Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis. J Child Psychol Psychiatry 47: 1051-1062

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DOI: 10.1111/j.1469-7610.2006.01671.x · Source: PubMed
Abstract
Attention deficit/hyperactivity disorder (ADHD) is one of the most prevalent and commonly studied forms of psychopathology in children and adolescents. Causal models of ADHD have long implicated dysfunction in fronto-striatal and frontal-parietal networks supporting executive function, a hypothesis that can now be examined systematically using functional neuroimaging. The present work provides an objective, unbiased statistically-based meta-analysis of published functional neuroimaging studies of ADHD. A recently developed voxel-wise quantitative meta-analytic technique known as activation likelihood estimation (ALE) was applied to 16 neuroimaging studies examining and contrasting patterns of neural activity in patients with ADHD and healthy controls. Voxel-wise results are reported using a statistical threshold of p < .05, corrected. Given the large number of studies examining response inhibition, additional meta-analyses focusing specifically on group differences in the neural correlates of inhibition were included. Across studies, significant patterns of frontal hypoactivity were detected in patients with ADHD, affecting anterior cingulate, dorsolateral prefrontal, and inferior prefrontal cortices, as well as related regions including basal ganglia, thalamus, and portions of parietal cortex. When focusing on studies of response inhibition alone, a more limited set of group differences were observed, including inferior prefrontal cortex, medial wall regions, and the precentral gyrus. In contrast, analyses focusing on studies of constructs other than response inhibition revealed a more extensive pattern of hypofunction in patients with ADHD than those of response inhibition. To date, the most consistent findings in the neuroimaging literature of ADHD are deficits in neural activity within fronto-striatal and fronto-parietal circuits. The distributed nature of these results fails to support models emphasizing dysfunction in any one frontal sub-region. While our findings are suggestive of the primacy of deficits in frontal-based neural circuitry underlying ADHD, we discuss potential biases in the literature that need to be addressed before such a conclusion can be fully embraced.
The neural correlates of attention deficit
hyperactivity disorder: an ALE meta-analysis
Steven G. Dickstein, Katie Bannon, F. Xavier Castellanos,
and Michael P. Milham
NYU Child Study Center, USA
Background: Attention deficit/hyperactivity disorder (ADHD) is one of the most prevalent and com-
monly studied forms of psychopathology in children and adolescents. Causal models of ADHD have long
implicated dysfunction in fronto-striatal and frontal-parietal networks supporting executive function, a
hypothesis that can now be examined systematically using functional neuroimaging. The present work
provides an objective, unbiased statistically-based meta-analysis of published functional neuroimaging
studies of ADHD. Methods: A recently developed voxel-wise quantitative meta-analytic technique
known as activation likelihood estimation (ALE) was applied to 16 neuroimaging studies examining and
contrasting patterns of neural activity in patients with ADHD and healthy controls. Voxel-wise results
are reported using a statistical threshold of p < .05, corrected. Given the large number of studies ex-
amining response inhibition, additional meta-analyses focusing specifically on group differences in the
neural correlates of inhibition were included. Results: Across studies, significant patterns of frontal
hypoactivity were detected in patients with ADHD, affecting anterior cingulate, dorsolateral prefrontal,
and inferior prefrontal cortices, as well as related regions including basal ganglia, thalamus, and por-
tions of parietal cortex. When focusing on studies of response inhibition alone, a more limited set of
group differences were observed, including inferior prefrontal cortex, medial wall regions, and the
precentral gyrus. In contrast, analyses focusing on studies of constructs other than response inhibition
revealed a more extensive pattern of hypofunction in patients with ADHD than those of response
inhibition. Conclusions: To date, the most consistent findings in the neuroimaging literature of ADHD
are deficits in neural activity within fronto-striatal and fronto-parietal circuits. The distributed nature of
these results fails to support models emphasizing dysfunction in any one frontal sub-region. While our
findings are suggestive of the primacy of deficits in frontal-based neural circuitry underlying ADHD, we
discuss potential biases in the literature that need to be addressed before such a conclusion can be fully
embraced. Keywords: Attention deficit/hyperactivity disorder (ADHD), meta-analysis, neuroimaging,
functional magnetic resonance imaging (fMRI), positron emission tomography (PET), executive function.
Models of attention-deficit/hyperactivity disorder
(ADHD) have long posited that a core deficit in frontal
lobe function underlies its various cognitive and
behavioral manifestations. In particular, fronto-
striatal and fronto-parietal networks supporting an
array of top-down or executive processes, such as
dorsolateral prefrontal cortices, anterior cingulate
cortices, and associated striatal regions, are fre-
quently cited as loci of dysfunction in ADHD (e.g.,
Barkley, 1997; Castellanos & Tannock, 2002). Until
recently, such models have relied heavily upon
anatomical and neuropsychological studies of ADHD
(see recent reviews, Willcutt, Doyle, Nigg, Faraone, &
Pennington, 2005; Seidman, Valera, & Makris, 2005)
as well as inferences based upon functional neuroi-
maging findings from healthy normal adults. How-
ever, with recent advances in non-invasive
neuroimaging techniques such as functional mag-
netic resonance imaging (fMRI), researchers have
begun examining the neural correlates of ADHD,
resulting in a rapidly growing literature.
Given both the strong association between execu-
tive function and the frontal lobes, and the sub-
stantial literature confirming the presence of
executive dysfunction in ADHD (Willcutt et al.,
2005), it is not surprising that nearly all neuro-
imaging studies have focused on cognitive para-
digms assessing executive processes. Amongst
these, response inhibition has been the most studied
of executive top-down functions, reflecting the im-
pact of an influential theory which posited inhibitory
dysfunction as the primary deficit in ADHD (Barkley,
1997).
Several recent reviews have examined the ADHD
neuroimaging literature. Bush, Valera, and Seidman
(2005) surveyed over a dozen neuroimaging studies
of ADHD, encompassing a variety of functional
imaging techniques (PET, SPECT, fMRI, EEG) and
cognitive paradigms (e.g., inhibitory control, select-
ive attention, working memory, and vigilance). They
found a consistent pattern of frontal dysfunction
with altered patterns of activity in anterior cingulate,
dorsolateral prefrontal, and ventrolateral prefrontal
cortices, as well as associated parietal, striatal, and
cerebellar regions. Despite broad consistencies,
Conflict of interest statement: No conflicts declared.
Journal of Child Psychology and Psychiatry 47:10 (2006), pp 1051–1062 doi:10.1111/j.1469-7610.2006.01671.x
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
Bush et al. noted multiple challenges in comparing
results across studies, and cited difficulties in
interpretation due to small sample sizes and meth-
odological issues related to statistical correction for
multiple comparisons that impact rates of false
positives and false negatives.
Aron and Poldrack (2005) examined the existent
literature with a narrower focus on the neural cor-
relates of inhibitory control rather than the broader
construct of executive processes. Their review
included imaging, behavioral, and genetic studies in
patients with ADHD as well as healthy participants,
along with basic animal studies. Their conclusions
support the hypothesis that dysfunctions in pre-
frontal cortex (specifically the right inferior pre-
frontal cortex), basal ganglia, and the related
neurotransmitter systems (dopaminergic, noradren-
ergic, and serotonergic) underlie inhibitory deficits in
ADHD.
Here we build upon these prior efforts by using the
recently developed activation likelihood estimation
(ALE) (Lancaster, Laird, Fox, Glahn, & Fox, 2005;
Laird et al., 2005a) technique to carry out a quanti-
tative voxel-wise meta-analysis of published func-
tional neuroimaging studies of ADHD. While prior
reviews have attempted to synthesize the literature,
a quantitative meta-analysis can provide a useful
method to assess the state of the field, and to provide
a plan for future research, for several reasons. First,
quantitative meta-analytic techniques provide an
objective, unbiased, statistically-based approach to
examine findings across studies, as opposed to the
traditional ‘box-score’ or label-based qualitative
methods (Laird et al., 2005b). Second, a voxel-wise
approach provides increased spatial resolution,
delineating not only large frontal sub-regions but
specific areas within them by optimally using data
generated across functional imaging studies. The
advantages of increased spatial distinction are sub-
stantial in the frontal lobes given the remarkable
functional heterogeneity that is increasingly being
mapped at the level of sub-lobar regions (Stuss,
Murphy, Binns, & Alexander, 2003; Miller & Cohen,
2001; Wagner, 1999). Finally, a quantitative meta-
analysis yields specific brain coordinates that are
confirmed across multiple studies, thus helping to
filter out otherwise unavoidable spurious activations
representing type I errors.
