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A review of fronto-striatal and fronto-cortical brainabnormalities in children and adults with Attention DeficitHyperactivity Disorder (ADHD) and new evidence fordysfunction in adults with ADHD during motivation andattention

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

Article history:

Received 14 October 2010

Revised 12 January 2011

Accepted 11 April 2011

Published online 27 April 2011

Keywords:

Adult ADHD

fMRI

Reward

Sustained attention

Review

* Corresponding author. Department of ChildLondon SE5 8AF, UK.

E-mail address: katya.rubia@kcl.ac.uk (K.0010-9452/$ e see front matter ª 2011 Elsevdoi:10.1016/j.cortex.2011.04.007

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-

striatal, fronto-parieto-temporal, fronto-cerebellar and fronto-limbic regions and

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-naıve 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.

ª 2011 Elsevier Srl. All rights reserved.

Psychiatry/SGDP, P046, King’s College London, Institute of Psychiatry, 16 De Crespigny Park,

Rubia).ier Srl. All rights reserved.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 195

1. Introduction

suggesting multiple developmental pathways for ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is charac-

terised by age-inappropriate symptoms of inattention,

impulsiveness and hyperactivity (Diagnostic Standard

manual-IV – DSM-IV)(American Psychiatric Association, 1994).

It disrupts academic and social development, and is associated

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)

and persists into adulthood in 65%of cases (Barkley et al., 2002;

Biederman et al., 2006), affecting 4% of the adult population

(Faraone and Biederman, 2005; Kessler et al., 2006).

2. Neuropsychological deficits in childrenand adults with ADHD

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”

motivationandreward-relatedEF,which involve theregulation

of affect and motivation (Zelazo and Muller, 2002). Thus, “hot”

EF consist of tasks of reward-related decisionmaking, 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

ADHDwhoare impaired ineither “cool” EF, “hot”EFor temporal

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

(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).

3. Structural and functional neuroimaging ofchildhood and adult ADHD

3.1. Structural studies

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,

theposterior inferior vermis of the cerebellum, the spleniumof

the corpus callosum and right caudate (Valera et al., 2007). A

subsequent meta-analysis of whole-brain voxel-based

morphometry studies in children with ADHD found that the

most consistent regional gray matter reduction in ADHD

patients compared to controlswas in right putamenandglobus

pallidus (Ellison-Wright et al., 2008). Longitudinal imaging

studies have provided some evidence that the structural

abnormalities observed in children with ADHD compared to

healthypeers in frontal, striatal, parietal andcerebellar regions

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 cognitivematuration, 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;

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5196

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.,

2008).

Diffusion tensor imaging studies furthermore have

demonstratedabnormalities at theneural network level. Thus,

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;

Pavuluri et al., 2009; for a reviewseeKonradandEickhoff, 2010).

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

associatedwithmedicationstatus, so that studies that included

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;

Suskauer et al., 2008a, 2008b), interference inhibition

(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

have shown 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 (Scheres et al., 2007), in ventrolateral

fronto-striato-thalamo-parieto-cerebellar network during

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, childrenwith 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-termmedication

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

regions duringmotor and interference inhibition (Banich et al.,

2009; Burgess et al., 2010; Bushet al., 1999; Cubillo et al., 2010, in

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

workingmemory tasks (Banich et al., 2009; Dibbets et al., 2009,

2010; Epstein et al., 2007;Hale et al., 2007; Schneider et al., 2010;

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 (Strohle 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-

naıve adultswith ADHD compared to 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

fronto-striato-thalamic, temporo-parietal and cerebellar

regions, thus overlapping with areas observed to be under-

activated during motor and interference inhibition, switching

andworkingmemory tasks (Cubillo et al., 2010, in press; Depue

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;

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 197

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).

3.4. Conclusions

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

regional interconnections with striatal, cerebellar, and

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.

4. New data: lateral inferior fronto-striataland orbitofrontal-ventromedial braindysfunction in adults with childhood ADHD andpersistent hyperactive/inattentive behavioursduring sustained attention and reward

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 in adult ADHD have

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

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 (Strohle 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-naıve

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 andmotivation 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

(Tomporowski and Tinsley, 1996). FunctionalMRI 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 childrenwith ADHD (Rubia et al.,

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5198

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-naıve 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.

