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Keywords:

  • Epilepsy;
  • Attention-deficit/hyperactivity disorder;
  • Functional imaging;
  • Working memory;
  • Methylphenidate

Summary

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

Purpose:  Children with epilepsy have a significant risk for attention-deficit/hyperactivity disorder (ADHD), which is often accompanied by deficits in working memory performance. However, it is not yet clear whether there are specific differences in the underlying mechanisms of working memory capability between children with epilepsy-related ADHD and those with developmental ADHD. There is evidence that methylphenidate can improve the behavioral difficulties in children with developmental ADHD. Whether this medication has the same effect on ADHD symptoms in patients with epilepsy is not yet well understood. The aim of the present study is, therefore, to evaluate whether boys with epilepsy-related ADHD and developmental ADHD share a common behavioral, pharmacoresponsive, and neurofunctional pathophysiology.

Methods:  Seventeen boys with diagnosed combined epilepsy/ADHD, 15 boys with developmental ADHD, and 15 healthy controls (aged 8–14 years) performed on working memory tasks (N-back) while brain activation was recorded using functional magnetic resonance imaging. Each patient was tested twice: once after the intake of methylphenidate and once without in a counterbalanced order.

Key Findings:  On a behavioral level, we show that boys with epilepsy-related ADHD as well as those with developmental ADHD performed similarly poorly on tasks with high cognitive load when compared to healthy controls, and that intake of methylphenidate improved performance almost to normal levels in both ADHD groups. On the functional level, both patient groups showed similar reductions of activation in all relevant parts of the functional network of working memory when compared to controls. Of interest, intake of methylphenidate did not significantly alter this activity pattern.

Significance:  Our data show strong similarities between epilepsy-related and developmental ADHD on the behavioral, pharmacoresponsive, and neural level, favoring the view that ADHD with and without epilepsy shares a common underlying neurobehavioral pathophysiology.

Children with epilepsy often experience attentional problems such as attention-deficit/hyperactivity disorder (ADHD). Approximately one-third of children with epilepsy, in addition to their diagnosis of epilepsy, also receive a diagnosis of ADHD (Dunn et al., 2003; Hermann et al., 2007) and a potential bidirectional association of those two disorders has become the focus of recently published studies. On one hand it has been shown that children with developmental ADHD have an incidence of electroencephalography (EEG) abnormalities significantly higher than in the normal pediatric population (Richer et al., 2002; Davis et al., 2010) and on the other hand, attention problems including ADHD have also been reported to exist before the first recognized seizure (Dunn et al., 1997; Austin et al., 2001; Hesdorffer et al., 2004). Various mechanisms that may account for this association are discussed. These include effects of antiepileptic medications, common genetic predisposition, biochemical factors, subclinical epileptiform discharges, or even the fact that ADHD and epilepsy are both common childhood disorders (Hamoda et al., 2009; Kaufmann et al., 2009; Parisi et al., 2010). However, most of the studies are exclusively based on behavioral and neuropsychological examinations and are limited by their methodologic procedures. Whether the associated ADHD in epilepsy and developmental ADHD share a common underlying neurobiology remains an open question.

Hermann et al. (2007) published the first results on the neurobiology of ADHD in children with idiopathic epilepsy using voxel-based morphometry (VBM). The authors found significantly decreased gray matter in the frontal lobes and smaller brainstem volumes in children with epilepsy/ADHD compared to healthy controls. However, they did not match the epileptic patients with a sample of patients with developmental ADHD and, therefore, no comparison could be drawn between these two clinical samples. In a recent published study, we compared a sample of patients with epilepsy/ADHD to those with developmental ADHD and a group of healthy controls using diffusion tensor imaging (DTI) (Bechtel et al., 2009). Results revealed deficient cerebellar connections in both patient groups compared to controls, suggesting that patients with epilepsy and/or ADHD may have similar cerebellar pathology. However, this study was restricted to cerebellar regions and permits conclusions exclusively on a structural level. Functional data on the topic are still missing.