It is important to note that this is a novel appli-
cation of the ALE meta-analytic methodology. Typ-
ically, researchers have used ALE to examine a
particular cognitive construct across a set of studies
within a relatively homogenous population (e.g.,
healthy adult volunteers) (Laird et al., 2005a,
2005b). Here, we use ALE to examine differences
associated with ADHD across a set of studies
assessing executive function. Ideally, a separate
meta-analysis would be conducted for each indivi-
dual executive process as it relates to ADHD. How-
ever, given the infancy of the current literature, with
the exception of response inhibition, this is not
possible, as no individual construct has a sufficient
number of studies to date. Instead, it is more prac-
tical to study the commonalities across executive
function tasks in a systematic statistically driven
fashion, with the realization that some regions spe-
cific to an individual executive process may have
decreased detectability using this method. Support
for this approach comes from the descriptive reviews
already discussed (Aron & Poldrack, 2005; Bush et
al., 2005), which noted a pattern of ADHD-related
hypofunction in specific frontal regions (e.g., anterior
cingulate cortex) across studies in the existent lit-
erature, despite the heterogeneity of methods. Given
the large number of studies examining response
inhibition in ADHD, we carried out a second meta-
analysis focusing on this construct alone.
One potential difficulty for a meta-analysis of
neuroimaging in ADHD is that about a third of cur-
rent studies do not provide direct comparisons be-
tween patients with ADHD and healthy controls.
Instead, those studies have typically reported acti-
vations for each group (ADHD, control) individually,
with qualitative assessments about differences in the
patterns observed for the two individual groups.
Alternatively, some studies have simply presented
region of interest analyses. Furthermore, the small
sample sizes included in several of the studies
reporting direct comparisons may limit their ability
to detect group differences. An important advantage
of the ALE approach is that we can overcome these
obstacles to some degree by carrying out separate
meta-analyses for each group (ADHD, controls) and
then comparing the two meta-analyses statistically.
Methods
Study selection
Neuroimaging studies comparing patterns of activity in
patients with ADHD and healthy comparisons were
found primarily by searching the PUBMED database
(http://www.pubmed.org) and Google Scholar (http://
scholar.google.com/schhp?hl¼en&tab¼ws&q¼) using
the keywords: ADHD, fMRI, PET, Executive Function,
Inhibition, Working Memory, Child, Adolescent, Adult,
and Imaging. We then reviewed the reference lists of
each of these articles to obtain additional papers. Four
additional articles were included after the initial review
process. Three articles currently in press at the Ameri-
can Journal of Psychiatry were provided by the editor
with the permission of the authors, and one article that
had not appeared during our database searches was
pointed out by an anonymous reviewer. Only articles
that reported activation foci as 3-D coordinates (x, y, z)
in stereotactic space, examined active cognitive con-
structs, and presented results for individual participant
groups were included. The 16 studies identified using
our criteria are listed in Table 1. These studies yielded a
total of 134 foci of activation for patients with ADHD
and 180 foci of activation for controls. For the purpose
of analysis, any foci that were reported according to the
1052 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
atlas of the Montreal Neurological Institute (MNI
coordinates) were converted to Talairach coordinates
using the algorithm implemented by Matt Brett
(mni2tal.m) (http://www.mrc-cbu.cam.ac.uk/Imaging/
Common/downloads/MNI2tal/mni2tal.m). Of the ten
functional neuroimaging studies of ADHD not included
in this meta-analysis, six studies were excluded be-
cause they did not provide stereotactic coordinates
(Ernst et al., 1994; Vaidya et al., 1998; Sunshine et al.,
1997; Zang et al., 2005; Rubia et al., 2001; Teicher
et al., 2000), one contained no active cognitive task
(Ernst, Cohen, Liebenauer, Jons, & Zametkin, 1997),
three examined only effects of medication treatment
rather than effects of cognitive tasks (Anderson, Polcari,
Lowen, Renshaw, & Teicher, 2002; Teicher et al., 2000;
Schweitzer et al., 2003), and a final study was excluded
from analyses because results were not reported for
individual participant groups (Rubia et al., 1999).
Meta-analytic techniques
All meta-analyses were carried out using the activation
likelihood estimation (ALE) technique (Turkeltaub,
Eden, Jones, & Zeffiro, 2002) implemented in BrainMap
(Laird et al., 2005a). Based on the Talairach stereotactic
coordinates reported by the studies listed in Table 1,
two separate meta-analyses were conducted, one using
the foci reported for patients with ADHD and one using
the foci reported for controls. More specifically, for each
group, activation likelihood estimates were calculated
for each voxel by modeling each coordinate with an
equal weighting using a 3-D Gaussian probability
density function with FWHM ¼ 10 mm. We next carried
out a permutation test to determine the voxel-wise sig-
nificance of the resulting ALE values. We made use of a
non-parametric statistical approach previously des-
cribed by Turkeltaub et al. (2002), in which 5000 per-
mutations were generated using the same number of
foci and FWHM as used to generate the ALE map. As
such, no assumptions were made with respect to the
distribution or spatial separation of these random foci
(Laird et al., 2005a; Turkeltaub et al., 2002). Resulting
statistical maps were corrected for multiple compari-
sons using false discovery rates (FDR), and then
thresholded at p < .05, corrected, with a cluster extent
threshold of 16 voxels. To directly compare patients
with ADHD and controls, we used the ALE maps gene-
rated for each group to calculate ALE difference maps,
controls ADHD and ADHD controls. Each of these
difference maps was entered into a permutation ana-
lysis to generate voxel-wise statistical scores, as was
previously done for the individual meta-analyses.
Table 1 Data sources
Article Imaging modality N Task(s) Contrast(s)
Booth et al., 2005 fMRI (1.5T) 12 ADHD
12 Controls
Visual search; Go-No go Nine stimuli vs. one
stimulus; No-go vs. Go
Bush et al., 1999 fMRI (1.5T) 8 ADHD 8 Controls
(adults)
Counting Stroop Interference vs. neutral
Durston et al., 2003 fMRI (1.5T) 7 ADHD 7 Controls Go-No go (Modified) No-go vs. Go
Ernst et al., 2003 PET 10 ADHD 12 Controls
(adults)
Decision-making gambling task Choice vs. No choice
Pliszka et al., in press FMRI (2.0T) 17 ADHD 15 Controls Stop task Stop vs. Go; Successful stop
vs. Unsuccessful stop
Rubia et al., 2005 FMRI (1.5T) 16 ADHD 21 Controls Stop task Stop vs. Go
Schulz et al., 2004 FMRI (1.5T) 10 ADHD 9 Controls Go-No go No-go vs. Go
Schulz et al., 2005 FMRI (1.5T) 8 ADHD 8 Controls Stimulus and response
Conflict tasks
Stimulus conflict
vs. control and
location; Response
conflict vs. control; Combined
conflict condition vs. control
and location
Schweitzer et al., 2000 PET 6 ADHD 6 Controls
(adults)
Paced auditory
Serial addition task
Serial addition vs. Random
number vocalization
Schweitzer et al., 2004 PET 10 ADHD 11 Controls
(adults)
Paced auditory
Serial addition task
Serial addition vs. Random
number vocalization
Silk et al., 2005 FMRI (3.0T) 7 ADHD 7 Controls Mental rotation Mental rotation vs.
Fourier-transformed
noise patch
Smith et al., in press FMRI 19 ADHD 27 Controls Go-No go Motor-Stroop
Switch task
No-go vs. oddball trials;
Incongruent vs. congruent;
Switch vs. Repeat trials
Tamm et al., 2004 FMRI (1.5T) 10 ADHD 12 Controls Go-No go (modified) No-go vs. Go
Tamm et al., in press FMRI (1.5T) 14 ADHD 12 Controls Oddball task Oddball vs. Standard
stimulus
Vaidya et al., 2005 FMRI (3.0T) 10 ADHD 10 Controls Eriksen Flanker + Go-No go Incongruent vs. Neutral;
No-go vs. Neutral
Valera et al., 2005 FMRI (1.5T) 20 ADHD 20 Controls
(adults)
N-Back 2-back vs. ‘X’ vigilance task
These studies contained a total of 134 foci of activation for patients with ADHD and 180 foci for controls. Unless specifically noted
above as ‘adults’, study participants were children and adolescents.
