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Original Article |

Longitudinal Mapping of Cortical Thickness and Clinical Outcome in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder FREE

Philip Shaw, MD; Jason Lerch, PhD; Deanna Greenstein, PhD; Wendy Sharp, MSW; Liv Clasen, PhD; Alan Evans, PhD; Jay Giedd, MD; F. Xavier Castellanos, MD; Judith Rapoport, MD
[+] Author Affiliations

Author Affiliations: Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Md (Drs Shaw, Greenstein, Clasen, Giedd, and Rapoport and Ms Sharp); Montreal Neurological Institute, McGill University, Montreal, Quebec (Drs Lerch and Evans); and New York University Child Study Center, New York (Dr Castellanos).


Arch Gen Psychiatry. 2006;63(5):540-549. doi:10.1001/archpsyc.63.5.540.
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Published online

Context  Data from a previous prospective study of lobar volumes in children with attention-deficit/hyperactivity disorder (ADHD) are reexamined using a measure of cortical thickness.

Objective  To determine whether regional differences in cortical thickness or cortical changes across time characterize ADHD and predict or reflect its clinical outcome.

Design, Setting, and Participants  Longitudinal study of 163 children with ADHD (mean age at entry, 8.9 years) and 166 controls recruited mainly from a local community in Maryland. Participants were assessed with magnetic resonance imaging. Ninety-seven patients with ADHD (60%) had 2 or more images and baseline and follow-up clinical evaluations (mean follow-up, 5.7 years).

Main Outcome Measures  Cortical thickness across the cerebrum. Patients with ADHD were divided into better and worse outcome groups on the basis of a mean split in scores on the Children's Global Assessment Scale and persistence/remission of DSM-IV–defined ADHD.

Results  Children with ADHD had global thinning of the cortex (mean reduction, −0.09 mm; P=.02), most prominently in the medial and superior prefrontal and precentral regions. Children with worse clinical outcome had a thinner left medial prefrontal cortex at baseline than the better outcome group (−0.38 mm; P=.003) and controls (−0.25 mm; P=.002). Cortical thickness developmental trajectories did not differ significantly between the ADHD and control groups throughout except in the right parietal cortex, where trajectories converged. This normalization of cortical thickness occurred only in the better outcome group.

Conclusions  Children with ADHD show relative cortical thinning in regions important for attentional control. Children with a worse outcome have “fixed” thinning of the left medial prefrontal cortex, which may compromise the anterior attentional network and encumber clinical improvement. Right parietal cortex thickness normalization in patients with a better outcome may represent compensatory cortical change.

Figures in this Article

Attention-deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder that affects 3% to 5% of school-aged children in the United States.1 It has been variously conceptualized as an inability to suppress inappropriate responses and thoughts,2,3 as a pathologic abnormality of executive “control” attentional networks,4 and as the result of an aversion to delay that stems from abnormal processing of rewards.5 These diverse models all implicate dysfunction of the prefrontal cortex (PFC) and the interconnected striatum. Structural change in the frontal lobe,6,7 particularly in the posterior cingulate, precentral gyrus, and superior and dorsolateral prefrontal gray matter, have all been found in ADHD.810 Functional imaging studies1114 report anomalous prefrontal activation in ADHD, most consistently in midline prefrontal regions during response inhibition, decision making based on reward contingencies,15 and complex motor control.16 These deficits are also linked to hyperactivity and impulsivity, combined perhaps with pathologic abnormalities at the level of motor output in the motor cortices.17 The right parietal cortex is another major component of the distributed attention system involved in orienting attention to visual locations and in the maintenance of a vigilant state.18,19 Its compromise in ADHD is suggested by reports of structural6 and functional20,21 anomalies.

A striking feature of ADHD is its tendency to improve with age, with symptomatic improvement occurring in 31% to 43% of children as they move into late adolescence.22 A previous longitudinal study6 that did not consider clinical outcome demonstrated that the disorder is characterized by nonprogressive deficits in gray and white matter, except in the caudate, which normalizes in volume by late adolescence. It is possible that clinical improvement in a neurodevelopmental disorder such as ADHD may be associated with convergence to the normal trajectory of cortical development, with normalization prominent in regions that control attention.

Awareness that lobar volumetric studies may miss more regional cortical changes has prompted the examination of smaller regions of interest, frequently measured manually. Although informative, such studies are prone to operator error and are unsuited to large data sets, and it is possible that the boundaries of actual change in ADHD may not overlap with the limits of the a priori–defined regions of interest. We thus used a fully automated measure of cortical thickness across the entire cerebrum, unconstrained by predefined regions of interest. The technique has been validated through manual measurements23 and a population simulation.24 In addition, the method has been found to be sensitive to processes of normal aging and cognitive variation25 and to cortical abnormalities,26 making it an ideal tool for longitudinal mapping of cortical development. The technique has already been applied in a cross-sectional study27 of 27 children with ADHD demonstrating highly localized cortical change.

Drawing inferences about developmental processes from cross-sectional data is fraught with methodological problems; thus, we used a longitudinal design, studying a large group of 163 children with ADHD and 166 controls. Most patients with ADHD (60%) had at least 2 magnetic resonance images (MRIs) and clinical evaluations acquired during mean follow-up of 5.7 years. We hypothesized that ADHD would be characterized by focal cortical anomalies in regions of the distributed neural system that mediate attention, specifically, the medial prefrontal and cingulate gyri and the right parietal cortex. On an exploratory basis, we speculated that different clinical functional outcomes in ADHD would be associated with differences in the pattern of cortical change at baseline and in trajectories of cortical development.

