# Cortical gray matter in attention-deficit/hyperactivity disorder: a structural magnetic resonance imaging study

Martin J Batty, Elizabeth B Liddle, Alain Pitiot, Roberto Toro, Madeleine J Groom, Gaia Scerif, Mario Liotti, Peter F Liddle, Tomáš Paus, Chris Hollis

► To cite this version:

Martin J Batty, Elizabeth B Liddle, Alain Pitiot, Roberto Toro, Madeleine J Groom, 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, 2010, 49 (3), pp.229-238. 10.1016/j.jaac.2009.11.008. pasteur-01967137

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<figure><img src="image_1.png" /></figure>
# Cortical Gray Matter in Attention-Deficit/Hyperactivity Disorder: A Structural Magnetic Resonance Imaging Study

Martin J. Batty, Ph.D, Elizabeth B. Liddle, Ph.D, Alain Pitiot, Ph.D, Roberto Toro, Ph.D, Madeleine J. Groom, Ph.D, Gaia Scerif, Ph.D, Mario Liotti, M.D., Ph.D, Peter F. Liddle, MBBCh., Ph.D, Tomáš Paus, M.D., Ph.D, Chris Hollis, MRCPsych, Ph.D

**Objective:** Previous studies have shown smaller brain volume and less gray matter in children with attention-deficit/hyperactivity disorder (ADHD). Relatively few morphological studies have examined structures thought to subserve inhibitory control, one of the diagnostic features of ADHD. We examined one such region, the pars opercularis, predicting a thinner cortex of the inferior frontal gyrus (IFG) in children with ADHD. **Method:** Structural images were obtained from 49 children (24 control; 25 ADHD combined subtype) aged 9 through 15 years. Images were processed using a volumetric pipeline to provide a fully automated estimate of regional volumes of gray and white matter. A further analysis using FreeSurfer provided measures of cortical thickness for each lobe, and for 13 regions in the frontal lobe. **Results:** Relative to controls, children with ADHD had smaller whole brain volume and lower gray matter, but not white matter, volumes in all lobes. An analysis of frontal regions showed a significant interaction of group by region. Planned contrasts showed bilateral thinner cortex in the pars opercularis in children with ADHD. **Conclusions:** Children with ADHD showed both diffuse and regional gray matter abnormalities. Consistent with its putative role in response inhibition, the cortex of the pars opercularis was thinner in children with ADHD who, as expected, had significantly poorer inhibitory performance on a Go/No-go task. These differences held for both hemispheres raising the possibility that a developmental abnormality of IFG might drive development of inhibition difficulties. J. Am. Acad. Child Adolesc. Psychiatry, 2010;49(3):229-238. **Key Words:** Attention-deficit/hyperactivity disorder, MRI, Cortical thickness, Inferior frontal gyrus, Gray matter.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmen- tal disorder affecting between 3% and 5% of children and young persons. It is characterized by pervasive and developmentally inappropriate levels of inattention, impulsivity, and hyperactiv- ity and wide-ranging behavioral and cognitive impairments, including deficits in working mem- ory, inhibitory control, and altered motivational style.¹ Disinhibited behavior is one of the diag- nostic criteria for ADHD combined subtype, and children with ADHD typically perform more poorly than controls on tasks that require inhibi- tion or suppression of a pre-potent response.² This can be readily observed using paradigms such as the Go/No-go and Stop-Signal task in which subjects are required to make speeded responses to frequently presented targets while

withholding responses to rarer "No-go" or "Stop" trials. Both tasks are thought to tap inhib- itory behavioral control, a process generally con- sidered to be supported by fronto-cortical re- gions.³

The frontal lobe is an important focus of research in ADHD for a number of reasons. Converging evidence implicates dopaminergic dysfunction of fronto-striatal circuits in the pathogenesis of the disorder.⁴ Studies using functional magnetic resonance imaging (fMRI) have shown differential task-related response in frontal regions in both children⁴ and adults⁵ with ADHD, relative to matched controls, in para- digms requiring response inhibition. Further ev- idence of the importance of the frontal lobes in ADHD comes from MR-based anatomical stud- ies. Although interpretation of the anatomical
findings is complicated by differences in meth-
odology, clinical subtype and comorbidities (re-
viewed by Krain et al.⁶), children with ADHD
appear to have smaller volumes of prefrontal⁷
and striatal⁴,⁸ gray matter (GM) as compared
with typically developing children. Moreover,
the presence of a relationship between perfor-
mance in “inhibitory” tasks and fronto-striatal
volume⁴ suggests a close association between the
structural development of the fronto-cortical sys-
tems and cognitive functions implicated in
ADHD symptoms.