Meta-analysis of neuroimaging in ADHD
1053
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
Given the existence of a sufficient number of studies
to examine response inhibition specifically, we carried a
second set of meta-analyses (ADHD only, controls only,
controls ADHD, ADHD controls) using the same
approach, but only included those studies examining
response inhibition (Go No-go and Stop Tasks). To more
fully measure the effect of response inhibition on the
overall meta-analysis, we also created meta-analyses
using those tasks excluded from the response-inhibi-
tion meta-analyses.
Finally, to examine other possible sources of hetero-
geneity, additional separate meta-analyses were carried
out for the studies that included only child and adol-
escent participants (11 studies) and the studies that
only included medication-naı
¨
ve participants (four
studies).
Results
Controls only
Consistent with current models of executive function
and prior work using the specific tasks selected by
ADHD investigators, our meta-analysis detected
significantly elevated probabilities of activation in a
distributed network of brain regions both in frontal
and posterior regions (see Table 2 and Figure 1).
Frontal regions showed significantly elevated prob-
abilities of activation in areas of anterior cingulate
cortex (BA 32, BA 24), left dorsolateral prefrontal
cortices (DLPFC)(BA 6, BA 8), and bilateral inferior
prefrontal cortices (BA 13, BA 45). Additionally, sig-
nificantly elevated probabilities of activation were
identified in right-sided thalamus, claustrum, insu-
lar cortex (BA 13), and striatum, as well as bilateral
sub-regions of parietal lobe (bilateral BA 7, right BA
40). An area of left occipital cortex (BA 19) was also
shown to have a significantly elevated probability of
activation.
ADHD only
For patients with ADHD, similar to controls, our
meta-analysis demonstrated a distributed pattern of
regions with significantly elevated probabilities of
activation (see Table 2 and Figure 1). With the
exception of two large clusters in the left middle
frontal gyrus, these clusters were generally smaller
and distributed over fewer structures than the
clusters identified in the ALE maps from controls.
Frontal regions showed significantly elevated prob-
abilities of activation in areas of bilateral middle
frontal gyrus, and left medial frontal lobe (bilateral
BA 10, left BA 46, and right BA 6). Activations in
anterior cingulate were particularly inconsistent,
with no cluster reaching significance. Significantly
elevated probabilities of activation in ventral pre-
frontal cortices were much more prominent in the
left hemisphere, unlike in controls where they were
detected bilaterally. Significantly elevated probabili-
ties of activation were detected in thalamus bilater-
ally and in the lentiform nucleus (portions of
putamen and globus pallidus) only on the right. A
significantly elevated probability of activation was
also noted in the posterior cerebellum on the right
which extended to the occipital lobe of patients with
ADHD.
Controls>ADHD
Consistent with models of hypofrontality, when the
individual ALE maps for the two groups (ADHD,
control) were compared statistically, controls dem-
onstrated significantly greater probability of activa-
tion in a variety of regions relative to patients with
ADHD, including bilateral areas of frontal lobe as
well as areas of parietal lobe, and parts of striatum
(see Table 2 and Figure 1). More specifically, areas of
left ventral prefrontal cortex and DLPFC (BA 6, BA 8,
BA 13, BA 44), anterior cingulate cortex (BA 24, BA
32), bilateral parietal lobe (bilateral BA 7, right BA
40), right thalamus, and left middle occipital gyrus
(BA 19) showed a significantly greater probability of
activation in controls than in patients with ADHD.
There was also a significantly greater probability of
activation in controls compared with patients with
ADHD in an area centered at the right claustrum,
covering 133 voxels, extending from insula (BA 13) to
striatum (see Figure 1).
ADHD>Controls
A few regions had a greater probability of activation
in patients with ADHD than in controls (see Table 2).
Within the left frontal lobe, greater probabilities of
activation were detected for insular cortex (BA 13)
and portions of middle frontal gyrus (BA 9, BA 10).
There was also an increased probability of activation
in the left thalamus and the right paracentral lobule
(BA 5).
Medication-naı
¨
ve patients only
Of the 16 studies included in our meta-analyses,
four examined the neural correlates of ADHD in
medication-naı
¨
ve patients (Pliszka et al., in press;
Rubia, Smith, Brammer, Toone, & Taylor, 2005; Silk
et al., 2005; Smith, Taylor, Brammer, Toone, & Ru-
bia, in press). Though not uncommon in the study of
psychiatric illness, the decision to include patients
with a history of prior psychotropic medication
treatment can limit a study’s ability to definitively
attribute group-differences to the underlying psycho-
pathology as opposed to past effects of treatment. In
order to gain some insight into the likelihood that
our findings could reflect past treatment with
psychotropic medications as opposed to ADHD, we
carried out an exploratory meta-analysis limited to
the four studies examining ADHD in medication-
naı
¨
ve patients. Though drastically less robust due to
small sample size, we once again noted a pattern of
1054 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
Table 2 Individual groups and group differences: all studies. Group differences: medication-naı
¨
ve participants. Regions of signi-
ficant elevated probability of activation
x y z Cluster size (voxels) p*
Controls only
Frontal lobe
Inferior frontal gyrus (BA 45)(L) )48 17 4 143 9.4 · 10
)3
Middle frontal gyrus (BA 8)(L) )34 17 46 74 9.4 · 10
)3
Insula (BA 13)(L) )34 25 13 65 8.5 · 10
)3
Middle frontal gyrus (BA 6)(L) )24 )1 43 44 9.8 · 10
)3
Insula (BA 13)(L) )40 )17 )7 34 8.2 · 10
)3
Middle frontal gyrus (BA 8)(L) )40 28 38 24 7.7 · 10
)3
Insula (BA 13)(R) 42 15 6 19 8.0 · 10
)3
Medial wall
Cingulate gyrus (BA 32)(L) )4 22 37 370 9.5 · 10
)3
Cingulate gyrus (BA 24)(L) )6 1 47 105 1.1 · 10
)3
Parietal lobe
Precuneus (BA 7)(L) )24 )53 51 191 1.0 · 10
)3
Parietal lobe (BA 40)(R) 28 )41 54 88 9.3 · 10
)3
Postcentral gyrus (BA 40)(R) 60 )21 19 42 8.8 · 10
)3
Superior parietal lobule (BA 7)(R) 26 )65 45 25 8.1 · 10
)3
Basal ganglia/thalamus
Claustrum(striatum/insula)(R)** 26 19 0 238 9.9 · 10
)3
Thalamus (R) 22 )28 1 76 8.8 · 10
)3
Occipital lobe
Middle occipital gyrus (BA 19)(L) )46 )60 )6 25 8.1 · 10
)3
ADHD only
Frontal lobe
Middle frontal gyrus (BA 46)(L) )40 14 20 827 9.9 · 10
)3
Middle frontal gyrus (BA 10)(L) )34 46 12 216 7.7 · 10
)3
Middle frontal gyrus (BA 6)(R) 36 3 38 77 8.1 · 10
)3
Medial frontal lobe (BA 10)(L) )16 47 )6 23 7.0 · 10
)3
Middle frontal gyrus (BA 10)(R) 36 38 5 17 6.9 · 10
)3
Parietal lobe
Paracentral lobule (BA 5)(R) 12 )34 48 62 8.6 · 10
)3