PARTICIPANTS

One hundred sixty-six children and adolescents with DSM-IV–defined ADHD were recruited using the Diagnostic Interview for Children and Adolescents28 and a Conners' Teacher Rating Scale hyperactivity rating greater than 2 SDs above age- and sex-specific mean ratings.29 Parental history of probable ADHD was determined using the Wender Utah Rating Scale.30 Exclusion criteria were a full-scale IQ score of less than 80 and evidence of medical or neurologic disorders. Neuroimaging data from 3 patients with ADHD could not be analyzed owing to motion artifact, and these patients were excluded from further analyses. Ninety-five percent of the patients had combined-type ADHD (n = 157), 4 (2%) had inattentive ADHD, and 2 (1%) had the hyperactive subtype of ADHD. Unrelated controls (n = 166) who had no personal or family history of psychiatric or neurologic disorders were also recruited from the community.

Approximately 60% of the individuals in each group underwent MRI at least twice. The institutional review board of the National Institute of Mental Health approved the research protocol, and written informed consent and assent to participate in the study were obtained from the parents and children, respectively.

CLINICAL OUTCOME MEASURES

The first outcome measure was the last available Children's Global Assessment Scale (CGAS) score,31 chosen because it provides a clinically relevant measure of outcome and was available at baseline and follow-up for most patients with ADHD (n = 107). Patients with ADHD were divided into better (n = 51) and worse (n = 56) outcome groups based on mean final CGAS scores (mean CGAS score, 64; better outcome CGAS score, ≥64; and worse outcome CGAS score, <64). The second outcome measure was whether patients continued to meet DSM-IV criteria for ADHD. Clinical assessments were performed independently of neuroimaging analyses. The mean ages of the groups at each wave of assessment and MRI did not differ significantly (Table 1).

Table Graphic Jump LocationTable 1 Demographic and Clinical Details of Patients With ADHD With Better vs Worse Outcome
MRI ACQUISITION AND ANALYSIS

T1-weighted images with contiguous 1.5-mm sections in the axial plane and 2.0-mm sections in the coronal plane were obtained using 3-dimensional spoiled gradient recalled echo in the steady state on a 1.5-T scanner (Signa; General Electric Medical Systems, Milwaukee, Wis) (echo time, 5 milliseconds; repetition time, 24 milliseconds; flip angle, 45°; acquisition matrix, 256 × 192; number of signals acquired, 1; and field of view, 24 cm). The native MRIs were registered into standardized stereotaxic space using a linear transformation and corrected for nonuniformity artifacts.32 The registered and corrected volumes were segmented into white matter, gray matter, cerebrospinal fluid, and background using an advanced neural net classifier.33 A surface deformation algorithm was applied that first fits the white matter surface and then expands outward to find the gray matter–cerebrospinal fluid intersection defining a known relationship between each vertex of the white matter surface and its gray matter surface counterpart; cortical thickness can thus be defined as the distance between these linked vertices (40 962 such vertices are calculated).34 The white and gray matter surfaces were resampled into native space by inverting the initial stereotaxic transformation. Cortical thickness was then computed in native space. To improve the ability to detect population changes, each patient's cortical thickness map was blurred using a 30-mm surface-based blurring kernel.24

STATISTICAL ANALYSIS

Differences between groups at baseline were examined using 2-sample t tests for continuous variables and χ2 tests of independence for categorical variables. Linear regression was used to examine the effects of outcome group and medication status on cortical thickness in baseline MRIs. Variables that significantly correlated with cortical thickness and that differed between groups were entered as covariates in regression analyses. For the longitudinal analyses, mixed-model regression was chosen because it permits the inclusion of multiple measurements per person, missing data, and irregular intervals between measurements, thereby increasing statistical power.35 In unadjusted analyses, the resulting statistical maps were thresholded to control for multiple comparisons using the false discovery rate procedure, with q = 0.05.36,37 For each regression model, all P values for all effects were pooled across all vertices, and a false discovery rate threshold was determined. Initial longitudinal analyses estimated the full quadratic model at each vertex, but because the squared age term did not contribute significantly to the model across the cortex, a linear model was used to fit the trajectories of the ADHD and control groups. Sex and the interaction of sex and diagnosis did not significantly affect the shape of growth curves across cortical regions and were excluded. Thus, in the final model for the ADHD vs control comparisons, the ith individual's jth cortical thickness at a given vertex was modeled as follows:

Thicknessij = Intercept + di + βcontrol (Diagnosis = Control) + β1(Age–Mean Age) + β2 (Diagnosis = Control×[Age − Mean Age]) + eij,

where di is a random-effects modeling within-person dependence; the intercept and β terms are fixed effects, and eij represents the residual error. Group differences in slope were determined by the significance of the interaction term (ie, β2). Group differences in height, representing difference in cortical thickness, were determined by the significance of the βcontrol term. The t statistics at every cortical point were visualized through projection onto a standard brain template. Such visualization showed clusters of cortical points that differed significantly between the ADHD outcome groups and controls in the baseline MRIs or in the trajectory of cortical development. Analyses selected and averaged all cortical points within each of these clusters. Graphs illustrating the developmental trajectories of clusters were generated using fixed-effects parameter estimates.

