Context Functional magnetic resonance imaging studies in attention-deficit/hyperactivity disorder (ADHD) revealed fronto-striato-parietal dysfunctions during tasks of inhibition and attention. However, it is unclear whether task-dissociated dysfunctions exist and to what extent they may be influenced by age and by long-term stimulant medication use.
Objective To conduct a meta-analysis of functional magnetic resonance imaging studies in ADHD during inhibition and attention tasks, exploring age and long-term stimulant medication use effects.
Data Sources PubMed, ScienceDirect, Web of Knowledge, Google Scholar, and Scopus databases were searched up to May 2012 for meta-analyses. Meta-regression methods explored age and long-term stimulant medication use effects.
Study Selection Twenty-one data sets were included for inhibition (287 patients with ADHD and 320 control subjects), and 13 data sets were included for attention (171 patients with ADHD and 178 control subjects).
Data Extraction Peak coordinates of clusters of significant group differences, as well as demographic, clinical, and methodological variables, were extracted for each study or were obtained from the authors.
Data Synthesis Patients with ADHD relative to controls showed reduced activation for inhibition in the right inferior frontal cortex, supplementary motor area, and anterior cingulate cortex, as well as striato-thalamic areas, and showed reduced activation for attention in the right dorsolateral prefrontal cortex, posterior basal ganglia, and thalamic and parietal regions. Furthermore, the meta-regression analysis for the attention domain showed that long-term stimulant medication use was associated with more similar right caudate activation relative to controls. Age effects could be analyzed only for the inhibition meta-analysis, showing that the supplementary motor area and basal ganglia were underactivated solely in children with ADHD relative to controls, while the inferior frontal cortex and thalamus were underactivated solely in adults with ADHD relative to controls.
Conclusions Patients with ADHD have consistent functional abnormalities in 2 distinct domain-dissociated right hemispheric fronto-basal ganglia networks, including the inferior frontal cortex, supplementary motor area, and anterior cingulate cortex for inhibition and dorsolateral prefrontal cortex, parietal, and cerebellar areas for attention. Furthermore, preliminary evidence suggests that long-term stimulant medication use may be associated with more normal activation in right caudate during the attention domain.
Attention-deficit/hyperactivity disorder (ADHD) is one of the most debilitating childhood disorders, defined by age-inappropriate impulsiveness, inattention, and hyperactivity,1 persisting into adulthood in about 65% of cases.2 Patients with ADHD have consistent deficits in motor response and interference inhibition, as well as in attention, in particular selective, sustained, and flexible attention.3,4
Patients with ADHD show fronto-striato-thalamo-parietal brain dysfunctions during inhibition tasks, most prominently in right inferior frontal cortex (IFC), supplementary motor area (SMA), caudate, and thalamus during go/no-go5-9 and stop10-16 tasks and in the bilateral IFC, anterior cingulate cortex (ACC), basal ganglia, and parieto-temporal regions during interference inhibition tasks.16-22 More recently, functional magnetic resonance imaging (FMRI) studies demonstrated reduced activation in the bilateral dorsolateral prefrontal cortex (DLPFC) and IFC, basal ganglia, and parieto-temporal regions during attention allocation18,19,23,24 and during sustained,25 selective,5,26 and flexible attention.9,10,27-29
However, it is unclear whether patients with ADHD have different or overlapping fronto-basal ganglia-parietal dysfunctions during these 2 cognitive domains, mediated by parallel fronto-basal ganglia-thalamo-parietal networks.30 Furthermore, there have been conflicting reports of increased activation in patients with ADHD relative to control subjects during inhibition tasks in mesial frontal, parieto-temporal, and cingulate regions6,7,12,17,31-33 and during attention tasks in posterior parieto-temporal, cerebellar, and occipital brain regions.25,29,34-36 Variability between studies may be due to differences in age and in long-term stimulant medication use, given that both were associated with more normal basal ganglia volumes in a structural meta-analysis.37
A previous meta-analysis38 using activation likelihood estimation, conducted in 16 FMRI studies in 2006, found consistent deficits in patients with ADHD relative to controls across a range of inhibition and attention tasks in bilateral DLPFC, IFC, ACC, parietal, and striato-thalamic regions, as well as overactivation in the left thalamus, insula, and medial frontal lobe. However, the analysis included region-of-interest FMRI analyses,38 which provide a constrained characterization of functional anatomy.39 Also, likely due to limited methods and study availability at the time, meta-analyses were performed across group maps, rather than on coordinates of differences from individual studies, and included a wide range of FMRI paradigms of inhibition, timing, and attention tasks, as well as other imaging modalities. Given that FMRI activation is critically paradigm dependent, it is paramount to meta-analyze FMRI studies that focus on the same underlying cognitive constructs. Finally, activation likelihood estimation meta-analysis does not allow for meta-regression analyses, which can assess potential confounds such as age and long-term stimulant medication use.
In this meta-analysis, we aimed to overcome these limitations by including only whole-brain analysis FMRI studies of inhibition and attention paradigms, analyzing both domains independently. In addition, we tested for effects of age and long-term stimulant medication history in meta-regression analyses.
For the inhibition domain, go/no-go and stop tasks were included for motor response inhibition, and Simon, Eriksen flanker, and Stroop tasks were included for interference inhibition. Both inhibitory domains involve the inhibition of a prepotent motor response to an infrequent stimulus among a string of high-frequent stimuli and measure conflict detection. A meta-analysis40 of these tasks in healthy adults has shown that motor and interference inhibition is mediated by overlapping fronto-striato-thalamo-parietal networks, including predominantly the right hemispheric IFC, SMA, ACC, caudate, thalamus, and inferior parietal regions. Because there were large enough numbers of FMRI studies on motor and interference inhibition tasks, we further subdivided the 2 inhibitory domains to increase homogeneity. Go/no-go and stop tasks are considered to tap into the same cognitive construct of motor response inhibition,41-47 the difference being the timing of the stop signal, which in the stop task occurs shortly after the go signal, making it more difficult to inhibit.47,48 This is reinforced by the cognitive neuroimaging literature, which shows that both tasks are mediated by overlapping networks of right IFC, SMA, ACC, caudate, and parietal areas.40,44,45,47 Interference inhibition tasks, rather than measuring motor response inhibition directly, measure the ability to inhibit conflicting information that interferes with the primary intended action and may only lead to an erroneous motor response, if not ignored.40,42 These tasks have a higher load on cognitive inhibition (ie, conflict detection and inhibition of distraction), as opposed to motor inhibition tasks that load higher on motor response suppression.42,44 Neurofunctionally, this subdivision is reinforced by differences in the underlying neural networks, with interference inhibition tasks showing stronger activation of the left ACC and left IFC, crucial for conflict inhibition, while motor response inhibition tasks in turn activate more strongly the SMA and right IFC.40,44
For the attention domain, the FMRI literature is more heterogeneous, and studies have used a larger variety of tasks. Therefore, we included a range of tasks measuring visuospatial selective attention (including oddball and divided attention tasks), sustained attention (continuous performance task), and flexible attention (ie, the ability to rotate representations of visual objects in attention in mental rotation tasks). Visuospatial attention tasks typically activate a network of lateral IFC and DLPFC, basal ganglia, thalamic, parieto-temporal, and cerebellar brain regions.49,50 Further subdivision of these attention tasks was not possible due to the limited number of studies.
