Special issue: Research report
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
Ana Cubillo, Rozmin Halari, Anna Smith, Eric Taylor and Katya Rubia*
Department of Child Psychiatry, Institute of Psychiatry, King’s College London, UK
a r t i c l e i n f o
Received 14 October 2010
Revised 12 January 2011
Accepted 11 April 2011
Published online 27 April 2011
a b s t r a c t
Attention Deficit Hyperactivity Disorder (ADHD) has long been associated with abnor-
malities in frontal brain regions. In this paper we review the current structural and
functional imaging evidence for abnormalities in children and adults with ADHD in fronto-
networks. While the imaging studies in children with ADHD are more numerous and
consistent, an increasing number of studies suggests that these structural and functional
abnormalities in fronto-cortical and fronto-subcortical networks persist into adulthood,
despite a relative symptomatic improvement in the adult form of the disorder.
We furthermore present new data that support the notion of a persistence of neurofunc-
tional deficits in adults with ADHD during attention and motivation functions. We show
that a group of medication-naı ¨ve young adults with ADHD behaviours who were followed
up 20 years from a childhood ADHD diagnosis show dysfunctions in lateral fronto-striato-
parietal regions relative to controls during sustained attention, as well as in ventromedial
orbitofrontal regions during reward, suggesting dysfunctions in cognitive-attentional as
well as motivational neural networks. The lateral fronto-striatal deficit findings, further-
more, were strikingly similar to those we have previously observed in children with ADHD
during the same task, reinforcing the notion of persistence of fronto-striatal dysfunctions
in adult ADHD. The ventromedial orbitofrontal deficits, however, were associated with
comorbid conduct disorder (CD), highlighting the potential confound of comorbid antiso-
cial conditions on paralimbic brain deficits in ADHD.
Our review supported by the new data therefore suggest that both adult and childhood
ADHD are associated with brain abnormalities in fronto-cortical and fronto-subcortical
systems that mediate the control of cognition and motivation. The brain deficits in
ADHD therefore appear to be multi-systemic and to persist throughout the lifespan.
and fronto-limbic regionsand
ª 2011 Elsevier Srl. All rights reserved.
* Corresponding author. Department of Child Psychiatry/SGDP, P046, King’s College London, Institute of Psychiatry, 16 De Crespigny Park,
London SE5 8AF, UK.
E-mail address: firstname.lastname@example.org (K. Rubia).
0010-9452/$ e see front matter ª 2011 Elsevier Srl. All rights reserved.
Journal homepage: www.elsevier.com/locate/cortex
cortex 48 (2012) 194e215
Attention Deficit Hyperactivity Disorder (ADHD) is charac-
manual-IV – DSM-IV)(AmericanPsychiatric Association, 1994).
with significant psychiatric comorbidities and mental health
problems in adult life (Faraone et al., 2007; Spencer et al., 2007;
Taylor et al., 1996). ADHD affects 3e8% school-aged children
(American Psychiatric Association, 1994; Froehlich et al., 2007)
Biederman et al., 2006), affecting 4% of the adult population
(Faraone and Biederman, 2005; Kessler et al., 2006).
and adults with ADHD
Neuropsychological deficits in children
Executive functions (EF) are defined as functions that are
necessary for mature adult goal-directed behaviour, such as
set-shifting and set maintenance, higher level and selective
attention, interference control, motor inhibition, integration
across space and time, planning, decision making, temporal
foresight and working memory (Stuss and Alexander, 2000). It
should be noted that we use the wider definition of EF that
includes attention functions as well as specific aspects of
temporal processing such as temporal foresight since they are
underlying basic functions for all goal-directed behaviours.
“Cool” EF are mediated by ventrolateral and dorsolateral
(DLPFC) fronto-striatal, fronto-cerebellar and fronto-parietal
neural networks. More recently, a differentiation has been
made between “cool” cognitive EF, typically elicited by rela-
tively abstract and descontextualized problems, and “hot”
of affect and motivation (Zelazo and Muller, 2002). Thus, “hot”
EF consist of tasks of reward-related decision making, reversal
of rewarded stimuluseresponse associations, temporal dis-
counting and other EF that are dependent on motivation and
reward. “Hot” EF are mediated by mesolimbic ventromedial
(VMPFC) and orbitofrontal (OFC)-striatal and limbic circuits
(Zelazo and Muller, 2002). In neuropsychological studies, as
a group, child and adult patients with ADHD have shown defi-
cits both in “cool” EF (Marchetta et al., 2008; Martinussen et al.,
2005; Rubia et al., 2001, 2007a; Sergeant et al., 2002; Valko et al.,
2010; Willcutt et al., 2005) and “hot” EF (Antrop et al., 2006;
Bitsakou et al., 2009; Dalen et al., 2004; Luman et al., 2005;
Marco et al., 2009; Sagvolden et al., 1998), for review see
(Rubia, 2010). Deficits have furthermore been observed in
temporal (for review see Rubia et al., 2009a) and perceptual
processes (Banaschewski et al., 2006; Boonstra et al., 2005).
However, while as a group ADHD children show impairments
in these functions, a proportion of ADHD children is not
impaired in any of these functions (Nigg et al., 2005; Sonuga-
Barke et al., 2010) and there are subgroups of children with
processes with only some of them having overlapping deficits
(Nigg et al., 2005; Sonuga-Barke et al., 2010). Different theoret-
ical approaches have attempted to explain this heterogeneity
suggesting multiple developmental pathways for ADHD
(Makris et al., 2009; Nigg and Casey, 2005; Sonuga-Barke et al.,
2010; Willcutt et al., 2005), with structural and functional
abnormalities in shared but dissociable functional networks
underlying the observed deficits (Makris et al., 2009).
childhood and adult ADHD
Structural and functional neuroimaging of
Using structural magnetic resonance imaging (sMRI), children
with ADHD relative to controls have shown consistent abnor-
malities in late developing fronto-striatal, fronto-temporo-
parietal and fronto-cerebellar networks. These brain regions
are known to mediate the above mentioned cognitive control
functions that are impaired in the disorder. Thus, reduced
volume and cortical thickness have been observed in several
frontal brain regions, in parieto-temporal areas, the basal
ganglia, posterior cingulate (PCC), the cerebellum and the
splenium of the corpus callosum (Batty et al., 2010; Carmona
et al., 2005, 2009; Castellanos et al., 2002; Mackie et al., 2007;
Shaw et al., 2006; for reviews see Krain and Castellanos, 2006;
Rubia, 2010). A meta-analysis of structural studies using
region of interest analyses showed that the largest volume
reductions in ADHD children relative to controls were in
several frontal brain regions, total and right cerebral volumes,
the corpus callosum and right caudate (Valera et al., 2007). A
morphometry studies in children with ADHD found that the
most consistent regional gray matter reduction in ADHD
pallidus (Ellison-Wright et al., 2008). Longitudinal imaging
studies have provided some evidence that the structural
abnormalities observed in children with ADHD compared to
may be due to a delay in structural maturation (Castellanos
et al., 2002; Shaw et al., 2007). The peak of cortical thickness
maturation was found to be delayed in ADHD children relative
to typical controls by an average of 3 years across all cortical
regions, with up to 4e5 years delay in frontal and temporal
areas, respectively (Shaw et al., 2007). This was further rein-
forced by findings that the rate of cortical thinning in these
regions, which is thought to mirror synaptic pruning and
reflect structural and cognitive maturation, has been shown to
be inversely associated with the severity of hyperactivity and
impulsiveness in normal development (Shaw et al., 2011).
Structural MRI studies in adult ADHD have observed
abnormalities in similar cortical brain regions, including
deficits in overall cortical gray matter, volumes and cortical
thickness of superior frontal and OFC, anterior cingulate
(ACC), Inferior frontal cortex (IFC), DLPFC, PCC, temporo-
parietal, cerebellar and occipital regions (Amico et al., 2010;
Biederman et al., 2008; Hesslinger et al., 2002; Makris et al.,
2010; Seidman et al., 2006), as well as in subcortical brain
areas including the caudate, nucleus accumbens and the
amygdala (Almeida Montes et al., 2010; Frodl et al., 2009;
cortex 48 (2012) 194e215
Seidman et al., 2006). However, there have also been negative
findings with respect to structural differences in frontal lobes,
basal ganglia, amygdala and hippocampus (Ahrendts et al., in
press; Amico et al., 2010; Depue et al., 2010b; Perlov et al.,
children and adults with ADHD compared to controls have
shown reduced white matter connectivity in fronto-striatal,
cingulate, as well as fronto-parietal, fronto-cerebellar and
parieto-occipital white matter tracts (Ashtari et al., 2005;
Davenport et al., 2010; Konrad et al., 2010; Makris et al., 2008;
Our recent meta-analysis of 14 whole-brain voxel-based
morphometry studies in children and adults with ADHD,
including in total 378 ADHD and 344 controls, showed that the
most consistent regional gray matter reduction in ADHD
patients compared to controls was in right lenticular nucleus,
including the caudate. A meta-regression analysis, however,
showed that the grey matter volume size was associated with
age, with volumes becoming progressively more normal in
older patients, resulting in a normalisation in the adult
subgroup. A meta-regression analysis on the effect of psy-
chostimulants showed that the morphological deficit was
a high proportion of stimulant-medicated patients no longer
showed the basal ganglia abnormalities (Nakao et al., in press).
3.2. Functional imaging studies
Functional magnetic resonance imaging (fMRI) studies have
provided evidence for the fronto-striatal deficit hypothesis of
ADHD in addition to providing evidence for wider deficits.
Thus, children with ADHD have shown underactivation rela-
tive to controls in the DLPFC/IFC, ACC, caudate, supplemen-
tary motor area (SMA) as well as in temporo-parietal cortices
during motor response inhibition (Booth et al., 2005; Durston
et al., 2003, 2006; Epstein et al., 2007; Pliszka et al., 2006;
Rubia et al., 1999, 2005, 2008, 2010b; Smith et al., 2006;
Suskaueret al., 2008a,2008b),
(Konrad et al., 2006; Rubia et al., 2009b, 2011; Vaidya et al.,
2005) as well as during vigilant, selective and flexible atten-
tion (Rubia et al., 2009b, 2009c, 2009d, 2010a, 2010b, 2011, in
press; Smith et al., 2006; Stevens et al., 2007; Tamm et al.,
2004, 2006; for meta-analysis and review see Dickstein et al.,
2006; and Rubia, 2010, respectively). Furthermore, during
tasks involving temporal processing, children with ADHD
haveshown reduced activation compared to controls in dorsal
and ventrolateral prefrontal cortex, SMA, ACC and cerebellum
(Durston et al., 2007; Rubia et al., 1999, 2001, 2009a; Smith
et al., 2008; Vloet et al., 2010). Using “hot” EF tasks, abnormal
activation has been observed in children with ADHD relative
to healthy controls in ventral striatum (VS) during reward
anticipation (Schereset al.,
a temporal discounting task (Rubia et al., 2009a), in precuneus,
PCC (Rubia et al., 2009c) and in OFC, temporal regions and
cerebellum during rewarded trials within a Continuous
Performance Task (CPT) (Rubia et al., 2009d). Very few fMRI
studies have tested for neurofunctional deficits during
emotion processing in ADHD. During fearful facial expression
processing, children with ADHD compared to healthy controls
have shown either no differences in brain activation (Marsh
et al., 2008) or enhanced activation in the amygdala
(Brotman et al., 2010), and during visualisation of negative
arousing pictures reduced activation was observed in insula,
basal ganglia and thalamus (Herpertz et al., 2008).
Fewer fMRI studies have been conducted in adult ADHD,
and findings are more inconsistent. This is likely due to the
impact of confounding factors, more pronounced in adult
compared to childhood ADHD imaging studies, such as small
sample sizes, high rates of comorbidity, long-term medication
history and the need for a retrospective diagnosis of ADHD in
childhood (Cubillo and Rubia, 2010). Adults with ADHD have
shown underactivation compared to controls in OFC, IFC,
DLPFC, ACC, striatal, premotor, parietal and cerebellar brain
press; Epstein et al., 2007; Schneider et al., 2010), inhibition of
memories (Depue et al., 2010a), working memory (Hale et al.,
2007; Valera et al., 2005, 2010a; Wolf et al., 2009), cognitive
switching (Cubillo et al., 2010; Dibbets et al., 2010) and senso-
rimotor timing (Valera et al., 2010b). Other studies, however,
observed increased activation in medial frontal, DLPFC, pre-
motor, parietal and occipital cortices during inhibitory and
working memory tasks (Banich et al., 2009; Dibbets et al., 2009,
for a review see Cubillo and Rubia, 2010). Functional abnor-
malities during reward-related tasks have been observed in
adults with ADHD in OFC and limbic regions. Thus, patients
compared to controls showed underactivation in VS during
gain anticipation in a monetary incentive delay task, but
increased activation in IFC, OFC, DLPFC and striatum during
gain outcome (Stro ¨hle et al., 2008). During temporal discount-
ing, reduced activation was found in VS and amygdala during
immediate choices whereas increased striatal and amygdala
activations were observed during delayed choices (Plichta
et al., 2009). During emotion processing tasks, medication-
naı ¨ve adultswith ADHDcomparedto healthy controls showed
underactivation in VS in response to unexpected positive
versus neutral pictures, and in subgenual cingulate in
response to unexpected negative versus neutral pictures
(Schlochtermeier et al., in press). Furthermore, symptom
severity has been found to be negatively correlated with key
regions, thus overlapping with areas observed to be under-
activated during motor and interference inhibition, switching
et al., 2010b; Valera et al., 2010a).
3.3. Functional connectivity
Recent evidence from functional connectivity fMRI studies
demonstrates that the functional abnormalities in ADHD not
only affect isolated brain regions but also the functional inter-
regional interconnectivity between these regions. Thus,
during the resting state, children and adults with ADHD
showed reduced functional connectivity relative to healthy
controls in fronto-striatal, cingulate, fronto-parietal, temporo-
parietal and fronto-cerebellar networks (Cao et al., 2006, 2009;
cortex 48 (2012) 194e215
Castellanos et al., 2008; Konrad et al., 2010; Uddin et al., 2008;
Zang et al., 2007; for review see Konrad and Eickhoff, 2010).