Several fronto-cortical regions, including the
ventrolateral prefrontal, dorsolateral prefrontal,
and anterior cingulate cortex (ACC), are impor-
tant in attention. In particular, several studies
have highlighted the role of the ventrolateral
regions of the inferior frontal gyrus (IFG)—par-
ticularly the right IFG—as being crucial for in-
hibiting behavioral responses.⁹,¹⁰ The IFG com-
prises three adjacent regions: pars opercularis
Brodmann area (BA 44), pars triangularis (BA
45), and pars orbitalis (BA 47/12). Using a group
of patients with lesions confined to the right
frontal lobe, Aron et al.⁹ investigated the putative
role of the IFG in response inhibition. They
hypothesized that damage to IFG, but not other
structures, would correlate with performance on
a Stop-signal task. As predicted, there was a
significant positive correlation between the ex-
tent of IFG damage and Stop Signal Reaction
Time (SSRT), an inferred measure of the time
taken to countermand a motor response. Closer
examination of specific regions within IFG re-
vealed that the damage of the pars opercularis,
but not the pars triangularis, was a significant
predictor of SSRT.

Further support for the importance of the pars opercularis in inhibitory control is provided by a recent repetitive transcranial magnetic stimulation (rTMS) study¹⁰ in which rTMS was applied over three regions of the right hemisphere: IFG (pars opercularis), middle frontal gyrus (MFG) and angular gyrus (AG). Participants completed two blocks of a Stop-signal task, each block preceded by a 15-minute period of 1Hz rTMS. Inhibitory performance was impaired only after applying rTMS over the IFG. Finally, as one might expect of a disorder in which inhibitory performance is compromised, structural¹¹,¹² and functional⁴,¹³ abnormalities in IFG have been noted in children with ADHD.

Although these findings highlight the signifi-

cance of the IFG in inhibitory control, a number
of other regions appear to be important for this
function, including pre-supplementary motor
area (preSMA) and right dorsolateral prefrontal
and inferior parietal areas, with activation in the
latter two regions being particularly pronounced
when working memory load is high.¹⁴ In addi-
tion, fMRI signal in frontal (anterior cingulate),
striatal and medial temporal regions has been
shown to correlate with Stop Signal Reaction
Time (SSRT) in children with ADHD.¹⁵ In sup-
port of this more “global” model of inhibitory
deficits in ADHD, reductions in total brain vol-
ume⁷,¹⁶ and gray matter (GM) volume¹⁷ are
among the most consistent structural findings in
ADHD. In addition, thinner cerebral cortex in
children with ADHD, as compared with typically
developing children, has been found both glo-
bally and locally, most notably in frontal and
central regions.¹⁸ Overall, the heterogeneity of
findings in ADHD suggests that the disorder is
likely to be characterized by widespread changes
in cortical GM.¹²,¹⁹ Because the attention system
of the human brain is thought to involve a
number of distinct but interconnected regions,²⁰
and because of the frequent occurrence of comor-
bid disorders in ADHD²¹ in which the neural
bases are largely unknown, an examination of
global changes in brain structure should prove
useful.

Here, we used MRI to obtain a number of
global and regional measures of brain structure
in a clinical group of children and adolescents
with ADHD characterized by clinical impulsivity/
hyperactivity and inattention (DSM-IV combined
subtype) and in healthy controls. In accordance
with previous studies and the heterogeneous
nature of the disorder, we predicted smaller
brain volume and global reductions in GM in
participants with ADHD. In addition, based on
the putative role of the IFG in inhibitory
control⁹,¹⁰,²² and the occurrence of inhibitory
deficits in the disorder,²³ we predicted lower
cortical thickness in the right pars opercularis in
participants with ADHD.

