

Missing data, for instance due to delayed reaction or slipping off the answer button, occurred in 1.75% of the neuropsychological data points and were replaced by group means. Furthermore, a few individual data sets were excluded due to excessive demand or deficient understanding of the instructions, resulting in slightly different case numbers for the different dependent variables (see next paragraph). Raw data were Z-transformed in order to examine outliers. No extreme outliers (Z > 4) were observed in the ten selected measures (Wilcox et al. 1998).

Data analysis

In order to investigate group and age effects as well as group × age interactions, a 2 (diagnosis) × 3 (age group) factorial multivariate analysis of variance was conducted, and results were Bonferroni-corrected for multiple comparisons. Significant group effects or group × age interactions were followed by univariate post-hoc analyses for each age group. As ADHD and healthy adolescents differed in terms of IQ, this variable was entered as a covariate in all analyses (MANCOVA). Subjects with missing values in any of the dependent variables were excluded listwise, resulting in slightly but not significantly different case numbers between cells (children 18 controls and 17 ADHD; adolescents 20 controls and 19 ADHD; adults 19 controls and 17 ADHD; χ2 = 0.40, p = 0.99, ns). As parameter-free methods do not allow for covariation with IQ, while we were additionally interested in age × diagnosis interactions, no parameter-free methods were used for those variables that violated the assumptions. However, as samples were sufficient in size (at least 70% more cases in each cell than dependent variables) and equal in terms of case number, the F test should be robust (Tabachnick and Fidell 1996). The global significance level was set at 0.05.

In the case of delay aversion, only data from subjects who recognised the best strategy were analysed. As this procedure would reduce the number of cases in multivariate analyses for another seven datasets due to listwise exclusion, we conducted a separate 2 (diagnosis) × 3 (age group) ANCOVA with Bonferroni correction for multiple comparisons. The resulting sample size was as follows: 18 control and 19 ADHD children, 17 control and 20 ADHD adolescents, and 19 control and 17 ADHD adults. Cell sizes did not differ significantly, χ2 = 0.40, p = 0.99, ns.

Effect size

Partial eta-squared (ηp2) is reported as a measure of effect size, with 0.01 representing a small, 0.06 a medium, and 0.14 a strong effect (Kittler et al. 2007).

Results

Omnibus MANCOVA: The omnibus MANCOVA of the nine neuropsychological variables revealed significant effects for group, F(7, 97) = 3.44, p = 0.002, and age, F(14, 196) = 3.00, p < 0.001, but no group × age interaction, F(14, 196) = 1.02, p = 0.43, ns. All group effects are depicted in Table 3.
Table 3

Measures of performance for six neuropsychological paradigms in subjects with and without ADHD across three age groups

 

Age

Controls M (SD)

ADHD M (SD)

Diagnostic group F(1, 103) (p)

Age group F(2, 103) (p)

Diagnosis × Age F(2, 103) (p)

Post-hoc comparisons

Working memory

 Discriminability 1-back

8–12

0.82 (0.15)

0.59 (0.24)

8.64** (0.004)

2.78 (0.07)

3.86* (0.02)

 

13–16

0.76 (0.15)

0.74 (0.17)

    

18–40

0.83 (0.15)

0.79 (0.20)

    

 Discriminability 2-back

8–12

0.41 (0.27)

0.29 (0.27)

1.96 (0.17)

4.44* (0.01)

0.88 (0.42)

Chi < Ado

13–16

0.51 (0.25)

0.50 (0.21)

    

18–40

0.47 (0.22)

0.38 (0.29)

    

Interference control

 RT improvement

8–12

20.14 (31.47)

18.29 (43.20)

0.00 (0.99)

3.10* (0.05)

0.11 (0.90)

 

13–16

6.85 (32.53)

9.00 (29.44)

    

18–40

19.95 (22.47)

22.38 (26.36)

    

 Interference errors

8–12

1.78 (5.13)

1.41 (4.05)

0.26 (0.61)

2.23 (0.11)

0.63 (0.54)

 

13–16

0.20 (2.33)

1.22 (2.60)

    

18–40

0.20 (1.01)

0.29 (3.26)

    

Time discrimination

 Sensitivity threshold

8–12

233.19 (41.93)

267.79 (41.67)

4.02* (0.05)

3.30* (0.04)

1.59 (0.21)

 

13–16

228.63 (44.42)

223.75 (54.88)

    

18–40

200.38 (70.97)

233.38 (60.18)

    

Time production

 Absolute error

8–12

35553.86 (19630.18)

46128,84 (25302.64)

0.00 (0.95)

1.45 (0.24)

0.77 (0.47)

 

13–16

38679.32 (31772.82)

28946.38 (16414.32)

    

18–40

32486.45 (52666.33)

30358.55 (32212.21)

    

Time reproduction

 Absolute error

8–12

24573.51 (15934.08)

35862.19 (18840.67)

14.55** (<0.001)

5.45** (0.006)

0.71 (0.50)

Chi < Ado, Adu

13–16

16916.40 (6507.27)

24032.36 (16220.23)

    

18–40

12218.37 (6772.08)

26251.24 (18751.76)

    

Delay aversion

 Large reward decision

8–12

12.50 (6.52)

7.74 (6.44)

14.22** (<0.001)

4.42* (0.01)

1.03 (0.36)

Chi < Ado

13–16

16.12 (6.27)

12.50 (7.76)

    

18–40

16.11 (5.34)

9.24 (7.93)

    

Discriminability = number of correctly identified targets (true positives/number of targets) − the number of wrongly selected distractors (false positives/number of distractors). RT improvement = mean interference trial reaction time − mean neutral trial reaction time. Interference errors = mean interference trial error − mean neutral trial error. Sensitivity threshold = point of subjective equality, measured in milliseconds. Absolute error = sum of absolute values of the deviation between the specified and the produced time intervals. Large reward decision = number of decisions for the more delayed reward. For the n-back discriminability scores and the number of large reward decisions in the delay aversion task, higher numbers represent better performance. For all other tasks, lower numbers represent better performance

p < 0.05; ** p < 0.01

Working memory

1-back: There was a main effect for group, ηp2 = 0.08, and a group × age interaction, ηp2 = 0.07, but no effect of age. Subjects with ADHD showed lower discriminability. For group × age interaction, it became evident that children, F(1, 35) = 14.93, p < 0.001, but not adolescents, F(1, 36) = 0.07, p = 0.79, ns, or adults, F(1, 35) = 1.25, p = 0.27, ns, with ADHD underperformed compared to healthy controls.