4.1. Methods

4.1.1. SubjectsThe 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

who met criteria for hyperactive/inattentive behaviour

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-III diagnosis 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>10 is associatedwith poor social functioning (Stringaris

Table 1 e Description of symptoms for adults with childhood

Childhood diagnosis AHI scores Hyperactivity symptoms

ADHD hyp þ CD 19 3

ADHD combined 24 3

ADHD hyp þ CD 24 3

ADHD combined 10 0

ADHD hyp þ CD 13 0

ADHD hyp 16 0

ADHD combined þ CD 10 2

CD þ ADHD hyp 18 2

ADHD combined 14 3

ADHD combined 25 3

ADHD hyp þ CD 14 2

Note: ADHD hyp ¼ ADHD hyperactive subtype.

a 3 ¼ level of problem impairing function and deserving diagnosis; 2 ¼ d

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 diagnostic criteria for 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 disturbance or

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-matchedmales 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

ADHD and persistent hyperactivity/inattention symptoms.

a Inattention symptomsa ADHD DSM-IV criteria

3 YES-combined

3 YES-combined

3 YES-combined

1 NO-inattentive symptoms

1 NO-inattentive symptoms

1 NO-inattentive symptoms

1 YES-predominantly Hyperactive

3 YES-combined

3 YES-combined

1 YES-predominantly Hyperactive

2 YES-combined

efinite problem; 1 ¼ moderate problem; 0 ¼ no problem.

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.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 199

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.

4.1.2. fMRI paradigm: rewarded CPTA 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;

Smith et al., 2011) consists of 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

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

“reward contrast”.

4.1.3. Analysis of performance dataRepeated 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

a t-test.

4.1.4. fMRI image acquisition and analysesGradient-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

Table 2 e Performance data for adults with childhoodADHD and persistent hyperactivity/inattentionsymptoms and healthy comparison adults.

Performance variable Controls(N ¼ 14)

ADHD(N ¼ 11)

Mean (SD) Mean (SD)

Rewarded trials MRT (msec)a 420 (48) 382 (42)

Non-rewarded trials MRT (msec)a 422 (39) 388 (43)

Rewarded trials omission errors 0 (1) 0 (0)

Non-rewarded trials omission errors 0 (1) 0 (0)

Commission errors 1(1) 2 (2)

MRT ¼ Mean Reaction Time.

a Significant between-groups differences.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5200

Level Dependent (BOLD) contrast covering the whole brain

were acquiredwith TE¼ 40msec, TR¼ 1.8 sec, flip angle¼ 90�,in-plane resolution ¼ 3.1 mm, slice thickness ¼ 7 mm, slice-

skip ¼ .7 mm. This EPI dataset provided complete brain

coverage.

For fMRI analysis, the software package of XBAMwas used

(www.brainmap.co.uk, Brammer et al., 1997) that usesmedian

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 (Bullmore et al., 1999) and smoothed using

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

method previously described (Bullmore et 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 data re-samplingmethod (Bullmore et al., 2001)

and applying themodel-fitting process to the re-sampled data.

This process was repeated 20 times at each voxel and the data

combined over all voxels, resulting in 20 null parametricmaps

of SSQ-ratio for each subject, whichwere combined 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

trials (non-rewarded target trials- non-target trials),

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

testing them against the null distribution of median

SSQ-ratios computed from the identically transformed

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

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 greymattermask of the Talairach

template used for normalisation. Thismaskwas subsequently

used to restrict the analysis to those voxels lying within grey

matter.

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 BOLDmeasureswere then correlatedwithin each group

with RT using Pearson correlations, and with commission

errors using Spearman’s rho, given the non-parametric nature

of these.

4.2. Results

4.2.1. Task performanceRepeated 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

speed.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 201

4.2.2. Brain activation4.2.2.1. 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].

4.2.2.2. SUSTAINED ATTENTION: NON-REWARDED TARGET TRIALS

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

and cerebellum.

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

Fig. 2 eWithin group activationmaps for controls and adults wit

symptoms. Axial sections showing within-group brain activatio

childhood ADHD and persistent hyperactivity/inattention symp

non-rewarded target e non-target trials, (b) Reward: rewarded e

indicated for slice distance (in mm) from the intercommissural

insula, putamen and thalamus, and one including right PCC,

precuneus, and parahippocampal gyrus (Fig. 3a, Table 3a). In

all these clusters, controls showed increased activation 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-

tive to non-rewarded target trials. Effect sizeswere large for all

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).