METHOD

Participants

Ethical approval was granted by the local Research
Ethics Committee and Research and Development
Departments of the Nottinghamshire Healthcare and
Lincolnshire Partnership NHS Trusts. After complete
description of the study, written informed consent and
<table><caption>TABLE 1 Clinical and Demographic Characteristics of Subjects</caption><thead><tr><th rowspan="2"></th><th colspan="2">Group</th><th rowspan="2">p Value</th></tr><tr><th>ADHD<br>N = 25</th><th>Control<br>N = 24</th></tr></thead><tbody><tr><td>Gender</td><td>M=24, F=1</td><td>M=23, F=1</td><td>n/s</td></tr><tr><td>Age (y)</td><td>12.48 (1.86)</td><td>12.82 (1.69)</td><td>.52</td></tr><tr><td>Weight (kg)</td><td>42.84 (15.30)</td><td>48.38 (11.87)</td><td>.17</td></tr><tr><td>Duration of stimulant medication (mo)</td><td>38.1 (23.7)</td><td>n/a</td><td>n/a</td></tr><tr><td>D<sup>a</sup></td><td>1.16</td><td>1.68</td><td>.012</td></tr><tr><td>FSIQ</td><td>89.84 (11.65)</td><td>104.67 (14.74)</td><td>.0003</td></tr><tr><td>Total digit span scaled</td><td>7.72 (2.76)</td><td>9.75 (3.05)</td><td>.018</td></tr><tr><td>TOWRE: Total Score</td><td>89.56 (22.34)</td><td>98.63 (13.99)</td><td>.097</td></tr><tr><td>Conners Parent DSM total</td><td>81.40 (7.50)</td><td>44.79 (6.17)</td><td>4.21E-23</td></tr><tr><td>SES classification (n)</td><td></td><td></td><td>.89</td></tr><tr><td>Higher professional</td><td>1</td><td>1</td><td></td></tr><tr><td>Lower professional</td><td>5</td><td>5</td><td></td></tr><tr><td>Self-employed</td><td>1</td><td>1</td><td></td></tr><tr><td>Manual/unemployed</td><td>18</td><td>17</td><td></td></tr><tr><td>Co-morbid diagnoses</td><td></td><td></td><td></td></tr><tr><td>ODD</td><td>11</td><td>0</td><td>n/a</td></tr><tr><td>CD</td><td>7</td><td>0</td><td>n/a</td></tr><tr><td>DCD</td><td>1</td><td>0</td><td>n/a</td></tr><tr><td>RD</td><td>1</td><td>0</td><td>n/a</td></tr><tr><td>GAD</td><td>3</td><td>0</td><td>n/a</td></tr><tr><td>Depression<sup>b</sup></td><td>1</td><td>0</td><td>n/a</td></tr><tr><td>Specific phobia</td><td>3</td><td>0</td><td>n/a</td></tr></tbody></table>

Note: Some subjects had more than one comorbidity. ADHD = attention-deficit/hyperactivity disorder; CD = conduct disorder; DCD = developmental coordination disorder; DSM = Diagnostic and Statistical Manual of Mental Disorders; FSIQ = full-scale intelligence quotient; GAD = generalized anxiety disorder; ODD = oppositional defiant disorder; RD = reading disorder; SES = socioeconomic status; TOWRE = Test of Word Reading Efficiency.

<sup>a</sup>D prime scores are taken from a Go/No-go task using 24 matched pairs (ADHD participants off medication; Cohen's Dz effect size = 0.76).

<sup>b</sup>Mild depressive episode.

verbal assent was obtained from parents and children, respectively. Thirty children with ADHD and 33 controls taking part in an fMRI/EEG study (reported elsewhere<sup>24</sup>) underwent MRI scanning. Eleven children (five subjects with ADHD and six controls) were excluded because of excessive movement during scanning and three further controls were removed from the FreeSurfer analysis due to poor registration of their scans. Thus, data from 52 subjects were included in the volumetric analysis and 49 subjects were included in the cortical thickness analysis; 25 ADHD (24 male; mean age, 12.48 years; SD, 1.86) and 24 controls (23 male; mean age, 12.82; SD, 1.69). Details of clinical and demographic data are summarized in Table 1.