We hypothesized that patients with ADHD would show consistent and dissociated domain-specific fronto-basal ganglia-parietal brain dysfunctions in IFC, ACC, SMA, and caudate networks for inhibition and in DLPFC and basal ganglia-parieto-cerebellar networks for attention. Furthermore, 2 recent meta-analyses37,51 of structural imaging studies showed that long-term stimulant medication use is associated with more normal basal ganglia structure. Given that basal ganglia function is also modulated by long-term medication use,52 we expected that across both cognitive domains, basal ganglia function would be less severely impaired in patients with long-term medication use. Finally, given evidence from longitudinal and meta-analytical structural imaging studies37,53 showing that basal ganglia deficits in ADHD appear to normalize with age, we expected younger patients with ADHD to have more severe basal ganglia dysfunctions than adults with ADHD.
A comprehensive literature search of FMRI studies in ADHD using inhibition and attention tasks was conducted on PubMed, ScienceDirect, Web of Knowledge, Google Scholar, and Scopus search engines up to May 2012. The search keywords were attention-deficit/hyperactivity disorder, ADHD or hyperkinetic, plus FMRI, plus inhibition, stop, Stroop, flanker, go/no-go, Simon, interference, attention, CPT [continuous performance task], selective attention, divided attention, target detection, mental rotation, and cognitive flexibility. In addition, manual searches were conducted within review articles and reference sections of individual studies. Excluded were studies that (1) contained subject overlap within the same task with other studies, (2) did not include healthy controls, (3) used a region-of-interest approach, (4) included medicated patients with no washout period before FMRI, and (5) did not report coordinates for the relevant contrasts and did not or could not supply these when the authors were contacted. The corresponding authors were asked to provide additional details not included in the original publications. Meta-analysis of Observational Studies in Epidemiology guidelines for meta-analyses of observational studies were followed in the study.54
The following 2 main meta-analyses were performed: (1) one for inhibition tasks, further divided into motor response and interference inhibition, including stop and go/no-go (motor inhibition), Stroop, Simon, or Eriksen flanker tasks (interference inhibition), and (2) the other for attention tasks, including cued target detection and oddball (attention allocation), selective and divided attention (including alerting and orienting), continuous performance task (sustained attention), and mental rotation tasks (attentional flexibility). For all meta-analyses, peak coordinates of activation differences between patients with ADHD and controls were extracted from each data set for the following contrasts: stop or go/no-go (motor inhibition) and incongruent-congruent (Simon, Eriksen flanker, and Stroop tasks). For attention tasks, the following were used: cue plus target minus target only (cued target detection), oddball minus standard trials (oddball tasks), target minus nontarget trials (continuous performance task), rotation minus baseline (mental rotation), alerting-orienting minus baseline trials (alerting-orienting), and divided or selective attention minus baseline trials (divided or selective attention). Peaks that were not statistically significant at the whole-brain level were excluded.
Regional group differences in activation during inhibition and attention tasks were analyzed using a software program (effect size signed differential mapping; http://www.sdmproject.com/), a voxel-based meta-analytic approach that uses the reported peak coordinates to re-create maps of the effect size of group differences in blood oxygenation level–dependent response. For peak coordinates, the re-creation is based on first converting the peak t value to Hedges effect size and then applying a nonnormalized gaussian kernel to the voxels close to the peak. The signed differential mapping methods have been described in detail elsewhere,55-58 and only the main points are summarized herein.
First, only data sets in which the same threshold was used throughout the whole brain were included. Second, activations and deactivations were re-created in the same map to correctly analyze those regions with higher between-study heterogeneity. If activations and deactivations were plotted in separate maps, noisy regions could falsely appear as activating and deactivating at the same time, which is logically impossible.56 Third, studies were combined with a random-effects model as in standard meta-analyses, taking into account sample size, intrastudy variability, and between-study heterogeneity.58
These analyses were complemented with analyses of robustness. In case of significant heterogeneity within a brain region found to abnormally respond in patients, we used funnel plots to check whether findings might have been driven by few or small studies, as well as to detect gross abnormalities such as studies reporting opposite results.56,57 Also, we conducted a jackknife sensitivity analysis consisting of iteratively repeating the same analysis, excluding one data set at a time to establish whether the results were replicable.55
Statistical significance was determined using standard permutation tests. Null distributions were created, from which P values could be directly obtained.
For inhibition, we conducted a meta-regression analysis with age, which could not be done for attention due to only 2 studies in adults. For both analyses, we conducted meta-regression analyses for the percentage of patients receiving long-term stimulant medication.
Included studies and characteristics
The search retrieved a total of 65 data sets (28 for motor inhibition, 18 for interference inhibition, 18 for attention, and 1 requiring both inhibition and attention). Excluded were 14 data sets due to the use of anatomical regions of interest or regions of interest for analysis of variance based on whole-brain group activation,7,17,59-70 6 studies9-11,16,19,71 due to patient overlap, 4 studies72-75 due to lack of control groups, 2 studies76,77 that could not provide coordinates for the relevant contrasts, 2 studies78,79 that used emotional Stroop tasks, 1 study34 that required both inhibitory and attentional processes, and 1 study33 that included 50% of patients with undiagnosed ADHD (eFigure).
Finally, 21 high-quality data sets were included in the inhibition meta-analysis, including 7 adult samples15,18,21,22,31,80,81 and 14 pediatric samples.5,6,9,12-14,16,20,32,82-86 Thirteen high-quality data sets were included in the attention meta-analysis (2 adult samples18,35 and 11 pediatric samples5,19,23-26,28,29,52,87,88). Combined, the inhibition studies included 287 patients with ADHD and 320 healthy controls (Table 1), and the attention studies included 171 patients with ADHD and 178 healthy controls (Table 2). All data sets that included medicated patients used a washout period of at least 18 hours before FMRI.
Meta-analysis for inhibition
For all inhibition tasks together, patients having ADHD compared with controls showed significantly decreased activation in the right IFC extending into insula, a cluster comprising the SMA and the cognitive division of the ACC (Brodmann area 32/24),89 and the left caudate extending into the putamen and insula and right mid-thalamus (Table 3, Figure 1A, and Figure 2A). No significantly increased activations were observed for patients with ADHD relative to controls.
During motor response inhibition only, patients with ADHD (n = 187) relative to controls (n = 206) showed significantly decreased activation in the right IFC and insula, right SMA and ACC, right thalamus, left caudate, and right occipital lobe (Table 3 and Figure 2B).
For interference inhibition only, patients with ADHD (n = 100) relative to controls (n = 114) showed significantly decreased activation in the left cognitive division of ACC, right IFC and insula, right caudate head, and left posterior insula and parietal lobe (Table 3 and Figure 2C). However, findings should be considered with caution given the small number of studies included in this meta-analysis.
Meta-analysis for attention
For attention tasks, patients with ADHD relative to controls showed decreased activation in the right DLPFC, left putamen and globus pallidus, right posterior thalamus (pulvinar) and caudate tail extending into the posterior insula, and right inferior parietal lobe, precuneus, and superior temporal lobe. Relative to controls, patients with ADHD showed significantly increased activation in the right cerebellum and left cuneus (Table 3 and Figure 1B).