2-back: For 2-back discriminability, only the age effect, ηp2 = 0.08, but not the effect for group, was significant. Children achieved lower values than adolescents (p = 0.02), whereas there were no differences between children and adults (p = 0.06, ns) or between adolescents and adults (p = 0.65, ns).

For both n-back conditions, reaction times for all stimuli were compared between the diagnostic groups, using a 2 (group) × 3 (age) factorial MANCOVA. This analysis revealed a significant effect of age, F(4, 214) = 12.21, p < 0.001, but not of group, F(2, 106) = 0.22, p = 0.80, ns, or group × age interaction, F(4, 214) = 0.95, p = 0.43, ns. Bonferroni-corrected post-hoc tests revealed that children answered more slowly than adolescents and adults, whereas there were no differences between adolescents and adults (1-back: children vs. adolescents: p < 0.001; children vs. adults: p < 0.001; adolescents vs. adults: p = 1.00, ns; 2-back: children vs. adolescents: p < 0.01; children vs. adults: p < 0.001; adolescents vs. adults: p = 0.15, ns).

Interference control

For interference control, no group effects were found for corrected mean reaction time or the corrected number of errors in the interference trials. Furthermore, an age effect was found for reaction time, ηp2 = 0.06, but not for errors. Bonferroni-adjusted pairwise comparisons between age groups revealed no significant differences.

Time discrimination

Subjects with ADHD were characterised by a higher sensitivity threshold, indicating poorer time discrimination, ηp2 = 0.04. Beyond this, there was an effect of age, ηp2 = 0.06, on time discrimination threshold. Again, Bonferroni-adjusted pairwise comparisons remained insignificant.

Time production

There was no effect of group or age on time production absolute error across all time intervals. In order to evaluate how errors develop with increasing time interval, depending on ADHD status and age group, and in order to examine error direction, i.e. over- or underproduction of interval length, 2 (diagnosis) × 3 (age group) × 6 (time intervals) × 2 (blocks) repeated-measures ANCOVAs with repeats on two factors (time intervals, blocks) were conducted for time production and time reproduction error as well as accuracy coefficient. The latter is computed by dividing the manually produced time intervals by the actual interval presented (Meaux and Chelonis 2003). An accuracy score higher than 1.00 indicates overreproduction and a score lower than 1.00 indicates underreproduction.

Analysis revealed no within-subject interaction effects with increasing interval length for production error either for group, F(5, 555) = 1.02, p = 0.41, ns, or for age, F(10, 555) = 0.96, p = 0.48, ns. In the case of accuracy score, a between-subjects effect for group, F(1, 111) = 10.36, p = 0.002, but not for age, F(2, 111) = 1.96, p = 0.15, ns, was found. As Fig. 1 shows, control subjects overproduced and ADHD subjects underproduced the presented temporal intervals. Post-hoc comparisons revealed significant differences at interval durations of 6 (p = 0.009), 12 (p = 0.003), 24 (p = 0.001), 36 (p = 0.001) and 48 (p = 0.005) s. No within-subjects interaction effects with increasing interval length for accuracy score were found either for group, F(5, 555) = 0.90, p = 0.48, ns, or for age, F(10, 555) = 0.37, p = 0.96, ns.
Fig. 1

Interaction effect between diagnostic group and error development with increasing interval length in the time production and time reproduction task. Interaction effect between diagnostic group and the direction of time production and time reproduction error (over- vs. underreproduction). *p < 0.05. **p < 0.01

Time reproduction

Time reproduction absolute error yielded a significant group, ηp2 = 0.12, and age, ηp2 = 0.10, effect. Subjects with ADHD produced larger errors. Furthermore, children produced larger errors than adolescents (p = 0.012) and adults (p = 0.03), whereas adolescents and adults did not differ from each other (p = 0.99, ns).

Additionally, there was a within-subjects interaction effect with increasing time interval for time reproduction error x group, F(5, 560) = 6.87, p < 0.001, indicating that subjects with ADHD produced larger errors with increasing interval length when compared with healthy controls. Post hoc comparisons revealed significant differences at interval durations of 36 (p = 0.04) and 48 s (p = 0.04) (see Fig. 1). No significant within-subjects interaction effect was found with increasing time interval for time reproduction error x age, F(10, 560) = 1.31, p = 0.22, ns.

The direction of time reproduction error depended on age, F(2, 112) = 4.67, p = 0.01, and on age × group interaction, F(2, 112) = 3.43, p = 0.04. No group effect was found, F(1, 112) = 2.87, p = 0.09, ns. Children underreproduced time intervals compared to adults (p = 0.01), whereas no differences were found between children and adolescents (p = 0.10, ns) or between adolescents and adults (p = 0.99, ns). However, Bonferroni-corrected post-hoc comparisons did not reveal significant differences at any time interval. The interaction effect denotes that subjects with ADHD underreproduced time intervals when compared with controls, although this was only true for adolescents, F(1, 37) = 7.06, p = 0.01, and adults, F(1, 37) = 4.58, p = 0.04, but not for children, F(1, 36) = 0.70, p = 0.41, ns. A reproduction accuracy x group within-subjects interaction effect with increasing time interval, F(5, 560) = 3.55, p = 0.004, indicates that subjects with ADHD showed an increasing underreproduction with increasing interval length when compared with controls. Post-hoc comparisons revealed significant differences at interval durations of 36 s (p = 0.04) and 48s  (p = 0.04) (see Fig. 1). There was no significant within-subjects interaction effect with increasing time interval for reproduction accuracy x age, F(5, 560) = 0.48, p = 0.91, ns.