*ADHD Group.* Children and adolescents with a clinical diagnosis of ADHD were recruited from child psychiatry and community paediatric clinics. An initial telephone call to parents screened for broad inclusion criteria (9 through 15 years, right handed, currently taking and responsive to stimulant medication) and exclusion criteria (presence or history of psycho-

sis, epilepsy or Tourette syndrome, diagnosis of moderate or severe learning disability, current use of nonstimulant or other psychotropic medication except melatonin). Eligible participants were invited to attend an assessment session in which we administered the Development and Well Being Assessment [DAWBA]<sup>25</sup> and a battery of questionnaires were administered: Social Communication Questionnaire [SCQ],<sup>26</sup> Strengths and Difficulties Questionnaire [SDQ],<sup>27</sup> and Conners long form.<sup>28</sup> Permission to access the child's medical records and contact their school was obtained, and teacher versions of the DAWBA, SDQ, and Conners were also completed for each child.

ADHD diagnosis was confirmed or overturned following a clinical consensus diagnostic meeting involving CH and another experienced child and adolescent psychiatrist. This included a full review of the child's medical history, parent and teacher DAWBA transcripts (including computer generated predictions) and questionnaires. Only right-handed participants with a DSM-IV diagnosis of ADHD combined
subtype and an established positive response to stim-
ulant medication (assessed using symptom severity
and clinical interview ratings on and off medication)
were included. Diagnosed or suspected comorbidities
were investigated where necessary, and any partici-
pants with psychosis, bipolar disorder, major depres-
sion, Tourette syndrome, Autistic Disorder/Asperg-
er's Disorder, major head trauma or epilepsy were
excluded. Participants with comorbid oppositional de-
fiant disorder (ODD), conduct disorder (CD), and
anxiety disorder were included. A separate session
assessed intelligence, reading ability and handedness
using, respectively, Weschler Abbreviated Scale of
Intelligence (WASI), Test of Word Reading Efficiency
(TOWRE), and Annett Handedness Questionnaire.
Any subjects with a full scale IQ <70 were excluded.
Non-ADHD Control Group. Letters detailing the
study were sent to approximately 600 families of
children in primary and secondary schools in the
Nottinghamshire region. From an initial sample who
volunteered to take part, we selected a group of
right-handed controls matched for age (±6 months),
sex, and parental socio-economic status (SES) to a
member of the ADHD group. Parental SES was as-
sessed using the eight groups identified in the ONS
SES manual (ONS, v 1.1, 2004), combined to form four
subgroups (Table 1) to enable accurate demographic
matching. Parents completed the same battery of ques-
tionnaires used in the ADHD assessment, including a
shortened version of the DAWBA. Potential partici-
pants with attention scores >1 SD above the mean on
the SDQ or Conners (n = 6), or with known or
suspected autistic or other major psychiatric disorders
(assessed using the SCQ and DAWBA) or a FSIQ <70
were excluded from the study.

Participants undertook additional tasks (not re-
ported in this paper) on two separate days as part of
the fMRI/EEG study, in which testing was conducted
on and off stimulant medication, the order of which
was counterbalanced across subjects. Therefore, ap-
proximately half on the structural scans for the ADHD
group were taken while the subjects were on medica-
tion (n = 13), while the remainder (n = 12) were
scanned following a ~36-hour stimulant-medication
withdrawal. No effects of medication status at the time
of scanning on brain structure were predicted for the
ADHD group; controls were never medicated.

MRI Acquisition and Analysis

T1-weighted (T1W) brain images in the sagittal plane were obtained with a Philips Achieva 1.5-T MRI scanner with an eight-channel SENSE head coil using a 3D TFE sequence with the following parameters: 160 contiguous slices; TR/TE 9.9/3.7 ms; matrix size, 256 × 256; voxel size, 1 × 1 × 1 mm. Head movement was minimized by the use of foam pads placed within the head coil.