We explored which baseline variables were significantly associated with CGAS scores at final follow-up, treating the CGAS score as a continuous variable (and thus included only the patients with ADHD). In addition, a linear discriminant analysis with leave-one-out cross-validation38,39 was used to assess the ability of the measures of cortical thickness to separate accurately the outcome groups, treated as categories, from each other and from controls.

COMPARISON OF THE ADHD AND CONTROL GROUPS

The groups were well matched on demographic and diagnostic characteristics, except for a significantly lower IQ score in the ADHD group (Table 2). Comorbid diagnoses were relatively mild and in no case were the focus of treatment. The ADHD group had a significantly smaller estimated mean overall cortical thickness, most prominently in the prefrontal and anterior temporal cortices (Table 3) (see Supplementary Table 1a for additional details of cortical thickness in 56 subregions across the entire cerebrum, available at: http://intramural.nimh.nih.gov/chp/cos/shaw2006archivessupplementarytables.htm). When adjustment was made for differences in mean overall cortical thickness and IQ, diagnostic differences survived at t values greater than 2.0 in areas in the superior and medial frontal gyri and cingulate region bilaterally, left precentral gyrus, and right anterior/mesial temporal cortex (Figure 1 and Table 3). There was no region of significant increase in cortical thickness for the ADHD group in the unadjusted data.

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Figure 1

Cortical thickness in attention-deficit/hyperactivity disorder (ADHD) compared with controls. A, Estimated difference in cortical thickness in millimeters between patients with ADHD and controls. Significantly thinner regions in the ADHD group, applying a false discovery rate of 0.05, are shown in yellow. B, Group differences (t > 2) after adjustment for IQ and mean overall cortical thickness.

Graphic Jump Location
Table Graphic Jump LocationTable 2 Demographic and Diagnostic Characteristics of Patients With ADHD and Controls
Table Graphic Jump LocationTable 3 Cortical Thickness in the Regions That Differed Significantly Between Groups*

Children with ADHD who were medication naïve at the time of the first MRI were younger (mean±SD age, 8.2±2.5 years) than those who were medicated (mean±SD age, 10.7±2.7 years) (F1,153 = 31.8; P<.001) but had a similar IQ, socioeconomic status, and sex mix. The t statistical maps showed no significant regional cortical differences between the medicated and nonmedicated groups after adjustment for age, except in a small region in the left anterior temporal cortex.

BETTER VS WORSE OUTCOME GROUPS: ANALYSES OF INITIAL MRIs

The better (n = 51) and worse (n = 56) outcome groups, defined on the basis of CGAS scores, had no significant baseline differences in any clinical measures, but the worse outcome group had a significantly lower mean IQ score, which was thus entered as a covariate in adjusted analyses. Follow-up DSM-IV diagnoses were available on 83 patients with ADHD. Mean duration of follow-up was approximately 5.7 years for both groups.

Patients who still met DSM-IV criteria for ADHD (any subtype) at follow-up had, on baseline MRIs, a significantly thinner medial prefrontal and cingulate cortex bilaterally relative to the control group. Patients with a worse clinical outcome, defined using CGAS scores, had a thinner cortex in similar medial and superior prefrontal regions, which was significant after adjustment for IQ and mean cortical thickness. In contrast, remitted patients, similar to those with CGAS-defined better outcome, showed a minimal significant difference in cortical thickness from controls. The better outcome group had a small region of cortical thinning in the left dorsolateral PFC relative to controls (Figure 2, Table 4, and Supplementary Table 1b, which gives the cortical thickness for outcome groups across 56 cortical region, available at: http://intramural.nimh.nih.gov/chp/cos/shaw2006archivessupplementarytables.htm).

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Figure 2

Contrasts between patients with attention-deficit/hyperactivity disorder (ADHD) with differing outcomes and controls. A, The t statistical maps of pairwise contrasts using persistence/remission of ADHD as the outcome measure. B, The t maps using Children's Global Assessment Scale scores as the outcome measure. Adjustment is made for IQ and mean cortical thickness.

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Table Graphic Jump LocationTable 4 Cortical Thickness in Regions That Show a Significant Difference in Baseline MRIs in the Different ADHD Outcome Groups

In the stepwise regression we assessed the variance in outcome CGAS scores attributable to the following variables: mean thickness of the 2 main cortical regions, which differed between outcome groups (the left medial prefrontal/medial cortex for the worse outcome group and the left dorsolateral prefrontal region for the better outcome group), and demographic (age, sex, and socioeconomic status), clinical (Conners' Teacher Rating Scale hyperactivity factor scores, Teacher Report Form attention problems t scores, and baseline CGAS scores), and neuropsychologic (estimated IQ) variables. Three variables entered the final model: thickness of the left medial PFC (standardized β = .46; adjusted R2 = 0.08; P<.001), thickness of the left dorsolateral prefrontal cluster (β = −.33; R2 = 0.07; P = .005), and hyperactivity factor scores at baseline (β = −.23; R2 = 0.04; P = .03). Thickness of the left medial and dorsolateral prefrontal regions thus accounts for approximately 15% of the variance in outcome scores.

Using the more stringent linear discriminant analysis with leave-one-out cross-validation, the mean thickness of the cluster of points in the left medial PFC (Figure 2A) as a single variable did not separate accurately the worse and better outcome groups from each other and from controls. This is not surprising given the modest amount of variance in final clinical outcome scores accounted for by each variable in the linear regression.