Delay aversion

The choice for the more delayed reward in the trial constraint condition yielded a significant group, ηp2 = 0.12, and age effect, ηp2 = 0.08. Subjects with ADHD chose the more delayed reward less frequently than control subjects. Analysing the age effect, adolescents chose the delayed reward more often compared to children (p = 0.01), whereas no such differences were found between children and adults (p = 0.98, ns) or between adolescents and adults (p = 0.15, ns).

Effect sizes (ηp2) for each age group with significant group differences are displayed in Fig. 2.
Fig. 2

Effect sizes separated for age groups for all significant overall group differences (ηp2)

To obtain a quantitative measure of impairment, we computed a cutoff score for each of the six neuropsychological paradigms and identified, for each age group, the percentage of subjects with ADHD falling below this score (see Table 4). This cutoff was defined as the subject showing a performance of 1.5 standard deviations from the mean of the corresponding control group in the particular neuropsychological task. For the purpose of comparison, one healthy control per age group was expected to fall below this cut-off. The highest percentage of neuropsychologically impaired subjects was found for delay aversion and working memory. Rather moderate numbers of ADHD subjects were classified as impaired in the interference control and time discrimination task. Neuropsychological impairment was most prominent in the groups of children and adults with ADHD.
Table 4

Percentage of ADHD subjects showing cognitive impairment across the six neuropsychological paradigms

 

Age

Group size

Number of impaired subjects

% of Group

Working memory

 Discriminability 1-back

8–12

19

11

58

13–16

20

3

15

18–40

19

5

26

 Discriminability 2-back

8–12

17

4

24

13–16

20

1

5

18–40

19

5

26

Interference control

 RT improvement

8–12

18

3

17

13–16

19

2

11

18–40

19

1

5

 Interference errors

8–12

17

1

6

13–16

19

0

0

18–40

16

2

11

Time discrimination

 Sensitivity threshold

8–12

19

0

0

13–16

20

1

5

18–40

20

2

10

Time production

 Absolute error

8–12

18

0

0

13–16

20

0

0

18–40

20

0

0

Time reproduction

 Absolute error

8–12

19

0

0

13–16

20

0

0

18–40

20

0

0

Delay aversion

 Large reward decision

8–12

19

8

42

13–16

20

3

15

18–40

17

8

47

Cognitive impairment was defined as showing a performance of 1.5 standard deviations below the mean of the corresponding age control group (Z < −1.5). % of Group denominates the percentage of impaired ADHD subjects in the relevant age group. Discriminability = number of correctly identified targets (true positives/number of targets) − the number of wrongly selected distractors (false positives/number of distractors). RT improvement = mean interference trial reaction time − mean neutral trial reaction time. Interference errors = mean interference trial error − mean neutral trial error. Sensitivity threshold = point of subjective equality, measured in milliseconds. Absolute error = sum of absolute values of the deviation between the specified and the produced time intervals. Large reward decision = number of decisions for the more delayed reward

Discussion

The aim of this neuropsychological study was to evaluate age-related patterns of cognitive dysfunction in ADHD. Six tasks, which were appropriate for all three age groups and which tapped into different domains according to a multi-pathway approach to ADHD were selected.

Working memory

The total group of adolescents identified more true positives and true negatives in the 2-back condition, and thus outperformed children in terms of working memory performance. Furthermore, adolescents and adults answered faster than children in both conditions. This is in line with data from Crone et al. (2006), who found developmental improvement in maintaining and manipulating information from childhood to adolescence.

Subjects with ADHD underperformed controls in the 1-back condition. This effect was primarily caused by a performance deficit in ADHD children, which was not apparent in adolescents and adults with ADHD. In fact, low task difficulty due to simple pair matching and low stimulus density rendered the 1-back condition somewhat similar to a continuous performance task (CPT), meaning that observed performance deficits in children with ADHD may be caused by deficits in sustained attention. Interestingly, in line with our results, previous studies demonstrated particularly strong effect sizes for sustained attention deficits in children with ADHD compared to adolescents or adults (Conners et al. 2003; Tucha et al. 2008).

In contrast to other authors (Boonstra et al. 2005; Dige and Wik 2005; Dowson et al. 2004), and contrary to our hypothesis, we did not find working memory impairments in subjects with ADHD in the more demanding 2-back condition. This might be due to task-specific effects; for example, we did not use even more challenging conditions, e.g., a 3-back condition. However, ceiling effects, which would have indicated that the 2-back condition was too easy (especially for adolescents and adults), were not present, as proven by the low detection accuracy across all age groups and subjects, irrespective of ADHD diagnosis.

Interference control

No differences in interference control were found between the different age groups or between subjects with and without ADHD. Other studies have reported lower interference control in ADHD; however, the effects were rather small and heterogeneous (Hervey et al. 2004; Homack and Riccio 2004; van Mourik et al. 2005). Accordingly, Albrecht et al. (2008) compared the performance of children with and without ADHD on two different kinds of interference tasks. Children with ADHD showed higher interference scores in the widely applied colour Stroop, but not in a counting Stroop task, as was used in our study. Thus, it may be that subjects with ADHD are not generally impaired in interference control, and that some of the effects reported in the literature are due to task-inherent attributes of the widely applied colour Stroop task. However, even for the colour Stroop task, there is a lack of clear empirical evidence; a recently conducted meta-analysis by Schwartz and Verhaeghen (2008), which included subjects aged 9–41, did not find the interference effect in ADHD subjects to be larger than in non-affected controls and did not find older subjects to be more affected than younger ones.