Precuneus (BA 7)(C) 0 )55 44 37 8.4 · 10
)3
Inferior parietal lobule (BA 40)(L) )34 )47 45 24 7.7 · 10
)3
Basal ganglia/thalamus
Thalamus (L) )12 )12 14 129 9.2 · 10
)3
Thalamus (R) 24 )24 13 63 7.3 · 10
)3
Lentiform nucleus (R) 10 3 1 40 7.7 · 10
)3
Cerebellum
Cerebellum posterior, declive (R) 36 )64 )15 141 8.1 · 10
)3
Controls>ADHD
Frontal lobe
Precentral gyrus (BA 44)(L) )50 15 6 73 9.5 · 10
)3
Middle frontal gyrus (BA 8)(L) )34 17 46 65 9.4 · 10
)3
Middle frontal gyrus (BA 6)(L) )24 )1 43 54 9.3 · 10
)3
Inferior frontal gyrus (BA 13)(L) )34 27 12 18 8.2 · 10
)3
Medial wall
Cingulate gyrus (BA 32)(L) )4 24 33 262 9.6 · 10
)3
Cingulate gyrus (BA 24)(L) )6 1 47 92 1.0 · 10
)2
Parietal lobe
Precuneus (BA 7)(L) )26 )53 53 121 1.0 · 10
)2
Parietal lobe (BA 40)(R) 30 )41 54 77 9.2 · 10
)3
Postcentral gyrus (BA 40)(R) 58 )21 19 32 8.6 · 10
)3
Superior parietal lobule (BA 7)(R) 26 )65 45 21 1.0 · 10
)4
Basal ganglia/thalamus
Thalamus (R) 22 )30 )1 37 8.6 · 10
)3
Claustrum(striatum/insula)(R)** 26 19 0 133 8.9 · 10
)3
Occipital lobe
Middle occipital gyrus (BA 19)(L) )46 )60 )6 26 7.9 · 10
)3
ADHD>Controls
Frontal lobe
Insula (BA 13)(L) )40 14 17 293 1.0 · 10
)2
Middle frontal gyrus (BA 9)(L) )42 9 35 30 8.6 · 10
)3
Middle frontal gyrus (BA 10)(L) )38 42 8 30 8.4 · 10
)3
Middle frontal gyrus (BA 9)(L) )32 20 33 21 9.0 · 10
)3
Parietal lobe
Paracentral lobule (BA 5)(R) 12 )32 50 38 9.2 · 10
)3
Meta-analysis of neuroimaging in ADHD 1055
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
significantly greater likelihood of activation in frontal
and parietal regions for controls than patients with
ADHD (see Table 2). Similar to our prior analyses, we
also noted a pattern of greater likelihood of activation
in some left prefrontal regions for patients with
ADHD relative to controls. As such, the medication-
(a)
(b)
(c)
! ylnOslortnoC
! yln
O
D
H
DA
slortnoC.svDHDA
DHDA>slortnoCslortnoC>DHDA
Figure 1 ALE meta-analyses across studies of ADHD and controls (a) Activation likelihood estimation (ALE) meta-
analyses revealed an extensive pattern of significantly elevated probabilities of activation in regions of frontal lobe
(bilaterally), medial wall, and right-sided striatum for Controls. (b) Patients with ADHD show more localized areas of
significantly elevated probabilities of activation, predominantly in left frontal lobe. (c) To better compare the groups, a
difference map of results for Controls vs. patients with ADHD is shown. For all images: x ¼ )6, y ¼ 13, z ¼ 1
respectively, p < .05 corrected
Table 2 Continued
x y z Cluster size (voxels) p*
Basal ganglia/thalamus
Thalamus (L) )12 )14 13 35 9.3 · 10
)3
Studies of medication-naı
¨
ve participants only:
Controls>ADHD
Frontal lobe
Insula (BA 13)(R) 42 15 6 73 6.6 · 10
)3
Medial frontal gyrus (BA 10)(R) 18 59 )5 18 5.5 · 10
)3
Precentral gyrus (BA 4)(L) )54 )11 27 17 5.7 · 10
)3
Medial wall
Cingulate gyrus (BA 32)(L) )2 26 27 17 5.5 · 10
)3
Medial frontal gyrus (BA 10) 0 59 )7 17 5.5 · 10
)3
Parietal lobe
Postcentral gyrus (BA 40)(R) 60 )22 17 83 7.4 · 10
)3
Inferior parietal lobule (BA 40)(L) )48 )44 42 18 5.7 · 10
)3
ADHD>Controls
Frontal lobe
Middle frontal gyrus (BA 9)(L) )38 16 22 179 6.7 · 10
)3
Medial frontal gyrus (BA 10)(L) )16 47 )6 71 6.1 · 10
)3
Temporal lobe
Fusiform gyrus (BA 37)(R) 42 )58 )10 20 6.0 · 10
)3
BA ¼ Brodmann area, L ¼ left, R ¼ right, cluster threshold ¼ 16 voxels.
*False Discovery Rate (FDR) corrected p values.
**Area centered at claustrum extended from striatum to insula.
1056 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
naı
¨
ve only analysis does not support the notion that
our findings in the primary meta-analyses reflect the
impact of medications as opposed to ADHD. Admit-
tedly, caution should be taken in interpreting the
findings of the medication-naı
¨
ve meta-analysis, as
the low number of studies (n ¼ 4) clearly limits our
ability to detect group differences and potentially
decreases the stability of the findings.
Response inhibition only
In ALE meta-analyses generated using only studies
that specifically examined response inhibition, a
more restricted pattern of increased likelihood of
activations was seen for controls compared to sub-
jects with ADHD in the prefrontal cortices bilaterally
(left BA 47 and BA 6, right BA 44), cingulate cortex
(BA 24), left parietal lobe (BA 7), and right caudate
(see Table 3). There were only two areas in which
ADHD subjects had significantly more likely activa-
tions than controls, the medial frontal gyrus (BA 10)
and the right paracentral lobule (BA 5).
Executive processes excluding response inhibition
ALE meta-analyses generated by excluding those
studies examining response inhibition specifically
revealed a pattern of increased likelihood of activa-
tions seen for controls compared to subjects with
ADHD which mirrored the results from the meta-
analyses of the total combined studies except for a
lack of significantly greater likelihood of activation in
the right inferior frontal lobe (a complete table of
results for this subset of analyses is available upon
request from the corresponding author). Controls
were significantly more likely to have activations in
areas of the left frontal lobe (BA 8, BA 9, BA 13),
cingulate cortex (BA 10, BA 24, BA 32), bilateral
parietal lobe (bilateral BA 7, right BA 5, left BA 3),
right lentiform nucleus, right thalamus, as well as
Table 3 Individual groups and group differences: response inhibition only. Regions of significant elevated probability of activation
x y z Cluster size (voxels) p*
Controls only
Frontal lobe
Inferior frontal gyrus (BA 47)(L) )44 19 2 104 6.8 · 10
)3
Precentral gyrus (BA 6)(L) )38 1 34 77 6.0 · 10
)3
Insula (BA 13)(R) 42 15 6 65 6.7 · 10
)3
Insula (BA 13)(L) )42 6 17 53 6.2 · 10
)3
Medial wall
Cingulate (BA 32) 0 12 35 18 5.9 · 10
)3
Basal ganglia/striatum
Caudate (R) 14 2 23 37 6.0 · 10
)3
ADHD only
Frontal lobe
Paracentral lobule (BA 5)(R) 12 )34 49 140 7.2 · 10
)3
Insula (BA 13)(L) )38 12 19 57 6.3 · 10
)3
Inferior frontal gyrus (BA 47)(R) 34 24 )7 21 5.5 · 10
)3
Inferior frontal gyrus (BA 47)(L) )32 25 )2 20 5.8 · 10
)3
Insula/Claustrum (L) )30 17 6 20 5.8 · 10
)3
Medial wall
Medial frontal gyrus (BA 9)(R) 4 45 15 16 5.6 · 10
)3
Temporal lobe
Superior temporal gyrus (BA 39)(R) 40 )58 28 18 5.6 · 10
)3
Cerebellum
Cerebellum posterior, declive (R) 34 )68 )19 17 5.6 · 10
)3
Controls>ADHD
Frontal lobe
Inferior frontal gyrus (BA 47)(L) )44 19 2 78 6.5 · 10
)3
Precentral gyrus (BA 44)(R) 44 15 6 67 6.6 · 10
)3
Precentral gyrus (BA 6)(L) )34 )1 38 22 6.0 · 10
)3
Medial wall
Cingulate gyrus (BA 24)(R) 2 9 34 36 6.0 · 10
)3
Parietal lobe
Superior parietal lobe (BA 7)(L) )42 )61 55 16 1.0 · 10
)3
Basal ganglia
Caudate (body) (R) 14 0 23 40 5.9 · 10
)3
ADHD>Controls
Frontal lobe
Medial frontal gyrus (BA 10)(L) )14 51 )7 16 1.0 · 10
)3
Parietal lobe
Paracentral lobule (BA 5)(R) 12 )34 48 132 7.2 · 10
)3
BA ¼ Brodmann area, L ¼ left, R ¼ right, cluster threshold ¼ 16 voxels.