A whole-brain jackknife sensitivity analysis for all inhibition tasks together showed that the findings in the IFC and SMA or ACC were highly replicable, preserved throughout all 21 combinations of data sets. The results in the left basal ganglia remained significant in all but 1 combination of data sets and in the right thalamus in all but 3 combinations of data sets (eTable 1). For motor response inhibition, the findings in the SMA were preserved throughout all 15 combinations of data sets. The results in the IFC remained significant in all but 1 combination of data sets, in the thalamus and fusiform gyrus in all but 2 combinations of data sets, and in the caudate in all but 2 combinations of data sets (eTable 2). For interference inhibition, the results in the ACC, posterior parietal lobe and insula, IFC, and caudate were preserved in all but 1 of 6 combinations of data sets (eTable 3).
The whole-brain jackknife sensitivity analysis for attention showed that the underactivation results in the left basal ganglia and right parietal and precuneus, as well as the overactivation findings in the right cerebellum and cuneus, were preserved throughout all of 13 combinations of data sets. The underactivation in the right thalamus and caudate was significant in all but 1 combination of data sets, and the underactivation in the right DLPFC was significant in all but 2 combinations of data sets (eTable 4).
Effect of long-term stimulant medication use
For inhibition, information on long-term stimulant medication use was available for all 21 data sets, with 97 patients (33.8%) receiving long-term stimulant medication at the time of the study (methylphenidate in 45, unidentified stimulants in 31, mixed amphetamine salts in 18, and D-amphetamine in 3). The meta-regression with medication was not significant.
For attention, information on long-term stimulant medication use was available for all 13 data sets, with 37 patients (21.6%) receiving stimulant medication at the time of the study (methylphenidate in 30, unidentified stimulants in 5, D-amphetamine in 1, and mixed amphetamine salts in 1) for periods ranging from 6 months to 3 years; patients were taken off medication between 18 hours and 2 weeks before the imaging. The meta-regression analysis with long-term stimulant medication use showed that the percentage of patients on long-term stimulant medication correlated significantly with increasing activation in the right caudate tail (r = 0.233, permutation-derived P < .001), so that medication-naive patients had significantly reduced activation compared with healthy controls (z = 2.149, P < .001) and with long-term medicated patients (z = 1.646, P < .001), who did not differ from each other (Figure 3). Given that long-term medication use may be confounded by age, the meta-regression analysis was repeated with age as a covariate. The primary regression finding remained.
Given that the right caudate was associated with long-term stimulant medication use in the attention analysis and was activated during the interference sub–meta-analysis, we conducted a meta-regression analysis with this cluster and long-term stimulant medication use. A trend was observed toward an association between long-term stimulant medication use and more normal right caudate activation, although this did not reach statistical significance, probably due to lack of power (it included only 6 studies).
Because there were only 2 adult studies for the attention analysis, meta-regression analysis with age (age range, 8.5-38.3 years) was performed only for the inhibition analysis but showed no effects. However, when the data set was split categorically into an adult group (100 patients with ADHD and 107 controls) and a child group (187 patients with ADHD and 213 controls), only children with ADHD had decreased activation relative to controls in the left putamen and right caudate, as well as in the SMA and ACC, while only adult patients with ADHD had decreased activation relative to controls in the right IFC and right thalamus (Figure 4).
This meta-analysis across FMRI studies of inhibition and attention functions shows task domain–specific, dissociated fronto-basal ganglia-thalamic dysfunctions in patients with ADHD. For inhibition tasks, patients with ADHD relative to controls showed consistent and replicable underactivation in typical regions of inhibitory control, in the right IFC, reaching into the anterior insula, SMA and ACC, left caudate, and thalamus. For attention functions, patients with ADHD showed consistent deficits in a different fronto-basal ganglia-parieto-cerebellar network that is typical for visuospatial attention, including the right DLPFC, left putamen and right posterior thalamus, caudate tail, and parietal areas, with enhanced cerebellar activation. Furthermore, long-term stimulant medication use was associated with more normal function in the right caudate during attention tasks and at a trend level during interference inhibition. The findings suggest that long-term stimulant medication use is associated with more normal basal ganglia function, in line with documented effects of more normal basal ganglia structure.37,51 Age effects could not be tested for attention due to small numbers of adult studies. For the inhibition domain, the linear age meta-regression was not significant, but a categorical comparison showed that basal ganglia and SMA deficits were observed only in children with ADHD, while the right IFC or insula and thalamus deficits were significant only in adults with ADHD relative to controls.
The reduced activation in patients with ADHD relative to controls in the right inferior frontal junction reaching into insula, SMA or ACC, left caudate head, and right thalamus suggests deficits in a typical adult and adolescent inhibitory network for motor response and interference inhibition,40,44,47,90-92 in line with individual studies.12,16,32,86,93 The more restricted right IFC and insula deficit finding narrows the wider bilateral IFC and DLPFC deficit findings of a previous meta-analysis38 of inhibition functions. The underfunctioning of the right inferior frontal junction or insula in particular is one of the most consistent deficits in the ADHD FMRI literature of inhibition,4 which may be a disorder-specific neurofunctional biomarker for ADHD4 relative to patients with conduct disorder25,27 and obsessive-compulsive disorder.10
While the right IFC or insula was underactivated during both inhibition domains, supporting a more generic role of the inferior frontal junction in motor or interference inhibition,40,94 the deficits in the ACC and SMA showed inhibitory domain-specific dysfunctions. The SMA, a key area for motor response inhibition,40,92 was dysfunctional only during motor response inhibition. The cognitive division of the ACC, crucial for interference and conflict inhibition,89,95 was underactivated during both tasks, but in the left hemisphere for interference inhibition and in a more right hemispheric location during motor inhibition.
During attention tasks, patients with ADHD showed consistently reduced activation in a different fronto-basal ganglia-thalamo-parietal network, in line with prior literature comprising the right DLPFC,19,23,25,27,29,35 left putamen and globus pallidus, right thalamic pulvinar and caudate tail,18,23,24,26,28,29,35,87,88 and inferior parietal lobe and precuneus.5,9,23,24,28,29,87,88 These regions form part of a visuospatial attention network, whereby posterior parietal, precuneus, and the thalamic pulvinar regions mediate the representation of and orienting toward spatial locations, while the anterior DLPFC is responsible for target detection and selective attention, alerting, and switching attention.49,96 The findings of domain-dissociated deficits in distinct IFC and SMA fronto-striato-thalamic and DLPFC fronto-basal ganglia-thalamo-parietal networks in patients with ADHD during inhibition and attention, respectively, support the notion of multisystem deficits in ADHD,4,35,97 compromising different fronto-basal ganglia-parieto-cerebellar networks that mediate at least 2 functional domains.
The dissociated but right hemispheric DLPFC and IFC deficits in ADHD for both cognitive domains support previous meta-analytical structural findings of high effect sizes for right frontal deficits in patients with ADHD.98 These results are in line with theories of predominantly right hemispheric deficits.99
The enhanced activation in patients with ADHD relative to controls in the cerebellum and occipital lobe during attention functions may reflect compensatory enhanced activation of the posterior part of a DLPFC–cerebellar network for sustained attention.49,100 This is supported by individual sustained attention FMRI studies25,35 that found that enhanced cerebellum activation in ADHD was anticorrelated with reduced prefrontal activation that correlated with attention performance, suggesting compensation. The finding of enhanced cerebellar activation during attention functions contrasts with evidence for reduced cerebellar activation during timing functions.69,101-103 It reinforces the notion that brain dysfunctions in ADHD, as well as the direction of their abnormality, appear to be task dependent, with different fronto-basal ganglia-parieto-cerebellar neural networks being deficient in patients with ADHD in the context of different cognitive domains.