Some studies, however, also reported increased inter-regional
connectivity between ACC, striatum and temporo-cerebellar
regions (Tian et al., 2006; Wang et al., 2009; Zang et al., 2007;
Zhu et al., 2005). Reduced functional connectivity has been
observed in the context of cognitive tasks in children with
ADHD relative to controls between IFC and the basal ganglia,
parietal lobes and cerebellum, and between cerebellum,
parietal and striatal brain regions during sustained attention
(Rubia et al., 2009d), interference inhibition and time estima-
tion (Vloet et al., 2010).
In adults with ADHD, deficits in functional inter-regional
connectivity relative to healthy controls were observed
between right and left IFC, and between the right IFC and
other areas including basal ganglia, cingulate, parieto-
temporal and cerebellar regions during motor response inhi-
bition and working memory (Cubillo et al., 2010; Wolf et al.,
2009). In adults, however, there is also additional evidence
for compensatory increased connectivity between ACC,
superior frontal lobe and cerebellum (Wolf et al., 2009).
Neuropsychological evidence shows that children and adults
with ADHD are impaired in “cool” as well as “hot” EF. Struc-
tural and functional imaging studies show that these abnor-
malities in cognitive control are mediated by abnormalities in
lateral inferior and dorsolateral prefrontal as well as some
medial frontal regions such as rostral ACC and SMA and their
parieto-temporal areas. Weaknesses in motivation control as
measured in “hot” EF appear to be related to lateral orbito-
frontal and ventromedial prefrontal regions and their associ-
ated ventral striatal and limbic areas. While studies in
children with ADHD are more numerous and consistent, the
emerging evidence from imaging studies in adult ADHD
suggests that the structural and functional brain abnormali-
ties observed in children with ADHD persist into adult ADHD
in those who do not grow out of the disorder.
and orbitofrontal-ventromedial brain
dysfunction in adults with childhood ADHD and
persistent hyperactive/inattentive behaviours
during sustained attention and reward
New data: lateral inferior fronto-striatal
Despite the reported persistence of inattention problems in
adult ADHD (Biederman et al., 2000), and consistent evidence
for deficits in adult ADHD in tasks of sustained and selective
attention such as the CPT (Hervey et al., 2004; Marchetta et al.,
2008), few studies in adults with ADHD have focused on the
neuroimaging correlates of sustained and selective attention
functions.A few fMRI studies
co-measured higher executive selective attention functions
embedded within cognitive control tasks such as conflict
detection tasks or flexible attention during tasks of cognitive
flexibility. These studies show that adults with ADHD
compared to healthy controls have underactivation in
in adultADHD have
IFC/DLPFC, caudate (Cubillo et al., in press) and ACC (Burgess
et al., 2010; Bush et al., 1999) during selective attention/
conflict inhibition in interference inhibition tasks, and
decreased inferior fronto-striatal and parietal activation
during flexible attention in cognitive switching tasks (Cubillo
et al., 2010; Dibbets et al., 2010). However, hardly any studies
have tested for neurofunctional deficits during tasks that are
purposely designed to measure selective and sustained
attention such as continuous performance or target detection
tasks. Given that deficits in sustained attention as measured
in the CPT are one of the most consistent findings of the child
and adult ADHD literature (Epstein et al., 2001; Hervey et al.,
2004; Marchetta et al., 2008; Willcutt et al., 2005), it is
surprising that no fMRI study in adult ADHD has tested the
neurofunctional correlates of this function. Furthermore, the
findings between fMRI studies of the reward system in adults
with ADHD are inconsistent. While functional abnormalities
in ADHD adults relative to controls were observed in similar
regions across studies, in particular in VS, amygdala and
VMPFC/OFC cortex, the direction of some of these dysfunc-
tions have been different. For example, some studies observed
OFC underactivation (Dibbets et al., 2009) while others
observed OFC overactivation (Stro ¨hle et al., 2008). The incon-
sistent results are likely due to typical confounds in adult
ADHD imaging studies, such as small sample sizes, previous
medication history, presence of comorbidities and retrospec-
tive diagnosis of ADHD in childhood (Cubillo and Rubia, 2010).
To avoid these confounds, we recruited medication-naı ¨ve
adults from an ongoing longitudinal study, with a confirmed
diagnosis of childhood ADHD, who were followed up 20 years
into adulthood, where they showed persistent symptoms of
inattention/hyperactivity. We aimed to investigate the neural
correlates of the interaction between “hot”, motivational and
“cool” cognitive processes within a rewarded CPT task that
measured the effect of reward upon sustained attention.
Furthermore, we aimed to investigate whether these adult
patients present similar functional abnormalities as those
observed previously in children with ADHD during the same
task (Rubia et al., 2009c, 2009d). Motivation is particularly
relevant for attention processes and motivation and attention
are hence closely interrelated. Reward and enhanced arousal
states have shown to potentiate selective (Engelmann and
Pessoa, 2007; Krawczyk et al., 2007; Lang et al., 1990;
Rothermund et al., 2001) and sustained attention functions
(Tomporowskiand Tinsley, 1996). Functional MRI studies have
demonstrated that motivation in the form of reward can
enhance activation within brain regions that mediate arousal
and selective attention such as ventrolateral prefrontal, pari-
etal and PCC cortices (Lang et al., 1990; Mohanty et al., 2008;
Pochon et al., 2002; Rothermund et al., 2001; Small et al.,
2005). Furthermore, we have shown that in healthy adoles-
cents and adults, reward within the CPT task further upre-
gulates the same inferior fronto-striatal and temporo-parietal
regions that mediate sustained attention under the non-
rewarded condition (Smith et al., 2011). In healthy children
and adults, sustained attention in the same and similar tasks
activates inferior frontal, striatal, temporal and parietal
regions and cerebellum (Lawrence et al., 2003; Smith et al.,
2011; Tana et al., 2010; Voisin et al., 2006) and elicits under-
activation in these regions in children with ADHD (Rubia et al.,
cortex 48 (2012) 194e215
2009c, 2009d). During the rewarded condition, healthy adults
activate VMPFC/OFC, ACC, striatal and temporo-parietal
regions (Smith et al., 2011), while children with ADHD show
abnormal activation relative to controls in PCC and precuneus
(Rubia et al., 2009c), cerebellum, OFC and temporal regions
(Rubia et al., 2009d).
We hypothesised that medication-naı ¨ve adults with
a confirmed childhood diagnosis of ADHD and persistent
hyperactive/inattentive behaviours in adulthood would show
reduced activation relative to healthy controls in lateral IFC,
striatal, temporo-parietal and cerebellar brain regions during
sustained attention, and abnormal function in OFC, VMPFC
and VS during reward, similar to the dysfunctions previously
observed in children with ADHD during the same task and in
adult ADHD during similar tasks.
The patient group and the diagnostic procedures have been
described previously (Cubillo et al., 2010, in press). In brief,
patients were 11 male right-handed adults (mean age
(years) ¼ 29, standard deviation (SD) ¼ 1, age range ¼ 26e30),
recruited from a 20-year prospective longitudinal epidemio-
logical study (Taylor et al., 1991). Six and 7 years old school
children were initially assessed (Taylor et al., 1991). Those
whomet criteriafor hyperactive/inattentive
according to both teacher and parent rating scales were fol-
lowed up, and re-assessed at age 16e18 years (Danckaerts
et al., 2000; Taylor et al., 1996) and 26e30 years (Stringaris
et al., in press). The subjects for this paper had also met
criteria for ADDH, which the DSM-IIIdiagnosis was at the time
corresponding to the contemporary ADHD.
The assessment in this 20 years follow-up included (i)
Schedule for Affective Disorders and Schizophrenia (SADS)
(Endicott and Spitzer, 1978), (ii) Adult Personality Functioning
Assessment (APFA) (Hill et al., 1989), (iii) Self-reported check-
list of the DSM-IV items comprising the criteria for ADHD, (iv)
Adult Hyperactivity Interview (AHI) (Stringaris et al., in press),
developed for this project which defines symptoms in terms
appropriate to adulthood. The scale ranges from 0 to 24. A
score>10isassociated with poorsocialfunctioning(Stringaris
et al., in press). A diagnostic conference was held for each
case, chaired by an experienced child psychiatrist (ET). All
relevant DSM-IV diagnoses were reviewed, including ADHD
(Table 1), which required four of the DSM-IV criteria for inat-
tentiveness, hyperactivity-impulsiveness or both, significant
functional impairment, and a score >10 on the AHI. As the
diagnostic process was blind to childhood status, the age of
onset (before age 7) criteria was not included. All subjects met
DSM-IV diagnosticcriteriafor ADHD,except for threewho had
subthreshold symptoms, coded in DSM-IV as “ADHD in partial
remission”. Like previous studies in adults with ADHD (i.e.,
Valera et al., 2010b), we decided to include these subjects,
taking into account the age-related decrease in hyperactivity/
impulsivity symptoms (Biederman et al., 2000), and the fact
that remission defined in DSM-IV refers to fewer symptoms
than required and not to functional improvement, which has
been criticised not to reflect adult characteristics of ADHD
(Faraone et al., 2000). There was no evidence for a selection
bias, since the scanned adults did not differ from the rest of
the group in their childhood measures of IQ, classroom or
home hyperactivity symptoms, conduct
emotional problems [F(7,82)<1, p ¼ n.s.].
Several patients presented a current Axis I comorbid
diagnosis: Anxiety (n ¼ 1), Mood (n ¼ 3), Conduct (n ¼ 1) and
non-stimulant Substance Related Disorders (n ¼ 2; Cannabis,
Alcohol), although only one subject had suffered enough
impairment to attend specialists services.
Controls were 15 adult right-handed age-matched males of
average intellectual ability, recruited through advertisement
in the community (mean age ¼ 28, SD ¼ 3). Exclusion criteria
for controls were present or past history of any mental
disorder, substance abuse or psychotropic medication.
Neurological abnormalities were exclusion criteria for all
subjects. All participants scored above cut-off on the Raven’s
Standard Progressive Matrices Intelligence Questionnaire
(Raven, 1960) (i.e., over 75; fifth percentile) (Converted IQ
estimate: Controls: mean IQ ¼ 108, SD ¼ 12, Patients: mean
IQ ¼ 92, SD ¼ 10). One-way analyses of variance (ANOVAs)
showed that the groups did not differ significantly in age
[F (1,23) ¼ 1.67, p ¼ n.s.], but in IQ estimate [F(1,23) ¼ 13.18,
p ¼ ?.001]. Since low IQ is associated with ADHD both in
children and adults (Bridgett and Walker, 2006; Crosbie and
Table 1 e Description of symptoms for adults with childhood ADHD and persistent hyperactivity/inattention symptoms.
Childhood diagnosisAHI scores Hyperactivity symptomsa
ADHD DSM-IV criteria
ADHD hyp þ CD
ADHD hyp þ CD
ADHD hyp þ CD
ADHD combined þ CD
CD þ ADHD hyp
ADHD hyp þ CD
Note: ADHD hyp ¼ ADHD hyperactive subtype.
a 3 ¼ level of problem impairing function and deserving diagnosis; 2 ¼ definite problem; 1 ¼ moderate problem; 0 ¼ no problem.
cortex 48 (2012) 194e215
Schachar, 2001), covarying for IQ would not be appropriate
(Miller and Chapman, 2001), while matching groups for IQ
would create unrepresentative groups (Dennis et al., 2009).
Therefore, as suggested by some authors (Bridgett and
Walker, 2006), fMRI data were analysed with and without IQ
as a covariate to assess the impact of IQ.
The study was approved by the local ethics committee and
written informed consent was obtained from all participants.
A rapid, mixed trial, event related fMRI design was used with
jittered inter-trial-intervals (ITI) and randomised presentation
to optimise statistical efficiency. Subjects practised the task
once prior to scanning.
In the CPT task, subjects have to detect and respond with
a button press to infrequent targets that are embedded in
highly frequent non-targets that require no action and can
therefore be simply being ignored. The difficulty of the task
therefore consists in detecting the rare targets. The task
measures selective and sustained attention and target detec-
tion (Conners, 1993). The computerised fMRI adaptation of the
rewarded CPT (Rubia et al., 2009c, 2009d; Schmitz et al., 2008;
Smithet al.,2011) consistsof a streamof 416 stimuli(letters)of
300 milliseconds (msec) presentation time each (mean ITI:
900 msec), including 48 target stimuli, the letters “X” (24) and
“O” (24) and 368 non-target letters (A, B, C, D, E, F, G, H, K, L, M,
N) (30 or 31 each). There are hence 11.5% target letters that are
randomly interspersed with 88.5% non-target letters. Each
letter had a presentation time of 300 msec and each of the 24
target letters ‘X’ and ‘O’ were separated in the stream by at
least 5400 msec and at most 9000 msec to allow for separa-
bility of haemodynamic response. Subjects have to respond
with the right hand button box to target letters only (X and O)
and ignore all other letters. One of the target letters was
rewarded (£1 for every three correct responses) and the
amount of money earned during the task (£8 for 100% correct
responses) was displayed throughout the task on the right
screen side by one of two differently coloured rising score-
bars (red/blue). Which target letter was rewarded and which
was not rewarded was counterbalanced across subjects.
Next to the letter frame two feedback bars appeared on
screen at all times with ascending panels numbered from 1 to
8, one of which indicated the accumulation of correct
responses to X (coloured red) and the other, the number of
correct responses to O (coloured blue). These feedback bars
would flash for each correct response to their associated letter
and on every third successful hit would move upwards to fill
a panel with colour. In the case of the rewarded feedback bar
each filled panel signalled to the participant that they had
made three successful responses to a rewarded letter and had
won a £1; in the case of the non-rewarded feedback bar this
signalled to the participant that they had made three
successful responses to the non-rewarded letter with no
associated winnings. It was emphasised to the subject which
feedback panel informed them of the accumulation of their
monetary reward (as well as providing feedback) and which
bar gave them feedback about the number of successful non-
rewarded targets. Since there were 24 rewarded target trials
there was a maximum of £8 to win. Regardless of their ex-
pected winnings, all participants were given this sum at the
fMRI paradigm: rewarded CPT
end of the scanning session. Single letters were chosen as
targets rather than complex letter combinations (CPT-AX) to
reduce the load on working memory (Rubia et al., 2009c;
Schmitz et al., 2008) (Fig. 1).
For the fMRI analysis the contrast between non-rewarded
target trials and non-target trials measures the brain
response to sustained attention and will be labelled “sus-
tained attention contrast”. The contrast between rewarded
and non-rewarded target trials measures the effect of reward
upon sustained attention functions and will be labelled
Repeated measures multiple two-way ANOVAs were per-
formed with group (adult ADHD; healthy controls) as
a between subjects factor and trial type (non-rewarded;
rewarded) as within subjects factor for the dependent vari-
ables of omission errors and reaction time (RT). Commission
errors (responses to non-targets) were compared using
Analysis of performance data
Gradient-echo echoplanar MR imaging (EPI) data were
acquired on a GE Signa 1.5T Horizon LX System (General Elec-
tric, Milwaukee, WI, USA) at the Maudsley Hospital, London.