*False Discovery Rate (FDR) corrected p values.
Meta-analysis of neuroimaging in ADHD
1057
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
areas of the left occipital cortex (BA 18, BA 19) and
sub-cortical areas of the right temporal lobe (BA 37).
Subjects with ADHD showed significantly higher
probability of activation than controls in areas of left
frontal lobe in insula (BA 13) and middle frontal gy-
rus (BA 9, BA 10) and right precentral gyrus (BA 9),
along with bilateral thalamus and right lentiform
nucleus.
Children and adolescents only
While most studies included in our meta-analyses
focused on ADHD in children and adolescents, five
studies examine ADHD in adult populations instead
(Bush et al., 1999; Ernst et al., 2003; Schweitzer et al.,
2000, 2004; Valera, Faraone, Biederman, Poldrack, &
Seidman, 2005). These studies were included in our
analyses in order to maximize our ability to detect
ADHD-related differences across studies. However,
one possible risk of this approach is that the neural
correlates of ADHD in adults may differ to some de-
gree from the findings in children and adolescents,
resulting in the introduction of unintended hetero-
geneity that may decrease our ability to detect ADHD-
related differences. In order to address this concern,
all meta-analyses were repeated with the five adult
studies excluded (all of which were non-inhibition
studies). Removal of the heterogeneity introduced by
the adult studies did not result in detection of addi-
tional regions reflecting group differences.
Overall, the results of our meta-analyses excluding
adult studies were highly similar to those obtained
when the adult studies were included, though
markedly less robust. Such reductions in effect size
were expected due to a smaller sample size (see
Table 4 and Figure 2). While most group differences
remained detectable at p < .05 corrected (see
Table 4), a more lenient threshold of p < .005 un-
corrected was employed in Figure 2 to demonstrate
the high degree of similarity to overall (adult + child/
adolescent) group differences. While these findings
do not exclude the possibility that the neural corre-
lates of ADHD may differ in children and adolescents
versus adults, they do suggest a reasonable degree of
overlap.
Discussion
In line with models implicating frontal lobe dys-
function in ADHD, our meta-analyses provided
objective, unbiased evidence of a consistent pattern
of frontal hypoactivity in patients with ADHD com-
pared to controls across 16 peer-reviewed neuro-
imaging studies. The frontal hypoactivity noted in
patients with ADHD is widely distributed, affecting
anterior cingulate, dorsolateral prefrontal, inferior
prefrontal, and orbitofrontal cortices, as well as
related regions, such as portions of the basal ganglia
and parietal cortices.
While our findings are primarily centered in frontal
regions, they should not be over-interpreted as sug-
gesting that frontal dysfunction alone underlies
ADHD. All of the tasks from studies included in our
meta-analysis were designed to isolate executive
processes, which are primarily supported by fronto-
striatal and fronto-parietal neural networks. Despite
differences in the specific executive process being
examined across studies, our findings confirm the
effectiveness of such an approach in detecting group
differences in a set of frontal regions common to the
processes examined. Of course, our findings may be
an underestimate for some individual processes that
may uniquely activate a broader range of regions.
Evidence for this comes from our findings of ADHD-
related caudate hypofunction in the response-inhi-
bition-only meta-analysis, but not in the larger
combined meta-analysis. To fully establish the pri-
macy of frontal dysfunction in ADHD, future studies
will need to provide a more comprehensive exam-
ination of executive function, as well as other cog-
nitive domains, using tasks known to produce
consistent patterns of activity in other regions con-
sidered putative sources of dysfunction (e.g., cere-
bellum, ventral striatum, parietal cortices). In the
absence of such a literature, we can only conclude
that robust evidence of frontal hypofunction exists in
ADHD.
Given the prominence of response-inhibition tasks
in the ADHD literature, the present work provided a
separate analysis focusing on this subset of studies
alone, once again finding evidence of frontal hypo-
function, albeit less widespread. More specifically, a
meta-analysis carried out across ten contrasts iso-
lating response inhibition revealed relatively small
areas of frontal hypofunction within inferior frontal,
medial wall regions (including anterior cingulate
cortex) and the precentral gyrus. The limited findings
for the meta-analysis focusing on inhibition alone
may reflect the smaller number of studies included,
though ten contrasts should be enough to obtain
relatively robust findings based on prior work.
Alternatively, the suggestion that response inhibition
relies on a fairly focal network within the brain (Aron
et al., 2005) may be responsible for the sparse re-
sults found here. In order to further examine this
possibility, we carried out a meta-analysis of the
non-inhibition studies alone, finding a highly similar
pattern to the combined meta-analysis, suggesting
that the findings for non-inhibition studies tend to
be more robust than those focusing on response
inhibition.
With respect to further examination of frontal
dysfunction in ADHD, we believe that a recent dis-
tinction between types of executive function may be
usefully explored in future efforts. Zelazo and col-
leagues differentiate between ‘hot’ (i.e., affective) and
‘cool’ (i.e., non-affective) subtypes of executive func-
tion, involving orbitofrontal and dorsolateral pre-
frontal areas, respectively. As reviewed elsewhere
1058 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
(Castellanos, Sonuga-Barke, Milham, & Tannock,
2006), despite the emphasis of the current ADHD
literature on deficits in ‘cool’ executive function, a
large-scale meta-analysis of behavioral studies
involving executive function found that while statis-
tically significant, effect sizes of these deficits were
modest at best (Willcutt et al., 2005). Of note, group
differences extending in the present meta-analysis
extended into left orbitofrontal cortex, a putative ‘hot’
area, despite the tendency of most studies to use
tasks designed to assess the ‘cool’ DLPFC and cin-
gulate regions. These findings support the expansion
of the scope of experimental paradigms to expressly
target regions associated with ‘hot’ executive func-
tion and affectively laden processing, such as the
orbitofrontal cortices and related ventral striatal
areas.