The acquired images were processed using a pipeline
adapted from the Montreal Neurological Institute (MNI)

approach.²⁹ First, we corrected nonuniformity in the
intensity of T1-weighted (T1W) images using the N3
algorithm.³⁰ After nonuniformity correction, the T1W
images were linearly and nonlinearly registered onto the
standard stereotaxic space. The template brain used here
is the average brain computed from a population
(SYS333) comprising 183 female adolescents (age [mean
± SD], 183 ± 24 months; FSIQ, 105 ± 12) and 150 male
adolescents (age, 183 ± 22 months; FSIQ, 105 ± 13).³¹ The
SYS333 template is aligned with the MNI-305 template,³²
which is aligned with the Talairach and Tournoux at-
las.³³ The tissues were then classified into GM, white
matter (WM), and cerebrospinal fluid (CSF) using the
fully automated landmark-based MNI classifier.³⁴ By
back-projecting these tissue maps and a standard-space
lobar atlas onto the native space of the original MR scans,
we could quantify the overall amount of GM, WM, and
CSF for each lobe in each subject. This provided a fully
automated estimate of lobar volumes of GM and WM. A
brain mask of the SYS333 template, obtained by manu-
ally adjusting the automatic extraction produced by the
BET algorithm (FSL software), was also nonlinearly back-
projected onto the native space of each scan to provide an
estimate of brain size for each subject.

Estimates of cortical thickness were obtained using FreeSurfer.³⁵ For each subject, GM, WM, and noncortical structures were segmented and a triangular mesh was used to measure the distance from the pial surface to the GM/WM boundary for each hemisphere.³⁶ For both the FreeSurfer and the volumetry pipeline, a quality control inspection assessed for gross structural abnormalities, accuracy of registration, and presence of artifacts.

All statistical analyses were conducted using SPSS
v.16 (SPSS Inc., Chicago, IL). Volumes of WM and GM
and mean cortical thickness were computed for each lobe
and combined across the left and right hemispheres. To
ascertain the presence of group differences in frontal
cortex, particularly inferior frontal gyrus (IFG), we also
tested differences between the two groups in cortical
thickness in 13 frontal regions segmented by the Free-
Surfer. If the data violated the assumptions of sphericity,
Greenhouse-Geisser adjusted degrees of freedom were
used and a corrected p value was reported (p<GG). All
reported analyses include data from all eligible partici-
pants. When the analyses were repeated omitting the one
female ADHD-control pair, the pattern of results re-
mained the same.

RESULTS

Participant Characteristics

There were no significant differences in age, sex,
or socioeconomic status (SES) between the two
groups (Table 1). Because GM and WM volume
changes during development are well estab-
lished, and as pairwise matching was not used in
order to maximise subject numbers, AGE was
<table><caption>TABLE 2 Absolute Gray Matter Volumes by Lobe and Group</caption><thead><tr><th rowspan="3"></th><th colspan="4">Group</th></tr><tr><th colspan="2">ADHD</th><th colspan="2">Control</th></tr><tr><th>Mean (cm³)</th><th>SD</th><th>Mean (cm³)</th><th>SD</th></tr></thead><tbody><tr><td>Total frontal GM absolute</td><td>232.87</td><td>21.67</td><td>260.07</td><td>21.55</td></tr><tr><td>Total parietal GM absolute</td><td>113.23</td><td>12.09</td><td>127.16</td><td>12.01</td></tr><tr><td>Total temporal GM absolute</td><td>161.03</td><td>15.91</td><td>174.43</td><td>15.82</td></tr><tr><td>Total occipital GM absolute</td><td>67.30</td><td>8.76</td><td>75.79</td><td>8.71</td></tr></tbody><tfoot><tr><td colspan="5">Note: ADHD = attention-deficit/hyperactivity disorder; GM = gray matter.</td></tr></tfoot></table>

used as a covariate (linear and quadratic) in all analyses. Only one analysis was improved by the inclusion of a quadratic term and in all analyses, the findings remained robust when this term was included. Controls (CTRL) had higher mean Full Scale Intelligence (FSIQ) than children with ADHD (105, SD = 15 vs. 90, SD = 12 respectively); t (50) = 4.27, p = .0003 and FSIQ was also used as a covariate in all subsequent analyses.

## Brain Volume

An analysis of covariance (ANCOVA) using the between subjects factor GROUP (ADHD vs. CTRL) and the covariates AGE and full-scale IQ (FSIQ), returned significant main effects of GROUP [F (1, 48) = 9.61, p = .003] and AGE [F (1, 48) = 15.52, p = .0003] but not FSIQ [F<1]. Participants with ADHD had smaller total brain volume than CONTROLS (ADHD = 1273.42 cm³, SD = 118.19; CTRL = 1387.89 cm³ SD = 112.12). Age was a significant predictor of brain volume, with larger brains in the older children. Including a quadratic term for age did not improve the fit of the model.