Consistent image quality was ensured by a semi-automated
quality control procedure. A quadrature birdcage head coil
was used for RF transmission and reception. In each of 16 non-
contiguous planes parallel to the anterioreposterior commis-
sural, 208 T2*-weighted MR images depicting Blood Oxygen
fMRI image acquisition and analyses
Fig. 1 e Schematic illustration of the Rewarded Continuous
Performance Test. Response required to “X” or “O”, not to
any other letters. Reward is given for each response to one
of the two target letters (which letter was rewarded was
randomised across subjects). Red/blue bars indicate correct
responses to targets (X/O). Three correct responses make
one score on the bar for the rewarded and non-rewarded
targets, but only the rewarded target scores are
remunerated with £1. Up to £8 can be won on the task.
cortex 48 (2012) 194e215
Level Dependent (BOLD) contrast covering the whole brain
were acquired with TE ¼ 40 msec, TR ¼ 1.8 sec, flip angle ¼ 90?,
in-plane resolution ¼ 3.1 mm, slice thickness ¼ 7 mm, slice-
skip ¼ .7 mm. This EPI dataset provided complete brain
For fMRI analysis, the software package of XBAM was used
(www.brainmap.co.uk, Brammeret al., 1997) that uses median
statistics to control outlier effects and permutation rather
than normal theory based inference, recommended for fMRI
(Thirion et al., 2007).
fMRI data were realigned to minimise motion-related
artefacts (Bullmoreet al.,
a Gaussian filter (full-width half maximum, 7.2 mm). Time-
series analysis of individual subject activation was per-
formed using XBAM, with a wavelet-based re-sampling
methodpreviouslydescribed (Bullmoreet al.,2001). Briefly,we
first convolved each experimental condition (i.e., rewarded
and non-rewarded target trials vs the implicit baseline of non-
target trials) with two Poisson model functions (delays of 4
and 8 sec). Only correct trials were included in the analyses.
We then calculated the weighted sum of these two convolu-
tions that gave the best fit (least-squares) to the time series at
each voxel. A goodness-of-fit statistic (the SSQ-ratio) was then
computed at each voxel consisting of the ratio of the sum of
squares of deviations from the mean intensity value due to
the model (fitted time series) divided by the sum of squares
due to the residuals (original time series minus model time
series). The appropriate null distribution for assessing signif-
icance of any given SSQ-ratio was established using the
wavelet-based datare-samplingmethod(Bullmoreetal., 2001)
and applyingthe model-fitting processto there-sampleddata.
This process was repeated 20 times at each voxel and the data
combined over all voxels, resulting in 20 null parametric maps
of SSQ-ratiofor each subject,which werecombined to give the
overall null distribution of SSQ-ratio. The same permutation
strategy was applied at each voxel to preserve spatial corre-
lation structure in the data. Activated voxels, at a <1 level of
Type I error, were identified through the appropriate critical
value of the SSQ-ratio from the null distribution. The first
contrast involved subtracting activation associated with non-
target trials (implicit baseline) from the non-rewarded target
measuring sustained attention. The second contrast sub-
tracted activation from non-rewarded target trials from
rewarded target trials (rewarded e non-rewarded target
trials), measuring effects of reward upon sustained attention.
A group activation map was then produced for the exper-
imental conditions by calculating the median observed
SSQ-ratio over all subjects at each voxel in standard space and
testingthem againstthe null
wavelet re-sampled data (Brammer et al., 1997). The voxel-
level threshold was first set to p < .05 to give maximum
sensitivity and to avoid type II errors. Next, a cluster-level
threshold was computed for the resulting 3D voxel clusters
such that the final expected number of type I error clusters
was <1 per whole brain. Cluster mass rather than a cluster
extent threshold was used, to minimise discrimination
against possible small, strongly responding foci of activation
(Bullmore et al., 1999). For the group activation analyses, less
1999)and smoothed using
than one false positive activation locus was expected for
p < .05 at voxel level and p < .01 at cluster level. ANOVA
analysis for between-group differences was conducted using
randomisation-based test for voxel or cluster-wise differences
(Bullmore et al., 1999). Less than 1 false activated cluster was
expected at a p-value of p < .05 for voxel and p < .01 for cluster
comparisons. Thus, an expected cluster-level type I error rate
of <1 per brain was achieved by first applying a voxel-level
threshold of p < .05 followed by thresholding the 3D clusters
formed from the voxels that survived this initial step at
a cluster-level threshold of p < .01. The cluster-level threshold
of p < .01, was therefore not applied to the whole brain (which
would be lenient) but rather to the data previously thresh-
olded at a voxel-wise level of p < .05. The necessary combi-
nation of voxel and cluster-level thresholds is not assumed
from theory but rather determined by direct permutation for
each data set. White matter regions were extracted from
analyses using the BET tool from the FSL software package
(Smith, 2002). This creates a grey matter mask of the Talairach
template used for normalisation. This mask was subsequently
used to restrict the analysis to those voxels lying within grey
In order to test whether the between-group differences in
brain activation were related to performance differences,
statistical measures of BOLD response for each participant
were extracted in each of the clusters of between-group
differences for the sustained attention and reward contrasts.
These BOLDmeasures werethen correlatedwithin each group
with RT using Pearson correlations, and with commission
errors using Spearman’s rho, given the non-parametric nature
Repeated measures ANOVA showed that there was no effect
of reward on omission errors or RT within each group or
reward by group interactions (see Table 2). However, patients
showed significantly reduced mean RT to targets (whether
rewarded or not) [F(1,23) ¼ 6, p ¼ .01], and a trend for an
increased number of commission errors [t(23) ¼ ?2, p ¼ .08],
suggesting a different speed-accuracy trade-off, favouring
Table 2 e Performance data for adults with childhood
ADHD and persistent hyperactivity/inattention
symptoms and healthy comparison adults.
(N ¼ 14)
(N ¼ 11)
Rewarded trials MRT (msec)a
Non-rewarded trials MRT (msec)a
Rewarded trials omission errors
Non-rewarded trials omission errors
MRT ¼ Mean Reaction Time.
a Significant between-groups differences.
cortex 48 (2012) 194e215
220.127.116.11. MOTION. Multivariate Analyses of Variance (MANOVA)
showed no between-group differences in any of the x, y, z
translation and rotation motion parameters [F(6, 18) ¼ 1,
p ¼ .44].
COMPARED TO NON-TARGET TRIALS. Within-group activation maps
for each group are shown in Fig. 2a and Supplementary Table
1a. Healthy control adults showed activation in IFC, ACC,
putamen and globus pallidus, PCC, primary motor cortex and
SMA, thalamus, temporal, parietal, and occipital cortices and
cerebellum. Adults with childhood ADHD showed activation
in IFC, medial and superior frontal cortices, ACC and PCC,
striatum, thalamus, temporal, parietal, and occipital cortices
The ANOVA comparison showed increased activation in
the control group during non-rewarded target compared to
non-target trials compared to adults with childhood ADHD in
three clusters, the largest in the left hemisphere including
IFC/insula reaching into pre-SMA and ACC, as well as deep
into caudate, putamen, globus pallidus, and thalamus, one
comprising right premotor and postcentral gyri, extending to
insula, putamen and thalamus, and one including right PCC,
precuneus, and parahippocampal gyrus (Fig. 3a, Table 3a). In
all theseclusters,controlsshowed increasedactivation during
non-rewarded target trials relative to non-target trials, while
patients with ADHD showed decreased activation for this
contrast or increased activation during non-target trials rela-
these clusters, with Cohen’s d values between .77 and 1.09
(see Table 3a).
Patients compared to controls showed increased activation
in bilateral posterior brain regions comprising cerebellum,
PCC, precuneus, inferior and superior parietal cortices, and
occipital regions (Fig. 3a, Table 3a). This increased activation
was due to patients showing more activation than control
subjects in these regions during non-rewarded targets
compared non-target trials while controls showed either less
activation or deactivation for this contrast. Effect sizes were
large for all these clusters, with Cohen’s d values between 1.05
and 1.81 (see Table 3a).
networks that mediate selective and sustained attention
(Arnsten and Rubia, in press; Smith et al., 2011; Tana et al.,
2010), we tested whether the increased cerebellar activation
Fig. 2 e Within group activation maps for controls and adults with childhood ADHD and persistent hyperactivity/inattention
symptoms. Axial sections showing within-group brain activation for the healthy comparison group and the adults with
childhood ADHD and persistent hyperactivity/inattention symptoms for the contrasts (a) Sustained attention:
non-rewarded target e non-target trials, (b) Reward: rewarded e non-rewarded target trials. Tailarach z-coordinates are
indicated for slice distance (in mm) from the intercommissural line.
cortex 48 (2012) 194e215
a compensatory effect related to the reduced inferior fronto-
striatal activation. For this purpose, average scalar measures
of the BOLD response was extracted for each subject in three
of the clusters that differed between groups: in the large left
inferior fronto-striatal activation cluster and in the two cere-
bellar clusters in left lateral cerebellum and right cerebellar
vermis (see Table 3). Then Pearson correlations were calcu-
lated between the scalar measures of the BOLD response in
the left inferior frontal cluster and each of the two cerebellar
clusters, separately within ADHD and control patients. In the
group of adults with ADHD, significant negative correlations
were observed between the activation in left inferior frontal
cortex and in right cerebellar vermis (r ¼ ?.71, p < .01), as well
as between the activation in left inferior frontal cortex and left
inADHD patientsrelative tocontrolswas
lateral cerebellum (r¼ ?.81,p < .01)(seeSupplementary Fig.1).
In healthy control subjects, there was a significant correlation
between activation in left inferior frontal cortex and left
lateral cerebellum (r ¼ ?.51, p < .03) (Supplementary Fig. 1),
whereas the correlation between the activation in left inferior
frontal cortex and right cerebellar vermis was not significant
(r ¼ ?.04, p ¼ n.s.).
When IQ was used as a covariate, the main findings
remained, at the same p-value of p < .05 for voxel and p < .01
for cluster comparisons, but with slightly smaller cluster
In order to correlate clusters of between-group differences
with performance measures we also extracted average scalar
measures of the BOLD response for each cluster that differed
between groups and then conducted two-tailed Pearson
Fig. 3 e Results of the ANOVA between-group difference analyses. Axial sections showing the ANOVA between-group
difference effects in brain activation between adults with persistent hyperactive/inattentive behaviours and childhood
ADHD for (a) Sustained attention: non-rewarded target e non-target trials contrast, and (b) Reward: rewarded e
non-rewarded target trials contrast. Tailarach z-coordinates are indicated for slice distance (in mm) from the
intercommissural line. Section (b) includes axial sections showing the ANOVA between-group difference effects in brain
activation for the Reward contrast between healthy controls and only those six adults with persistent hyperactive/
inattentive behaviours and childhood ADHD who also had comorbid childhood CD (N [ 6). Adults with persistent symptoms
in adulthood and with childhood ADHD without comorbid childhood CD showed no brain differences when compared to
controls. Tailarach z-coordinates are indicated for slice distance (in mm) from the intercommissural line.
cortex 48 (2012) 194e215
correlations between those measures and performance vari-
ables within each group. There was a significant negative
correlation within the ADHD group between commission
errors (that were trend-wise increased in ADHD compared to
controls) and activation in the right PCC/precuneus activation
difference cluster (r ¼ ?.63, p < .03) (that was reduced in ADHD
patients relative to control). No significant correlation was
observed between activation in this cluster and commission
errors in the healthy adults group (r ¼ ?.06, p ¼ n.s.). However,
within the control subject group there was a positive corre-
lation between activation in this PCC/precuneus cluster and
RT (that was increased relative to patients) (r ¼ .65, p < .01),
whereas this correlation was not significant within the ADHD
group (r ¼ .03, p ¼ n.s.). RT also correlated negatively within
the healthy adults group with activation in right inferior
parietal lobe (that was decreased in controls relative to ADHD
patients) (r ¼ ?.65, p < .01). No significant correlation was
observed between RT and parietal activation within the ADHD
group (r ¼ .21, p ¼ n.s.). Differences between the correlations
observed within the two groups were only significant for the
correlation between RT and activation in parietal cortex
(z ¼ ?2.16, p < .03) (Supplementary Fig. 1).
18.104.22.168. EFFECTS OF REWARD: REWARDED TARGETS COMPARED TO NON-
TRIALS. Within-group activation maps for
each group are shown in Fig. 2b and Supplementary Table 1b.
Healthy adults showed activation in OFC, IFC, medial frontal
lobe, striatum, ACC, SMA, insula, thalamus, amygdala,
temporal, parietal and occipital cortices and cerebellum.
Adults with childhood ADHD showed activation in IFC, medial
frontal regions, ACC and PCC, SMA, striatum, thalamus,
insula, temporal, parietal, occipital cortices and cerebellum.
The ANOVA comparison showed increased activation in
the control group compared to adults with childhood ADHD in
two clusters, one comprising right OFC and VMPFC and
a second one including right medial and superior frontal
cortices (Fig. 3b, Table 3b). No areas of increased activation
were detected for the patient group when compared to
controls. The group differences were due to an increased
activation in these clusters for controls during the rewar-
dedenon-rewarded target contrast, while ADHD patients
showed increased activation in these regions during non-
rewarded target compared to rewarded target trials. No
brain regions were increased in adults with childhood ADHD
compared to controls. The effect sizes were large (Cohen’s
d ¼ 1.35 and 1.24) (see Table 3b).