TRAJECTORY OF CORTICAL DEVELOPMENT

Parallel trajectories of cortical thickness for the ADHD and control groups were found for the overall cortical thickness and at individual vertices across the entire cortex except in the right parietal cortex (Figure 3). In this region, the entire ADHD group started at a significantly lower point, but the thickness of the right parietal cortex converged with that of the control group by age 17 years. Normalization of cortical thickness in the right parietal cortex noted for the ADHD group as a whole was attributable to the morphologic changes in the better outcome group (Figure 3).

Place holder to copy figure label and caption
Figure 3

Trajectory of change in cortical thickness in patients with attention-deficit/hyperactivity disorder (ADHD) and controls. A, Estimated trajectories for mean overall cortical thickness. There was a significant difference in height (P=.02) but not in the gradient of the lines (P=.78). Dashed lines indicate 95% confidence intervals. B, The t map indicates vertices where there was a significant interaction in the contrast between the better outcome and control groups and age. The graph illustrates group trajectories in this region (difference in gradients: better outcome group vs controls, P=.001; better vs worse outcome groups, P=.03; and worse outcome group vs controls, P=.60).

Graphic Jump Location

The cortical thickness gradients in the right parietal cortex differed significantly between the 2 outcome groups and between the better outcome group and controls. For the remaining cortex, the better outcome and control groups had parallel cortical thickness developmental trajectories, without significant differences between the gradients of the fitted lines. The worse outcome group and those with persistent ADHD showed no significant deviation from a trajectory parallel to that of the control group in any region, including the right parietal cortex. Those who had full remission showed cortical normalization in the same region of the right parietal cortex as the better outcome group.

Given the wide range of duration of follow-up, the analyses were repeated using only the central 66% (follow-up, 3.5-8.4 years) and central 80% (follow-up, 2.7-9.3 years) of the outcome group data. The same pattern of results held with converging trajectories for the better outcome and control groups (with significant differences in the gradients of the trajectories, P<.02 for all) compared with parallel trajectories for the worse outcome and control groups (with no significant differences in the gradients of trajectories, P>.10 for all). The difference in outcome was not attributable to regular stimulant use during follow-up, which did not differ significantly between outcome groups (Table 1).

Using fully automated computational techniques, we examined the relationships among cortical thickness, baseline diagnosis, and clinical outcome in a large cohort of children and adolescents with ADHD. We replicate earlier findings of cortical anomalies in the disorder, prominent in prefrontal regions important for the control of attention and motor output. A thinner medial PFC in baseline MRIs discriminated poor from good outcome in patients with ADHD and controls, whether outcome was defined on the basis of overall functioning or persistence of DSM-IV–defined ADHD. A measure of cortical thickness in this region was significantly associated with future clinical outcome scores in a linear regression, although the amount of variance accounted for by cortical thickness was modest. The outcome groups also differed in the trajectory of development of cortical thickness: the good outcome group alone showed normalization of right parietal cortical thickness in a pivotal region in posterior attentional systems.

DIAGNOSTIC DIFFERENCES

The thinner PFC we report is congruent with previous volumetric studies demonstrating reduction in frontal lobe volume. The regions of cortical thinning overlap with reductions in gray matter density found in studies that obtained a high degree of spatial resolution, specifically, loss in the superior frontal gryus, posterior cingulate, and dorsolateral PFC.27,40 However, unlike the present study, Sowell et al27 also report an increase in cortical density in the posterior temporal and inferior parietal regions, a discrepancy that may reflect in part our native space analyses rather than the use of stereotaxic space.

Cortical change in the precentral gyrus is of interest because motor hyperactivity is a cardinal feature of the disorder. Unlike the findings for the prefrontal regions, previous volumetric studies with a smaller sample size have not detected morphometric changes in the precentral gyrus.9,41 However, transcranial magnetic stimulation studies have demonstrated reduced intracortical inhibition in the motor cortex in ADHD, which may represent a neurophysiologic correlate of reduced behavioral inhibition.42 Stimulants have also been shown to correct abnormally high resting levels of cerebral blood flow in the precentral gyrus in patients with ADHD.17 Cortical thinning in the precentral gyrus may thus represent a substrate for impaired behavioral control at the level of motor output.

The thinner right mesial and left lateral temporal cortex is harder to interpret. Anomalous activation of the temporal lobes has been noted at rest and during functional MRI studies of response inhibition and working memory.4345 As part of the lateral paralimbic motivational system the region may also contribute indirectly to delay aversion in ADHD.6 We did not find any significant effects of treatment with stimulants on cortical thickness at the time of the initial MRI or on the course of cortical development.

CORTICAL DIFFERENCES AND CLINICAL OUTCOME

It is striking that cortical thickness in the left medial prefrontal and cingulate cortex at baseline discriminated between children with ADHD who had differing clinical outcomes 5 years later. This does not reflect greater symptomatic severity at baseline, medication status, or comorbidities. A more plausible explanation of the findings invokes the increasing developmental importance of attentional processing modulated by the prefrontal regions.46,47 Performance on tasks measuring response inhibition and susceptibility to interference show a marked improvement stretching into adolescence.4851 Increased or more focused activation of the PFC (including the left middle and inferior prefrontal and cingulate gyri) may support this cognitive maturation.5257 In ADHD, the development of this system is delayed, with reports of decreased and more diffuse activation of the medial prefrontal and cingulate regions during tasks that require response inhibition or higher-order motor control.12,13,16,58 In the present study, the medial and cingulate cortical thinning in patients with poor outcome persists into adolescence and, thus, could represent a compromised neural substrate that prevents age-appropriate attentional skills from coming “online” in early adolescence. In contrast, patients with ADHD with a better clinical outcome have a morphologically intact medial cortical wall, which might support the development of more refined cognitive control, leading to symptomatic relief and clinical improvement.