Time perception

Whereas time discrimination abilities generally improved with age, the entire group of subjects with ADHD was impaired. No age or group effects were found for overall time production accuracy. Control subjects overproduced and ADHD subjects underproduced the presented temporal intervals, with the absolute amount of errors being equal across the two groups. In the time reproduction task, children produced a larger overall error compared with adolescents and adults, as they underreproduced the presented time intervals. Similarly, subjects with ADHD produced a larger overall error compared with healthy controls. Furthermore, subjects with ADHD produced larger errors with increasing interval length, as adolescents and adults successively underreproduced the presented time intervals. Notably, both control and ADHD subjects underestimated the presented time intervals. Accordingly, we did not find an overestimation of short temporal intervals, in contrast to other studies (McInerney and Kerns 2003; Mullins et al. 2005), but in line with other investigations, we did detect an underestimation of long intervals.

Interestingly, no differences between ADHD subjects and controls were found in time production accuracy, whereas subjects with ADHD were less accurate in time reproduction, especially for long intervals. This was also found in other studies, but was never examined along with time production (Barkley et al. 2001a; Bauermeister et al. 2005; Meaux and Chelonis 2003). This qualitatively different performance in two timing tasks with quite comparable demands is a remarkable finding, as deficits in time reproduction with concomitant intact time production abilities argue against a pure time perception deficit. As subjects with ADHD show marked delay aversion, it might instead be that deficits in time reproduction are caused by a lack of motivation to wait patiently, i.e., to infer the duration of the stimulus presented and then again to press a button for the same amount of time. Correspondingly, the age effect might also be explained by more pronounced deficits in volitional control of motivational functions in children compared to adolescents or adults. A further explanation could be that subjects with ADHD had a dysfunction in the analysis of temporal characteristics of the stimuli used for temporal estimations.

Delay aversion

Delay aversion was more pronounced in subjects with ADHD. Although ADHD adolescents showed fewer deficits in this task than ADHD children, contrary to our hypothesis, our data did not show a normalisation of task performance with age. Thus, the results of our study suggest, on the behavioural level, that even adults with ADHD cannot compensate for their aversion towards delay. As dysfunctional patterns of reward processing had been demonstrated on the neural level in adolescents and adults with ADHD (Scheres et al. 2007; Ströhle et al. 2008), the question remains open whether their tendency towards immediacy results from a stronger delay aversion per se, from a stronger reward dependency, or both, when compared with healthy controls. As the impact of reward significance, task interest, reward magnitude and probability of reward was demonstrated by several authors (Carlson and Tamm 2000; Drechsler et al. 2009; Scheres et al. 2006), the influence of contextual factors on delay aversion needs to be explored in more detail.

General discussion

This study was one of the first to comparatively examine children, adolescents and adults with ADHD and the first study to investigate different aspects of time perception in adults with ADHD. Based on neuroimaging findings, we hypothesised that impairments in working memory and interference control would persist into adulthood and that deficits in time perception would even increase in adult years, whereas delay aversion should normalise with increasing age in subjects with ADHD.

From a neurodevelopmental perspective, some of the functions examined, i.e., interference control and time perception, seemed to be already fully developed in children, whereas others improved as a function of age. Working memory, time reproduction abilities and delay tolerance improved from childhood to adolescence, and time discrimination abilities also improved as a function of age. Improvement in working memory performance might be related to the development of the prefrontal cortex, and increasing abilities in delay tolerance and time reproduction might be linked to the maturation of frontal top–down control mechanisms of motivational and emotional networks.

In line with our hypothesis, we found deficient time perception in subjects with ADHD across the life span. Contrary to our expectations, delay aversion did not improve as a function of age, but remained stable into adulthood. Whereas deficits in time discrimination may be traced to impairment of the fronto-striatal-cerebellar circuitry, as evidenced by others (Neufang et al. 2008; Smith et al. 2003), intolerance to delayed reward delivery might be attributable to persistent dysfunction in the reward system, including fronto-striatal and fronto-amygdalar interconnections (Halperin and Schulz 2006; Nigg and Casey 2005; Plichta et al. 2009).

We did not find significant impairment in interference control and working memory, which we had suspected would be evident across all age groups in ADHD. In the case of working memory, it might be that the task we applied was not sufficiently challenging to produce differences between the diagnostic groups. For interference control, the effects reported in the literature are generally rather small, and counting Stroop tasks might measure different aspects of interference control compared with colour Stroop tasks (Albrecht et al. 2008).

Contrary to our hypotheses, virtually no developmental dissociations in cognitive functioning were found between subjects with ADHD and healthy controls, except for an interaction effect between age and diagnostic group in the 1-back task. Interestingly, effect sizes were lowest for adolescents with ADHD across all tasks compared to age-matched controls. This finding, which is not attributable to lower ADHD symptoms in this age group, suggests that cognitive dysfunction might be modulated by age, possibly due to developmental changes in brain maturation patterns and/or better compensation for deficits during certain developmental periods. For a shorter time window, this has already been demonstrated by Drechsler et al. (2005). Using a simple reaction time task and examining a group of children with ADHD longitudinally, the authors found reaction time variability to be most pronounced before adolescence (at time 1, when the mean age was 11.0 years), and then to decline over the next 2 years, with no differences between ADHD and control subjects emerging at time 3. Although their study contributes to the understanding of cognitive development from childhood to adolescence, it is limited by the fact that at time 3, only 11 of 28 children formerly diagnosed as having ADHD still fulfilled the diagnostic criteria. Thus, the decrease of reaction time variability might have paralleled the remission of ADHD symptoms. However, authors who used larger samples found reaction time variability to be more pronounced in ADHD adolescents (Martel et al. 2007) and adults (Nigg et al. 2005) compared with healthy controls.