When covarying for IQ at a p-value of p < .05 for voxel and
at a more lenient p < .03 for cluster comparisons, the findings
remained essentially unchanged. No significant correlations
were observed between brain activation in these clusters and
Given that patients with CD have been shown to have
consistent functional and structural abnormalities in ventro-
medial and orbitofrontal cortices (Rubia, 2010); and, further-
more, that ventromedial orbitofrontal activation (in a similar
location to this one) has been shown to be disorder-
specifically underactivated in children with CD compared to
children with ADHD during the same reward contrast of this
CPT task (Rubia et al., 2009c), we tested for the potential
impact of the presence of comorbid CD in childhood on the
functional brain activation abnormalities. For this purpose,
two exploratory between-group ANOVAs were conducted,
comparing brain activation during the reward condition in
controls (N ¼ 14) with activation in the subsample of patients
with additional comorbid CD in childhood (N ¼ 6), and with
the activation observed in patients without comorbid CD in
childhood (N ¼ 5). Less than 1 false activated cluster was ex-
pected at a p-value of p < .05 for voxel and p < .01 for cluster
The subgroup of patients with comorbid CD in childhood
when compared to controls showed underactivation in one
large cluster, comprising the same regions as previously
observed in the whole group, in bilateral OFC/VMPFC and
superior frontal cortices, including ACC and reaching deep
into left caudate (Fig. 3b). Despite the small sample size, the
effect size was large (Cohen’s d ¼ 2.8). No areas of increased
Table 3 e Differences in brain activation between adults with childhood ADHD and healthy comparison adults.
Brain regions of activationBrodman
coordinates (x; y; z)
(Cohen’s d )
(a) Sustained attention: non-rewarded target e non-target trials
C > ADHD
L inferior frontal/insula/premotor/putamen/globus pallidus/
R premotor/postcentral gyrus/insula/putamen
R PCC/precuneus/parahippocampal gyrus
ADHD > C
L inferior/superior parietal gyrus
R inferior/superior parietal gyrus
R þ L cuneus/precuneus/PCC
R vermis cerebellum/occipital/PCC
L cerebellum/occipital/inferior temporal
(b) Reward: rewarded target e non-rewarded target trials
C > ADHD
R medial/superior frontal gyrus
R ventromedial orbitofrontal
?29; 4; 26 136
58; ?4; 15
11; ?33; 20
?43; ?37; 42
47; ?41; 42
?7; ?74; 26
4; ?67; ?13
?25; ?63; ?13
36; 52; 15
25; 48; ?13
N voxels ¼ number of voxels. L ¼ left; R ¼ right. The maps are thresholded to give less than 1 Type I error cluster per map.
cortex 48 (2012) 194e215
activation in ADHD patients with comorbid CD compared to
controls were observed. The subgroup of ADHD patients
without comorbid CD, however, showed no differences in
brain activation when compared to controls.
Medication-naı ¨ve adults with a confirmed diagnosis of child-
hood hyperactivity who displayed persistent ADHD symp-
toms in adulthood showed reduced activation compared to
healthy controls in lateral fronto-striatal and superior parietal
regions during “cool” sustained attention processes and in
paralimbic VMPFC/OFC areas during “hot” reward-associated
functions. Subsequent exploratory analyses furthermore
showed that the VMPFC/OFC underactivation was observed
only in those adults who had comorbid CD in childhood.
During sustained attention, adults with ADHD seemed to
compensate with cerebellar overactivation, which correlated
negatively with the underactivation observed in IFC. The
reduced activation in PCC, furthermore, appeared to be asso-
ciated with a more impulsive performance style, as it was
negatively associated with commission errors in patients but
positively with slower RTs in controls.
For the sustained attention contrast, ADHD patients
showed reduced activation in key areas of sustained atten-
tion, in IFC, striatum, thalamus, anterior insula and PCC
(Lawrence et al., 2003; Tana et al., 2010; Voisin et al., 2006). IFC-
striatal underactivation has previously been observed in adult
ADHD during inhibitory and attention processes (Banich et al.,
2009; Cubillo et al., 2010, in press; Depue et al., 2010a; Dibbets
et al., 2010; Epstein et al., 2007; for review see Cubillo and
Rubia, 2010). IFC dysfunction during cognitive tasks is one of
the most consistent fMRI findings in children with ADHD
(Booth et al., 2005; Durston et al., 2006; Konrad et al., 2006;
Pliszka et al., 2006; Rubia et al., 1999, 2001, 2005, 2008; Smith
et al., 2006; Vaidya et al., 2005; for review see Rubia, 2010),
and has been shown to be disorder-specific compared to
children with CD (Rubia et al., 2008, 2009b, 2009c, 2010a) and
OCD (Rubia et al., 2010b, 2011; for review see Rubia, 2010). The
findings thus show that the key abnormality of IFC dysfunc-
tion in childhood ADHD, which may potentially be a disorder-
specific neurofunctional biomarker, persists into adulthood.
The underactivation findings in ACC is in line with
previous fMRI findings in adults with ADHD during tasks of
interference inhibition (Banich et al., 2009; Burgess et al., 2010;
Bush et al., 1999; Cubillo et al., in press), motor response
inhibition (Cubillo et al., 2010) and working memory (Valera
et al., 2010a) and may be associated with the generic role of
this area in output related attention functions (MacDonald
et al., 2000; Ridderinkhof et al., 2003).
The observed underactivation in the group of adults with
ADHD extended to pre-SMA. Reduced activation in SMA and
pre-SMA has previously observed in children with ADHD
2009d) and selective attention in conflict tasks (Rubia et al.,
2011, in press), attentional switching (Rubia et al., 2010b), as
Tamm et al., 2004). It has also been found to be underactivated
during motor and perceptual temporal processes (Rubia et al.,
1999; Smith et al., 2008). In the same group of adults with
ADHD studied here, we observed underactivation in the SMA
during motor response inhibition (Cubillo et al., 2010).
However, increased activation in the SMA has also been
observed in children with ADHD after failed motor response
inhibition (Spinelli et al., in press) and in adults with ADHD
during an attentional switching task (Dibbets et al., 2010). The
pre-SMA has beenshown to be involved in sustainedattention
as motor response inhibition (Mostofsky and Simmonds, 2008;
Sharp et al., 2010; Simmonds et al., 2008; Tabu et al., in press).
The pre-SMA in particular has been associated with free
response selection as well as attention processes such as
attention to intention and attention to action (Lau et al., 2004a,
2004b). The abnormal activation in ADHD adults during this
target detection task may hence reflect abnormalities in exec-
utive attention and response selection networks.
ADHD patients, on the other hand, showed increased
activation in several posterior brain regions, comprising
lateral and medial cerebellum, PCC, parietal and occipital
areas. The enhanced activation in cerebellar regions is likely
to be compensatory, as it correlated negatively with the
(reduced) activation in inferior frontal regions in both patients
and controls. The cerebellum as part of fronto-cerebellar
neural networks (Arnsten and Rubia, in press) is involved in
higher cognitive processes (Steinlin, 2007), including sus-
tained attention (Lawrence et al., 2003; Voisin et al., 2006). The
underactivation in the frontal parts of this network in patients
may hence have triggered a compensatory activation increase
in the cerebellum. Functional abnormalities in the cerebellum
have previously been observed in adults with ADHD during
other functions that involve attention such as working
memory and timing (Valera et al., 2005, 2010a, 2010b; Wolf
et al., 2009). The PCC together with the ACC forms part of
the midline attention network, and mediates visualespatial
attention to saliency (Mesulam et al., 2001; Mohanty et al.,
2008; Small et al., 2003), whereas precuneus has been
predominantly associated with voluntary attention shifting
and directed spatial attention (Cavanna and Trimble, 2006).
The PCC and precuneus are typically reduced in activation in
children with ADHD during salient stimuli such as stop errors
(Rubia et al., 2005, 2008), oddball or incongruent targets (Rubia
et al., 2007b, 2009b, 2011; Tamm et al., 2006), rare targets in
sustained attention tasks (Rubia et al., 2009c) as well as during
a motor delay task (Rubia et al., 1999).
The pattern of underactivation in IFC and striatum
together with enhanced activation in cerebellar and occipital
brain regions are strikingly similar to that we have previously
observed in children with ADHD relative to the healthy
controls during the same rewarded sustained attention task
(Rubia et al., 2009c, 2009d) (see Fig. 4). These findings of similar
brain under- and overactivation during the same task in
children and adults with ADHD relative to their respective
age-matched controls support the notion of a continuity of
dysfunctions that are typically observed in children with
ADHD into adult ADHD.
During the rewarded relative to the non-rewarded sus-
activation in paralimbic VMPFC and OFC regions, key areas of
reward processing, which are typically activated in this task
cortex 48 (2012) 194e215
(Smith et al., 2011). The lateral OFC mediates stimulus-
Schoenbaum et al., 2006). The VMPFC is associated specifi-
callywith rewardas opposed
processes (Christakou et al., 2009; Knutson et al., 2003;
O’Doherty, 2004; Windmann et al., 2006). Furthermore, the
lateral and VMPFC/OFC modulate interconnected paralimbic
brain regions and mediate topedown affect regulation that is
typically weak in disorders of impulsiveness and aggression
(Davidson et al., 2000a, 2000b; Haber, 2008; Haber et al., 2006;
Catani et al., 2012; Thiebaut de Schotten et al., 2012). These
networksof affect regulation
typically involved in “hot” EF (Zelazo and Muller, 2002).
The underactivation in OFC cortex during the reward
contrast in adult ADHD is in line with the underactivation
and motivation are
finding by Dibbets et al. (2009) in adults with ADHD in bilateral
IFC/OFC during positive feedback during a motor inhibition
task. However, it is not in line with finding of increased acti-
vation in OFC in adults with ADHD during reward outcome
(Stro ¨hle etal., 2008).WearguethatunderactivationinOFCmay
potentially be associated with the presence of CD comorbidity,
given that children with CD relative to healthy controls typi-
cally show underactivation in VMPFC/OFC (Rubia, 2010).
Furthermore, this region has been shown to be disorder-
specifically underactivated in children with pure CD relative
to pure ADHD and control children during the reward compo-
nent of the same task (Rubia et al., 2009c). In this sample, six
subjects had comorbid CD in childhood, although it only per-
sisted into adulthood in one case. Our exploratory analysis
supported this hypothesis of an association between CD and
Fig. 4 e Similarities in reduced and increased brain activation in medication-naı ¨ve adults with childhood ADHD and
persistent hyperactivity/inattention symptoms relative to their age-matched controls and in medication-naı ¨ve children
with ADHD during the Sustained Attention condition compared to their respective age-matched healthy comparison
cortex 48 (2012) 194e215
OFC dysfunction. Only those patients with ADHD with comor-
bid CD in childhood showed underactivation in VMPFC/OFC
regions compared to healthycontrolsduring thereward aspect
of the task, while patients without comorbid CD in childhood
showed no abnormalities in this condition (Fig. 3). The findings
hence suggest that comorbid CD in childhood may have
accounted for the OFC/VMPFC abnormalities, despite the
symptomatic improvement of CD symptoms in adulthood.
Previous studies of ADHD in adulthood did not assess or report
of comorbid CD thus could potentially explain the differences
in OFC findings across studies (Sundram et al., 2012). Our
findings of a significant impact of childhood CD on reward-
associated functional brain activation in OFC/VMPFC stresses
the importance of assessing childhood CD in addition to
childhood ADHD in studies of adult ADHD.
The data thus provide additional evidence for dysfunctions
in overlapping “cool” attention and “hot” motivation brain
networks in a group of adult patients with ADHD who were
followed up from childhood. However, potential caveats of
a relatively small sample size, of IQ differences and of the
potential impact of CD comorbidity on the OFC deficit findings
need to be taken into consideration. The findings of lateral
fronto-striatal deficits during “cool” EF, however, are in line
with previousfronto-striatal deficit findings in the same group
of patients during other “cool” EF, including motor and inter-
ference inhibition, cognitive switching and oddball detection
(Cubillo et al., 2010, in press). Across all tasks, these adults
with childhood ADHD showed underactivation in inferior
prefrontal cortex and the basal ganglia (Fig. 5). However, the
exact location of the inferior prefrontal location differed
between tasks and task conditions. During motor inhibition,
switching and sustained attention, the location was in
predominantly right hemispheric or bilateral deep inferior
prefrontal junction reaching into insula, in line with the
known role of the right IFC in inhibitory control and sustained
attention processes (Derrfuss et al., 2005; Rubia et al., 2003,
2007c; Voisin et al., 2006). During an interference inhibition
and oddball tasks, the location was exclusively left hemi-
spheric and in a more superior, dorsolateral prefrontal loca-
tion and included also ACC, in line with evidence implicating
left DLPFC and ACC in conflict monitoring (MacDonald et al.,
2000). In line with the role of ventromedial orbitofrontal
cortex with emotional-driven action selection and reward
processing (Bechara et al., 2000; Kringelbach, 2005), this loca-
tion was underactivated in adult ADHD patients during the
only “hot” EF, the reward contrast in the sustained attention
task. Furthermore, this activation may have been due to
comorbidchildhoodCDrather thanADHD.With respectto the
basal ganglia deficits, the inhibition tasks (motor and inter-
ference inhibition) elicited reduced activation in the caudate
in the group of ADHD adults, while during sustained and
flexible attention the putamen was also underactivated, in
line with fronto-caudate implications in cognitive control
(Aron et al., 2007; Rubia et al., 2007c) and putamen involve-
ment in attention functions (Adler et al., 2001).
The underactivation observed in caudate and putamen
across tasks (Cubillo et al., 2010, in press) suggests persistent
Fig. 5 e Reduced brain activation in adults with childhood ADHD relative to healthy controls across a series of cognitive
tasks in fMRI. For details on images a, b, eeg see Cubillo et al., 2010, in press.
cortex 48 (2012) 194e215
striatal dysfunctions in adults with symptoms of ADHD. This is
neither in line with longitudinal findings of structural caudate
normalisation in young ADHD adulthood (Castellanos et al.,
2002) nor with our meta-regression analysis showing that the
is in line with caudate and putamen abnormality findings from
recent cross-sectional structural (Almeida Montes et al., 2010;
Seidman et al., 2011) and functional (Epstein et al., 2007;
Schneider et al., 2010) imaging studies in adults with ADHD.
As discussed above, the SMA was underactivated during both
sustained attention and successful inhibition (Cubillo et al.,
2010), in line with the role of this region in both motor
response inhibition and attention to response (Lau et al., 2004a,
2004b; Mostofsky and Simmonds, 2008; Sharp et al., 2010;
Simmonds et al., 2008; Tabu et al., in press). Overall, the find-
as simple (oddball task) and higher level executive attention
functions (interference inhibition and sustained attention
tasks), adults with childhood ADHD have deficits in their acti-
vation of fronto-striatal neural networks. However, the exact
location of frontal and striatal dysfunctions is task-dependent,
with dorsolateral fronto-striatal deficits during cognitive
dysfunctions during inhibitory control and sustained atten-
tion and ventromedial orbitofrontal deficits during reward
processing. The findings suggest that overlapping fronto-
striatal neural networks, mediating both “cool” attentional
dysfunctional in ADHD, in line with models of multisystem
The findings have to be interpreted in light of some limi-
tations. One is the small sample size. Large numbers were
difficult to obtain due to factors inherent to long-term follow-
up studies: many patients grew out of the disorder, changed
their geographic area, making contact impossible, or refused
to participate in the scanning study. Despite the small sample
size, however, we observed significant differences in brain
activation between cases and controls, which are consistent
with previous findings. Furthermore, the effect sizes for the
between-group findings were relatively large.