In patients with developmental ADHD a substantial literature confirms the presence of executive dysfunctions, including working memory deficits (Willcutt et al., 2005), which are also supported by functional magnetic resonance imaging (fMRI) studies. Although healthy controls showed robust increased activation patterns in frontal, parietal, and cerebellar regions during working memory tasks (Owen et al., 2005), several studies reported decreased activation in this functional network in patients with ADHD (Valera et al., 2005; Ehlis et al., 2008; Kobel et al., 2009). Working memory tasks seem to be sensitive in differentiating between healthy controls and patients with ADHD and may also give valuable insights into the neurofunctional pathology of patients with epilepsy-related ADHD.

Methylphenidate (MPH) is the best studied and widely prescribed stimulant for the treatment of ADHD (Weber & Lutschg, 2002), and a number of studies have shown that it is highly effective in alleviating the symptoms of the disorder (Solanto, 1998). Recent controlled trials of MPH in patients with well-controlled epilepsy and ADHD have shown significant improvements in ADHD symptoms without an exacerbation of seizures (Gross-Tsur et al., 1997; Baptista-Neto et al., 2008). In higher doses and in patients with frequent seizures, the safety of MPH needs further evaluation (Gonzalez-Heydrich et al., 2010). Although MPH is the most prominent medication to improve the symptoms of ADHD, studies examining the effect of stimulants in children with epilepsy by functional neuroimaging techniques have not yet been reported.

The aim of the present study was to clarify whether ADHD in epilepsy shares the pathophysiology of ADHD seen in nonepileptic populations. To answer this question, we examined behavioral differences in working memory performance, pharmacologic efficiency profiles of MPH, and functional brain organization in children with epilepsy/ADHD, children with developmental ADHD, and healthy controls.

Methods

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

Clinical participants were recruited from different sites in Switzerland and Germany: University Children’s Hospital Basel, Children’s Hospital of Luzern, Children’s Hospital of Aarau, and the St. Elisabethen Clinic of Lörrach. Healthy children agreed to participate upon announcement or personal query. All participants were investigated at the University Hospital Basel. Children and their parents gave informed written consent and the local ethics committee approved the study.

Participants

Seventeen boys with epilepsy/ADHD, 15 boys with developmental ADHD, and 15 healthy boys were investigated. Ages ranged from 8 to 14 years. Initial inclusion criteria for boys with epilepsy were: Diagnosis of epilepsy based on a history of two unprovoked cerebral seizures, idiopathic or cryptogenic cause, EEG features of hypersynchronous activity, and no identifiable lesions on MRI. Further, patients had to be seizure-free for at least 6 months and had to fulfil the diagnosis of ADHD predominantly inattentive type (ADD) or combined type (ADHD) according to DSM-IV criteria (American Psychiatric Association, 2000). This diagnosis was based on clinical features including typical history and behavioral reports and elevated scores on the Conners’ Rating Scales for Parents (Conners, 2001). Comparisons of basic demographic characteristics are provided in Table 1. Additional information about characteristics of patients with combined epilepsy/ADHD is provided in the Table S1.

Table 1.   Demographic and clinical characteristics of groups
 Epilepsy/ADHD (n = 17) Mean (SD)ADHD (n = 15) Mean (SD)Controls (n = 15) Mean (SD)
  1. ADHD, attention-deficit/hyperactivity disorder; ADD, attention-deficit/hyperactivity disorder—predominantly inattentive type.

  2. *Significant difference between patients with epilepsy/ADHD and controls (t15.1 = −6.80, p < 0.01) and patients with developmental ADHD and healthy controls, respectively (t15.3 = −9.23, p < 0.01).

Age in years11.54 (±1.65)11.03 (±1.35)11.49 (±1.82)
Raven’s progressive matrices in percent ranks32.5 (±22.1)47.3 (±35.5)44.2 (±26.6)
Conners’ score parents13.86 (±5.71)16.86 (±5.35)3.08 (±1.56)*
Diagnosis of ADHD6 ADHD9 ADHD_
11 ADD6 ADD
Age at diagnosis of ADHD9.33 (±1.95)8.23 (±2.61)
Age at diagnosis of epilepsy7.51 (±2.85)
Duration of epilepsy4.72 (±2.26)

Patients with ADHD were selected according to the same diagnostic criteria of ADHD/ADD. We included six patients with ADD and nine patients with ADHD. In the developmental ADHD group, 93% were taking medication before study, and in the combined epilepsy/ADHD, 53% were.