The findings support recent neurobiological theories of ADHD that suggest that the disorder is multisystemic, characterized by multiple parallel deficits in several fronto-striatal, fronto-cortical, and fronto-cerebellar networks that mediate the different cognitive functions that are impaired in the disorder.4,35,97,104,105 Within the inhibitory domain, the findings reconcile theories of predominant IFC,4 ACC,106 and SMA deficits86 in ADHD, by showing that SMA deficits are specifically related to motor response inhibition, while IFC and ACC dysfunctions underlie both motor and interference inhibition. While in this study we have delineated the different fronto-basal ganglia-parietal networks that are deficient for attention and inhibition functions, future meta-analysis studies should investigate potential fronto-cortical and fronto-subcortical neural network deficiencies during other tasks such as timing102 and reward-associated functions.4
Effect of long-term stimulant medication use
The meta-regression analysis for attention showed that long-term stimulant medication use (for periods ranging from 6 months to 3 years) was associated with more normal right but not left caudate function, and this survived age correction. The results parallel previous meta-analysis findings of normal right caudate structure in a similar location (Talairach x, y, and z coordinates of 16, 2, and 20) in patients with long-term medication relative to never-medicated patients and controls.37 Together, they suggest a right-lateralized positive plastic effect of long-term stimulant medication use on basal ganglia structure and function. The gradual normalization of right caudate function with long-term stimulant medication use may also be related to meta-analytic positron emission tomography findings of higher striatal dopamine transporter levels in patients with long-term medication use relative to controls and medication-naive patients, who had reduced striatal dopamine transporter levels relative to controls.107 The right-lateralized effect may also explain why long-term stimulant medication use did not normalize the abnormal caudate function in the meta-regression analysis of the inhibition tasks, which was left hemispheric, as is typical for motor inhibition tasks.6,12 Furthermore, a right-lateralized effect would be in line with the trend-level finding toward an association between long-term stimulant medication use and more normal right caudate activation in the interference inhibition tasks, which may not have reached statistical significance due to lack of power. This would also echo evidence that methylphenidate has a stronger effect on right basal ganglia blood flow and metabolism, rather than left.108,109
However, the significant meta-regression finding for the association between long-term stimulant medication use and right caudate activation for the attention meta-analysis should be considered preliminary and be interpreted with caution given that it was based on 37 medicated patients, which were only 21.6% of the entire sample of 171 patients. Also, the association between long-term stimulant medication use and more normal striatal activation was observed only at a trend level for the interference inhibition regression analysis, which was also underpowered. In addition, while there was a significant linear association, the correlation figure is not suggestive of a linear dose-response effect.
Linear age effects could not be tested for the attention domain because there were only 2 adult studies. While linear age effects on brain activation were not observed for the inhibition meta-regression analysis, a categorical age group meta-analysis showed that basal ganglia and SMA or ACC underactivation was more prominently associated with pediatric ADHD, while IFC-thalamic deficits were more pronounced in adult ADHD relative to their age-matched controls. The findings are in line with structural findings of normal basal ganglia gray matter in adults with ADHD relative to children with ADHD, who had reduced basal ganglia gray matter relative to controls.37 The results are also in line with longitudinal data in ADHD showing normalization of basal ganglia structural deficits in early adulthood.53 Together, these data suggest that basal ganglia deficits may normalize in ADHD adulthood, while frontal deficits may become more prominent, in line with theories that suggest that frontal lobe deficits in ADHD may be secondary to primary subcortical deficits.110
Overall, the findings illustrate that age, long-term stimulant medication use, and differences in the cognitive domain tested all have important effects on the brain activation deficits in patients with ADHD. Future studies need to bear this in mind as follows: (1) by including only medication-naive patients with ADHD to assess ADHD pathology and not potential brain-adaptive responses to long-term stimulant medication use (or at least to test for medication effects in subgroups of medicated and medication-naive samples in sufficiently large sample sizes), (2) by assessing narrowly defined and homogeneous age groups for a better stratification of ADHD deficits according to age groups, and (3) by using similar comparable cognitive tasks to elucidate deficits in specific cognitive domains.
This study has several limitations, inherent to all meta-analyses. First, peak-based meta-analyses are based on coordinates from published studies, rather than raw statistical brain maps, providing less accurate results.58 Second, different studies used different statistical thresholds. Third, while voxelwise meta-analytical methods provide excellent control for false-positive results, it is more difficult to avoid false-negative results.58 Fourth, regression in voxel-based meta-analyses should be considered with caution; however, spurious results were minimized herein by using more conservative thresholds and by reporting only those findings that were significant both when comparing medicated vs unmedicated patients and patients vs controls. We were able to combine similar tasks for the inhibition domain, and there were a sufficient number of studies to further subdivide articles into motor and interference inhibition sub–meta-analyses. However, for the attention domain, fewer FMRI studies were available on a larger range of different tasks, so that we had to combine tasks of a range of different visuospatial attention domains, including selective, sustained, and flexible attention. Future meta-analytic studies should subcategorize the attention domain into more homogeneous attention tasks once the field of FMRI of attention functions in ADHD has expanded. Fifth, we conducted a meta-analysis of only inhibition and attention studies, and future meta-analyses will need to investigate other compromised functions such as timing102 and motivation.4
In conclusion, patients with ADHD have cognitive domain–specific dissociated dysfunctions in distinct fronto-basal ganglia-thalamic networks, involving the right IFC, SMA, and anterior caudate for inhibition functions and the right DLPFC, posterior basal ganglia, and parietal areas for attention functions. Furthermore, long-term stimulant medication use appears to be associated with a gradual normalization of right caudate deficits during attention.
Correspondence: Katya Rubia, PhD, Department of Child and Adolescent Psychiatry, Institute of Psychiatry, King's College London, Social Genetic and Developmental Psychiatry Center, PO Box 46, De Crespigny Park, London SE5 8AF, England (katya.rubia@kcl.ac.uk).
Submitted for Publication: January 16, 2012; final revision received June 4, 2012; accepted June 6, 2012.
Published Online: December 17, 2012. doi:10.1001/jamapsychiatry.2013.277
Author Contributions: Dr Hart performed the statistical analysis, with help from Dr Radua, and had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Hart took responsibility for writing the “Methods” and “Results” sections, and Dr Rubia wrote the introduction and the “Comment” section, both of them with contributions from coauthors.
Conflict of Interest Disclosures: Dr Rubia is the recipient of a grant from Lilly for an unrelated project and has received speakers honoraria from Lilly, Shire, Novartis, and Medice.
Funding/Support: This study was supported by a research grant from the Kids Company (Dr Hart) and the Reta Lila Weston Medical Trust.
Role of the Sponsor: The funding organization had no influence on the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
1.Achenbach TM. Manual for the Child Behaviour Checklist/4-18 and 1991 Profile. Burlington: University of Vermont; 1994
2.Faraone SV, Biederman J. What is the prevalence of adult ADHD? results of a population screen of 966 adults.