The presence of comorbid conditions constitutes another
limitation. However, comorbidity is extremely common with
up to 87% both in children (Blackman et al., 2005; Kadesjo and
Gillberg, 2001; Spencer, 2006) and in adults with ADHD
(Biederman et al., 2006; Kessler et al., 2006; Sobanski et al.,
2007), most commonly mood (40e61%), substance related
(15e70%), and anxiety disorders (30e47%) (Kessler et al., 2006;
Miller et al., 2007; Sobanski et al., 2007, 2008; Wilens et al.,
2009). Pure cases are therefore the rare exception, and the
selection of an ADHD group without any psychiatric comor-
bidities would not be representative for ADHD in adulthood
(Biederman et al., 2006), since it would only include subjects
with milder forms of the disorder, or of higher functioning.
That is also the reason why most previous fMRI studies of
adult ADHD have typically included samples with comorbid
psychiatric disorders (Dibbets et al., 2009; Epstein et al., 2007;
Hale et al., 2007; Valera et al., 2010a).
The groups differed significantly in IQ. However, given the
association between low IQ and ADHD, matching for IQ would
create unrepresentative groups. The similarity in the findings
with and without IQ as covariate, however, suggests that the
observed dysfunctions are associated with the disorder and
A common problem with fMRI adaptations of CPT tasks is
that motor responses to targets are not controlled for since
a motor response to non-targets would place unwanted atten-
tion demands. While this does not affect the reward contrast
that was well controlled,this could haveaffectedthesustained
attention contrast. Thus, some activation differences between
groupsfor this contrastcouldpotentiallybemotor- rather than
purely attention-related, such as the differences in premotor
and SMA regions. The majority of ANOVA findings, however,
were not in motor regions. Inferior prefrontal, cingulate and
striato-thalamic activation for example that was reduced in
activation in ADHD adults, is known to mediate sustaining
attention in motor-controlled vigilance and parametric sus-
A key strength of this study is the medication-naivety of
patients, which allowed us to avoid the common confound of
the long-term effects of stimulant medication history on brain
function. This, together with the clearly established presence
of hyperactivity during childhood, which avoids the potential
recall bias present in retrospective diagnosis (Mannuzza et al.,
2002), make this a valuable sample. Additionally, the
enhanced homogeneity of the sample by the inclusion of
males only, and a restricted age range, helped us to avoid the
confounding effects of gender and age.
In conclusion, we observed that medication-naı ¨ve adults
who were known to have ADHD symptoms in childhood and
persistent inattention/hyperactivity symptoms in adult life
showed “cool” attention-related inferior fronto-striatal brain
dysfunctions as well as presumably compensatory hyper-
activation in cerebellum, strikingly similar to previous find-
ings in adolescents with ADHD. We also observed abnormal
brain function in “hot” reward-related EF, with reduced par-
alimbic VMPFC/OFC activation during rewarded sustained
attention trials. However, these lateral OFC and VMPFC acti-
vation dysfunctions were only present in those subjects with
comorbid CD during childhood, suggesting that childhood CD
problems may have accounted for these. The findings stress
the importance for assessing CD in childhood and for testing
their impact on brain deficits. Overall, the findings therefore
suggest that attention and motivation-related lateral and
ventromedial fronto-striatal brain abnormalities observed in
children with ADHD may persist into adulthood, despite
a relative symptomatic improvement.
We show in this review that structural and functional imaging
studies provide evidence for abnormalities in children and
adults with ADHD both in lateral inferior/dorsolateral and
cerebellar neural networks that mediate “cool” abstract-
cognitive EF, including higher level motor, attention, and
temporal processes, as well as in lateral orbitofrontal and
ventromedial networks that mediate “hot” motivation control
functions, all of which are behaviourally and cognitively
compromised in the disorder. We furthermore provide new
cortex 48 (2012) 194e215
functional imaging evidence in support of the notion of the
adult persistence of dysfunctions previously observed in chil-
dren with ADHD. Medication-naı ¨ve adults with hyperactive/
of ADHD symptoms in childhood, showed underactivation in
both “cool” and “hot” lateral inferior fronto-striatal and
ventromedial fronto-striatal networks involved in sustained
attention and motivation, respectively, in association with
other cognitive functions. They were furthermore strikingly
similar to those findings previously observed in childhood
ADHD during the same tasks and hence are in support of our
same abnormalities in lateral fronto-striato-parietal and
fronto-cerebellar cognitive control networks as well as in
networks during motivation control as children with ADHD.
childhood and adult ADHD, as a group,are characterised by the
impairment of several overlapping neural networks, affecting
fronto-subcortical, fronto-cortical and fronto-cerebellar neural
circuitries that mediate “cool” attention and cognitive control
functions as well as fronto-temporo-limbic neural networks of
affect and motivation control (see schematic Fig. 6). The “cool”
neural circuitries furthermore are known to interact closely
with the “hot” neural circuitries (Goel and Dolan, 2003; Krain
et al., 2006). The findings are therefore in line with previously
suggested concepts of multiple neural system impairment in
ADHD associated with the different motor, attention, cognitive
control and motivational processes that are impaired in the
disorder (Makris et al., 2009; Nigg and Casey, 2005).
literature of ADHD that need to be considered. The majority of
with ADHD have not excluded comorbidity with conduct
disorder (CD)/antisocial personality disorder. We have shown
in a previous review that antisocial behaviours are associated
that mediate affect and motivation (i.e., “hot” EF), comprising
ventromedial frontal cortex, superior temporal lobe and
underlying limbic structures (Rubia, 2010). Furthermore,
abnormalities in these paralimbic regions appear to be
disorder-specific when compared with non-comorbid ADHD
patients, while “cool” EF lateral inferior fronto-cortical and
fronto-subcortical networks appear to be disorder-specific in
non-comorbid ADHD patients relative to pure CD cases (Rubia,
2010). While some ADHD patients, in particular those with
emotion dysregulation or irritability, or comorbid emotional
and antisocial problems, are likelyto suffer fromabnormalities
in both “cool” and “hot” EF neurocircuitries, it is possible that
pure, non-comorbid ADHD groups may only be impaired in
studies have included high rates of comorbid cases with
Fig. 6 e Schematic representation of the MRI evidence for structural and functional brain abnormalities in children and
adults with ADHD in overlapping neural networks that mediate “cool” cognitive-abstract and “hot” reward-associated
executive and cognitive functions. “Cool” cognitive networks of dysfunction in ADHD include inferior, dorsolateral and
medial fronto-striatal, fronto-parieto-temporal and fronto-cerebellar regions and networks that mediate functions such
as motor response and interference inhibition, cognitive flexibility, temporal foresight, selective and sustained attention,
working memory, motor and timing processes. “Hot” EF network dysfunctions in ADHD patients have been observed in
the context of temporal discounting, reward processing and reward anticipation. IFG [ inferior frontal gyrus;
OFC [ orbitofrontal cortex; DLPFC [ dorsolateral prefrontal cortex; vmOFC [ ventromedial orbitofrontal cortex;
d/vACC [ dorsal/ventral ACC cortex; SMA [ Supplementary Motor Area.
cortex 48 (2012) 194e215
emotional and affective problems. Our findings of no orbito-
frontal deficits in adults with ADHD without antisocial prob-
lems in the reward condition would be in line with the notion
that some ADHD patients, who have no association with anti-
social or affective behaviours, may not share affective circuit
impairment. This would also be in line with meta-analysis
studies of whole brain structural imaging studies that do not
find fronto-limbic but predominantly basal ganglia impair-
ments both in children with ADHD (Ellison-Wright et al., 2008)
and across the lifespan (Nakao et al., in press). On the other
hand, however, there has been a bias in structural MRI studies
of ADHD towards the selection of regions of interest that
mediate “cool” cognitive functions or fMRI paradigms of
abstractecognitive control. Only relatively recent structural
and functional imaging studies have focussed their interest on
limbic regions and on fMRI tasks of motivation and affect
limbic regions and limbic white matter networks as well as
emotional fMRI paradigms to assess more thoroughly to what
A limitation of the field is furthermore the fact that most
imaging studies are based on group statistics on relatively
small numbers. This applies particularly to the adult imaging
literature. There are as yet few studies that tested for neural
networks in subgroups of ADHD patients. There is likely to be
heterogeneity in neural network impairments between ADHD
patients and subgroups, similar to the observed heterogeneity
in neuropsychological impairments (Nigg et al., 2005; Sonuga-
Barke et al., 2010). Thus, some children may have no brain
abnormalities, while others may have abnormalities in
specific fronto-striatal circuitries of specific “cool” EF, timing,
motor and/or attention functions, and yet others may suffer
circuits or in both, overlapping cognitive and affective neu-
rocircuitries. This would be in line with the notion of ADHD as
a multisystem developmental disorder, where the clinical
expression is based on the degree and heterogeneity of the
neural system dysfunction (Makris et al., 2009).
An important confound in the majority of adult ADHD
imaging studies is long-term medication history. Our recent
meta-regression analysis showed that basal ganglia abnor-
malities were correlated with the percentage of medication-
naı ¨ve patients included in the studies (Nakao et al., in press).
This would suggest that the current literature, mostly con-
ducted in adult ADHD patients with a medication history,
shows a more lenient deficit picture from what would be
observed in untreated patients. The findings of striking simi-
larities in functional deficits between our previously scanned
medication-naı ¨ve ADHD children and the here presented
medication-naı ¨ve adults with ADHD are in line with this and
suggest that deficit findings may be more similar when
medication-naı ¨ve patients are being compared.
It also remains to be investigated to what degree ADHD is
a delay of normal brain structure or function maturation.
brain regions, functions and functional and structural neural
networks that develop late between childhood and adulthood
(Geier and Luna, 2009; Giedd et al., 2001; Konrad and Eickhoff,
2010; Makris et al., 2009). Longitudinal imaging studies have
demonstrated a delay in cortical thickness maturation in
children with ADHD (Shaw et al., 2007). Unfortunately, these
studies have not scanned ADHD patients beyond the age of 20.
If a delay is defined as a maturational lag that normalises
eventually with age (i.e., catches up), then this seems not to
structural and functional interconnectivity deficits are still
observed in adults with ADHD in cross-sectional imaging
studies. One possible exception may be basal ganglia struc-
tures, where there is some evidence for normalisation from
longitudinal (Castellanos et al., 2002) and meta-regression
studies (Nakao et al., in press). The findings of persisting
brain abnormalities in adult ADHD seem to suggest that the
life. Also, to our knowledge, no imaging studies have been
structural and functional imaging studies will be needed to
elucidate whether there is a structural and functional matu-
rational delay in ADHD patients that persists throughout the
lifespan or whether this normalises at any given age. Such
a potential normalisationprocess,
severity, environmental factors, cognitive faculties, genetic
predisposition or pharmacological and behavioural treatment.
The research was supported by grants from the Medical
Research Council(G9900839)andThe WellcomeTrust (053272/
Z/98/Z/JRS/JP/JAT). AC and ABS were supported by PHD
studentship/post-doctoral fellowships by the National Insti-
tute for Health Research (NIHR) Biomedical Research Centre
(BRC) for Mental Health at the South London and Maudsley
NHS Foundation Trust (SLaM) and the Institute of Psychiatry
at King’s College, London.
Supplementary data related to this article can be found online
r e f e r e n c e s
Adler CM, Sax KW, Holland SK, Schmithorst V, Rosenberg L, and
Strakowski SM. Changes in neuronal activation with
increasing attention demand in healthy volunteers: An fMRI
study. Synapse, 42(4): 266e272, 2001.
Ahrendts J, Ru ¨sch N, Wilke M, Philipsen A, Eickhoff SB, Glauche V,
Perlov E, Ebert D, Hennig J, Tebartz van Elst L. Visual cortex
abnormalities in adults with ADHD: A structural MRI study.
World Journal of Biological Psychiatry, in press [epub ahead of
printing, doi:10.3109/15622975.2010.518624, 2010].
Almeida Montes LG, Ricardo-Garcell J, Barajas De La Torre LB,
Prado AH, Martinez Garcia RB, Fernandez-Bouzas A, et al.
Clinical correlations of grey matter reductions in the caudate
cortex 48 (2012) 194e215
nucleus of adults with attention deficit hyperactivity disorder.
Journal of Psychiatry Neuroscience, 35(4): 238e246, 2010.
American Psychiatric Association Diagnostic and Statistical Manual
of Mental Disorders. Washington, DC: American Psychiatric
Amico F, Stauber J, Koutsouleris N, and Frodl T. Anterior cingulate
cortex gray matter abnormalities in adults with attention
deficit hyperactivity disorder: A voxel-based morphometry
study. Psychiatry Research, 191(1): 31e35, 2010.
Antrop I, Stock P, Verte S, Wiersema JR, Baeyens D, and
Roeyers H. ADHD and delay aversion: The influence of non-
temporal stimulation on choice for delayed rewards. Journal of
Child Psychology and Psychiatry, 47(11): 1152e1158, 2006.
Arnsten A and Rubia K. Neurobiological circuits regulating
attention, movement and emotion and their disruptions in
pediatric neuropsychiatric disorders. Journal of the American
Academy of Child and Adolescent Psychiatry, in press.
Aron AR, Durston S, Eagle DM, Logan GD, Stinear CM, and
Stuphorn V. Converging evidence for a fronto-basal-ganglia
network for inhibitory control of action and cognition. Journal
of Neuroscience, 27(44): 11860e11864, 2007.
Ashtari M, Kumra S, Bhaskar SL, Clarke T, Thaden E,
Cervellione KL, et al. Attention-deficit/hyperactivity disorder:
A preliminary diffusion tensor imaging study. Biological
Psychiatry, 57(5): 448e455, 2005.
Banaschewski T, Ruppert S, Tannock R, Albrecht B, Becker A,
Uebel H, et al. Colour perception in ADHD. Journal of Child
Psychology and Psychiatry, 47(6): 568e572, 2006.