Limitations

The sample size in this study was quite small, thus reducing the power to detect group differences. Moreover, correcting for IQ differences in our analyses may have contributed to decreased group differences, in particular within the group of adolescents. However, statistical analysis without IQ as a covariate revealed a marginally stronger, but similar, effect size pattern. We included only male subjects, meaning that our results cannot be generalised to female subjects with ADHD. In addition, since our study design was cross-sectional, we cannot completely rule out that our results might have been influenced by cohort effects, although age groups were highly comparable with respect to ADHD symptomatology and subtype classification. However, subjects with ADHD differed in the kind of prescribed drugs, frequency and degree of dosage adaptation, continuity of intake of medication, experience with inpatient treatment and therapeutic interventions, making it difficult to evaluate the influence of these factors on performance. Furthermore, it should be taken into consideration that in children and adolescents, medication began while brain maturation was still ongoing. In adults, medication mostly began after maturation processes had already been completed. The question here is whether drugs might have exerted an influence on brain maturation in early medicated subjects (Konrad et al. 2007).

Due to the large age range of the subjects examined in our study, we cannot rule out a differing level of task difficulty between the three age groups. We chose rather simple tasks in order to avoid placing excessive demands on the group of children, which implies the risk of underchallenging adolescents and adults. However, an examination of our results shows that we mostly managed to avoid floor and ceiling effects, with the latter merely being indicated in the 1-back discriminability score and the number of interference errors.

Conclusions

Subjects with ADHD displayed performance deficits up to adulthood; deficits were most pronounced in delay tolerance and timing abilities. The observed enduring deficits in the ability to wait patiently are consistent with the clinical persistence of impulsivity in ADHD throughout life. As these abilities had been previously associated with frontostriatal, frontocerebellar and frontoamygdalar networks in healthy subjects, from a neuropsychological perspective our data support current multiple-pathway approaches, which assume altered information processing in these networks in subjects with ADHD. Future longitudinal neuroimaging studies should clarify how enduring performance deficits are reflected in brain maturation processes.

Notes

Acknowledgments

This study was supported by a grant to K.K., S.C.H. and B.H–D. by the Interdisciplinary Center of Clinical Research Aachen (IZKF N65) in Germany. K.K., B.H.-D. gratefully acknowledge further support by the Deutsche Forschungsgemeinschaft (DFG-KFO112-II, TP 5).

Conflict of interest statement

The authors declare that they have no conflict of interest.