Exclusion criteria for both patient groups and controls were: developmental disorder (e.g., autism), neurologic disorder (i.e., cerebral palsy), intelligence below average (intelligence quotient [IQ] <70), genetic disorder, psychotropic medication (e.g., atomoxetine), and the need to wear fixed braces.

Materials and procedure

Behavioral data

All participants were examined for intelligence using the Raven’s progressive matrices (Raven et al., 2002). To measure working memory performance, three different N-back tasks with increasing load (0-back, 2-back, 3-back) were applied during functional imaging. In all tasks the participants had to respond to visually projected numbers by pressing a button. In the 0-back task, they had to press the button when the number 2 appeared among a series of other numbers, in the 2-back and the 3-back task, whenever the presented number was identical to the second or third last number, respectively. Each task consisted of five blocks of resting and four blocks of activation in alteration; in resting blocks, the projected numbers were replaced by a fixation cross. Each block comprised 10 trials. One trial included the visual presentation of a series of numbers (or a cross in resting trials) for 500 ms followed by a blank screen for 2,500 ms. Responses and reaction times were recorded on a Lumina LP-400 response box (Cedrus). All tasks were programmed using E-PRIME Software (version 1.1.3; Psychology Software Tools, Pittsburgh, PA, U.S.A.) and generated by a PC. Stimuli were back-projected onto a screen that could be viewed through a mirror attached above the scanner’s head-coil.

Image acquisition

Due to replacement, the measurements had to be performed on two different 3T MRI systems. The first 32 participants (12 healthy controls, 14 patients with developmental ADHD, and 8 patients with epilepsy/ADHD) were scanned on a SIEMENS Magnetom Allegra system using a circular polarized birdcage radiofrequency (rf) head coil, and 12 participants (3 healthy controls, 1 patient with developmental ADHD, and 9 patients with epilepsy/ADHD) were scanned on a SIEMENS Magnetom Verio scanner (Siemens, Erlangen, Germany) using a 12-channel array radiofrequency coil. Most acquisition parameters for functional imaging could be kept constant on both scanners; however, the lower gradient performance of the Verio system resulted in the acquisition of one slice less per volume repetition time. The imaging protocol included a scout, a high-resolution T1-weighted image and functional imaging. The sagittal T1-weighted three-dimensional high-resolution data set was acquired by a magnetization prepared rapid gradient echo (MPRAGE) sequence (TI: 1,000 ms) providing an isotropic resolution of 1 × 1 × 1 mm on both scanners. For functional imaging, an echo planar imaging (EPI) sequence with repetition time/echo time (TR/TE) 2,500/32 ms, field of view (FOV) of 240 × 240 mm, matrix size of 96 × 96, and slice thickness of 3 mm with gap of 0.6 mm was applied. Thirty-six slices (Allegra)/35 slices (Verio) were positioned parallel to the AC-PC line. For each task, 112 volumes were acquired.

Data analyses of imaging data

Statistical parametric mapping (SPM5) software package (Wellcome Department of Imaging Neuroscience, London, United Kingdom) was used for data analyses. The first four volumes of each session were discarded to avoid transient signal changes, yielding 108 volumes per session. These volumes were subjected to standard preprocessing procedures (Ashburner & Friston, 1997) including realignment and unwarping, normalization to the standard Montreal Neurological Institute template (resampled voxel size: 2 × 2 × 2 mm), and smoothing with an 8-mm full-width-at-half-maximum isotropic Gaussian kernel. Then, the smoothed images of each person were subjected to a first-level analysis in order to extract the contrasts of interest. Thereby, vectors of stimulus onsets were entered, reflecting the experimental design, and convolved with the hemodynamic response function as basis-function. To remove residual variance, caused by head movements during the image acquisition, the movement parameters extracted in the realignment procedure were included in the model as additional covariates. During fitting of the data, the time series were filtered with a high-pass filter of 128 s to remove artifacts due to cardiorespiratory and other cyclical influences. The model was then convolved with the canonical hemodynamic response function and corrected for serial correlations. Then contrasts were specified to extract the activation in the active condition by subtracting the resting condition separately for each task. Those contrast images were then submitted to a full factorial analysis of variance (ANOVA) in order to extract effects of group, medication condition, and of load. To analyze a potential bias due to the measurement on two different scanners, scanner type was implemented as a covariate in the analysis. An effect could be almost ruled out because none of the scanner explained for any variance.