Banich MT, Burgess GC, Depue BE, Ruzic L, Bidwell LC,
Hitt-Laustsen S, et al. The neural basis of sustained and
transient attentional control in young adults with ADHD.
Neuropsychologia, 47(14): 3095e3104, 2009.
Barkley RA, Fischer M, Smallish L, and Fletcher K. The persistence
of attention-deficit/hyperactivity disorder into young
adulthood as a function of reporting source and definition of
disorder. Journal of Abnormal Psychology, 111(2): 279e289, 2002.
Batty MJ, Liddle EB, Pitiot A, Toro R, Groom MJ, Scerif G, et al.
Cortical gray matter in attention-deficit/hyperactivity
disorder: A structural magnetic resonance imaging study.
Journal of the American Academy of Child and Adolescent
Psychiatry, 49(3): 229e238, 2010.
Baxter MG and Murray EA. The amygdala and reward. Nature
Reviews. Neuroscience, 3(7): 563e573, 2002.
Bechara A, Tranel D, and Damasio H. Characterization of the
decision-making deficit of patients with ventromedial
prefrontal cortex lesions. Brain, 123(11): 2189e2202, 2000.
Biederman J, Mick E, and Faraone SV. Age-dependent decline of
symptoms of attention deficit hyperactivity disorder: Impact
of remission definition and symptom type. American Journal of
Psychiatry, 157(5): 816e818, 2000.
Biederman J, Monuteaux MC, Mick E, Spencer T, Wilens TE,
Silva JM, et al. Young adult outcome of attention deficit
hyperactivity disorder: A controlled 10-year follow-up study.
Psychological Medicine, 36(2): 167e179, 2006.
Biederman J, Makris N, Valera EM, Monuteaux MC, Goldstein JM,
Buka S, et al. Towards further understanding of the co-
morbidity between attention deficit hyperactivity disorder and
bipolar disorder: A MRI study of brain volumes. Psychological
Medicine, 38(7): 1045e1056, 2008.
Bitsakou P, Psychogiou L, Thompson M, and Sonuga-Barke EJ.
Delay aversion in attention deficit/hyperactivity disorder: An
empirical investigation of the broader phenotype.
Neuropsychologia, 47(2): 446e456, 2009.
Blackman GL, Ostrander R, and Herman KC. Children with ADHD
and depression: A multisource, multimethod assessment of
clinical, social, and academic functioning. Journal of Attention
Disorders, 8(4): 195e207, 2005.
Boonstra A, Oosterlaan J, Sergeant J, and Buitelaar J. Executive
functioning in adult ADHD: A meta-analytic review.
Psychological Medicine, 35(8): 1097e1108, 2005.
Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL,
Davenport ND, et al. Larger deficits in brain networks for
response inhibition than for visual selective attention in
attention deficit hyperactivity disorder (ADHD). Journal of Child
Psychology and Psychiatry, 46(1): 94e111, 2005.
Brammer M, Bullmore ET, Simmons A, Williams SC, Grasby PM,
Howard RJ, et al. Generic brain activation mapping in
functional magnetic resonance imaging: A nonparametric
approach. Magnetic Resonance Imaging, 15(7): 763e770, 1997.
Bridgett DJ and Walker ME. Intellectual functioning in adults with
ADHD: A meta-analytic examination of full scale IQ
differences between adults with and without ADHD.
Psychological Assessment, 18(1): 1e14, 2006.
Brotman MA, Rich BA, Guyer AE, Lunsford JR, Horsey SE,
Reising MM, et al. Amygdala activation during emotion
processing of neutral faces in children with severe mood
dysregulation versus ADHD or bipolar disorder. American
Journal of Psychiatry, 167(1): 61e69, 2010.
Bullmore E, Long C, Suckling J, Fadili J, Calvert G, Zelaya F, et al.
Colored noise and computational inference in
neurophysiological (fMRI) time series analysis: Resampling
methods in time and wavelet domains. Human Brain Mapping,
12(2): 61e78, 2001.
Bullmore ET, Suckling J, Overmeyer S, Rabe-Hesketh S, Taylor E,
and Brammer M. Global, voxel, and cluster tests, by theory and
permutation, for a difference between two groups of
structural MR images of the brain. IEEE Transactions on Medical
Imaging, 18(1): 32e42, 1999.
Burgess GC, Depue BE, Ruzic L, Willcutt EG, Du YP, and
Banich MT. Attentional control activation relates to working
memory in attention-deficit/hyperactivity disorder. Biological
Psychiatry, 67(7): 632e640, 2010.
Bush G, Frazier JA, Rauch SL, Seidman LJ, Whalen PJ, Jenike MA,
et al. Anterior cingulate cortex dysfunction in attention-
deficit/hyperactivity disorder revealed by fMRI and the
Counting Stroop. Biological Psychiatry, 45(12): 1542e1552, 1999.
Cao QJ, Zang YF, Sun L, Sui MQ, Long XY, Zou QH, et al. Abnormal
neural activity in children with attention deficit hyperactivity
disorder: A resting-state functional magnetic resonance
imaging study. NeuroReport, 17(10): 1033e1036, 2006.
Cao X, Cao Q, Long X, Sun L, Sui M, Zhu C, et al. Abnormal resting-
state functional connectivity patterns of the putamen in
medication-naive children with attention deficit hyperactivity
disorder. Brain Research, 1303: 195e206, 2009.
Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M,
et al. Global and regional gray matter reductions in ADHD:
A voxel-based morphometric study. Neuroscience Letters,
389(2): 88e93, 2005.
Carmona S, Proal E, Hoekzema EA, Gispert JD, Picado M, Moreno I,
et al. Ventro-striatal reductions underpin symptoms of
disorder. Biological Psychiatry, 66(10): 972e977, 2009.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK,
Clasen LS, et al. Developmental trajectories of brain volume
abnormalities in children and adolescents with attention-
deficit/hyperactivity disorder. Journal of the American Medical
Association, 288(14): 1740e1748, 2002.
Castellanos FX, Margulies DS, Kelly C, Uddin LQ, Ghaffari M,
Kirsch A, et al. Cingulate-precuneus interactions: A new locus
of dysfunction in adult attention-deficit/hyperactivity
disorder. Biological Psychiatry, 63(3): 332e337, 2008.
Catani M, Dell’Acqua F, Vergani F, Malik F, Hodge H, Roy P, et al.
Short frontal lobe connections of the human brain. Cortex,
48(2): 273e291, 2012.
cortex 48 (2012) 194e215
Cavanna AE and Trimble MR. The precuneus: A review of its
functional anatomy and behavioural correlates. Brain,
129(Pt 3): 564e583, 2006.
Christakou A, Brammer M, Giampietro V, and Rubia K. Right
ventromedial and dorsolateral prefrontal cortices mediate
adaptive decisions under ambiguity by integrating choice
utility and outcome evaluation. Journal of Neuroscience,
29(35): 11020e11028, 2009.
Conners CK. The Conners Continuous Performance Test. North
Tonawanda, NY: Multi-Health Systems, 1993.
Crosbie J and Schachar R. Deficient inhibition as a marker for
familial ADHD. American Journal of Psychiatry, 158(11):
Cubillo A, Halari R, Ecker C, Giampietro V, Taylor E, and 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. Journal of Psychiatry Research, 44(10): 629e639, 2010.
Cubillo A, Halari R, Giampietro V, Taylor E and Rubia K. Fronto-
striatal hypo-activation during interference inhibition and
attention allocation in a group of grown up children with
ADHD with persistent hyperactive/inattentive behaviours in
adulthood. Psychiatry Research: Neuroimaging, in press.
Cubillo A and Rubia K. Structural and functional brain imaging
in adult Attention deficit hyperactivity disorder (ADHD):
A review. Expert Reviews of Neurotherapeutics, 10(4):
Dalen L, Sonuga-Barke EJ, Hall M, and Remington B. Inhibitory
deficits, delay aversion and preschool AD/HD: Implications for
the dual pathway model. Neural Plasticity, 11(1e2): 1e11, 2004.
Danckaerts M, Heptinstall E, Chadwick O, and Taylor E. A natural
history of hyperactivity and conduct problems: Self-reported
Davenport ND, Karatekin C, White T, and Lim KO. Differential
fractional anisotropy abnormalities in adolescents with ADHD
or schizophrenia. Psychiatry Research, 181(3): 193e198, 2010.
Davidson RJ, Putnam KM, and Larson CL. Dysfunction in the
neural circuitry of emotion regulation e A possible prelude to
violence. Science, 289(5479): 591e594, 2000a.
Davidson RJ, Jackson DC, and Kalin NH. Emotion, plasticity,
context, and regulation: Perspectives from affective
neuroscience. Psychological Bulletin, 126(6): 890e909, 2000b.
Dennis M, Francis DJ, Cirino PT, Schachar R, Barnes MA, and
Fletcher JM. Why IQ is not a covariate in cognitive studies of
neurodevelopmental disorders. Journal of the International
Neuropsychological Society: JINS, 15(3): 331e343, 2009.
Depue BE, Burgess GC, Willcutt EG, Ruzic L, and 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,
48(13): 3909e3917, 2010a.
Depue BE, Burgess GC, Willcutt EG, Bidwell LC, Ruzic L, and
Banich MT. Symptom-correlated brain regions in young adults
with combined-type ADHD: Their organization, variability,
and relation to behavioral performance. Psychiatry Research,
182(2): 96e102, 2010b.
Derrfuss J, Brass M, Neumann J, and von Cramon DY.
Involvement of the inferior frontal junction in cognitive
control: Meta-analyses of switching and Stroop studies.
Human Brain Mapping, 25(1): 22e34, 2005.
Dibbets P, Evers L, Hurks P, Marchetta N, and Jolles J. Differences
in feedback- and inhibition-related neural activity in adult
ADHD. Brain and Cognition, 70(1): 73e83, 2009.
Dibbets P, Evers EA, Hurks PP, Bakker K, and Jolles J. Differential
brain activation patterns in adult attention-deficit
hyperactivity disorder (ADHD) associated with task switching.
Neuropsychology, 24(4): 413e423, 2010.
Dickstein SG, Bannon K, Castellanos FX, and Milham MP. The
neural correlates of attention deficit hyperactivity disorder:
An ALE meta-analysis. Journal of Child Psychology and Psychiatry,
47(10): 1051e1062, 2006.
Durston S, Tottenham NT, Thomas KM, Davidson MC, Eigsti IM,
Yang YH, et al. Differential patterns of striatal activation in
young children with and without ADHD. Biological Psychiatry,
53(10): 871e878, 2003.
Durston S, Mulder M, Casey BJ, Ziermans T, and van Engeland H.
Activation in ventral prefrontal cortex is sensitive to genetic
vulnerability for attention-deficit hyperactivity disorder.
Biological Psychiatry, 60(10): 1062e1070, 2006.
Durston S, Davidson MC, Mulder MJ, Spicer JA, Galvan A,
Tottenham N, et al. Neural and behavioral correlates of
expectancy violations in attention-deficit hyperactivity
disorder. Journal of Child Psychology and Psychiatry, 48(9):
Ellison-Wright I, Ellison-Wright Z, and Bullmore E. Structural
brain change in Attention deficit hyperactivity disorder
identified by meta-analysis. BMC Psychiatry, 8: 51e58, 2008.
Endicott J and Spitzer RL. A diagnostic interview: The schedule for
affective disorders and schizophrenia. Archives of General
Psychiatry, 35(7): 837e844, 1978.
Engelmann JB and Pessoa L. Motivation sharpens exogenous
spatial attention. Emotion, 7(3): 668e674, 2007.
Epstein JN, Johnson DE, Varia IM, and Conners CK.
Neuropsychological assessment of response inhibition in
adults with ADHD. Journal of Clinical and Experimental
Neuropsychology, 23(3): 362e371, 2001.
Epstein JN, Casey BJ, Tonev ST, Davidson MC, Reiss AL, Garrett A,
et al. ADHD- and medication-related brain activation effects in
concordantly affected parent-child dyads with ADHD. Journal
of Child Psychology and Psychiatry, 48(9): 899e913, 2007.
Faraone SV, Biederman J, Spencer T, Wilens T, Seidman LJ, Mick E,
et al. Attention-deficit/hyperactivity disorder in adults: An
overview. Biological Psychiatry, 48(1): 9e20, 2000.
Faraone SV and Biederman J. What is the prevalence of adult
ADHD? Results of a population screen of 966 adults. Journal of
Attention Disorders, 9(2): 384e391, 2005.
Faraone SV, Wilens TE, Petty C, Antshel K, Spencer T, and
Biederman J. Substance use among ADHD adults: Implications
of late onset and subthreshold diagnoses. American Journal of
Addictions, 16(Suppl 1): 24e32, 2007.
Frodl T, Stauber J, Schaaff N, Koutsouleris N, Scheuerecker J,
Ewers M, et al. Amygdala reduction in patients with ADHD
compared with major depression and healthy volunteers. Acta
Psychiatrica Scandinavica, 121(2): 111e118, 2009.
Froehlich TE, Lanphear BP, Epstein JN, Barbaresi WJ, Katusic SK,
and Kahn RS. Prevalence, recognition, and treatment of
attention-deficit/hyperactivity disorder in a national sample
of US children. Archives of paediatrics and adolescent medicine,
161(9): 857e864, 2007.
Geier C and Luna B. The maturation of incentive processing
and cognitive control. Pharmacology Biochemistry and Behavior,
93(3): 212e221, 2009.
Giedd JN, Blumenthal J, Molloy E, and Castellanos FX. Brain
imaging of attention deficit/hyperactivity disorder. Annals of
the New York Academy of Sciences, 931: 33e49, 2001.
Goel V and Dolan RJ. Reciprocal neural response within lateral
and ventral medial prefrontal cortex during hot and cold
reasoning. NeuroImage, 20(4): 2314e2321, 2003.
Haber S. Functional anatomy and physiology of the basal ganglia:
Non-motor functions. In Tarsy D (Ed), Deep Brain Stimulation in
Neurological and Psychiatric Disorders. Humana Press, 2008:
Haber SN, Kim KS, Mailly P, and Calzavara R. Reward-related
cortical inputs define a large striatal region in primates that
interface with associative cortical connections, providing
cortex 48 (2012) 194e215
a substrate for incentive-based learning. Journal of
Neuroscience, 26(32): 8368e8376, 2006.
Hale ST, Bookheimer S, McGough JJ, Phillips JM, and
McCracken JT. Atypical brain activation during simple &
complex levels of processing in adult ADHD: An fMRI study.