References

  1. Albrecht B, Rothenberger A, Sergeant J, Tannock R, Uebel H, Banaschewski T (2008) Interference control in attention-deficit/hyperactivity disorder: differential Stroop effects for colour-naming versus counting. J Neural Transm 115:241–247CrossRefPubMedGoogle Scholar
  2. Barkley RA (1997) Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 121:65–94CrossRefPubMedGoogle Scholar
  3. Barkley RA (1998) Attention-deficit hyperactivity disorder: a handbook for diagnosis and treatment, 2nd edn. Guilford Press, New YorkGoogle Scholar
  4. Barkley RA, Edwards G, Laneri M, Fletcher K, Metevia L (2001a) Executive functioning, temporal discounting, and sense of time in adolescents with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). J Abnorm Child Psychol 29:541–556CrossRefPubMedGoogle Scholar
  5. Barkley RA, Murphy KR, Bush T (2001b) Time perception and reproduction in young adults with attention deficit hyperactivity disorder. Neuropsychology 15:351–360CrossRefPubMedGoogle Scholar
  6. Bauermeister JJ, Barkley RA, Martinez JV, Cumba E, Ramirez RR, Reina G et al (2005) Time estimation and performance on reproduction tasks in subtypes of children with attention deficit hyperactivity disorder. J Clin Child Adolesc Psychol 34:151–162CrossRefPubMedGoogle Scholar
  7. Biederman J, Faraone SV, Spencer T, Wilens T, Norman D, Lapey KA, Mick E et al (1993) Patterns of psychiatric comorbidity, cognition, and psychosocial functioning in adults with attention deficit hyperactivity disorder. Am J Psychiatry 150:1792–1798PubMedGoogle Scholar
  8. Biederman J, Mick E, Faraone SV (2000) Age-dependent decline of symptoms of attention deficit hyperactivity disorder: impact of remission definition and symptom type. Am J Psychiatry 157:816–818CrossRefPubMedGoogle Scholar
  9. Biederman J, Petty CR, Fried R, Doyle AE, Spencer T, Seidman LJ et al (2007) Stability of executive function deficits into young adult years: a prospective longitudinal follow-up study of grown up males with ADHD. Acta Psychiatr Scand 116:129–136CrossRefPubMedGoogle Scholar
  10. Blasi G, Goldberg TE, Weickert T, Das S, Kohn P, Zoltick B et al (2006) Brain regions underlying response inhibition and interference monitoring and suppression. Eur J Neurosci 23:658–1664CrossRefGoogle Scholar
  11. Boonstra AM, Oosterlaan J, Sergeant JA, Buitelaar JK (2005) Executive functioning in adult ADHD: a meta-analytic review. Psychol Med 35:1097–1108CrossRefPubMedGoogle Scholar
  12. Bunge SA, Ochsner KN, Desmond JE, Glover GH, Gabrieli JD (2001) Prefrontal regions involved in keeping information in and out of mind. Brain 124:2074–2086CrossRefPubMedGoogle Scholar
  13. Bush G, Frazier JA, Rauch SL, Seidman LJ, Whalen PJ, Jenike MA et al (1999) Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop. Biol Psychiatry 45:1542–1552CrossRefPubMedGoogle Scholar
  14. Carlson CL, Tamm L (2000) Responsiveness of children with attention deficit-hyperactivity disorder to reward and response cost: differential impact on performance and motivation. J Consult Clin Psychol 69:73–83Google Scholar
  15. Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS et al (2002) Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 288:1740–1748CrossRefPubMedGoogle Scholar
  16. Conners C, Erhardt D, Sparrow E (1998) The Conners Adult ADHD Rating Scale-Long Version (CAARS-S-L). Multi-Health Systems, TorontoGoogle Scholar
  17. Conners CK, Epstein JN, Angold A, Klaric J (2003) Continuous performance test performance in a normative epidemiological sample. J Abnorm Child Psychol 31:555–562CrossRefPubMedGoogle Scholar
  18. Crone EA, Wendelken C, Donohue S, van Leijenhorst L, Bunge SA (2006) Neurocognitive development of the ability to manipulate information in working memory. Proc Natl Acad Sci USA 103:9315–9320CrossRefPubMedGoogle Scholar
  19. Dige N, Wik G (2005) Adult attention deficit hyperactivity disorder identified by neuropsychological testing. Int J Neurosci 115: 169–183CrossRefPubMedGoogle Scholar
  20. Doepfner M, Lehmkuhl G (1998) Diagnostic system for mental disorders in child and adolescence according to ICD-10 and DSM-IV (DISYPS-KJ). Huber, BernGoogle Scholar
  21. Dowson JH, McLean A, Bazanis E, Toone B, Young S, Robbins TW et al (2004) Impaired spatial working memory in adults with attention-deficit/hyperactivity disorder: comparisons with performance in adults with borderline personality disorder and in control subjects. Acta Psychiatr Scand 110:45–54CrossRefPubMedGoogle Scholar
  22. Drechsler R, Brandeis D, Foldenyi M, Imhof K, Steinhausen HC (2005) The course of neuropsychological functions in children with attention deficit hyperactivity disorder from late childhood to early adolescence. J Child Psychol Psychiatry 46:824–836CrossRefPubMedGoogle Scholar
  23. Drechsler R, Rizzo P, Steinhausen HC (2009) Decision making with uncertain reinforcement in children with attention deficit/hyperactivity disorder (ADHD). Child Neuropsychol (in press)Google Scholar
  24. Ernst M, Zametkin AJ, Matochik JA, Jons PH, Cohen RM (1998) DOPA decarboxylase activity in attention deficit hyperactivity disorder adults. A [fluorine-18]fluorodopa positron emission tomographic study. J Neurosci 18:5901–5907PubMedGoogle Scholar
  25. Faraone SV, Biederman J, Mick E (2006) The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies. Psychol Med 36:159–165CrossRefPubMedGoogle Scholar
  26. Fayyad J, De Graaf R, Kessler R, Alonso J, Angermeyer M, Demyttenaere K et al (2007) Cross-national prevalence and correlates of adult attention-deficit hyperactivity disorder. Br J Psychiatry 190:402–409CrossRefPubMedGoogle Scholar
  27. Garrett A, Penniman L, Epstein JN, Casey BJ, Hinshaw SP, Glover G et al (2008) Neuroanatomical abnormalities in adolescents with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 47:1321–1328CrossRefPubMedGoogle Scholar
  28. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A et al (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 2:861–863CrossRefPubMedGoogle Scholar
  29. Gillberg C, Gillberg IC, Rasmussen P, Kadesjö B, Söderström H, Rastam M et al (2004) Co-existing disorders in ADHD–implications for diagnosis and intervention. Eur Child Adolesc Psychiatry 13 Suppl 1:180–192Google Scholar
  30. Halperin JM, Schulz KP (2006) Revisiting the role of the prefrontal cortex in the pathophysiology of attention-deficit/hyperactivity disorder. Psychol Bull 132:560–581CrossRefPubMedGoogle Scholar