## Gray Matter

Absolute GM volumes for each lobe were entered into a multivariate analysis of covariance. There were significant main effects of AGE, F (4, 45) = 6.03, p = .001 and GROUP, F (4, 45) = 5.71, p = .001. Younger children had lower GM volume than older children, and children with ADHD had less GM than CONTROLS. Univariate analysis showed smaller GM volumes in all four lobes in ADHD, as compared with CONTROLS: Frontal [F (1, 48) = 17.78, p = .0001]; Parietal [F (1, 48) = 14.90, p = .0003]; Temporal [F (1, 48) = 8.01, p = .007] and Occipital [F (1, 48) = 10.60, p = .002] (Table 2). Although this GM difference is

partially accounted for by the reduction in brain volume in the ADHD group, when brain volume was entered as a covariate and total GM volume for the four lobes was entered as a dependent variable, ADHD participants still had smaller (relative) GM volumes [F (4, 44) = 2.91, p = .032]. Using univariate statistics, only parietal [F (1, 47) = 4.76, p = .034] (ADHD = 117.06 cm³, SD = 9.25; CTRL = 123.57 cm³, SD = 10.08) and frontal lobe [F (1, 47) = 6.67, p = .0013] (ADHD = 240.42 cm³, SD = 15.90; CTRL = 253.08 cm³; SD = 15.75) relative GM volumes were significantly different between the two groups. The same was also true when total surface (cortical) area was entered as a covariate (instead of brain volume). Overall, total relative GM volume is significantly lower in ADHD children, as compared with CONTROLS, after covarying for surface area (or brain volume), surface ratio, and mean thickness.

## White Matter

When the analysis was repeated using WM volume, there was no main effect of GROUP (F<1). The smaller brain volume in the ADHD cases appears to be driven by lower GM volume in all lobes.

## Cortical Thickness

Cortical thickness measures derived from FreeSurfer were analysed in a series of ANCOVAs. Data from three control subjects were not included in this analysis because of poor registration of their scans. To test our a priori hypothesis that children with ADHD would show abnormalities in the Inferior Frontal Gyrus, specifically, the pars opercularis, a repeated-measures ANCOVA was conducted with HEMISPHERE as a within-subjects factor and GROUP as a between-subjects factor, using the covariates Age
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** Developmental trajectory of cortical thickness of the right pars opercularis in ADHD and control groups (quadratic fit). ADHD = attention-deficit/hyperactivity disorder.</figcaption></figure>

and FSIQ. Hemisphere was included in this analysis owing to the putative lateralised role of the right pars opercularis in inhibitory control. The ANCOVA returned a significant main effect of GROUP, F (1, 45) = 6.00, p = .018; children with ADHD had significantly thinner cortex in the pars opercularis than CONTROLS (2.71 mm, SD = .14 vs 2.81 mm, SD = .14 respectively). There was no significant difference in the thickness of the pars opercularis between the left and right hemisphere, as evidenced by the nonsignificant GROUP × HEMISPHERE interaction (F = 1.1) but univariate analysis conducted on each hemisphere separately showed a significant main effect of GROUP in the right hemisphere, F (1, 45) = 5.79, p = .02, and a trend in the left hemisphere, F (1, 45) = 3.30, p = .076. Including a quadratic term for age improved the fit of the model,

whereby the left pars opercularis had a nonsignificant positive value and the right pars opercularis had an inverted U function (significant quadratic effect) of age, peaking at around 148 months (Figure 1).

Mean cortical thickness across the 13 frontal cortical regions (caudal anterior cingulate, caudal middle frontal, frontal pole, lateral orbitofrontal, medial orbitofrontal, paracentral, pars opercularis, pars orbitalis, pars triangularis, precentral, rostral anterior cingulate, rostral middle frontal, and superior frontal) were then entered into a 2 (GROUP) × 13 (REGION) × 2 (HEMISPHERE) repeated-measures ANCOVA using the covariates AGE, and FSIQ and returned a significant REGION × GROUP interaction, F (5.82, 262.04) = 2.49, $p_{(GG)}$ = .024. In a planned comparison, the thickness of the pars opercularis was com-
**FIGURE 2** Ranked effect sizes for mean differences in cortical thickness for frontal regions in ADHD and control groups after controlling for FSIQ and Age. Positive effect sizes indicate thinner cortex in ADHD and negative effects sizes mean thicker cortex in ADHD. ADHD = attention-deficit/hyperactivity disorder; FSIQ = full-scale intelligence quotient.