The thickness of the left medial PFC in baseline MRIs was more strongly associated with outcome scores than baseline clinical and demographic variables. However, in a discriminant analysis, this cortical measure, as a single variable, did not accurately predict outcome. Although this highlights the current limitations of anatomic imaging in predicting outcome, it is hoped that future multivariate discriminant analyses in a larger sample incorporating other neuroanatomic variables (such as white matter) may result in a more powerful predictive model.

TRAJECTORY OF CORTICAL DEVELOPMENT IN ADHD

The previous findings6 of fixed nonprogressive lobar cortical deficits in ADHD are partially modified by the demonstration of cortical normalization in portions of the right parietal cortex. Because we did not collect cognitive and behavioral data in tandem with neuroanatomic data, we cannot give a definitive answer to the functional significance of this structural change. However, recent studies58 suggest that activation of the right parietal cortex during tasks of alerting and reorienting of attention is not fully mature until adulthood and that this posterior component of the attentional network may develop during adolescence. In ADHD, structural6,8,27 and functional anomalies at rest20 and during tasks of selective attention12 and response inhibition21 have been shown in the right parietal cortex. In addition, previous studies have established links between structure and function in ADHD; for example, reduction in prefrontal gray matter volume and metabolism additionally correlate with deficits in response inhibition59,60 and symptom severity.6,61 Thus, normalization of the right parietal cortex, noted only in children with better clinical outcome, may support the maturation of components of the attentional network through adolescence. The striking difference in cortical development in the better and worse outcome groups might alternately suggest 2 distinct entities in ADHD, in different underlying neural substrates.

The apparent lack of normalization in the motor cortex warrants comment given the prominence of improvement in hyperactivity and impulsivity in ADHD during adolescence. First, the inferior portion of the right motor cortex and the dorsolateral cortices bilaterally were the only regions that showed a trend to normalization in the better outcome group (revealed by relaxing the false discovery rate to 0.10). Second, structural change in the right parietal cortex may have distal effects on the richly interconnected cingulate/medial PFCs,6264 regions important for response inhibition and interference suppression, which in turn may contribute to impulsivity and higher-order motor control.

Cortical thinning in adolescence, underpinned possibly by synaptic pruning,65 and increased myelination66,67 may accompany cognitive maturation,25 and, thus, the lack of thinning in the better outcome group may seem counterintuitive. However, the exact nature of the relationships among cellular events, cortical dimensions, and cognitive change in humans is largely speculative. It is possible that a relatively late persistence of synapses in the better outcome group is associated with normalization of cortical thickness and affords an extended period for the sculpting of complex neural circuits supporting attention.

The changing cortical thickness we report is likely to reflect alterations in the gray/white boundary related to myelination and changes in the cortical mantle itself. The T1-weighted MRIs used cannot disentangle the relative contribution of these factors to changes in gray and white matter at the cortical border. This will require an MRI protocol that includes measures of high-resolution relaxometry and diffusion tensor imaging that can quantify changes in myelination. However, the link between clinical outcome and cortical change in a region pivotal for the control of attention suggests that the finding is biologically meaningful and deserves further exploration.

The present study is limited partly by the attrition rate in the study of 35% for the ADHD group, although those lost to follow-up were representative of the inception cohort. The similarity in results between the 2 different definitions of clinical outcome (CGAS scores and DSM-IV definition) is reassuring but reflects in part the large overlap in membership between the worse outcome and persistent ADHD groups. We did not collect clinical measures on the typically developing controls, and, thus, a similar, if much attenuated, pattern of cortical change may occur in typically developing children who show improvement in (subthreshold) attention and hyperactivity symptom scores. The sample was composed almost entirely of children who had combined-type ADHD, and, thus, we cannot address the possibility of different developmental trajectories of the different subtypes of ADHD.

In estimating cortical thickness we chose a 30-mm-bandwidth blurring kernel on the basis of population simulations that showed that this bandwidth maximized statistical power while minimizing false positives.24 Although this bandwidth filter may seem large, 30-mm blurring along the surface using a diffusion smoothing operator represents considerably less cortex than the equivalent volumetric gaussian blurring kernel because it preserves cortical topologic features. Repeating the analyses with a 15-mm blurring kernel showed a similar pattern of results.

In conclusion, we demonstrate a pattern of cortical thinning in ADHD, predominantly in prefrontal regions, which comprise key regions associated with attentional mechanisms. A fixed nonprogressive deficit of the medial prefrontal and cingulate regions, which might compromise the anterior attentional network, was associated with relatively poor clinical outcome. In contrast, normalization of the right parietal cortex, which might support compensatory change in the posterior attentional network, was associated with relative clinical improvement.

Correspondence: Philip Shaw, MD, Child Psychiatry Branch, National Institute of Mental Health, Bldg 10, Room 3N202, Bethesda, MD 20892-1500 (shawp@mail.nih.gov).

Submitted for Publication: March 7, 2005; final revision received October 5, 2005; accepted October 25, 2005.