J Atten Disord. 2005;9(2):384-39116371661
PubMedGoogle ScholarCrossref 3.Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review.
Biol Psychiatry. 2005;57(11):1336-134615950006
PubMedGoogle ScholarCrossref 5.Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL, Davenport ND, Li W, Parrish TB, Gitelman DR, Mesulam MM. Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD).
J Child Psychol Psychiatry. 2005;46(1):94-11115660647
PubMedGoogle ScholarCrossref 6.Durston S, Tottenham NT, Thomas KM, Davidson MC, Eigsti IM, Yang YH, Ulug AM, Casey BJ. Differential patterns of striatal activation in young children with and without ADHD.
Biol Psychiatry. 2003;53(10):871-87812742674
PubMedGoogle ScholarCrossref 7.Epstein JN, Casey BJ, Tonev ST, Davidson MC, Reiss AL, Garrett A, Hinshaw SP, Greenhill LL, Glover G, Shafritz KM, Vitolo A, Kotler LA, Jarrett MA, Spicer J. ADHD- and medication-related brain activation effects in concordantly affected parent-child dyads with ADHD.
J Child Psychol Psychiatry. 2007;48(9):899-91317714375
PubMedGoogle ScholarCrossref 8.Mostofsky SH, Schafer JGB, Abrams MT, Goldberg MC, Flower AA, Boyce A, Courtney SM, Calhoun VD, Kraut MA, Denckla MB, Pekar JJ. fMRI evidence that the neural basis of response inhibition is task-dependent.
Brain Res Cogn Brain Res. 2003;17(2):419-43012880912
PubMedGoogle ScholarCrossref 9.Smith AB, Taylor E, Brammer M, Toone B, Rubia K. Task-specific hypoactivation in prefrontal and temporoparietal brain regions during motor inhibition and task switching in medication-naive children and adolescents with attention deficit hyperactivity disorder.
Am J Psychiatry. 2006;163(6):1044-105116741205
PubMedGoogle ScholarCrossref 10.Rubia K, Cubillo A, Smith AB, Woolley J, Heyman I, Brammer MJ. Disorder-specific dysfunction in right inferior prefrontal cortex during two inhibition tasks in boys with attention-deficit hyperactivity disorder compared to boys with obsessive-compulsive disorder.
Hum Brain Mapp. 2010;31(2):287-29919777552
PubMedGoogle ScholarCrossref 11.Rubia K, Halari R, Smith AB, Mohammed M, Scott S, Giampietro V, Taylor E, Brammer MJ. Dissociated functional brain abnormalities of inhibition in boys with pure conduct disorder and in boys with pure attention deficit hyperactivity disorder.
Am J Psychiatry. 2008;165(7):889-89718413706
PubMedGoogle ScholarCrossref 12.Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC, Simmons A, Bullmore ET. Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI.
Am J Psychiatry. 1999;156(6):891-89610360128
PubMedGoogle Scholar 13.Rubia K, Smith AB, Brammer MJ, Toone B, Taylor E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD.
Am J Psychiatry. 2005;162(6):1067-107515930054
PubMedGoogle ScholarCrossref 14.Passarotti AM, Sweeney JA, Pavuluri MN. Neural correlates of response inhibition in pediatric bipolar disorder and attention deficit hyperactivity disorder.
Psychiatry Res. 2010;181(1):36-4319926457
PubMedGoogle ScholarCrossref 15.Cubillo A, Halari R, Ecker C, Giampietro V, Taylor E, Rubia K. Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
J Psychiatr Res. 2010;44(10):629-63920060129
PubMedGoogle ScholarCrossref 16.Rubia K, Halari R, Cubillo A, Smith AB, Mohammad AM, Brammer M, Taylor E. Methylphenidate normalizes fronto-striatal underactivation during interference inhibition in medication-naïve boys with attention-deficit hyperactivity disorder.
Neuropsychopharmacology. 2011;36(8):1575-158621451498
PubMedGoogle ScholarCrossref 17.Konrad K, Neufang S, Hanisch C, Fink GR, Herpertz-Dahlmann B. Dysfunctional attentional networks in children with attention deficit/hyperactivity disorder: evidence from an event-related functional magnetic resonance imaging study.
Biol Psychiatry. 2006;59(7):643-65116197925
PubMedGoogle ScholarCrossref 18.Cubillo A, Halari R, Giampietro V, Taylor E, Rubia K. Fronto-striatal underactivation during interference inhibition and attention allocation in grown up children with attention deficit/hyperactivity disorder and persistent symptoms.
Psychiatry Res. 2011;193(1):17-2721601434
PubMedGoogle ScholarCrossref 19.Rubia K, Halari R, Smith AB, Mohammad M, Scott S, Brammer MJ. Shared and disorder-specific prefrontal abnormalities in boys with pure attention-deficit/hyperactivity disorder compared to boys with pure CD during interference inhibition and attention allocation.
J Child Psychol Psychiatry. 2009;50(6):669-67819236528
PubMedGoogle ScholarCrossref 20.Rubia K, Cubillo A, Woolley J, Brammer MJ, Smith AB. Disorder-specific dysfunctions in patients with attention-deficit/hyperactivity disorder compared to patients with obsessive-compulsive disorder during interference inhibition and attention allocation.
Hum Brain Mapp. 2011;32(4):601-61121391250
PubMedGoogle ScholarCrossref 21.Banich MT, Burgess GC, Depue BE, Ruzic L, Bidwell LC, Hitt-Laustsen S, Du YP, Willcutt EG. The neural basis of sustained and transient attentional control in young adults with ADHD.
Neuropsychologia. 2009;47(14):3095-310419619566
PubMedGoogle ScholarCrossref 22.Burgess GC, Depue BE, Ruzic L, Willcutt EG, Du YPP, Banich MT. Attentional control activation relates to working memory in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2010;67(7):632-64020060961
PubMedGoogle ScholarCrossref 23.Cao QJ, Zang YF, Zhu CZ, Cao XH, Sun L, Zhou XL, Wang YF. Alerting deficits in children with attention deficit/hyperactivity disorder: event-related fMRI evidence.
Brain Res. 2008;1219:159-16818534567
PubMedGoogle ScholarCrossref 24.Rubia K, Smith AB, Brammer MJ, Taylor E. Temporal lobe dysfunction in medication-naïve boys with attention-deficit/hyperactivity disorder during attention allocation and its relation to response variability.
Biol Psychiatry. 2007;62(9):999-100617585887
PubMedGoogle ScholarCrossref 25.Rubia K, Smith AB, Halari R, Matsukura F, Mohammad M, Taylor E, Brammer MJ. Disorder-specific dissociation of orbitofrontal dysfunction in boys with pure conduct disorder during reward and ventrolateral prefrontal dysfunction in boys with pure attention-deficit/hyperactivity disorder during sustained attention.
Am J Psychiatry. 2009;166(1):83-9418829871
PubMedGoogle ScholarCrossref 26.Shafritz KM, Marchione KE, Gore JC, Shaywitz SE, Shaywitz BA. The effects of methylphenidate on neural systems of attention in attention deficit hyperactivity disorder.
Am J Psychiatry. 2004;161(11):1990-199715514398
PubMedGoogle ScholarCrossref 27.Rubia K, Halari R, Cubillo A, Mohammad A, Scott S, Brammer M. Disorder-specific inferior prefrontal hypofunction in boys with pure ADHD compared to boys with pure conduct disorder during cognitive flexibility.