Data analysis of behavioral data

During scanning, reaction times and reaction accuracy in the N-back tasks were recorded. Errors were counted when the answer was not correct, or participants failed to press the button. All behavioral data was analyzed using standard statistical software (SPSS, version 17.0.4; SPSS Inc, Chicago, IL, U.S.A.).

Procedure

All patients were scanned twice in a counterbalanced order: once with MPH and once without. Healthy boys were tested only once. On scanning sessions without MPH, the last intake of MPH had to date back at least 24 h. On scanning sessions with MPH, the medication was taken approximately one and one half hours before scanning. The two scanning sessions for patients with ADHD were scheduled at least 14 days apart (mean, 48.8 days, standard deviation [SD] 25.1). Before scanning, each child was familiarized with the scanner. Then, the N-back tasks were explained and each child completed a shortened version outside the scanner. Once all questions were settled, scanning was performed. After scanning and a short break, the participants were asked to complete the psychological examination.

Results

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

Behavioral data

N-back tasks

In a first step, reaction times were analyzed using one-way ANOVAs with group as a factor. Then a priori contrasts were performed to evaluate the effects between groups.

Results yielded no significant differences in any of the tasks between healthy controls and both patient groups, neither in the unmedicated (all p > 0.49) nor in the medicated condition (all p > 0.42). In a next step, we compared accuracy measures between the groups. (Descriptives of accuracy in percentages are listed in the Table S2.) With respect to the subtypes, no differences were found between patients with ADHD or ADD neither in the ADHD only group nor in the combined ADHD/epilepsy group (all p > 0.16).

Comparisons of healthy controls and unmedicated patients with epilepsy/ADHD revealed that controls performed significantly better in the 2-back task (t16.2 = 2.33, p < 0.05) and pointed out a strong trend for the 3-back task (t42 = 1.96, p = 0.057); contrasts between healthy controls and patients with developmental ADHD exposed significant differences in both tasks (t20.2 = 3.45, p < 0.01; t42 = 2.58, p < 0.05, respectively). However, when patients performed the tasks medicated, there were no longer any significant differences between controls and patients detectable in any of the tasks (all p > 0.083). A within-subject comparison for the patient groups showed an effect of MPH on performance for the two more difficult tasks, as depicted in Fig. 1.

image

Figure 1.   Accuracy measures in percentages for patients with combined epilepsy/ADHD and patients with developmental ADHD in the 2- and the 3-back tasks in the unmedicated and the medicated condition. In addition, the performance of healthy controls is depicted as reference.

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Paired-sample t-tests revealed that, when medicated, both patient groups performed significantly better than when unmedicated in the most difficult 3-back task (t12 = 2.46, p < 0.05; t12 = 2.22, p < 0.05, respectively). In the 2-back task, only a trend for better performance, and in the easy 0-back task, no differences due to medication were found. Between the two patient groups, no significant difference was detectable. The same holds true for the comparisons between patients with long-term dose and patients with single dose of MPH (all p > 0.16).

Imaging data

With performance of one-sample t-tests, activation maps per task and per group were extracted. All contrasts were thresholded with applying a family-wise error (FWE) correction with p < 0.001 and a minimal cluster size of 100 voxels. In the 0-back task, all groups showed a consistent activation pattern, where the main activation foci were located in right occipital regions and in frontal midline structures. This pattern was present in all groups with no significant between-group differences. Given its underload and its nonspecificity, the 0-back task turned out to be not sensitive enough to differentiate between controls and patients. Therefore, the 0-back task has not been considered in the analyses presented herein.