Given evidence for inferior fronto-cerebellar neural

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

h childhood ADHD and persistent hyperactivity/inattention

n for the healthy comparison group and the adults with

toms for the contrasts (a) Sustained attention:

non-rewarded target trials. Tailarach z-coordinates are

line.

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 ADHDwho 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.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5202

observed in ADHD patients relative to controls was

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

lateral cerebellum (r¼�.81, p< .01) (see Supplementary 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

sizes.

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

Table 3 e Differences in brain activation between adults with childhood ADHD and healthy comparison adults.

Brain regions of activation Brodmanarea (BA)

Peak Talairachcoordinates (x; y; z)

N ofvoxels

pvalues

Effect Sizes(Cohen’s d )

(a) Sustained attention: non-rewarded target e non-target trials

C > ADHD

L inferior frontal/insula/premotor/putamen/globus pallidus/

caudate/thalamus/insula/ACC/pre-SMA

6/44/9/24/32 �29; 4; 26 136 >.001 1.09

R premotor/postcentral gyrus/insula/putamen 4/6/43 58; �4; 15 43 .004 .93

R PCC/precuneus/parahippocampal gyrus 18/19/31/7 11; �33; 20 64 .01 .77

ADHD > C

L inferior/superior parietal gyrus 40/7 �43; �37; 42 28 .001 1.05

R inferior/superior parietal gyrus 40/7 47; �41; 42 109 .002 1.08

R þ L cuneus/precuneus/PCC 18/19/7/29/30 �7; �74; 26 408 >.001 1.43

R vermis cerebellum/occipital/PCC 18/23/30/31 4; �67; �13 244 .003 1.43

L cerebellum/occipital/inferior temporal 19/18/37 �25; �63; �13 309 >.001 1.81

(b) Reward: rewarded target e non-rewarded target trials

C > ADHD

R medial/superior frontal gyrus 10/46/9/8 36; 52; 15 140 .014 1.35

R ventromedial orbitofrontal 11/9/10 25; 48; �13 105 .011 1.24

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.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 203

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 (r¼�.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 (r¼�.06, p¼ 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).

4.2.2.3. EFFECTS OF REWARD: REWARDED TARGETS COMPARED TO NON-REWARDED TARGET 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

performance measures.

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

comparisons.

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

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5204

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.

4.3. Discussion

Medication-naıve 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

during tasks of cognitive control such as sustained (Rubia et al.,

2009d) and selective attention in conflict tasks (Rubia et al.,

2011, in press), attentional switching (Rubia et al., 2010b), as

well asmotor response inhibition (Suskaueret al., 2008a, 2008b;

Tammet 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-SMAhas been shown to be involved in sustained attention

in healthy adults (Lawrence et al., 2003; Tana et al., 2010) and to

have a role inmotor responsepreparationand selectionaswell

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-

tained attention condition, adultswith ADHD showed reduced

activation in paralimbic VMPFC and OFC regions, key areas of

reward processing, which are typically activated in this task

Fig. 4 e Similarities in reduced and increased brain activation in medication-naıve adults with childhood ADHD and

persistent hyperactivity/inattention symptoms relative to their age-matched controls and in medication-naıve children

with ADHD during the Sustained Attention condition compared to their respective age-matched healthy comparison

subjects.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 205

(Smith et al., 2011). The lateral OFC mediates stimulus-

reinforcement learning (Baxter and Murray, 2002;

Schoenbaum et al., 2006). The VMPFC is associated specifi-

cally with reward as opposed to punishmentedriven

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

networks of affect regulation and motivation are

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

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

(Strohle et al., 2008).We argue that underactivation inOFCmay

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

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5206

OFC dysfunction. Only those patients with ADHD with comor-

bid CD in childhood showed underactivation in VMPFC/OFC

regions compared to healthy controls during the reward 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

the presence of comorbid CD in childhood. Presence or absence

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 previous fronto-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

Fig. 5 e Reduced brain activation in adults with childhood ADH

tasks in fMRI. For details on images a, b, eeg see Cubillo et al.,

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

comorbid childhood CD rather thanADHD.With respect to 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

D relative to healthy controls across a series of cognitive

2010, in press.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 207

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

caudate normaliseswith age (Nakao et al., in press). However, it

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-

ingsshowthatacross tasks, including “cool”and“hot”EFaswell

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

control and selective attention, inferior fronto-striatal

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

and cognitive control aswell “hot”motivationprocessesmaybe

dysfunctional in ADHD, in line with models of multisystem

dysfunctions inADHD(Makrisetal., 2009;NiggandCasey, 2005).