Journal of Attention Disorders, 11(2): 125e139, 2007.
Herpertz SC, Huebner T, Marx I, Vloet TD, Fink GR, Stoecker T,
et al. Emotional processing in male adolescents with
childhood-onset conduct disorder. Journal of Child Psychology
and Psychiatry, 49(7): 781e791, 2008.
Hervey AS, Epstein JN, and Curry JF. Neuropsychology of adults
with attention-deficit/hyperactivity disorder: A meta-analytic
review. Neuropsychology, 18(3): 485e503, 2004.
Hesslinger B, Tebartz van Elst L, Thiel T, Haegele K, Hennig J, and
Ebert D. Frontoorbital volume reductions in adult patients
with attention deficit hyperactivity disorder. Neuroscience
Letters, 328(3): 319e321, 2002.
Hill J, Harrington R, Fudge H, Rutter M, and Pickles A. Adult
personality functioning assessment (APFA). An investigator-
based standardised interview. British Journal of Psychiatry,
155(1): 24e35, 1989.
Kadesjo B and Gillberg C. The comorbidity of ADHD in the general
population of Swedish school-age children. Journal of Child
Psychology and Psychiatry, 42(4): 487e492, 2001.
Kessler RC, Adler L, Barkley R, Biederman J, Conners CK, Demler O,
States: Results from the National Comorbidity Survey
Knutson B, Fong GW, Bennett SM, Adams CM, and Hommer D. A
region of mesial prefrontal cortex tracks monetarily
rewarding outcomes: Characterization with rapid event-
related fMRI. NeuroImage, 18(2): 263e272, 2003.
Konrad A, Dielentheis TF, El Masri D, Bayerl M, Fehr C, Gesierich T,
et al. Disturbed structural connectivity is related to inattention
and impulsivity in adult attention deficit hyperactivity
disorder. European Journal of Neuroscience, 31(5): 912e919, 2010.
Konrad K, Neufang S, Hanisch C, Fink GR, and Herpertz-
Dahlmann B. Dysfunctional attentional networks in children
with attention deficit/hyperactivity disorder: Evidence from
an event-related functional magnetic resonance imaging
study. Biological Psychiatry, 59(7): 643e651, 2006.
Konrad K and Eickhoff SB. Is the ADHD brain wired differently? A
review on structural and functional connectivity in attention
deficit hyperactivity disorder. Human Brain Mapping, 31(6):
Krain AL and Castellanos FX. Brain development and ADHD.
Clinical Psychology Review, 26(4): 433e444, 2006.
Krain AL, Wilson AM, Arbuckle R, Castellanos FX, and Milham MP.
Distinct neural mechanisms of risk and ambiguity: A meta-
analysis of decision-making. NeuroImage, 32(1): 477e484, 2006.
Krawczyk DC, Gazzaley A, and D’Esposito M. Reward modulation
of prefrontal and visual association cortex during an incentive
working memory task. Brain Research, 1141: 168e177, 2007.
Kringelbach ML. The human orbitofrontal cortex: Linking reward
to hedonic experience. Nature Reviews. Neuroscience, 6(9):
Lang W, Obrig H, Lindinger G, Cheyne D, and Deecke L.
Supplementary motor area activation while tapping
bimanually different rhythms in musicians. Experimental Brain
Research, 79(3): 504e514, 1990.
Lau HC, Rogers RD, Ramnani N, and Passingham RE. Willed action
and attention to the selection of action. NeuroImage, 21(4):
Lau HC, Rogers RD, Haggard P, and Passingham RE. Attention to
intention. Science, 303(5661): 1208e1210, 2004b.
Lawrence NS, Ross TJ, Hoffmann R, Garavan H, and Stein EA.
Multiple neuronal networks mediate sustained attention.
Journal of Cognitive Neuroscience, 15(7): 1028e1038, 2003.
Luman M, Oosterlaan J, and Sergeant JA. The impact of
reinforcement contingencies on AD/HD: A review and
theoretical appraisal. Clinical Psychology Review, 25(2):
MacDonald 3rd AW, Cohen JD, Stenger VA, and Carter CS.
Dissociating the role of the dorsolateral prefrontal and
anterior cingulate cortex in cognitive control. Science,
288(5472): 1835e1838, 2000.
Mackie S, Shaw P, Lenroot R, Pierson R, Greenstein DK, Nugent TF,
et al. Cerebellar development and clinical outcome in
attention deficit hyperactivity disorder. American Journal of
Psychiatry, 164(4): 647e655, 2007.
Makris N, Buka SL, Biederman J, Papadimitriou GM, Hodge SM,
Valera EM, et al. Attention and executive systems
abnormalities in adults with childhood ADHD: A DTeMRI
study of connections. Cerebral Cortex, 18(5): 1210e1220, 2008.
Makris N, Biederman J, Monuteaux MC, and Seidman LJ. Towards
conceptualizing a neural systems-based anatomy of
attention-deficit/hyperactivity disorder. Developmental
Neuroscience, 31(1e2): 36e49, 2009.
Makris N, Seidman LJ, Valera EM, Biederman J, Monuteaux MC,
Kennedy DN, et al. Anterior cingulate volumetric alterations in
treatment-naive adults with ADHD: A pilot study. Journal of
Attention Disorders, 13(4): 407e413, 2010.
Mannuzza S, Klein RG, Klein DF, Bessler A, and Shrout P. Accuracy
of adult recall of childhood attention deficit hyperactivity
disorder. American Journal of Psychiatry, 159(11):
Marchetta ND, Hurks PP, De Sonneville LM, Krabbendam L, and
Jolles J. Sustained and focused attention deficits in adult
ADHD. Journal of Attention Disorders, 11(6): 664e676, 2008.
Marco R, Miranda A, Schlotz W, Melia A, Mulligan A, Muller U,
et al. Delay and reward choice in ADHD: An experimental
test of the role of delay aversion. Neuropsychology, 23(3):
Marsh AA, Finger EC, Michell DGV, Sims C, Kosson DS, Towbin KE,
et al. Reduced amygdala response to fearful expressions in
children and adolescents with callous-unemotional traits and
disruptive behaviour disorders. American Journal of Psychiatry,
165(6): 712e720, 2008.
Martinussen R, Hayden J, Hogg-Johnson S, and Tannock R. A
meta-analysis of working memory impairments in children
with attention-deficit/hyperactivity disorder. Journal of the
American Academy of Child and Adolescent Psychiatry, 44(4):
Mesulam MM, Nobre AC, Kim YH, Parrish TB, and Gitelman DR.
Heterogeneity of cingulate contributions to spatial attention.
NeuroImage, 13(6): 1065e1072, 2001.
Miller G and Chapman J. Misunderstanding analysis of
covariance. Journal of Abnormal Psychology, 110(1): 40e48, 2001.
Miller TW, Nigg JT, and Faraone SV. Axis I and II comorbidity in
adults with ADHD. Journal of Abnormal Psychology, 116(3):
Mohanty A, Gitelman DR, Small DM, and Mesulam MM. The
spatial attention network interacts with limbic and
monoaminergic systems to modulate motivation-induced
attention shifts. Cerebral Cortex, 18(11): 2604e2613, 2008.
Mostofsky SH and Simmonds DJ. Response inhibition and
response selection: Two sides of the same coin. Journal of
Cognitive Neurosciences, 20(5): 751e761, 2008.
Nakao T, Radua J, Rubia K and Mataix-Cols D. Gray matter volume
abnormalities in ADHD and the effects of stimulant
medication: Voxel-based meta-analysis. American Journal of
Psychiatry, in press.
Nigg JT, Willcutt EG, Doyle AE, and Sonuga-Barke EJ. Causal
heterogeneity in attention-deficit/hyperactivity disorder: Do
we need neuropsychologically impaired subtypes? Biological
Psychiatry, 57(11): 1224e1230, 2005.
cortex 48 (2012) 194e215
Nigg JT and Casey BJ. An integrative theory of attention-deficit/
hyperactivity disorder based on the cognitive and affective
neurosciences. Development and Psychopathology, 17(3):
O’Doherty JP. Reward representations and reward-related
learning in the human brain: Insights from neuroimaging.
Current Opinion in Neurobiology, 14(6): 769e776, 2004.
Pavuluri MN, Yang S, Kamineni K, Passarotti AM, Srinivasan G,
Harral EM, et al. Diffusion tensor imaging study of white
matter fiber tracts in pediatric bipolar disorder and attention-
deficit/hyperactivity disorder. Biological Psychiatry, 65(7):
Perlov E, Philipsen A, Tebartz van Elst L, Ebert D, Henning J,
Maier S, et al. Hippocampus and amygdala morphology in
adults with attention-deficit hyperactivity disorder. Journal of
Psychiatry and Neuroscience, 33(6): 509e515, 2008.
Plichta MM, Vasic N, Wolf RC, Lesch KP, Brummer D, Jacob C, et al.
Neural hyporesponsiveness and hyperresponsiveness during
immediate and delayed reward processing in adult attention-
deficit/hyperactivity disorder. Biological Psychiatry, 65(1):
Pliszka SR, Glahn DC, Semrud-Clikeman M, Franklin C, Perez R,
and Xiong JJ. Neuroimaging of inhibitory control areas in
children with attention deficit hyperactivity disorder who
were treatment naive or in long-term treatment. American
Journal of Psychiatry, 163(6): 1052e1060, 2006.
Pochon JB, Levy R, Fossati P, Lehericy S, Poline JB, Pillon B, et al.
The neural system that bridges reward and cognition in
humans: An fMRI study. Proceedings of the National Academy of
Sciences of the United States of America, 99(8): 5669e5674, 2002.
Raven J. Guide to the Standard Progressive Matrices. London: HK
Ridderinkhof KR, Nieuwenhuis S, and Bashore TR. Errors are
foreshadowed in brain potentials associated with action
monitoring in cingulate cortex in humans. Neuroscience Letters,
348(1): 1e4, 2003.
Rothermund K, Wentura D, and Bak PM. Automatic attention to
stimuli signalling chances and dangers: Moderating effects of
positive and negative goal and action contexts. Cognition and
Emotion, 15(2): 231e248, 2001.
Rubia K. “Cool” inferior fronto-striatal dysfunction in attention-
deficit hyperactivity disorder (ADHD) versus “hot”
ventromedial orbitofronto-limbic dysfunction in conduct
disorder: A review. Biological Psychiatry,. epub ahead of
printing, doi:10.1016/j.biopsych.2010.09.023 2010.
Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC,
Simmons A, et al. Hypofrontality in attention deficit
hyperactivity disorder during higher-order motor control: A
study with functional MRI. American Journal of Psychiatry,
156(6): 891e896, 1999.
Rubia K, Taylor E, Smith A, Oksanen H, Overmeyer S, and
Newman S. Neuropsychological analyses of impulsiveness in
childhood hyperactivity. British Journal of Psychiatry, 179(2):
Rubia K, Smith A, Brammer M, and Taylor E. Right inferior
prefrontal cortex mediates response inhibition while mesial
prefrontal cortex is responsible for error detection.
NeuroImage, 20(1): 351e358, 2003.
Rubia K, Smith A, Brammer M, Toone B, and Taylor E. Abnormal
brain activation during inhibition and error detection in
medication-naive adolescents with ADHD. American Journal of
Psychiatry, 162(6): 1067e1075, 2005.
Rubia K, Smith A, Brammer M, and Taylor E. Performance of
children with attention deficit hyperactivity disorder (ADHD)
on a test battery for impulsiveness. Child Neuropsychology,
30(2): 659e695, 2007a.
Rubia K, Smith A, Brammer M, and Taylor E. Temporal lobe
dysfunction in medication-naive boys with attention-deficit/
hyperactivity disorder during attention allocation and its
relation to response variability. Biological Psychiatry, 62(9):
Rubia K, Smith A, Taylor E, and Brammer M. Linear age-correlated
functional development of right inferior fronto-striato-
cerebellar networks during response inhibition and anterior
cingulate during error-related processes. Human Brain
Mapping, 28(11): 1163e1177, 2007c.
Rubia K, Halari R, Smith A, Mohammed M, Scott S, Giampietro V,
et al. Dissociated functional brain abnormalities of inhibition
in boys with pure conduct disorder and in boys with pure
attention deficit hyperactivity disorder. American Journal of
Psychiatry, 165(7): 889e897, 2008.
Rubia K, Halari R, Christakou A, and Taylor E. Impulsiveness as
a timing disturbance: Neurocognitive abnormalities in
attention-deficit hyperactivity disorder during temporal
processes and normalization with methylphenidate.
Philosophical Transactions of the Royal Society of London,
364(1525): 1919e1931, 2009a.
Rubia K, Halari R, Smith A, Mohammad M, Scott S, and
Brammer M. 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. Journal of Child
Psychology and Psychiatry, 50(6): 669e678, 2009b.
Rubia K, Smith A, Halari R, Matukura F, Mohammad M, Taylor E,
et al. 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. American Journal of Psychiatry, 166(1): 83e94, 2009c.
Rubia K, Halari R, Cubillo A, Mohammad M, and Taylor E.
Methylphenidate normalises activation and functional
connectivity deficits in attention and motivation networks in
medication-naı ¨ve children with ADHD during a Rewarded
Continuous Performance Task. Neuropharmacology, 57(7e8):
Rubia K, Halari R, Cubillo A, Mohammad M, Scott S, and
Brammer M. Disorder-specific inferior prefrontal
hypofunction in boys with pure attention-deficit/hyperactivity
disorder compared to boys with pure conduct disorder during
cognitive flexibility. Human Brain Mapping, 31(12):
Rubia K, Cubillo A, Smith A, Woolley J, Heyman I, and
Brammer M. 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. Human Brain Mapping, 31(2):
Rubia K, Cubillo A, Woolley J, Halari R, Smith A, and Brammer M.
Disorder-specific dysfunctions in boys with attention-deficit/
hyperactivity disorder compared to boys with obsessive-
compulsive disorder during interference inhibition and
attention allocation. Human Brain Mapping, 32(4):
Rubia K, Halari R, Cubillo A, Mohammad AM, M B and E T.
Methylphenidate normalises fronto-striatal underactivation
during interference inhibition in medication-naive children
with attention-deficit hyperactivity disorder.
Neuropsychopharmacology, in press [epub ahead of printing, doi:
Sagvolden T, Aase H, Zeiner P, and Berger D. Altered
reinforcement mechanisms in attention-deficit/hyperactivity
disorder. Behavioural Brain Research, 94(1): 61e71, 1998.