Although a consistent theme in the current ADHD
literature, some caution should be taken in making
inferences about the significance of ‘frontal hypo-
activity.’ While typically thought to reflect a decrease
in the intensity of activation in a particular region, it
may also reflect decreases in the spatial extent of
activations, more spatial dispersion of activations,
decreases in functional connectivity, or more statis-
tical noise (possibly due to factors such as more
Table 4 Group differences: child/adolescent participants only. Regions of significant elevated probability of activation
x y z Cluster size (voxels) p*
Controls>ADHD
Frontal lobe
Middle frontal gyrus (BA 9)(R) 50 18 29 34 5.0 · 10
)3
Inferior frontal gyrus (BA 47)(L) )24 28 )9 31 5.1 · 10
)3
Superior frontal gyrus (BA 6)(R) 20 )3 71 28 5.3 · 10
)3
Inferior frontal gyrus (BA 9)(R) 52 4 30 27 5.3 · 10
)3
Precentral gyrus (BA 4)(L) )34 )27 56 26 5.3 · 10
)3
Middle frontal gyrus (BA 6)(L) )20 )2 56 24 5.3 · 10
)3
Middle frontal gyrus (BA 10)(L) )42 41 23 22 5.4 · 10
)3
Insula (BA 13)(L) )34 25 9 21 5.5 · 10
)3
Superior frontal gyrus (BA 6)(L) )4 12 57 21 5.4 · 10
)3
Superior frontal gyrus (BA 8)(L) )2 30 44 21 5.3 · 10
)3
Medial wall
Cingulate (BA 31) 0 )24 39 29 5.3 · 10
)3
Cingulate gyrus (BA 24)(R) 12 )4 49 24 5.4 · 10
)3
Medial frontal gyrus (BA 6)(L) )6 0 49 21 5.5 · 10
)3
Parietal lobe
Precuneus (BA 7)(L) )16 )71 50 33 5.1 · 10
)3
Postcentral gyrus (BA 3)(R) 38 )28 52 31 5.1 · 10
)3
Superior parietal lobule (BA 5)(L) )18 )39 60 28 5.2 · 10
)3
Precuneus (BA 7)(L) )8 )63 39 26 5.4 · 10
)3
Postcentral gyrus (BA 3)(R) 24 )35 52 25 5.4 · 10
)3
Superior parietal lobule (BA7)(R) 26 )65 48 25 5.3 · 10
)3
Basal ganglia
Lentiform nucleus (R) 24 15 0 21 5.5 · 10
)3
Occipital lobe
Middle occipital gyrus (BA 19)(L) )28 )78 12 76 5.6 · 10
)3
Temporal lobe
Superior temporal gyrus (BA 22)(L) )50 )47 14 32 5.3 · 10
)3
ADHD>Controls
Frontal lobe
Inferior frontal gyrus (BA 9)(L) )44 16 22 201 5.8 · 10
)3
Middle frontal gyrus (BA 10)(L) )38 44 8 109 6.0 · 10
)3
Middle frontal gyrus (BA 9)(L) )30 22 33 32 5.1 · 10
)3
Middle frontal gyrus (BA 46)(R) 38 40 3 31 5.1 · 10
)3
Precentral gyrus (BA 9)(R) 36 5 36 27 5.2 · 10
)3
Inferior frontal gyrus (BA 44)(R) 52 10 15 25 5.4 · 10
)3
Middle frontal gyrus (BA 6)(L) )26 14 57 21 5.1 · 10
)3
Medial wall
Anterior cingulate gyrus (BA 32)(R) 6 35 19 25 5.3 · 10
)3
Medial frontal gyrus (BA 8)(L) )2 19 46 25 5.1 · 10
)3
Parietal lobe
Postcentral gyrus (BA 2)(R) 40 )25 32 25 5.3 · 10
)3
Basal ganglia
Caudate (R) 6 17 15 31 5.2 · 10
)3
Lentiform nucleus (R) 10 1 ) 1 16 5.6 · 10
)3
Temporal lobe
Superior temporal gyrus (BA 38)(L) )40 6 )15 18 5.5 · 10
)3
BA ¼ Brodmann area, L ¼ left, R ¼ right, cluster threshold ¼ 16 voxels.
*False Discovery Rate (FDR) corrected p values.
Meta-analysis of neuroimaging in ADHD
1059
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
variable responses in or greater motion in patients).
Similarly, it is important to note that our analyses
suggested that some regions show locally greater
activations for patients with ADHD when compared
to controls, suggesting that ADHD is not purely
accounted for by hypofunction. These increases may
reflect compensatory recruitment of accessory brain
regions to accomplish a given cognitive task or
alternatively an abnormally increased activation that
interferes with typically recruited brain regions.
Further work is merited to clarify the nature of both
findings of decreased and increased activity associ-
ated with ADHD.
Despite our success in demonstrating consisten-
cies in findings across studies, specific methodo-
logical limitations should be considered with respect
to the current functional neuroimaging literature
examining ADHD. First, only nine of the studies in-
cluded in this meta-analysis reported direct com-
parisons between participants with ADHD and
healthy controls and those that do provide such di-
rect comparisons tend to use small sample sizes,
thereby limiting their ability to detect subtle be-
tween-group differences. By creating a difference
map between ALE estimates of participants with
ADHD and controls we were able to include the seven
studies that did not report direct comparisons and
thus create a meta-analysis across all 16 of the
studies in the literature that examined cognitive
constructs reported results for individual participant
groups and reported results in stereotactic coordi-
nates. Second, multiple studies in the literature did
not report their results in standard 3-D stereotactic
coordinates, and only reported their findings in
terms of anatomically determined regions of interest.
Without the use of standardized 3-D coordinates,
comparisons between these studies must rely on
more subjective approaches and cannot be included
in voxel-based, statistical meta-analyses such as the
present study. Despite exclusion of these studies,
our results are broadly consistent with those of
qualitative reviews that included both studies with
and without stereotactic coordinates. Finally, an-
other related concern is that even studies reporting
stereotactic coordinates focused their results based
on regions of interest rather than on whole brain
analyses, potentially missing other putative sources
of dysfunction, such as cerebellum and ventral stri-
atum.
Variability in statistical approaches employed in
the literature is another potential methodological
concern, in particular with respect to corrections for
multiple comparisons and threshold selection. These
differences impact rates for false positives and false
negatives. ALE addresses this issue by weighting the
findings of each peer-reviewed study equally and
relying upon patterns of consistency across studies
to overcome this concern. Two notable risks of this
approach are that studies with less stringent
thresholds can introduce a larger number of foci,
and that higher- and lower-powered studies have
equal weighting. In our meta-analyses, such con-
founds are not more likely to impact one group than
the other, as we only included studies that provide
results for patients with ADHD and controls. In a
study with a lenient threshold, both groups will
benefit and have a greater number of foci, thus
nullifying the effect on group differences. Further-
more, as a test of the possibility that differences in
number of foci alone may be driving our group dif-
ferences, we matched the two groups for number of
foci by randomly selecting and dropping foci from
the control group and reran the meta-analyses (Laird
et al., 2005a). No marked differences in the pattern
of results were observed.
Finally, the presence of substantial heterogeneity
with regard to study samples is another notable
limitation of the current literature. In particular,
differences in the age ranges examined across stud-
ies may be a source of concern. Both age-related
differences in the expression of ADHD symptoms
and potential age-related differences in patterns of
functional activity can complicate the integration of
findings across studies. At present, there are not yet
a sufficient number of studies of ADHD in either
adult or pediatric populations to distinguish age-
slortnoC.svDHDA
DH
DAvs.
s
l
or
tnoC
s
lo
r
tnoC
>
DHDA
Figure 2 ALE meta-analyses for child and adolescent participants only. While similar in distribution to meta-ana-
lyses including both adults and children, results from the child-only analyses were less robust. For all images: x ¼
)5, y ¼ 13, z ¼ 1 respectively, p < .005 uncorrected
1060 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
related or developmental effects. Other notable
sources of heterogeneity include medication history,
ADHD subtypes, medication status during imaging,
and age of onset. Despite these multiple sources of
heterogeneity, our meta-analyses suggests that
commonalities across patients with ADHD in the
range of tasks examined to date are not obliterated
by the undoubted heterogeneity encompassed within
this diagnostic category.
In conclusion, our findings draw attention to the
presence of consistent differences in the neural
substrates of cognitive function between partici-
pants with and without a diagnosis of ADHD across
studies. This has significant implications for future
studies of the neuropathophysiology of ADHD,
highlighting consistently identified regions across
studies. The results of this meta-analysis do not
support simpler models which posit that ADHD is
strictly a disorder resulting from deficits of activity in
a few isolated brain regions. Thus, a continued
examination of the cognitive substrates of ADHD is
needed.
Acknowledgements
This work was supported by the National Institute of
Mental Health (T32 MH 067763).
The authors would like to thank Dr. Michael Sey-
ffert, Dr. Amy Krain, Dr. Clare Kelly, and Dr. Manely
Ghaffari for their assistance in the preparation of the
manuscript.
Correspondence to
Michael P. Milham, NYU Child Study Center, 14th
Floor, 215 Lexington Avenue, New York, NY 10016,
USA; E-mail: milham01@med.nyu.edu
References
Anderson, C.M., Polcari, A., Lowen, S.B., Renshaw,
P.F., & Teicher, M.H. (2002). Effects of methylpheni-
date on functional magnetic resonance relaxometry of
the cerebellar vermis in boys with ADHD. American
Journal of Psychiatry, 159, 1322–1328.
Aron, A.R., & Poldrack, R.A. (2005). The cognitive
neuroscience of response inhibition: Relevance for
genetic research in attention-deficit/hyperactivity
disorder. Biological Psychiatry, 57, 1285–1292.
Barkley, R.A. (1997). Behavioral inhibition, sustained
attention, and executive functions: Constructing a
unifying theory of ADHD. Psychological Bulletin, 121,
65–94.