  31. Halperin JM, Trampush JW, Miller CJ, Marks DJ, Newcorn JH (2008) Neuropsychological outcome in adolescents/young adults with childhood ADHD: profiles of persisters, remitters and controls. J Child Psychol Psychiatry 49:958–966CrossRefPubMedGoogle Scholar
  32. Hervey AS, Epstein JN, Curry JF (2004) Neuropsychology of adults with attention-deficit/hyperactivity disorder: a meta-analytic review. Neuropsychologia 18:485–503CrossRefGoogle Scholar
  33. Himpel S, Banaschewski T, Grüttner A, Becker A, Heise A, Üebel A et al (2009) Duration discrimination in the range of milliseconds and seconds in children with ADHD and their unaffected siblings. Psychol Med 39:1745–1751CrossRefPubMedGoogle Scholar
  34. Homack S, Riccio CA (2004) A meta-analysis of the sensitivity and specificity of the Stroop Color and Word Test with children. Arch Clin Neuropsychol 19:725–743CrossRefPubMedGoogle Scholar
  35. Ivry R (1996) The representation of temporal information in perception and motor control. Curr Opin Neurobiol 6:851–857CrossRefPubMedGoogle Scholar
  36. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P et al (1997) Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36:980–988CrossRefPubMedGoogle Scholar
  37. Kittler JE, Menard W, Phillips KA (2007) Weight concerns in individuals with body dysmorphic disorder. Eat Behav 8:115–120CrossRefPubMedGoogle Scholar
  38. Konrad K, Neufang S, Fink GR, Herpertz-Dahlmann B (2007) Long-term effects of methylphenidate on neural networks of attention in children with ADHD: results from a longitudinal functional MRI study. J Am Acad Child Adolesc Psychiatry 46:1633–1641CrossRefPubMedGoogle Scholar
  39. Levitt H (1971) Transformed up-down methods in psychoacoustics. J Acoust Soc Am 49:467–477CrossRefPubMedGoogle Scholar
  40. Lijffijt M, Kenemans JL, Verbaten MN, van Engeland H (2005) A meta-analytic review of stopping performance in attention-deficit/hyperactivity disorder: deficient inhibitory motor control? J Abnorm Psychol 114:216–222CrossRefPubMedGoogle Scholar
  41. Mackie S, Shaw P, Lenroot R, Pierson R, Greenstein DK, Nugent TF et al (2007) Cerebellar development and clinical outcome in attention deficit hyperactivity disorder. Am J Psychiatry 164:647–655CrossRefPubMedGoogle Scholar
  42. Martel M, Nikolas M, Nigg JT (2007) Executive function in adolescents with ADHD. J Am Acad Child Adolesc Pschiatry 46:1437–1444CrossRefGoogle Scholar
  43. Martinussen R, Hayden J, Hogg-Johnson SH, Tannock R (2005) A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 44:377–384CrossRefPubMedGoogle Scholar
  44. McClure SM, Laibson DI, Loewenstein G, Cohen JD (2004) Separate neural systems value immediate and delayed monetary rewards. Science 306:503–507CrossRefPubMedGoogle Scholar
  45. McInerney RJ, Kerns KA (2003) Time reproduction in children with ADHD: motivation matters. Child Neuropsychol 9:91–108PubMedGoogle Scholar
  46. McLoughlin G, Albrecht B, Banaschewski T, Rothenberger A, Brandeis D, Asherson P et al (2009) Performance monitoring is altered in adult ADHD: a familial event-related potential investigation. Neuropsychologia 47:3134–3142CrossRefPubMedGoogle Scholar
  47. Meaux JB, Chelonis JJ (2003) Time perception differences in children with and without ADHD. J Pediatr Health Care 17:64–71PubMedGoogle Scholar
  48. Mullins C, Bellgrove MA, Gill M, Robertson IH (2005) Variability in time reproduction: difference in ADHD combined and inattentive subtypes. J Am Acad Child Adolesc Psychiatry 44:169–176CrossRefPubMedGoogle Scholar
  49. Neufang S, Fink GR, Herpertz-Dahlmann B, Willmes K, Konrad K (2008) Developmental changes in neural activation and psychophysiological interaction patterns of brain regions associated with interference control and time perception. Neuroimage 43:399–409CrossRefPubMedGoogle Scholar
  50. Nigg JT (2006) Temperament and developmental psychopathology. J Child Psychol Psychiatry 47:395–422CrossRefPubMedGoogle Scholar
  51. Nigg JT, Casey BJ (2005) An integrative theory of attention-deficit/hyperactivity disorder based on the cognitive and affective neurosciences. Dev Psychopathol 17:785–806PubMedGoogle Scholar
  52. Nigg JT, Stavro G, Ettenhofer M, Hambrick DZ, Miller T, Henderson JM (2005) Executive functions and ADHD in adults: evidence for selective effects on ADHD symptom domains. J Abnorm Psychol 114:706–717CrossRefPubMedGoogle Scholar
  53. Pennington BF, Ozonoff S (1996) Executive functions and developmental psychopathology. J Child Psychol Psychiatry 37:51–87CrossRefPubMedGoogle Scholar
  54. Peterson BS, Kane MJ, Alexander GM, Lacadie C, Skudlarski P, Leung HC et al (2002) An event-related functional MRI study comparing interference effects in the Simon and Stroop tasks. Cogn Brain Res 13:427–440CrossRefGoogle Scholar
  55. Plichta MM, Vasic N, Wolf C, Lesch KP, Brummer D, Jacob C et al (2009) Neural hyporesponsiveness and hyperresponsiveness during immediate and delayed reward processing in adult attention-deficit/hyperactivity disorder. Biol Psychiatry 65:7–14CrossRefPubMedGoogle Scholar
  56. Pliszka SR (1998) Comorbidity of attention-deficit/hyperactivity disorder with psychiatric disorder: an overview. J Clin Psychiatry 59:50–58PubMedGoogle Scholar
  57. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA (2007) The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164:942–948CrossRefPubMedGoogle Scholar
  58. Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L et al (2002) Working memory for complex figures: an fMRI comparison of letter and fractal n-back tasks. Neuropsychology 16:370–379CrossRefPubMedGoogle Scholar
  59. Retz-Junginger P, Retz W, Blocher D, Weijers HG, Trott GE, Wender PH et al (2002) Wender Utah rating scale. The short-version for the assessment of the attention-deficit hyperactivity disorder in adults. Nervenarzt 73:830–838CrossRefPubMedGoogle Scholar
  60. Retz-Junginger P, Retz W, Blocher D, Stieglitz RD, Supprian T, Wender PH et al (2003) Reliabilität und Validität der Wender Utah Rating Scale Kurzform. Nervenarzt 74:987–993CrossRefPubMedGoogle Scholar
  61. Rosler M, Retz W, Retz-Junginger P, Thome J, Supprian T, Nissen T et al (2004) Instrumente zur Diagnostik der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) im Erwachsenenalter: Selbstbeurteilungsskala (ADHS-SB) und Diagnosecheckliste (ADHS-DC). Nervenarzt 75:888–895PubMedGoogle Scholar