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

not IQ.

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 waswell controlled, this could have affected the sustained

attention contrast. Thus, some activation differences between

groups for this contrast could potentially bemotor- 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-

tained attention tasks (Lawrence et al., 2003; Voisin et al., 2006).

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 stimulantmedication 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-naıve 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.

5. Overall conclusions

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

dorsomedial fronto-striatal, fronto-parietal and fronto-

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

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5208

functional imaging evidence in support of the notion of the

adult persistence of dysfunctions previously observed in chil-

dren with ADHD. Medication-naıve adults with hyperactive/

inattentive behaviours, followedup fromaconfirmedpresence

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

amore impulsive performance style. The findingswere similar

to thosepreviouslyobserved in the samegroupof adultsduring

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

reviewedevidence thatadultswithADHDcontinue to showthe

same abnormalities in lateral fronto-striato-parietal and

fronto-cerebellar cognitive control networks as well as in

ventromedial fronto-striatal and fronto-temporo-limbic

networks during motivation control as children with ADHD.

Thereviewtogetherwith thenewdata thereforesuggest that

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

Fig. 6 e Schematic representation of the MRI evidence for struc

adults with ADHD in overlapping neural networks that mediate

executive and cognitive functions. “Cool” cognitive networks o

medial fronto-striatal, fronto-parieto-temporal and fronto-cereb

as motor response and interference inhibition, cognitive flexibi

working memory, motor and timing processes. “Hot” EF netwo

the context of temporal discounting, reward processing and rew

OFC [ orbitofrontal cortex; DLPFC [ dorsolateral prefrontal cor

d/vACC [ dorsal/ventral ACC cortex; SMA [ Supplementary M

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).

There are, however, several potential caveats in the imaging

literature of ADHD that need to be considered. The majority of

structural and functional imagingstudies inchildrenandadults

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

withabnormalities inneural networksof theparalimbic system

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 likely to suffer from abnormalities

in both “cool” and “hot” EF neurocircuitries, it is possible that

pure, non-comorbid ADHD groups may only be impaired in

“cool” EF brain systems. In adultADHD, themajority of imaging

studies have included high rates of comorbid cases with

tural and functional brain abnormalities in children and

“cool” cognitive-abstract and “hot” reward-associated

f dysfunction in ADHD include inferior, dorsolateral and

ellar regions and networks that mediate functions such

lity, temporal foresight, selective and sustained attention,

rk dysfunctions in ADHD patients have been observed in

ard anticipation. IFG [ inferior frontal gyrus;

tex; vmOFC [ ventromedial orbitofrontal cortex;

otor Area.

c o r t e x 4 8 ( 2 0 1 2 ) 1 9 4e2 1 5 209

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

control.More imaging studiesofADHDareneededthat focuson

limbic regions and limbic white matter networks as well as

emotional fMRI paradigms to assess more thoroughly to what

degree thesesystemsareaffected inADHDandtheir subgroups.

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

from dysfunctions in fronto-limbic emotion/motivation

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-

naıve 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-naıve ADHD children and the here presented

medication-naıve adults with ADHD are in line with this and

suggest that deficit findings may be more similar when

medication-naıve patients are being compared.

It also remains to be investigated to what degree ADHD is

a delay of normal brain structure or function maturation.

Overall, the cross-sectional findings from thedifferent imaging

modalities suggest thatADHDchildrenhavedeficits in the very

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

apply toADHD, given that similar brain structure, function and

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

maturational “delay”, if it is a “delay” persists throughout adult

life. Also, to our knowledge, no imaging studies have been

conducted inelderlypatientswithADHD,but theremaywellbe

differences innormal ageingprocesses too. Future longitudinal

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

studies should also test for different factors thatmay influence

a potential normalisation process, including symptom

severity, environmental factors, cognitive faculties, genetic

predisposition or pharmacological and behavioural treatment.

Acknowledgements

The research was supported by grants from the Medical

Research Council (G9900839) and TheWellcomeTrust (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

Supplementary data related to this article can be found online

at doi:10.1016/j.cortex.2011.04.007.

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