Booth, J.R., Burman, D.D., Meyer, J.R., Lei, Z., Trom-
mer, B.L., Davenport, N.D. et al. (2005). Larger def-
icits in brain networks for response inhibition than
for visual selective attention in attention deficit
hyperactivity disorder (ADHD). Journal of Child Psy-
chology and Psychiatry, 46, 94–111.
Bush, G., Frazier, J.A., Rauch, S.L., Seidman, L.J.,
Whalen, P.J., Jenike, M.A., et al. (1999). Anterior
cingulate cortex dysfunction in attention-deficit/
hyperactivity disorder revealed by fMRI and the
counting stroop. Biological Psychiatry, 45, 1542–
1552.
Bush, G., Valera, E.M., & Seidman, L.J. (2005).
Functional neuroimaging of attention-deficit/hyper-
activity disorder: A review and suggested future
directions. Biological Psychiatry, 57, 1273–1284.
Castellanos, F.X., Sonuga-Barke, E.J., Milham, M.P., &
Tannock, R. (2006). Characterizing cognition in
ADHD: Beyond executive dysfunction. Trends in
Cognitive Sciences, 10, 117–123.
Castellanos, F.X., & Tannock, R. (2002). Neuroscience
of attention-deficit/hyperactivity disorder: The
search for endophenotypes. Nature Reviews Neuro-
science, 3, 617–628.
Durston, S., Tottenham, N.T., Thomas, K.M., Davidson,
M.C., Eigsti, I.M., Yang, Y.H., et al. (2003). Differen-
tial patterns of striatal activation in young children
with and without ADHD. Biological Psychiatry, 53,
871–878.
Ernst, M., Cohen, R.M., Liebenauer, L.L., Jons, P. H., &
Zametkin, A.J. (1997). Cerebral glucose metabolism
in adolescent girls with attention-deficit/hyperactiv-
ity disorder. Journal of the American Academy of
Child and Adolescent Psychiatry, 36 , 1399–1406.
Ernst, M., Kimes, A.S., London, E.D., Matochik, J.A.,
Eldreth, D., Tata, S., et al. (2003). Neural substrates
of decision making in adults with attention deficit
hyperactivity disorder. American Journal of Psychia-
try, 160, 1061–1070.
Ernst, M., Liebenauer, L.L., King, A.C., Fitzgerald, G.A.,
Cohen, R.M., & Zametkin, A. J. (1994). Reduced brain
metabolism in hyperactive girls. Journal of the Amer-
ican Academy of Child and Adolescent Psychiatry, 33,
858–868.
Laird, A.R., Fox, P.M., Price, C.J., Glahn, D.C., Uecker,
A.M., Lancaster, J.L., et al. (2005a). ALE meta-ana-
lysis: Controlling the false discovery rate and per-
forming statistical contrasts. Human Brain Mapping,
25, 155–164.
Laird, A.R., McMillan, K.M., Lancaster, J.L., Kochunov,
P., Turkeltaub, P.E., Pardo, J.V., et al. (2005b). A
comparison of label-based review and ALE meta-
analysis in the stroop task. Human Brain Mapping,
25, 6–21.
Lancaster, J.L., Laird, A.R., Fox, P.M., Glahn, D.E., &
Fox, P.T. (2005). Automated analysis of meta-analysis
networks. Human Brain Mapping, 25, 174–184.
Miller, E.K., & Cohen, J.D. (2001). An integrative theory
of prefrontal cortex function. Annual Review of
Neuroscience, 24, 167–202.
Pliszka, S.R., Glahn, D.C., Semrud-Clikeman, M.,
Franklin, C., Perez, R., Xiong, J., et al. (2006) Neu-
roimaging of inhibitory control areas in children with
attention deficit hyperactivity disorder who were
threatment niave or in long-term treatment. American
Journal of Psychiatry, 163, 1052–1060.
Rubia, K., Overmeyer, S., Taylor, E., Brammer, M.,
Williams, S.C.R., Simmons, A., et al. (1999). Hypo-
frontality in attention deficit hyperactivity disorder
during higher-order motor control: A study with
Meta-analysis of neuroimaging in ADHD 1061
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
functional MRI. American Journal of Psychiatry, 156,
891–896.
Rubia, K., Smith, A.B., Brammer, M.J., Toone, B., &
Taylor, E. (2005). Abnormal brain activation during
inhibition and error detection in medication-naive
adolescents with ADHD. American Journal of Psy-
chiatry, 162, 1067–1075.
Rubia, K., Taylor, E., Smith, A.B., Oksannen, H.,
Overmeyer, S., & Newman, S. (2001). Neuropsycho-
logical analyses of impulsiveness in childhood hyper-
activity. British Journal of Psychiatry, 179, 138–143.
Schulz, K.P., Fan, J., Tang, C.Y., Newcorn, J.H., Buc-
hsbaum, M.S., Cheung, A.M., et al. (2004). Response
inhibition in adolescents diagnosed with attention
deficit hyperactivity disorder during childhood: An
event-related fMRI study. American Journal of
Psychiatry, 161, 1650–1657.
Schulz, K.P., Tang, C.Y., Fan, J., Marks, D.J., Cheung,
A.M., Newcorn, J.H., et al. (2005). Differential pre-
frontal cortex activation during inhibitory control in
adolescents with and without childhood attention-
deficit/hyperactivity disorder. Neuropsychology, 19,
390–402.
Schweitzer, J.B., Faber, T.L., Grafton, S.T., Tune, L.E.,
Hoffman, J.M., & Kilts, C. D. (2000). Alterations in
the functional anatomy of working memory in adult
attention deficit hyperactivity disorder. American
Journal of Psychiatry, 157, 278–280.
Schweitzer, J.B., Lee, D.O., Hanford, R.B., Tagamets,
M.A., Hoffman, J.M., Grafton, S. T., et al. (2003). A
positron emission tomography study of methylpheni-
date in adults with ADHD: Alterations in resting blood
flow and predicting treatment response. Neuropsy-
chopharmacology, 28, 967–973.
Schweitzer, J.B., Lee, D.O., Hanford, R.B., Zink, C.F.,
Ely, T.D., Tagamets, M.A., et al. (2004). Effect of
methylphenidate on executive functioning in adults
with attention-deficit/hyperactivity disorder: Normal-
ization of behavior but not related brain activity.
Biological Psychiatry, 56, 597–606.
Seidman, L.J., Valera, E.M., & Makris, N. (2005).
Structural brain imaging of attention-deficit/hyper-
activity disorder. Biological Psychiatry, 57, 1263–
1272.
Silk, T., Vance, A., Rinehart, N., Egan, G., O’Boyle, M.,
Bradshaw, J. L., et al. (2005). Fronto-parietal activa-
tion in attention-deficit hyperactivity disorder, com-
bined type: Functional magnetic resonance imaging
study. British Journal of Psychiatry, 187, 282–283.
Smith, A.B., Taylor, E., Brammer, M., Toone, B., &
Rubia, K. (2006). Task specific hypoactivation in
prefrontal and temporo-parietal brain regions during
motor inhibition and task switching in medication-
naive children and adolesents with attention deficit
hyperactivity disorder. American Journal of Psychi-
atry, 163, 1044–1051.
Stuss, D.T., Murphy, K.J., Binns, M.A., & Alexander,
M.P. (2003). Staying on the job: The frontal lobes
control individual performance variability. Brain,
126, 2363–2380.
Sunshine, J.L., Lewin, J.S., Wu, D.H., Miller, D.A.,
Findling, R.L., Manos, M.J., et al. (1997). Functional
MR to localize sustained visual attention activation in
patients with attention deficit hyperactivity disorder:
A pilot study. American Journal of Neuroradiology, 18,
633–637.
Tamm, L., Menon, V., & Reiss, A.L. (2006). Parietal
attentional system aberrations during target detec-
tion in adolescents with attention-deficit/hyperactiv-
ity disorder: Event-related fMRI evidence. American
Journal of Psychiatry, 163, 1033–1043.
Tamm, L., Menon, V., Ringel, J., & Reiss, A.L. (2004).
Event-related fMRI evidence of frontotemporal invol-
vement in aberrant response inhibition and task
switching in attention-deficit/hyperactivity disorder.
Journal of the American Academy of Child and
Adolescent Psychiatry, 43, 1430–1440.