Hum Brain Mapp. 2010;31(12):1823-183320205245
PubMedGoogle ScholarCrossref 28.Vance A, Silk TJ, Casey M, Rinehart NJ, Bradshaw JL, Bellgrove MA, Cunnington R. Right parietal dysfunction in children with attention deficit hyperactivity disorder, combined type: a functional MRI study.
Mol Psychiatry. 2007;12(9):826-832, 79317471290
PubMedGoogle ScholarCrossref 29.Silk T, Vance A, Rinehart N, Egan G, O’Boyle M, Bradshaw JL, Cunnington R. Fronto-parietal activation in attention-deficit hyperactivity disorder, combined type: functional magnetic resonance imaging study.
Br J Psychiatry. 2005;187:282-28316135867
PubMedGoogle ScholarCrossref 30.Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR, Fischl B, Liu H, Buckner RL. The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
J Neurophysiol. 2011;106(3):1125-116521653723
PubMedGoogle ScholarCrossref 31.Kooistra L, van der Meere JJ, Edwards JD, Kaplan BJ, Crawford S, Goodyear BG. Preliminary fMRI findings on the effects of event rate in adults with ADHD.
J Neural Transm. 2010;117(5):655-66220157743
PubMedGoogle ScholarCrossref 32.Tamm L, Menon V, Ringel J, Reiss AL. Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder.
J Am Acad Child Adolesc Psychiatry. 2004;43(11):1430-144015502603
PubMedGoogle ScholarCrossref 33.Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM, Halperin JM. Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related FMRI study.
Am J Psychiatry. 2004;161(9):1650-165715337656
PubMedGoogle ScholarCrossref 34.Schneider MF, Krick CM, Retz W, Hengesch G, Retz-Junginger P, Reith W, Rösler M. Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults—a functional magnetic resonance imaging (fMRI) study.
Psychiatry Res. 2010;183(1):75-8420558047
PubMedGoogle ScholarCrossref 35.Cubillo A, Halari R, Smith A, Taylor E, Rubia K. A review of fronto-striatal and fronto-cortical brain abnormalities in children and adults with attention deficit hyperactivity disorder (ADHD) and new evidence for dysfunction in adults with ADHD during motivation and attention.
Cortex. 2012;48(2):194-21521575934
PubMedGoogle ScholarCrossref 36.Hale TS, Bookheimer S, McGough JJ, Phillips JM, McCracken JT. Atypical brain activation during simple & complex levels of processing in adult ADHD: an fMRI study.
J Atten Disord. 2007;11(2):125-14017494829
PubMedGoogle ScholarCrossref 37.Nakao T, Radua J, Rubia K, Mataix-Cols D. Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication.
Am J Psychiatry. 2011;168(11):1154-116321865529
PubMedGoogle Scholar 38.Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis.
J Child Psychol Psychiatry. 2006;47(10):1051-106217073984
PubMedGoogle ScholarCrossref 39.Friston KJ, Rotshtein P, Geng JJ, Sterzer P, Henson RN. A critique of functional localisers.
Neuroimage. 2006;30(4):1077-108716635579
PubMedGoogle ScholarCrossref 40.Nee DE, Wager TD, Jonides J. Interference resolution: insights from a meta-analysis of neuroimaging tasks.
Cogn Affect Behav Neurosci. 2007;7(1):1-1717598730
PubMedGoogle ScholarCrossref 41.MacLeod CM, Dodd MD, Sheard ED, Wilson DE, Bibi U. In opposition to inhibition. In: Ross BH, ed. The Psychology of Learning and Motivation. San Diego, CA: Academic Press; 2003:163-214
42.Nigg JT. On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy.
Psychol Bull. 2000;126(2):220-24610748641
PubMedGoogle ScholarCrossref 43.Eagle DM, Bari A, Robbins TW. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks.
Psychopharmacology (Berl). 2008;199(3):439-45618542931
PubMedGoogle ScholarCrossref 44.Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience.
Neurosci Biobehav Rev. 2009;33(5):631-64618835296
PubMedGoogle ScholarCrossref 45.Ridderinkhof KR, van den Wildenberg WPM, Segalowitz SJ, Carter CS. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning.
Brain Cogn. 2004;56(2):129-14015518930
PubMedGoogle ScholarCrossref 46.Aron AR, Poldrack RA. The cognitive neuroscience of response inhibition: relevance for genetic research in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2005;57(11):1285-129215950000
PubMedGoogle ScholarCrossref 47.Rubia K, Russell T, Overmeyer S, Brammer MJ, Bullmore ET, Sharma T, Simmons A, Williams SCR, Giampietro V, Andrew CM, Taylor E. Mapping motor inhibition: conjunctive brain activations across different versions of go/no-go and stop tasks.
Neuroimage. 2001;13(2):250-26111162266
PubMedGoogle ScholarCrossref 50.Cabeza R, Nyberg L. Imaging cognition, II: an empirical review of 275 PET and fMRI studies.
J Cogn Neurosci. 2000;12(1):1-4710769304
PubMedGoogle ScholarCrossref 51.Frodl T, Skokauskas N. Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects.
Acta Psychiatr Scand. 2012;125(2):114-12622118249
PubMedGoogle ScholarCrossref 52.Konrad K, Neufang S, Fink GR, Herpertz-Dahlmann B. Long-term effects of methylphenidate on neural networks associated with executive attention in children with ADHD: results from a longitudinal functional MRI study.
J Am Acad Child Adolesc Psychiatry. 2007;46(12):1633-164118030085
PubMedGoogle ScholarCrossref 53.Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS, Blumenthal JD, James RS, Ebens CL, Walter JM, Zijdenbos A, Evans AC, Giedd JN, Rapoport JL. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder.
JAMA. 2002;288(14):1740-174812365958
PubMedGoogle ScholarCrossref 54.Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB.Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.
JAMA. 2000;283(15):2008-201210789670
PubMedGoogle ScholarCrossref 55.Radua J, Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder.
Br J Psychiatry. 2009;195(5):393-40219880927
PubMedGoogle ScholarCrossref 56.Radua J, Mataix-Cols D. Heterogeneity of coordinated-based meta-analyses of neuroimaging data: an example from studies in OCD [authors' reply].
Br J Psychiatry. 2010;197(1):77
Google ScholarCrossref 57.Radua J, Via E, Catani M, Mataix-Cols D. Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls.
Psychol Med. 2011;41(7):1539-155021078227
PubMedGoogle ScholarCrossref 58.Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, Surguladze S. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps.
Eur Psychiatry. 2012;27(8):605-61121658917
PubMedGoogle ScholarCrossref 59.Braet W, Johnson KA, Tobin CT, Acheson R, McDonnell C, Hawi Z, Barry E, Mulligan A, Gill M, Bellgrove MA, Robertson IH, Garavan H. fMRI activation during response inhibition and error processing: the role of the
DAT1 gene in typically developing adolescents and those diagnosed with ADHD.
Neuropsychologia. 2011;49(7):1641-165021232548
PubMedGoogle ScholarCrossref 60.Bush G, Spencer TJ, Holmes J, Shin LM, Valera EM, Seidman LJ, Makris N, Surman C, Aleardi M, Mick E, Biederman J. Functional magnetic resonance imaging of methylphenidate and placebo in attention-deficit/hyperactivity disorder during the multi-source interference task.