Scheres A, Milham MP, Knutson B, and Castellanos FX. Ventral
striatal hyporesponsiveness during reward anticipation in
attention-deficit/hyperactivity disorder. Biological Psychiatry,
61(5): 720e724, 2007.
cortex 48 (2012) 194e215
Schlochtermeier L, Stoy M, Schlagenhauf F, Wrase J, Park SQ,
Friedel E, Huss M, Lehmkuhl U, Heinz A and Strohle A.
Childhood methylphenidate treatment of ADHD and response
to affective stimuli. European Neuropsychopharmacology, in press
[epub ahead of printing, doi:10.1016/j.euroneuro.2010.05.001].
Schmitz N, Rubia K, van Amelsvoort T, Daly E, Smith A, and
Murphy DG. Neural correlates of reward in autism. British
Journal of Psychiatry, 192(1): 19e24, 2008.
Schneider MF, Krick CM, Retz W, Hengesch G, Retz-Junginger P,
Reith W, et al. Impairment of fronto-striatal and parietal
cerebral networks correlates with attention deficit
hyperactivity disorder (ADHD) psychopathology in adults e
a functional magnetic resonance imaging (fMRI) study.
Psychiatry Research, 183(1): 75e84, 2010.
Schoenbaum G, Roesch MR, and Stalnaker TA. Orbitofrontal
cortex, decision-making and drug addiction. Trends in
Neurosciences, 29(2): 116e124, 2006.
Seidman LJ, Valera EM, Makris N, Monuteaux MC, Boriel DL,
Kelkar K, et al. Dorsolateral prefrontal and anterior cingulate
cortex volumetric abnormalities in adults with attention-
deficit/hyperactivity disorder identified by magnetic resonance
imaging. Biological Psychiatry, 60(10): 1071e1080, 2006.
Seidman LJ, Biederman J, Liang L, Valera EM, Monuteaux MC,
Brown A, Kaiser J, Spencer T, Faraone SV, and Makris N. Gray
matter alterations in adults with attention-deficit/
hyperactivitydisorder identified by voxel based morphometry.
Biological Psychiatry, 69(9): 857e866, 2011.
Sergeant JA, Geurts H, and Oosterlaan J. How specific is a deficit of
executive functioning for attention-deficit/hyperactivity
disorder? Behavioural Brain Research, 130(1e2): 3e28, 2002.
Sharp DJ, Bonnelle V, De Boissezon X, Beckmann CF, James SG,
Patel MC, et al. Distinct frontal systems for response
inhibition, attentional capture, and error processing.
Proceedings of the National Academy of Sciences of the United States
of America, 107(13): 6106e6111, 2010.
Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, et al.
Longitudinal mapping of cortical thickness and clinical
outcome in children and adolescents with attention-deficit/
hyperactivity disorder. Archives of General Psychiatry, 63(5):
Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP,
Greenstein D, et al. Attention-deficit/hyperactivity disorder is
characterized by a delay in cortical maturation. Proceedings of
the National Academy of Sciences of the United States of America,
104(49): 19649e19654, 2007.
Shaw P, Gilliam M, Liverpool M, Weddle C, Malek M, Sharp W,
et al. Cortical development in typically developing children
with symptoms of hyperactivity and impulsivity: Support for
a dimensional view of attention deficit hyperactivity disorder.
American Journal of Psychiatry, 168(2): 143e151, 2011.
Simmonds DJ, Pekar JJ, and Mostofsky SH. Meta-analysis of
Go/No-go tasks demonstrating that fMRI activation associated
with response inhibition is task-dependent. Neuropsychologia,
46(1): 224e232, 2008.
Small DM, Gitelman DR, Gregory MD, Nobre AC, Parrish TB, and
Mesulam MM. The posterior cingulate and medial prefrontal
cortex mediate the anticipatory allocation of spatial attention.
NeuroImage, 18(3): 633e641, 2003.
Small DM, Gitelman D, Simmons K, Bloise SM, Parrish T, and
Mesulam MM. Monetary incentives enhance processing in
brain regions mediating topedown control of attention.
Cerebral Cortex, 15(12): 1855e1865, 2005.
Smith A, Taylor E, Brammer M, Toone B, and 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. American Journal of Psychiatry,
163(6): 1044e1051, 2006.
Smith A, Taylor E, Brammer M, Halari R, and Rubia K. Reduced
activation in right lateral prefrontal cortex and anterior
cingulate gyrus in medication-naive adolescents with
attention deficit hyperactivity disorder during time
discrimination. Journal of Child Psychology and Psychiatry,
49(9): 977e985, 2008.
Smith A, Halari R, Giampietro V, Brammer M, and Rubia K.
Developmental effects of reward on sustained attention
networks. NeuroImage, 56(3): 1693e1704, 2011.
Smith SM. Fast robust automated brain extraction. Human Brain
Mapping, 17(3): 143e155, 2002.
et al. Psychiatric comorbidity and functional impairment in
a clinically referred sample of adults with attention-deficit/
hyperactivity disorder (ADHD). European Archives of Psychiatry
and Clinical Neurosciences, 257(7): 371e377, 2007.
Sobanski E, Bruggemann D, Alm B, Kern S, Philipsen A,
Schmalzried H, et al. Subtype differences in adults with
attention-deficit/hyperactivity disorder (ADHD) with regard to
ADHD-symptoms, psychiatric comorbidity and psychosocial
adjustment. European Psychiatry, 23(2): 142e149, 2008.
Sonuga-Barke E, Bitsakou P, and Thompson M. Beyond the dual
pathway model: Evidence for the dissociation of timing,
inhibitory, and delay-related impairments in attention-deficit/
hyperactivity disorder. Journal of the American Academy of Child
and Adolescent Psychiatry, 49(4): 345e355, 2010.
Spencer T, Biederman J, and Mick E. Attention-deficit/
hyperactivity disorder: Diagnosis, lifespan, comorbidities, and
neurobiology. Ambulatory Pediatrics, 7(1): 73e81, 2007.
Spencer TJ. ADHD and comorbidity in childhood. Journal of Clinical
Psychiatry, 67(Suppl. 8): 27e31, 2006.
Spinelli S, Vasa RA, Joel S, Nelson TE, Pekar JJ and Mostofsky SH.
Variability in post-error behavioral adjustment is associated
with functional abnormalities in the temporal cortex in
children with ADHD. Journal of Child Psychology and Psychiatry,
in press [epub ahead of printing, doi:10.1111/j.1469-7610.2010.
Steinlin M. The cerebellum in cognitive processes: Supporting
studies in children. Cerebellum, 6(3): 237e241, 2007.
Stevens MC, Pearlson GD, and Kiehl KA. An FMRI auditory
oddball study of combined-subtype attention deficit
hyperactivity disorder. American Journal of Psychiatry, 164(11):
Stringaris A, Hawkins A, McLoughlin L, Moya Querejeta J,
Asherson P, Sandberg S and Taylor E. Childhood hyperactivity
as a risk factor for adult psychopathology: results from a 20
year prospective community-based study in London. Journal of
Child Psychology and Psychiatry, in press.
Stro ¨hle A, Stoy M, Wrase J, Schwarzer S, Schlagenhauf F, Huss M,
et al. Reward anticipation and outcomes in adult males with
attention-deficit/hyperactivity disorder. NeuroImage, 39(3):
Stuss DT and Alexander MP. Executive functions and the frontal
lobes: A conceptual view. Psychological Research, 63(3e4):
Sundram F, Deeley Q, Sarkar S, Daly E, Latham R, Craig M, et al.
White matter microstructural abnormalities in the frontal
lobe of adults with antisocial personality. Cortex, 48(2):
Suskauer SJ, Simmonds DJ, Caffo BS, Denckla MB, Pekar JJ, and
Mostofsky SH. fMRI of intrasubject variability in ADHD:
Anomalous premotor activity with prefrontal compensation.
Journal of the American Academy of Child and Adolescent
Psychiatry, 47(10): 1141e1150, 2008a.
Suskauer SJ, Simmonds DJ, Fotedar S, Blankner JG, Pekar JJ,
Denckla MB, et al. Functional magnetic resonance imaging
evidence for abnormalities in response selection in attention
deficit hyperactivity disorder: Differences in activation
cortex 48 (2012) 194e215
associated with response inhibition but not habitual motor Download full-text
response.Journal ofCognitive Neuroscience,20(3): 478e493,2008b.
Tabu H, Mima T, Aso T, Takahashi R and Fukuyama H. Functional
relevance of pre-supplementary motor areas for the choice to
stop during Stop signal task. Neuroscience Research, in press
[epub ahead of printing, doi:10.1016/j.neures.2011.03.007].
Tamm L, Menon V, Ringel J, and Reiss AL. Event-related FMRI
evidence of frontotemporal involvement in aberrant response
inhibition and task switching in attention-deficit/
hyperactivity disorder. Journal of the American Academy of Child
and Adolescent Psychiatry, 43(11): 1430e1440, 2004.
Tamm L, Menon V, and Reiss AL. Parietal attentional system
aberrations during target detection in adolescents with
attention deficit hyperactivity disorder: Event-related fMRI
evidence. American Journal of Psychiatry, 163(6):
Tana MG, Montin E, Cerutti S, and Bianchi AM. Exploring cortical
attentional system by using fMRI during a Continuous
Performance Test. Computational Intelligence and Neuroscience,
2010(329213): 1e6, 2010.
Taylor E, Sandberg S, Thorley G, and Giles S. The Epidemiology of
Childhood Hyperactivity. Oxford, England: Oxford University
Taylor E, Chadwick O, Heptinstall E, and Danckaerts M.
Hyperactivity and conduct problems as risk factors for
adolescent development. Journal of the American Academy of
Child and Adolescent Psychiatry, 35(9): 1213e1226, 1996.
Thiebaut de Schotten M, Dell’Acqua F, Valabregue R, and
Catani M. Monkey to human comparative anatomy of the
frontal lobe association tracts. Cortex, 48(1): 82e96, 2012.
Thirion B, Pinel P, Meriaux S, Roche A, Dehaene S, and Poline JB.
Analysis of a large fMRI cohort: Statistical and methodological
issues for group analyses. NeuroImage, 35(1): 105e120, 2007.
Tian LX, Jiang TZ, Wang YF, Zang YF, He Y, Liang M, et al. Altered
resting-state functional connectivity patterns of anterior
cingulate cortex in adolescents with attention deficit
Tomporowski PD and Tinsley VF. Effects of memory demand and
motivation on sustained attention in young and older adults.
American Journal of Psychology, 109(2): 187e204, 1996.
Uddin LQ, Kelly AM, Biswal BB, Margulies DS, Shehzad Z, Shaw D,
et al. Network homogeneity reveals decreased integrity of
default-mode network in ADHD. Journal of Neuroscience
Methods, 169(1): 249e254, 2008.
Vaidya CJ, Bunge SA, Dudukovic NM, and Zalecki CA. Altered
neural substrates of cognitive control in childhood ADHD:
Evidence from functional magnetic resonance imaging.
American Journal of Psychiatry, 162(9): 1605e1613, 2005.
Valera EM, Faraone SV, Biederman J, Poldrack RA, and Seidman LJ.
Functional neuroanatomy of working memory in adults with
attention-deficit/hyperactivity disorder. Biological Psychiatry,
57(5): 439e447, 2005.
Valera EM, Faraone SV, Murray KE, and Seidman LJ. Meta-analysis
disorder. Biological Psychiatry, 61(12): 1361e1369, 2007.
Valera EM, Brown A, Biederman J, Faraone SV, Makris N,
Monuteaux MC, et al. Sex differences in the functional
neuroanatomy of working memory in adults with ADHD.
American Journal of Psychiatry, 167(1): 87e94, 2010a.
Valera EM, Spencer RM, Zeffiro TA, Makris N, Spencer TJ,
Faraone SV, et al. Neural substrates of impaired sensorimotor
timing in adult attention-deficit/hyperactivity disorder.
Biological Psychiatry, 68(4): 359e367, 2010b.
Valko L, Schneider G, Doehnert M, Muller U, Brandeis D,
Steinhausen HC, et al. Time processing in children and
adults with ADHD. Journal of Neural Transmission, 117(10):
Vloet TD, Gilsbach S, Neufang S, Fink GR, Herpertz-Dahlmann B,
and Konrad K. Neural mechanisms of interference control and
time discrimination in attention-deficit/hyperactivity
disorder. Journal of the American Academy of Child and Adolescent
Psychiatry, 49(4): 356e367, 2010.
Voisin J, Bidet-Caulet A, Bertrand O, and Fonlupt P. Listening in
silence activates auditory areas: A functional magnetic
resonance imaging study. Journal of Neuroscience, 26(1):
Wang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, et al.
Parcellation-dependent small-world brain functional
networks: A resting-state fMRI study. Human Brain Mapping,
30(5): 1511e1523, 2009.
Wilens TE, Biederman J, Faraone SV, Martelon M, Westerberg D,
and Spencer TJ. Presenting ADHD symptoms, subtypes, and
comorbid disorders in clinically referred adults with ADHD.
Journal of Clinical Psychiatry, 70(11): 1557e1562, 2009.
Willcutt EG, Doyle AE, Nigg JT, Faraone SV, and Pennington BF.
Validity of the executive function theory of attention-deficit/
hyperactivity disorder: A meta-analytic review. Biological
Psychiatry, 57(11): 1336e1346, 2005.
Windmann S, Kirsch P, Mier D, Stark R, Walter B, Gunturkun O,
et al. On framing effects in decision making: Linking lateral
versus medial orbitofrontal cortex activation to choice
outcome processing. Journal of Cognitive Neuroscience, 18(7):
Wolf RC, Plichta MM, Sambataro F, Fallgatter AJ, Jacob C,
Lesch KP, et al. Regional brain activation changes and
abnormal functional connectivity of the ventrolateral
prefrontal cortex during working memory processing in adults
with attention-deficit/hyperactivity disorder. Human Brain
Mapping, 30(7): 2252e2266, 2009.
Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, et al. Altered
baseline brain activity in children with ADHD revealed by
resting-state functional MRI. Brain and Development, 29(2):
Zelazo PD and Muller U. Executive function in typical and atypical
development. In Goswami U (Ed), Handbook of Cognitive
Development. Oxford: Blackwell, 2002: 445e469.
analysis of brain function at resting-state for attention-deficit/
hyperactivity disorder. Medical Image Computing and Computer-
Assisted Intervention e Miccai 2005, 3750(Pt 2): 468e475, 2005.
cortex 48 (2012) 194e215