Direct comparisons between the 2- and the 3-back tasks revealed no difference in activation due to load in any of the groups. Therefore data were pooled in the further analyses to highlight the effects between the groups. Activation maps for all groups are depicted in Fig. 2.

image

Figure 2.   Activation maps for the collapsed 2- and 3-back task of healthy controls, patients with combined epilepsy/ADHD without and with MPH (top line), and patients with developmental ADHD without and with MPH (bottom-line). Activation maps are FWE corrected (p < 0.001, cluster size >100 voxels).

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In healthy controls, the pooled working memory task activated an extended area within the frontal lobe including bilateral premotor cortices (Brodmann’s area [BA] 6/8), dorsal anterior cingulate cortex, and medial premotor cortex including supplementary motor area (SMA; BA 32/6), bilateral insular regions (BA 48), left frontal pole (BA 10), bilateral dorsolateral prefrontal cortex (DLPFC; BA 46), ventrolateral prefrontal cortex (VLPFC; BA 47), and Broca’s area (BA 45). In addition, bilateral inferior parietal lobules (BA 7/40) were activated as well as subcortical regions at the junction of the right thalamus and caudate. Furthermore, medial and lateral cerebellar regions were found to be active (Crus1/Vermis 4_5). Patients with epilepsy/ADHD also activated regions within this network, but to a lesser extent. In the unmedicated condition, the following regions were active: bilateral premotor cortices (BA 6/8), medial premotor cortex including SMA (BA 32/6), bilateral insular regions (BA 48/47), bilateral DLPFC (BA 46), VLPFC (BA 47), and Broca’s area (BA 44/45). Further, bilateral inferior and superior parietal lobules (BA 7/40) were activated. When patients were medicated, the same described activation patterns with no additional areas were activated. Boys with developmental ADHD (unmedicated) showed a very similar pattern. In the frontal lobe, BAs 32/6/8/45/46/47/48 were active and in the parietal lobe BA 7/40. In addition, in that patient sample, activation of medicated patients revealed the same pattern without involvement of further regions. To account for intragroup and intergroup differences in patient groups, analyses on the second level were performed. A threshold of p < 0.0001 uncorrected at the voxel level and a cluster size of 20 were chosen. Intragroup comparisons revealed no alteration due to medication condition in both groups. In the between-group comparisons, the contrasts in the unmedicated conditions (epilepsy/ADHD >ADHD; epilepsy/ADHD <ADHD) exposed no significant differences in activation patterns. In the medicated condition, the contrast epilepsy/ADHD <ADHD revealed no difference either. Only the comparison epilepsy/ADHD >ADHD showed group differences in the Heschl’s gyrus (BA 48; x = −42, y = −18, z = 10) and in the parahippocampal region (BA 37; x = −30, y = −34, −8) on the left side. On the right side, differences in the superior temporal lobe (BA 41; x = 32, y = −32, z = 20) were evident. Overall, results of intragroup and intergroup comparisons showed very similar activation patterns during working memory performance.

In a second step, comparisons were conducted between healthy controls and our group of interest, the boys with epilepsy/ADHD in the clinically pure condition (unmedicated). The resulting contrast revealed more pronounced activation in healthy controls than in patients, mainly in all three parts of the brain that are crucial for working memory performance as well as in the insular region and the cingulum. Significant activation foci are listed in Table 2. Conversely, patients showed no enhanced activation compared to controls.

Table 2.   Activation of healthy controls in the 2- and 3-back task compared to patients with combined epilepsy/ADHD in the unmedicated condition
AreaBAAnatomyMNI CoordinatesPeak tk
xyz
  1. MNI, Montreal Neurological Institute; BA, Brodmann`s area; k, cluster extent; R, right; L, left; Sup, superior; Inf, inferior; Mot, motor; Mid, middle; Oper, operculum.

  2. p uncorrected < 0.0001.