  62. Rubia K, Smith AB, Woolley J, Nosarti C, Heyman I, Taylor E et al (2006) Progressive increase of frontostriatal bran activation from childhood to adulthood during event-related tasks of cognitive control. Hum Brain Mapp 27:973–993CrossRefPubMedGoogle Scholar
  63. Scahill L, Schwab-Stone M (2000) Epidemiology of ADHD in school-age children. Child Adolesc Psychiatr Clin N Am 9:541–555PubMedGoogle Scholar
  64. Scheres A, Dijkstra M, Ainslie E, Balkan J, Reynolds B, Sonuga-Barke E et al (2006) Temporal and probabilistic discounting of rewards in children and adolescents: effects of age and ADHD symptoms. Neuropsychologia 44:2092–2103CrossRefPubMedGoogle Scholar
  65. Scheres A, Milham MP, Knutson B, Castellanos FX (2007) Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. Biol Psychiatry 61:720–724CrossRefPubMedGoogle Scholar
  66. Schwartz K, Verhaeghen P (2008) ADHD and Stroop interference from age 9 to age 41 years: a meta-analysis of developmental effects. Psychol Med 38:1607–1616CrossRefPubMedGoogle Scholar
  67. Sergeant JA, Oosterlaan J, van der Merre J (1999) Information processing and energetic factors in attention-deficit/hyperactivity disorder. In: Quay HC, Hogan AE (eds) Handbook of disruptive behaviour disorders. Kluwer Academic/Plenum Publishers, New York, pp 75–104Google Scholar
  68. Smith A, Taylor E, Rogers JW, Newman S, Rubia K (2002) Evidence for a pure time perception deficit in children with ADHD. J Child Psychol Psychiatry 43:529–542CrossRefPubMedGoogle Scholar
  69. Smith A, Taylor E, Lidzba K, Rubia K (2003) A right hemispheric frontocerebellar network for time discrimination of several hundreds of milliseconds. Neuroimage 20:344–350CrossRefPubMedGoogle Scholar
  70. Smith AB, Taylor E, Brammer M, Halari R, Rubia K (2008) Reduced activation in right lateral prefrontal cortex and anterior cingulate gyrus in medication-naive adolescents with attention deficit hyperactivity disorder during time discrimination. J Child Psychol Psychiatry 49:977–985CrossRefPubMedGoogle Scholar
  71. Solanto MV, Abikoff H, Sonuga-Barke E, Schachar R, Logan GD, Wigal T et al (2001) The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multi-modal treatment study of AD/HD. J Abnorm Child Psychol 29:215–228CrossRefPubMedGoogle Scholar
  72. Sonuga-Barke EJS (2002) Psychological heterogeneity in AD/HD: a dual pathway model of behaviour and cognition. Behav Brain Res 130:29–36CrossRefPubMedGoogle Scholar
  73. Sonuga-Barke EJS, Taylor E, Sembi S, Smith J (1992) Hyperactivity and delay aversion–I. The effect of delay on choice. J Child Psychol Psychiatry 33:387–398CrossRefPubMedGoogle Scholar
  74. Sonuga-Barke EJS, Dalen L, Remington B (2003) Do executive deficits and delay aversion make independent contributions to preschool attention-deficit/hyperactivity disorder symptoms? J Am Acad Child Adolesc Psychiatry 42:1335–1342CrossRefPubMedGoogle Scholar
  75. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW (2003) Mapping cortical change across the human life span. Nat Neurosci 6:309–315CrossRefPubMedGoogle Scholar
  76. Spencer T, Biederman J, Wilens T (1999) Attention-deficit/hyperactivity disorder and comorbidity. Pediatr Clin N Am 46:915–927CrossRefGoogle Scholar
  77. Steinhausen HC, Metzke CW, Meier M, Kannenberg R (1998) Prevalence of child and adolescent psychiatric disorders: the Zurich epidemiological study. Acta Psychiatr Scand 98:262–271CrossRefPubMedGoogle Scholar
  78. Strohle A, Stoy M, Wrase J, Schwarzer S, Schlagenhauf F, Huss M et al (2008) Reward anticipation and outcomes in adult males with attention-deficit/hyperactivity disorder. Neuroimage 39:966–972CrossRefPubMedGoogle Scholar
  79. Tabachnick BG, Fidell LS (1996) Using multivariate statistics, 3rd edn. HarperCollins College Publishers, New YorkGoogle Scholar
  80. Tewes U (1994) Hamburg-Wechsler-Intelligenztest für Erwachsene–Revision 1991 (HAWIE-R). Huber, BernGoogle Scholar
  81. Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga AW (2000) Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature 404:190–193CrossRefPubMedGoogle Scholar
  82. Toplak ME, Tannock R (2005) Time perception: modality and duration effects in attention-deficit/hyperactivity disorder (ADHD). J Abnorm Child Psychol 33:639–654CrossRefPubMedGoogle Scholar
  83. Tripp G, Alsop B (2001) Sensitivity to reward delay in children with attention deficit hyperactivity disorder (ADHD). J Child Psychol Psychiatry 42:691–698CrossRefPubMedGoogle Scholar
  84. Tucha L, Tucha O, Walitza S, Sontag TA, Laufkötter R, Linder M et al (2009) Vigilance and sustained attention in children and adults with ADHD. J Atten Disord 12:410–421CrossRefPubMedGoogle Scholar
  85. Valera EM, Faraone SV, Biederman J, Poldrack RA, Seidman LJ (2005) Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder. Biol Psychiatry 57: 439–447CrossRefPubMedGoogle Scholar
  86. Van Mourik R, Oosterlaan J, Sergeant JA (2005) The Stroop revisited: a meta-analysis of interference control in AD/HD. J Child Psychol Psychiatry 46:150–165CrossRefPubMedGoogle Scholar
  87. Wager TD, Smith EE (2003) Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci 3:255–274CrossRefPubMedGoogle Scholar
  88. Weiss RH (1998) Grundintelligenztest Skala 2 (CFT 20). Hogrefe, GöttingenGoogle Scholar
  89. Weiss RH, Osterland J (1997) Grundintelligenztest Skala 1 (CFT1). Hogrefe, GöttingenGoogle Scholar
  90. Wilcox RR, Keselman HJ, Kowalchuk RK (1998) Can tests for treatment group equality be improved? The bootstrap and trimmed means conjecture. Br J Math Stat Psychol 51:123–134Google Scholar
  91. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF (2005) Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry 57:1336–1346CrossRefPubMedGoogle Scholar
  92. Wittchen H, Zaudig M, Fydrich T (1997) Strukturiertes Klinisches Interview für DSM IV (SKID). Hogrefe, GöttingenGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ivo Marx
    • 1
  • Thomas Hübner
    • 2
  • Sabine C. Herpertz
    • 3
  • Christoph Berger
    • 1
  • Erik Reuter
    • 1
  • Tilo Kircher
    • 4
  • Beate Herpertz-Dahlmann
    • 5
  • Kerstin Konrad
    • 5
  1. 1.Department of Psychiatry and PsychotherapyUniversity of RostockRostockGermany
  2. 2.Technical University of DresdenDresdenGermany
  3. 3.Department of General PsychiatryUniversity of HeidelbergHeidelbergGermany
  4. 4.Department of Psychiatry and PsychotherapyUniversity of MarburgMarburgGermany
  5. 5.RWTH Aachen University HospitalAachenGermany

Personalised recommendations