<figure><img src="image_3.png" /></figure>

pared with the mean thickness of all the other frontal areas and a significant effect was found, *F* (1, 45) = 4.58, *p* = .038. Children with ADHD had thinner cortex in the pars opercularis relative to CONTROLS in both hemispheres. The effect sizes for each of the 13 regions are presented in Figure 2. As predicted, the largest effect was for pars opercularis (Cohen's *D* = 0.6).

A vertex-based analysis using the between-subjects factor GROUP failed to reveal any significance difference in cortical thickness for any voxels in any of the lobes after correcting for multiple comparisons across the entire cerebral cortex.

## Behavioral Correlate

On a Go/No-go task, inhibition rates were significantly lower in children with ADHD than controls (Table 1). Pearson correlations were computed separately for each group to determine whether there was an association between mean cortical thickness of the pars opercularis and discriminability (measured using D prime scores)

on the Go/No-go task from the 39 children with available D prime data. In the children with ADHD, task performance did not correlate with thickness of the pars opercularis (r = .201, n = 21, *p* = .383), whereas in the control group, there was a nonsignificant trend (r = .44, n = 18, *p* = .071).

## DISCUSSION

Consistent with previous studies,¹⁶,¹⁷ the mean total brain volume in children with ADHD was less than that of the typically developing controls. In addition, GM (but not WM) volume was lower in all four lobes. Smaller global GM volumes have been observed in other studies,¹⁷ particularly in frontal areas, as reviewed elsewhere.¹⁹,³⁷

The global lower GM volume in the ADHD group might be explained using a model of delayed brain maturation.³⁸ In both typically and atypically developing children, GM and WM development follows a similar trajectory,
with GM increases in childhood followed by a reduction in adolescence. Shaw et al.³⁸ measured the peak age at which cortical thickness occurred as a proxy for cortical maturation. Peak cortical thickness in children with ADHD was found to lag by approximately 4 years relative to controls, with the largest difference (~5 years) observed in prefrontal cortex, suggestive of a maturational lag. However, cortical thinning in networks thought to subserve attention has also been found in adults with ADHD,³⁹ indicating that developmental structural anomalies in ADHD might persist into adult life rather than normalise with age. In support of this view, Castellanos et al.¹⁶ used a longitudinal design and found that volume abnormalities present in children with ADHD continued into adulthood. Although the neurobiological underpinnings of these group differences in GM are unknown, they could be related, for example, to fewer synapses and/or reduced dendritic branching in children with ADHD, with corresponding decreases in cerebral metabolism accompanied by a reduction in the numbers of glial cells.⁴⁰ Whereas widespread reductions in GM appear robust and are of interest in their own right, we were particularly interested in determining whether cortical abnormalities in specific structures associated with control of inhibition might also be present in ADHD. We began with an *a priori* interest in the frontal lobe, specifically, the pars opercularis, because of its putative role in inhibitory control.⁹,¹⁰,²² As hypothesized, the cortex was thinner throughout the frontal lobe in the children with ADHD, but was significantly thinner in the pars opercularis, which also had the greatest effect size. Of note, and in accord with its proposed role in inhibitory function, not only did the children with ADHD show structural differences in the pars opercularis, but their performance on a Go/no-go task was also significantly poorer (d' scores in Table 1). Furthermore, although there was a nonsignificant trend for performance to be positively correlated with cortical thickness in the pars opercularis in the control group, no such trend was observed in the ADHD group. This finding is not wholly unexpected, given that participants were selected on the basis of having good (control participants) or poor (ADHD participants) inhibitory control. Thus, the weak or absent association between structure and be-

haviour in each group is likely to reflect homogeneity within groups. Similar findings were evidenced in a study by Durston et al.¹³ Using fMRI, children and adolescents with ADHD, their unaffected siblings and controls undertook a Go/No-go task. Correlations between right IFG and performance were evident in controls and unaffected siblings only and not in participants with ADHD. In the current study, thinner cortex was present in both the left and right hemisphere in the children with ADHD. Our finding of a larger effect in right IFG but no significant hemispheric difference, is consistent with prior functional imaging studies of Go/No-go tasks, which more commonly show engagement of right IFG¹⁴ but in some instances report engagement of bilateral IFG.⁴¹