Frontal6/44Precentral L−524307.321,541
6Sup Mot L−20−4665.64292
32Sup Mot L−412504.9799
9Mid R3616584.8550
6Sup R242664.878
45Mid R4840204.5833
11Mid R305804.2965
6Inf Oper548164.1532
Insula48Insula L−282246.79309
47/48Insula R322285.31171
Parietal40SupraMarginal R58−30466.43654
40Inf L−44−56585.5693
5Precuneus R4−46624.3499
40Inf L−38−38384.3087
Cerebellum Crus 1 R14−74−304.8948
 Vermis 4_52−48−164.3353
Cingulum32Mid L−1218404.5866

To illustrate the hemodynamic response of all groups in the essential parts of the functional network of working memory, activation of the two most significant clusters within each region of interest (ROI) including frontal, parietal, as well as cerebellar regions of the performed contrast was computed (Fig. 3). The plots illustrate that controls exhibited more pronounced activation within all ROIs compared to both patient groups. Again, within the patient groups, group membership and medication condition did not allow predictions about the intensity of hemodynamic response.

image

Figure 3.   ROI and corresponding hemodynamic response of all groups, displayed on the contrast between healthy controls and unmedicated boys with combined epilepsy/ADHD (p < 0.0001 uncorrected). Bar graphs are representing the hemodynamic responses (in arbitrary units) for the different groups, with the following order: (1) healthy controls, (2) boys with combined epilepsy and ADHD unmedicated /medicated, and (3) boys with developmental ADHD unmedicated/medicated in the two most significant clusters within each ROI. (A) Precentral (left), BA 6/44 (B) Superior motor area (left), BA 6 (C) Parietal supramarginal (right), BA 40 (D) Parietal inferior (left), BA 40 (E) Cerebellum Crus 2 (right) (F) Vermis 4_5.

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Discussion

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

Although there is an increasing interest in the clinical diagnosis of ADHD in epilepsy, this is to our knowledge the first study evaluating its mechanisms using neuropsychological and functional imaging methods. When we applied working memory tasks, our data showed that behavioral and functional performance of patients with epilepsy/ADHD were comparable to patients with developmental ADHD but differed from healthy controls.

Our behavioral results are consistent with those of studies that reported working memory deficits in nonepileptic ADHD (Martinussen et al., 2005; Willcutt et al., 2005; Kobel et al., 2009). In children with idiopathic epilepsy, cognitive problems have been reported before as well (MacAllister & Schaffer, 2007); however, with respect to working memory performance, findings are contradictory. Pascalicchio et al. (2007) have found deficits in digit span in children with juvenile myoclonic epilepsy; in contrast, patients in the study of Myatchin et al. (2009) performed as well as controls. Nevertheless, few studies have controlled for the confounding effect of the comorbidity ADHD. The importance of this issue is nicely illustrated by Hermann et al. (2007) who compared patients with epilepsy with and without ADHD. The authors found significant differences in motor/psychomotor speed, as well as in executive functions between patient groups implying, that ADHD has a disruptive effect on cognition in children with epilepsy. In the present study, patients with epilepsy/ADHD performed as poorly as boys with developmental ADHD, leading to the assumption that both patient groups show a comparable impairment in working memory functioning. No differences in performance were found due to the different subtypes of ADHD. This finding is in accordance with an earlier study by Pasini et al. (2007).

MPH-treatment led to an improvement in performance in both patient groups, which became similar to the one of healthy controls in the more difficult tasks. In addition, within-subject comparisons for the patient groups showed the effect of MPH on performance, in particular for the most difficult task. From these results it can be concluded, that more demanding tasks seem to be especially sensitive to the improving effect of MPH. This improvement has been already shown for a variety of cognitive domains in children with ADHD without epilepsy (Pietrzak et al., 2006; Kobel et al., 2009). Our data showed no difference between the two patient groups in performance, implying that all patients clearly benefited from MPH to the same extent during tasks requiring higher cognitive processes. Data therefore support a clear effect of this medication also in epilepsy-related ADHD.