Buitelaar  JK Epidemiology: what have we learned over the past decade? In:Sandberg  Jed.Hyperactivity and Attention-Deficit Disorders. Cambridge, Mass Cambridge University Press2002;
Barkley  RA Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 1997;12165- 94
PubMed Link to Article
Casey  BJTottenham  NFossella  J Clinical, imaging, lesion, and genetic approaches toward a model of cognitive control. Dev Psychobiol 2002;40237- 254
PubMed Link to Article
Posner  MIDiGirolamo  GJ Executive attention: conflict, target detection and cognitive control. In:Parasuraman  Red.The Attentive Brain Cambridge, Mass MIT Press1998;
Sonuga-Barke  EJDalen  LRemington  B Do executive deficits and delay aversion make independent contributions to preschool attention-deficit/hyperactivity disorder symptoms? J Am Acad Child Adolesc Psychiatry 2003;421335- 1342
PubMed Link to Article
Castellanos  FXLee  PPSharp  WJeffries  NOGreenstein  DKClasen  LSBlumenthal  JDJames  RSEbens  CLWalter  JMZijdenbos  AEvans  ACGiedd  JNRapoport  JL Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 2002;2881740- 1748
PubMed Link to Article
Durston  SHulshoff Pol  HESchnack  HGBuitelaar  JKSteenhuis  MPMinderaa  RBKahn  RSvan Engeland  H Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry 2004;43332- 340
PubMed Link to Article
Filipek  PASemrud-Clikeman  MSteingard  RJRenshaw  PFKennedy  DNBiederman  J Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls. Neurology 1997;48589- 601
PubMed Link to Article
Mostofsky  SHCooper  KLKates  WRDenckla  MBKaufmann  WE Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biol Psychiatry 2002;52785- 794
PubMed Link to Article
Hill  DEYeo  RACampbell  RAHart  BVigil  JBrooks  W Magnetic resonance imaging correlates of attention-deficit/hyperactivity disorder in children. Neuropsychology 2003;17496- 506
PubMed Link to Article
Bush  GFrazier  JARauch  SLSeidman  LJWhalen  PJJenike  MARosen  BRBiederman  J Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop. Biol Psychiatry 1999;451542- 1552
PubMed Link to Article
Booth  JRBurman  DMeyer  JLei  ZTrommer  BLDavenport  NDLi  WParrish  TBGitelman  DRMesulam  MM Larger deficits in brain networks selective for response inhibition than for visual selective attention in ADHD. J Child Psychol Psychiatry 2005;4694- 111
PubMed Link to Article
Durston  STottenham  NTThomas  KMDavidson  MCEigsti  IMYang  YUlug  AMCasey  BJ Differential patterns of striatal activation in young children with and without ADHD. Biol Psychiatry 2003;53871- 878
PubMed Link to Article
Schulz  KPFan  JTang  CYNewcorn  JHBuchsbaum  MSCheung  AMHalperin  JM Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related FMRI study. Am J Psychiatry 2004;1611650- 1657
PubMed Link to Article
Ernst  MKimes  ASLondon  EDMatochik  JAEldreth  DTata  SContoreggi  CLeff  MBolla  K Neural substrates of decision making in adults with attention deficit hyperactivity disorder. Am J Psychiatry 2003;1601061- 1070
PubMed Link to Article
Rubia  KOvermeyer  STaylor  EBrammer  MWilliams  SCSimmons  ABullmore  ET Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. Am J Psychiatry 1999;156891- 896
PubMed
Langleben  DDActon  PDAustin  GElman  IKrikorian  GMonterosso  JRPortnoy  ORidlehuber  HWStrauss  HW Effects of methylphenidate discontinuation on cerebral blood flow in prepubescent boys with attention deficit hyperactivity disorder. J Nucl Med 2002;431624- 1629
PubMed
Mesulam  MM Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci 1999;3541325- 1346[published correction appears in Philos Trans R Soc Lond B Biol Sci. 1999;354:2083]
PubMed Link to Article
Posner  MIPetersen  SE The attention system of the human brain. Annu Rev Neurosci 1990;1325- 42
PubMed Link to Article
Ernst  MCohen  RMLiebenauer  LLJons  PHZametkin  AJ Cerebral glucose metabolism in adolescent girls with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1997;361399- 1406
PubMed Link to Article
Vaidya  CJBunge  SADudukovic  NMZalecki  CAElliott  GRGabrieli  JD Altered neural substrates of cognitive control in childhood ADHD: evidence from functional magnetic resonance imaging. Am J Psychiatry 2005;1621605- 1613
PubMed Link to Article
Mannuzza  SKlein  RGBonagura  NMalloy  PGiampino  TLAddalli  KA Hyperactive boys almost grown up, V: replication of psychiatric status. Arch Gen Psychiatry 1991;4877- 83
PubMed Link to Article
Kabani  NLe Goualher  GMacDonald  DEvans  AC Measurement of cortical thickness using an automated 3-D algorithm: a validation study. Neuroimage 2001;13375- 380
PubMed Link to Article
Lerch  JPEvans  AC Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage 2005;24163- 173
PubMed Link to Article
Sowell  ERThompson  PMLeonard  CMWelcome  SEKan  EToga  AW Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 2004;248223- 8231
PubMed Link to Article
Lerch  JPPruessner  JCZijdenbos  AHampel  HTeipel  SJEvans  A Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. Cereb Cortex 2005;15995- 1001
PubMed Link to Article
Sowell  ERThompson  PMWelcome  SEHenkenius  ALToga  AWPeterson  BS Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. Lancet 2003;3621699- 1707
PubMed Link to Article
Reich  W Diagnostic Interview for Children and Adolescents (DICA). J Am Acad Child Adolesc Psychiatry 2000;3959- 66
PubMed Link to Article
Werry  JSSprague  RLCohen  MN Conners' Teacher Rating Scale for use in drug studies with children: an empirical study. J Abnorm Child Psychol 1975;3217- 229
PubMed Link to Article
Ward  MFWender  PHReimherr  FW The Wender Utah Rating Scale: an aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. Am J Psychiatry 1993;150885- 890[published correction appears in Am J Psychiatry. 1993;150:1280]
PubMed
Shaffer  DGould  MSBrasic  JAmbrosini  PFisher  PBird  HAluwahlia  S A Children's Global Assessment Scale (CGAS). Arch Gen Psychiatry 1983;401228- 1231
PubMed Link to Article
Sled  JGZijdenbos  APEvans  AC A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;1787- 97
PubMed Link to Article
Zijdenbos  APForghani  REvans  AC Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 2002;211280- 1291
PubMed Link to Article
MacDonald  DKabani  NAvis  DEvans  AC Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 2000;12340- 356