The strengths of this study include a well-defined clinical sample, the inclusion of only combined type ADHD and careful matching of controls. There are, however, also some limitations. Although the sample size is larger than that of many similar studies, it lacked the statistical power required to explore multiple *a priori* regions or to detect small effects post hoc.¹⁶,³⁸ Although the groups were carefully matched in terms of demographic factors, they were not matched for IQ. However, as participants came from similar areas and backgrounds, the lower IQ scores in the ADHD group are likely to be a consequence of the disorder rather than other factors such as social disadvantage, and thus reflect “typical” ADHD. Indeed, attention and learning problems are highly interrelated and typically coexist.⁴² As expected,²¹ comorbidity—particularly behavioral disorders—was present in most participants with ADHD, all of whom were taking long-term stimulant medication. Nonetheless, when the analysis was repeated including only those participants with externalising disorders (ODD/CD), the results remained robust despite the reduction in sample size. This finding is in keeping with other studies in which comorbid behavioral disorders such as oppositional defiant disorder and conduct disorder have evidenced relatively little additional effect on brain structure in ADHD.¹⁵

With respect to medication, in one of the largest morphologic studies to date,¹⁶ no significant differences were found between medicated and treatment-naive subjects, suggesting that medication has little effect on brain structure. In
contrast, Semrud-Clikeman et al.⁴³ found reductions in right anterior cingulate cortex volume in treatment-naive relative to medicated ADHDs and controls, raising the intriguing possibility that medication may “normalize” deficient structures by strengthening connections within the structure in much the same way that synaptic plasticity may increase the size of local structures through demand.⁴⁴ Further support for this notion is provided in a recent study by Shaw et al.,⁴⁵ who found more rapid cortical thinning in excess of age-appropriate rates in children with ADHD not taking stimulant medication. If so, then the morphological reductions observed in our ADHD participants may be an underestimate of the true effects of ADHD rather than a consequence of stimulant medication.

Our findings demonstrate that an ADHD combined type subgroup, with clinical features including impulsivity/hyperactivity have both a generalized deficit in gray matter compared with healthy controls, but there is some evidence of nonuniformity, with the deficits being most marked in IFG. Future work using a single het-

erogeneous group with a spectrum of severity of impulsivity deficits would enable us to test for the anatomical correlates of impulsivity in ADHD. &

Accepted November 19, 2009.

Drs. Batty, E. Liddle, Pitiot, Groom, P. Liddle, Paus, and Hollis are with the University of Nottingham. Dr. Toro is with the Pasteur Institute, Paris, France. Dr. Scerif is with the University of Oxford. Dr. Liotti is with Simon Fraser University, Canada. Dr. Paus is also with the Montreal Neurological Institute, Montreal, Quebec, Canada.

This study was supported by a grant from the Wellcome Trust (grant number: 076448/Z/05/Z).

We are grateful for the assistance of Drs Carolyn Nahmen and Rachel Duffy in the clinical assessment of patients, John Totman and Carolyn Costigan for scanning, Dr Pierre-Yves Herve for assistance with FreeSurfer, and the comments of two anonymous reviewers. We also extend our gratitude to the families who took part.

Disclosure: Drs. Batty, E. Liddle, Pitiot, Toro, Groom, Scerif, Liotti, P. Liddle, Paus, and Hollis report no biomedical financial interests or potential conflicts of interest.

Correspondence to: Dr. Batty, Developmental Psychiatry, E Floor, South Block, Queen's Medical Centre, Derby Road, NG7 2UH, United Kingdom; e-mail: martin.batty@nottingham.ac.uk.

0890-8567/10/Crown Copyright ©2010 Published by Elsevier Inc on behalf of American Academy of Child and Adolescent Psychiatry. All rights reserved.

DOI: 10.1016/j.jaac.2009.11.008

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