Arch Gen Psychiatry. 2008;65(1):102-11418180434
PubMedGoogle ScholarCrossref 61.Depue BE, Burgess GC, Willcutt EG, Bidwell LC, Ruzic L, Banich MT. Symptom-correlated brain regions in young adults with combined-type ADHD: their organization, variability, and relation to behavioral performance.
Psychiatry Res. 2010;182(2):96-10220399622
PubMedGoogle ScholarCrossref 62.Dillo W, Göke A, Prox-Vagedes V, Szycik GR, Roy M, Donnerstag F, Emrich HM, Ohlmeier MD. Neuronal correlates of ADHD in adults with evidence for compensation strategies—a functional MRI study with a Go/No-Go paradigm.
Ger Med Sci. 2010;8:Doc0920421953
PubMedGoogle Scholar 63.Epstein JN, Delbello MP, Adler CM, Altaye M, Kramer M, Mills NP, Strakowski SM, Holland S. Differential patterns of brain activation over time in adolescents with and without attention deficit hyperactivity disorder (ADHD) during performance of a sustained attention task.
Neuropediatrics. 2009;40(1):1-519639521
PubMedGoogle ScholarCrossref 64.Mulder MJ, Baeyens D, Davidson MC, Casey BJ, van den Ban E, van Engeland H, Durston S. Familial vulnerability to ADHD affects activity in the cerebellum in addition to the prefrontal systems.
J Am Acad Child Adolesc Psychiatry. 2008;47(1):68-7518174827
PubMedGoogle ScholarCrossref 65.Pliszka SR, Glahn DC, Semrud-Clikeman M, Franklin C, Perez R III, Xiong JJ, Liotti M. Neuroimaging of inhibitory control areas in children with attention deficit hyperactivity disorder who were treatment naive or in long-term treatment.
Am J Psychiatry. 2006;163(6):1052-106016741206
PubMedGoogle ScholarCrossref 66.Schulz KP, Newcorn JH, Fan J, Tang CY, Halperin JM. Brain activation gradients in ventrolateral prefrontal cortex related to persistence of ADHD in adolescent boys.
J Am Acad Child Adolesc Psychiatry. 2005;44(1):47-5415608543
PubMedGoogle ScholarCrossref 67.Stevens MC, Pearlson GD, Kiehl KA. An fMRI auditory oddball study of combined-subtype attention deficit hyperactivity disorder.
Am J Psychiatry. 2007;164(11):1737-174917974940
PubMedGoogle ScholarCrossref 68.Vaidya CJ, Austin G, Kirkorian G, Ridlehuber HW, Desmond JE, Glover GH, Gabrieli JDE. Selective effects of methylphenidate in attention deficit hyperactivity disorder: a functional magnetic resonance study.
Proc Natl Acad Sci U S A. 1998;95(24):14494-144999826728
PubMedGoogle ScholarCrossref 69.Vloet TD, Gilsbach S, Neufang S, Fink GR, Herpertz-Dahlmann B, Konrad K. Neural mechanisms of interference control and time discrimination in attention-deficit/hyperactivity disorder.
J Am Acad Child Adolesc Psychiatry. 2010;49(4):356-36720410728
PubMedGoogle Scholar 70.Zang YF, Jin Z, Weng XC, Zhang L, Zeng YW, Yang L, Wang YF, Seidman LJ, Faraone SV. Functional MRI in attention-deficit hyperactivity disorder: evidence for hypofrontality.
Brain Dev. 2005;27(8):544-55015876503
PubMedGoogle ScholarCrossref 71.Schulz KP, Tang CY, Fan J, Marks DJ, Newcorn JH, Cheung AM, Halperin JM. Differential prefrontal cortex activation during inhibitory control in adolescents with and without childhood attention-deficit/hyperactivity disorder.
Neuropsychology. 2005;19(3):390-40215910125
PubMedGoogle ScholarCrossref 72.Beauregard M, Lévesque J. Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder.
Appl Psychophysiol Biofeedback. 2006;31(1):3-2016552626
PubMedGoogle ScholarCrossref 73.Depue BE, Burgess GC, Willcutt EG, Ruzic L, Banich MT. Inhibitory control of memory retrieval and motor processing associated with the right lateral prefrontal cortex: evidence from deficits in individuals with ADHD.
Neuropsychologia. 2010;48(13):3909-391720863843
PubMedGoogle ScholarCrossref 74.Lee YS, Han DH, Lee JH, Choi TY. The effects of methylphenidate on neural substrates associated with interference suppression in children with ADHD: a preliminary study using event related fMRI.
Psychiatry Investig. 2010;7(1):49-5420396433
PubMedGoogle ScholarCrossref 75.Solanto MV, Schulz KP, Fan J, Tang CY, Newcorn JH. Event-related FMRI of inhibitory control in the predominantly inattentive and combined subtypes of ADHD.
J Neuroimaging. 2009;19(3):205-21219594667
PubMedGoogle ScholarCrossref 76.Bush G, Frazier JA, Rauch SL, Seidman LJ, Whalen PJ, Jenike MA, Rosen BR, Biederman J. Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop.
Biol Psychiatry. 1999;45(12):1542-155210376114
PubMedGoogle ScholarCrossref 77.Vaidya CJ, Bunge SA, Dudukovic NM, Zalecki CA, Elliott GR, Gabrieli JD. Altered neural substrates of cognitive control in childhood ADHD: evidence from functional magnetic resonance imaging.
Am J Psychiatry. 2005;162(9):1605-161316135618
PubMedGoogle ScholarCrossref 78.Passarotti AM, Sweeney JA, Pavuluri MN. Differential engagement of cognitive and affective neural systems in pediatric bipolar disorder and attention deficit hyperactivity disorder.
J Int Neuropsychol Soc. 2010;16(1):106-11719849880
PubMedGoogle ScholarCrossref 79.Whalen PJ, Bush G, McNally RJ, Wilhelm S, McInerney SC, Jenike MA, Rauch SL. The emotional counting Stroop paradigm: a functional magnetic resonance imaging probe of the anterior cingulate affective division.
Biol Psychiatry. 1998;44(12):1219-12289861465
PubMedGoogle ScholarCrossref 80.Dibbets P, Evers L, Hurks P, Marchetta N, Jolles J. Differences in feedback- and inhibition-related neural activity in adult ADHD.
Brain Cogn. 2009;70(1):73-8319201515
PubMedGoogle ScholarCrossref 81.Karch S, Thalmeier T, Lutz J, Cerovecki A, Opgen-Rhein M, Hock B, Leicht G, Hennig-Fast K, Meindl T, Riedel M, Mulert C, Pogarell O. Neural correlates (ERP/fMRI) of voluntary selection in adult ADHD patients.
Eur Arch Psychiatry Clin Neurosci. 2010;260(5):427-44019907927
PubMedGoogle ScholarCrossref 82.Durston S, Mulder M, Casey BJ, Ziermans T, van Engeland H. Activation in ventral prefrontal cortex is sensitive to genetic vulnerability for attention-deficit hyperactivity disorder.