PubMed Link to Article
Pinheiro  JCBates  DM Mixed-Effects Models in S and S-PLUS.  New York, NY Springer-Verlag NY Inc2000;
Genovese  CRLazar  NANichols  T Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002;15870- 878
PubMed Link to Article
Keselman  HJCribbie  RHolland  B Controlling the rate of type I error over a large set of statistical tests. Br J Math Stat Psychol 2002;5527- 39
PubMed Link to Article
Venables  WNRipley  BD Modern Applied Statistics With S. 4th New York, NY Springer-Verlag NY Inc2002;
Hastie  TTibshirani  RFriedman  J The Elements of Statistical Learning.  New York, NY Springer-Verlag NY Inc2001;
Overmeyer  SBullmore  ETSuckling  JSimmons  AWilliams  SCSantosh  PJTaylor  E Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychol Med 2001;311425- 1435
PubMed Link to Article
Kates  WRFrederikse  MMostofsky  SHFolley  BSCooper  KMazur-Hopkins  PKofman  OSinger  HSDenckla  MBPearlson  GDKaufmann  WE MRI parcellation of the frontal lobe in boys with attention deficit hyperactivity disorder or Tourette syndrome. Psychiatry Res 2002;11663- 81
PubMed Link to Article
Moll  GHHeinrich  HTrott  GEWirth  SBock  NRothenberger  A Children with comorbid attention-deficit-hyperactivity disorder and tic disorder: evidence for additive inhibitory deficits within the motor system. Ann Neurol 2001;49393- 396
PubMed Link to Article
Schweitzer  JBFaber  TLGrafton  STTune  LEHoffman  JMKilts  CD Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. Am J Psychiatry 2000;157278- 280
PubMed Link to Article
Tamm  LMenon  VRingel  JReiss  AL Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2004;431430- 1440
PubMed Link to Article
Kaya  GCPekcanlar  ABekis  RAda  EMiral  SEmiroglu  NDurak  H Technetium-99m HMPAO brain SPECT in children with attention deficit hyperactivity disorder. Ann Nucl Med 2002;16527- 531
PubMed Link to Article
Diamond  A Normal development of prefrontal cortex from birth to young adulthood: cognitive functions, anatomy and biochemistry. In:Knight  SAed.The Frontal Lobes. London, England Oxford University Press2002;
Casey  BJGiedd  JNThomas  KM Structural and functional brain development and its relation to cognitive development. Biol Psychol 2000;54241- 257
PubMed Link to Article
Carver  ACLivesey  DJCharles  M Age related changes in inhibitory control as measured by stop signal task performance. Int J Neurosci 2001;10743- 61
PubMed Link to Article
Tipper  SPBourque  TAAnderson  SHBrehaut  JC Mechanisms of attention: a developmental study. J Exp Child Psychol 1989;48353- 378
PubMed Link to Article
van der Molen  MW Developmental changes in inhibitory processing: evidence from psychophysiological measures. Biol Psychol 2000;54207- 239
PubMed Link to Article
Rueda  MRFan  JMcCandliss  BDHalparin  JDGruber  DBLercari  LPPosner  MI Development of attentional networks in childhood. Neuropsychologia 2004;421029- 1040
PubMed Link to Article
Rubia  KOvermeyer  STaylor  EBrammer  MWilliams  SCSimmons  AAndrew  CBullmore  ET Functional frontalisation with age: mapping neurodevelopmental trajectories with fMRI. Neurosci Biobehav Rev 2000;2413- 19
PubMed Link to Article
Bunge  SADudukovic  NMThomason  MEVaidya  CJGabrieli  JD Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron 2002;33301- 311
PubMed Link to Article
Durston  SThomas  KMYang  YUlug  AMZimmerman  RDCasey  BJ A neural basis for the development of inhibitory control. Dev Sci 2002;5F9- F16
Link to Article
Tamm  LMenon  VReiss  AL Maturation of brain function associated with response inhibition. J Am Acad Child Adolesc Psychiatry 2002;411231- 1238
PubMed Link to Article
Luna  BThulborn  KRMunoz  DPMerriam  EPGarver  KEMinshew  NJKeshavan  MSGenovese  CREddy  WFSweeney  JA Maturation of widely distributed brain function subserves cognitive development. Neuroimage 2001;13786- 793
PubMed Link to Article
Casey  BJTrainor  RJOrendi  JLSchubert  ABNystrom  LEGiedd  JCastellanos  FXHaxby  JNoll  DCCohen  JDForman  SDDahl  RERapoport  JL A developmental functional MRI study of prefrontal activation during performance of a Go-No-Go task. J Cogn Neurosci 1997;9835- 847
Link to Article
Konrad  KNeufang  SThiel  CSpecht  KHanisch  CFan  JHerpertz-Dahlmann  BFink  GR Development of attentional networks: an fMRI study with children and adults. Neuroimage 2005;28429- 439
PubMed Link to Article
Casey  BJCastellanos  FXGiedd  JNMarsh  WLHamburger  SDSchubert  ABVauss  YCVaituzis  ACDickstein  DPSarfatti  SERapoport  JL Implication of right frontostriatal circuitry in response inhibition and attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1997;36374- 383
PubMed Link to Article
Semrud-Clikeman  MSteingard  RJFilipek  PBiederman  JBekken  KRenshaw  PF Using MRI to examine brain-behavior relationships in males with attention deficit disorder with hyperactivity. J Am Acad Child Adolesc Psychiatry 2000;39477- 484
PubMed Link to Article
Zametkin  AJLiebenauer  LLFitzgerald  GAKing  ACMinkunas  DVHerscovitch  PYamada  EMCohen  RM Brain metabolism in teenagers with attention-deficit hyperactivity disorder. Arch Gen Psychiatry 1993;50333- 340
PubMed Link to Article
Kim  YHGitelman  DRNobre  ACParrish  TBLaBar  KSMesulam  MM The large-scale neural network for spatial attention displays multifunctional overlap but differential asymmetry. Neuroimage 1999;9269- 277
PubMed Link to Article
Mesulam  MMNobre  ACKim  YHParrish  TBGitelman  DR Heterogeneity of cingulate contributions to spatial attention. Neuroimage 2001;131065- 1072
PubMed Link to Article
Small  DMGitelman  DRGregory  MDNobre  ACParrish  TBMesulam  MM The posterior cingulate and medial prefrontal cortex mediate the anticipatory allocation of spatial attention. Neuroimage 2003;18633- 641
PubMed Link to Article
Huttenlocher  PRDabholkar  AS Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 1997;387167- 178
PubMed Link to Article
Benes  FMTurtle  MKhan  YFarol  P Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch Gen Psychiatry 1994;51477- 484
PubMed Link to Article
Yakovlev  PILecours  AR The myelinogenetic cycles of regional maturation of the brain. In:Minokowski  Aed.Regional Development of the Brain in Early Life. Oxford, England Blackwell Scientific1967;