Teicher, M.H., Anderson, C.M., Polcari, A., Glod, C.A.,
Maas, L.C., & Renshaw, P.F. (2000). Functional
deficits in basal ganglia of children with attention-
deficit/hyperactivity disorder shown with functional
magnetic resonance imaging relaxometry. Nature
Medicine, 6, 470–473.
Turkeltaub, P.E., Eden, G.F., Jones, K.M., & Zeffiro,
T.A. (2002). Meta-analysis of the functional neuro-
anatomy of single-word reading: Method and valida-
tion. NeuroImage, 16, 765–780.
Vaidya, C.J., Austin, G., Kirkorian, G., Ridlehuber,
H.W., Desmond, J.E., Glover, G.H., et al. (1998).
Selective effects of methylphenidate in attention defi-
cit hyperactivity disorder: A functional magnetic
resonance study. Proceedings of the National Acad-
emy of Sciences of the United States of America, 95,
14494–14499.
Vaidya, C.J., Bunge, S.A., Dudukovic, N.M., & Zalecki,
C.A. (2005). Altered neural substrates of cognitive
control in childhood ADHD: Evidence from functional
magnetic resonance imaging. American Journal of
Psychiatry, 162, 1605–1613.
Valera, E.M., Faraone, S.V., Biederman, J., Poldrack,
R.A., & Seidman, L.J. (2005). Functional neuro-
anatomy of working memory in adults with attention-
deficit/hyperactivity disorder. Biological Psychiatry,
57, 439–447.
Wagner, A.D. (1999). Working memory contributions to
human learning and remembering. Neuron, 22, 19–
22.
Willcutt, E.G., Doyle, A.E., Nigg, J.T., Faraone, S.V., &
Pennington, B.F. (2005). Validity of the executive
function theory of attention-deficit/hyperactivity dis-
order: A meta-analytic review. Biological Psychiatry,
57, 1336–1346.
Zang, Y.F., Jin, Z., Weng, X.C., Zhang, L., Zeng, Y.W.,
Yang, L., et al. (2005). Functional MRI in attention-
deficit hyperactivity disorder: Evidence for hypofron-
tality. Brain and Development, 27, 544–550.
Manuscript accepted 1 June 2005
1062 Steven G. Dickstein et al.
2006 The Authors
Journal compilation 2006 Association for Child and Adolescent Mental Health.
    • "One of the most consistent findings from studies of anatomical connectivity, in children and adolescents with ADHD, is reduced fractional anisotropy (Hamilton et al., 2008; Konrad et al., 2010; Luders et al., 2009; Makris et al., 2008) of fronto-striatal tracts (within the cortico-striatal network) and fronto-parietal tracts (within the ventral and dorsal attention network). These findings have been supported by some (Cubillo, Halari, Smith, Taylor, & Rubia, 2012; Dickstein, Bannon, Castellanos, & Milham, 2006; Rubia, 2011) but not all studies of functional connectivity (Tian et al., 2006; Uddin et al., 2008). Studies of functional connectivity have employed standard, task-activation, fMRI (task-fMRI), or resting-state fMRI (rsfMRI ). "
    [Show abstract] [Hide abstract] ABSTRACT: Two core symptoms characterize Attention Deficit Hyperactivity Disorder (ADHD) subtypes: inattentiveness and hyperactivity-impulsivity. While previous brain imaging research investigated ADHD as if it was a homogenous condition, its two core symptoms may originate from different brain mechanisms. We, therefore, hypothesized that the functional connectivity of cortico-striatal and attentional networks would be different between ADHD subtypes. We studied 165 children (mean age 10.93 years; age range, 7-17 year old) diagnosed as having ADHD based on their revised Conner’s rating scale score and 170 typical developing individuals (mean age 11.46 years; age range, 7-17 year old) using resting state functional fMRI. Groups were matched for age, IQ and head motion during the MRI acquisition. We fractionated the ADHD group into predominantly inattentive, hyperactive-impulsive and combined subtypes based on their revised Conner’s rating scale score. We then analyzed differences in resting state functional connectivity of the cortico-striatal and attentional networks between these subtypes. We found a double dissociation of functional connectivity in the cortico-striatal and ventral attentional networks, reflecting the subtypes of the ADHD participants. Particularly, the hyperactive-impulsive subtype was associated with increased connectivity in cortico-striatal network, whereas the inattentive subtype was associated with increased connectivity in the right ventral attention network. Our study demonstrated for the first time a right lateralized, double dissociation between specific networks associated with hyperactivity-impulsivity and inattentiveness in ADHD children, providing a biological basis for exploring symptom dimensions and revealing potential targets for more personalized treatments.
    Full-text · Article · Jun 2016
    • "TD girls also activated the middle temporal gyrus (BA 21), a region activated in controls in the Durston et al. (2007) study [18], which investigated expectancy violation in ADHD, a condition similar to the incongruent situation of forethought. TD girls also activated the claustrum, a region highlighted by Dickstein et al. (2006) [11] as being more activated in controls compared to participants with ADHD during EF performance. According to Crick and Koch (2005) [45], this part of basal ganglia plays a regulatory role in consciousness and cognition. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective . The majority of studies investigating neurocognitive processing in attention deficit/hyperactivity disorder (ADHD) have been conducted on male participants. Few studies evaluated females or examined sex differences. Among various cognitive anomalies in ADHD, deficit in forethought seems particularly important as children with ADHD often fail to adequately use previous information in order to prepare for responses. The main goal of this study was to assess sex-specific differences in behavioral and neural correlates of forethought in youth with ADHD. Methods . 21 typically developing (TD) youth and 23 youth with ADHD were asked to judge whether two pictures told a congruent or incongruent story. Reaction time, performance accuracy, and cerebral activations were recorded during functional magnetic resonance imaging (fMRI). Results . Significant sex-specific differences in cerebral activations appeared, despite equivalent performance. Relative to the boys TD participants, boys with ADHD had extensive bilateral frontal and parietal hypoactivations, while girls with ADHD demonstrated more scattered hypoactivations in the right cerebral regions. Conclusion . Present results revealed that youth with ADHD exhibit reduced cerebral activations during forethought. Nevertheless, the pattern of deficits differed between boys and girls, suggesting the use of a different neurocognitive strategy. This emphasizes the importance of including both genders in the investigations of ADHD.
    Full-text · Article · Jan 2016
    • "Neural substrates associated with these deficits can be investigated with techniques such as functional magnetic resonance imaging (fMRI) (Bush et al., 2005). The neural circuitry supporting CC tasks in individuals with ADHD generally resembles that of control groups with differences emerging in regions such as inferior, middle, and superior frontal gyri, anterior cingulate cortex, and striatum (Dickstein et al., 2006; Bush, 2011; Cortese et al., 2012). Reduced brain activation in these regions is frequently reported in unmedicated children and adolescents with ADHD when compared to control groups (Rubia et al., 1999; Tamm et al., 2004; Booth et al., 2005; Rubia et al., 2011a), although greater activation also has been observed (Durston et al., 2003) and may indicate additional neural recruitment associated with augmented cognitive effort (Schulz et al., 2005; Fassbender and Schweitzer, 2006). "
    [Show abstract] [Hide abstract] ABSTRACT: While antisaccade paradigms invoke circuitry associated with cognitive control and attention-deficit/hyperactivity disorder (ADHD), there is a dearth of functional magnetic resonance imaging (fMRI) investigations using antisaccade tasks among children with ADHD. Neural correlates associated with antisaccade performance were examined with fMRI in 11 children with ADHD (10 medicated) matched to 11 typically developing children. Significantly greater brain activation in regions in right dorsolateral prefrontal cortex and caudate nucleus was observed in children with ADHD relative to the control group. This pattern separated the children into their respective groups in a taxonomic manner. Sensitivity analyses probing comorbidity and medication-specific effects showed that results were consistent; however, the caudate nucleus difference was only detectable in the full sample, or in subsets with a more relaxed cluster threshold. Antisaccade performance did not significantly differ between the groups, perhaps as a result of greater brain activation or medication effects in the ADHD group. Thus, antisaccade paradigms may have sensitivity and specificity for the investigation of cognitive control deficits and associated neural correlates in ADHD, and may contribute towards the development of new treatment approaches for children with the disorder.
    Article · Oct 2015
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