The evaluation of working memory using fMRI is well established and induces prominent blood oxygen level dependent (BOLD) activations in frontal, parietal, and cerebellar regions (Owen et al., 2005). For the first time in the epilepsy literature, functional brain correlates of ADHD were revealed using working memory tasks. Boys with epilepsy/ADHD recruited substantially less cortical regions that are crucial for working memory performance compared to healthy controls and showed in addition no involvement of the cerebellum. Of interest, these activation patterns did not differ from the ones of boys with developmental ADHD and replicate the robust findings of previous studies reporting hypoactivation in the functional network of working memory in developmental ADHD (Valera et al., 2005; Dickstein et al., 2006; Cherkasova & Hechtman, 2009; Kobel et al., 2009; Wolf et al., 2009). Due to the functional similarity, data support the idea that ADHD with or without epilepsy show a common aberrant network of working memory. It has to be mentioned though, that our results are based on a block-design and, therefore, it was not possible to exclude error trials. In the future an event-related approach could lead to even more sensitive findings.

Concerning the intake of MPH, we did not find any alterations in activation patterns in patients. These findings of improved performance but not normalized brain activity have been already reported for patients with ADHD (Schweitzer et al., 2004; Kobel et al., 2009). However, in the literature, findings are heterogeneous, reporting also normalization of brain activation (Rubia et al., 2011a,b). Such discrepancies may result from heterogeneity in patient populations, such as different mean age, familiarity with medication, and methodologic differences such as different cognitive paradigms and different MRI scanners/scanning parameters. In addition, Bush et al. (2005) mentioned that especially fMRI data with a strong focus on lateral frontal cortex is much more inconsistent and produces high variability.

Altogether the present study revealed behavioral, pharmacoresponsive, and functional similarities between patients with isolated ADHD and epilepsy-related ADHD. Our findings, therefore, clearly support the assumption that ADHD with and without epilepsy share a common underlying neurobehavioral pathophysiology. This stands in contrast to postulations indicating that the symptoms of ADHD in epilepsy might rather originate from the epilepsy itself and its related neurologic damage or use of AEDs causing functional impairment within the brain (Kaufmann et al., 2009). In our study we cannot rule out subclinical effects of AEDs on attention (Glauser et al., 2010), but we assume that they are not the determining factors for the attentional problems in our sample of patients with epilepsy. First, all patients were seizure free for at least half a year, thereby minimizing the effect of electrical bursts on cognition, and secondly, patients were treated with AEDs that are considered not to have a substantial influence on cognitive, especially attention functions (Nadkarni et al., 2005; Donati et al., 2007), as diagnosed by the clinical diagnostic procedure. Moreover, it has been shown that behavioral problems including ADHD often exist before the first recognized seizure and, therefore, before the intake of AEDs (Austin et al., 2001; Hesdorffer et al., 2004). We assume that the above-mentioned factors might have an aggravating effect on ADHD symptomatology but may not be the underlying cause. The assumption of a common underlying pathophysiology is also supported by the work of Gonzalez-Heydrich et al. (2007). The authors found a strong similarity in comorbidity and clinical presentation between ADHD with and without epilepsy, suggesting that attentional/hyperactive and impulsive behavior commonly observed in boys with epilepsy truly constitute ADHD. To keep our sample as homogeneous as possible, we included only boys. Whether these data can be transferred unchanged to girls remains to be shown. In conclusion, our data support the assumption that both conditions might represent epiphenomena of a common underlying functional as well as neurobiologic network abnormality. Therefore, in terms of clinical relevance, our findings support the idea that attention deficits that emerge in the context of a developmental disorder are directly comparable to attention deficits in epileptic children and that treatment with MPH shows equivalent effectiveness in both patient samples.

Acknowledgments

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

This work was supported by the Swiss National Science Foundation [Grant: 3200B0-113897]. We thank Dr. Hubert Fahnenstich for his help in patient recruitment as well as all participants for their kind participation in our study.

Disclosure

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

None of the authors have conflicts of interest to declare. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

References

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  9. Supporting Information

Table S1. Characteristics of patients with combined epilepsy/ADHD.

Table S2. Means and standard deviations of accuracy (%).

FilenameFormatSizeDescription
EPI_3377_sm_TableS1.doc42KSupporting info item
EPI_3377_sm_TableS2.doc32KSupporting info item

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