Biol Psychiatry. 2006;60(10):1062-107016712804
PubMedGoogle ScholarCrossref 83.Peterson BS, Potenza MN, Wang ZS, Zhu HT, Martin A, Marsh R, Plessen KJ, Yu S. An FMRI study of the effects of psychostimulants on default-mode processing during Stroop task performance in youths with ADHD.
Am J Psychiatry. 2009;166(11):1286-129419755575
PubMedGoogle ScholarCrossref 84.Rubia K, Halari R, Mohammad AM, Taylor E, Brammer M. Methylphenidate normalizes frontocingulate underactivation during error processing in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2011;70(3):255-26221664605
PubMedGoogle ScholarCrossref 85.Spinelli S, Vasa RA, Joel S, Nelson TE, Pekar JJ, Mostofsky SH. Variability in post-error behavioral adjustment is associated with functional abnormalities in the temporal cortex in children with ADHD.
J Child Psychol Psychiatry. 2011;52(7):808-81621175614
PubMedGoogle ScholarCrossref 86.Suskauer SJ, Simmonds DJ, Fotedar S, Blankner JG, Pekar JJ, Denckla MB, Mostofsky SH. Functional magnetic resonance imaging evidence for abnormalities in response selection in attention deficit hyperactivity disorder: differences in activation associated with response inhibition but not habitual motor response.
J Cogn Neurosci. 2008;20(3):478-49318004945
PubMedGoogle ScholarCrossref 87.Rubia K, Halari R, Cubillo A, Mohammad AM, Brammer M, Taylor E. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a rewarded continuous performance task.
Neuropharmacology. 2009;57(7-8):640-65219715709
PubMedGoogle ScholarCrossref 88.Tamm L, Menon V, Reiss AL. Parietal attentional system aberrations during target detection in adolescents with attention deficit hyperactivity disorder: event-related fMRI evidence.
Am J Psychiatry. 2006;163(6):1033-104316741204
PubMedGoogle ScholarCrossref 89.Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex.
Trends Cogn Sci. 2000;4(6):215-22210827444
PubMedGoogle ScholarCrossref 90.Rubia K, Smith AB, Taylor E, Brammer M. Linear age-correlated functional development of right inferior fronto-striato-cerebellar networks during response inhibition and anterior cingulate during error-related processes.
Hum Brain Mapp. 2007;28(11):1163-117717538951
PubMedGoogle ScholarCrossref 91.Rubia K, Smith AB, Woolley J, Nosarti C, Heyman I, Taylor E, Brammer M. Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control.
Hum Brain Mapp. 2006;27(12):973-99316683265
PubMedGoogle ScholarCrossref 92.Simmonds DJ, Pekar JJ, Mostofsky SH. Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent.
Neuropsychologia. 2008;46(1):224-23217850833
PubMedGoogle ScholarCrossref 93.Suskauer SJ, Simmonds DJ, Caffo BS, Denckla MB, Pekar JJ, Mostofsky SH. fMRI of intrasubject variability in ADHD: anomalous premotor activity with prefrontal compensation.
J Am Acad Child Adolesc Psychiatry. 2008;47(10):1141-115018724253
PubMedGoogle ScholarCrossref 94.Derrfuss J, Brass M, Neumann J, von Cramon DY. Involvement of the inferior frontal junction in cognitive control: meta-analyses of switching and Stroop studies.
Hum Brain Mapp. 2005;25(1):22-3415846824
PubMedGoogle ScholarCrossref 95.Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S. The role of the medial frontal cortex in cognitive control.
Science. 2004;306(5695):443-44715486290
PubMedGoogle ScholarCrossref 96.Arnsten AF, Rubia K. Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: disruptions in neurodevelopmental psychiatric disorders.
J Am Acad Child Adolesc Psychiatry. 2012;51(4):356-36722449642
PubMedGoogle ScholarCrossref 97.Makris N, Biederman J, Monuteaux MC, Seidman LJ. Towards conceptualizing a neural systems-based anatomy of attention-deficit/hyperactivity disorder.
Dev Neurosci. 2009;31(1-2):36-4919372685
PubMedGoogle ScholarCrossref 98.Valera EM, Faraone SV, Murray KE, Seidman LJ. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2007;61(12):1361-136916950217
PubMedGoogle ScholarCrossref 99.Stefanatos GA, Wasserstein J. Attention deficit/hyperactivity disorder as a right hemisphere syndrome. Selective literature review and detailed neuropsychological case studies.
Ann N Y Acad Sci. 2001;931:172-19511462741
PubMedGoogle ScholarCrossref 100.Middleton FA, Strick PL. Basal ganglia and cerebellar loops: motor and cognitive circuits.
Brain Res Brain Res Rev. 2000;31(2-3):236-25010719151
PubMedGoogle ScholarCrossref 101.Durston S, Davidson MC, Mulder MJ, Spicer JA, Galvan A, Tottenham N, Scheres A, Xavier Castellanos F, van Engeland H, Casey BJ. Neural and behavioral correlates of expectancy violations in attention-deficit hyperactivity disorder.
J Child Psychol Psychiatry. 2007;48(9):881-88917714373
PubMedGoogle ScholarCrossref 102.Rubia K, Halari R, Christakou A, Taylor E. Impulsiveness as a timing disturbance: neurocognitive abnormalities in attention-deficit hyperactivity disorder during temporal processes and normalization with methylphenidate.
Philos Trans R Soc Lond B Biol Sci. 2009;364(1525):1919-193119487194
PubMedGoogle ScholarCrossref 103.Valera EM, Spencer RMC, Zeffiro TA, Makris N, Spencer TJ, Faraone SV, Biederman J, Seidman LJ. Neural substrates of impaired sensorimotor timing in adult attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2010;68(4):359-36720619827
PubMedGoogle ScholarCrossref 104.Durston S, van Belle J, de Zeeuw P. Differentiating frontostriatal and fronto-cerebellar circuits in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2011;69(12):1178-118420965496
PubMedGoogle ScholarCrossref 105.Konrad K, Eickhoff SB. Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder.
Hum Brain Mapp. 2010;31(6):904-91620496381
PubMedGoogle ScholarCrossref 106.Bush G. Cingulate, frontal, and parietal cortical dysfunction in attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2011;69(12):1160-116721489409
PubMedGoogle ScholarCrossref 107.Fusar-Poli P, Rubia K, Rossi G, Sartori G, Balottin U. Striatal dopamine transporter alterations in ADHD: pathophysiology or adaptation to psychostimulants? A meta-analysis.
Am J Psychiatry. 2012;169(3):264-27222294258
PubMedGoogle ScholarCrossref 108.Volkow ND, Wang GJ, Fowler JS, Hitzemann R, Angrist B, Gatley SJ, Logan J, Ding YS, Pappas N. Association of methylphenidate-induced craving with changes in right striato-orbitofrontal metabolism in cocaine abusers: implications in addiction.
Am J Psychiatry. 1999;156(1):19-269892293
PubMedGoogle Scholar 109.Kim BN, Lee JS, Cho SC, Lee DS. Methylphenidate increased regional cerebral blood flow in subjects with attention deficit/hyperactivity disorder.
Yonsei Med J. 2001;42(1):19-2911293498
PubMedGoogle Scholar 110.Halperin JM, Schulz KP. Revisiting the role of the prefrontal cortex in the pathophysiology of attention-deficit/hyperactivity disorder.
Psychol Bull. 2006;132(4):560-58116822167
PubMedGoogle ScholarCrossref