Figures

Place holder to copy figure label and caption
Figure 1

Cortical thickness in attention-deficit/hyperactivity disorder (ADHD) compared with controls. A, Estimated difference in cortical thickness in millimeters between patients with ADHD and controls. Significantly thinner regions in the ADHD group, applying a false discovery rate of 0.05, are shown in yellow. B, Group differences (t > 2) after adjustment for IQ and mean overall cortical thickness.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2

Contrasts between patients with attention-deficit/hyperactivity disorder (ADHD) with differing outcomes and controls. A, The t statistical maps of pairwise contrasts using persistence/remission of ADHD as the outcome measure. B, The t maps using Children's Global Assessment Scale scores as the outcome measure. Adjustment is made for IQ and mean cortical thickness.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3

Trajectory of change in cortical thickness in patients with attention-deficit/hyperactivity disorder (ADHD) and controls. A, Estimated trajectories for mean overall cortical thickness. There was a significant difference in height (P=.02) but not in the gradient of the lines (P=.78). Dashed lines indicate 95% confidence intervals. B, The t map indicates vertices where there was a significant interaction in the contrast between the better outcome and control groups and age. The graph illustrates group trajectories in this region (difference in gradients: better outcome group vs controls, P=.001; better vs worse outcome groups, P=.03; and worse outcome group vs controls, P=.60).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1 Demographic and Clinical Details of Patients With ADHD With Better vs Worse Outcome
Table Graphic Jump LocationTable 2 Demographic and Diagnostic Characteristics of Patients With ADHD and Controls
Table Graphic Jump LocationTable 3 Cortical Thickness in the Regions That Differed Significantly Between Groups*
Table Graphic Jump LocationTable 4 Cortical Thickness in Regions That Show a Significant Difference in Baseline MRIs in the Different ADHD Outcome Groups

References

Buitelaar  JK Epidemiology: what have we learned over the past decade? In:Sandberg  Jed.Hyperactivity and Attention-Deficit Disorders. Cambridge, Mass Cambridge University Press2002;
Barkley  RA Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 1997;12165- 94
PubMed Link to Article
Casey  BJTottenham  NFossella  J Clinical, imaging, lesion, and genetic approaches toward a model of cognitive control. Dev Psychobiol 2002;40237- 254
PubMed Link to Article
Posner  MIDiGirolamo  GJ Executive attention: conflict, target detection and cognitive control. In:Parasuraman  Red.The Attentive Brain Cambridge, Mass MIT Press1998;
Sonuga-Barke  EJDalen  LRemington  B Do executive deficits and delay aversion make independent contributions to preschool attention-deficit/hyperactivity disorder symptoms? J Am Acad Child Adolesc Psychiatry 2003;421335- 1342
PubMed Link to Article
Castellanos  FXLee  PPSharp  WJeffries  NOGreenstein  DKClasen  LSBlumenthal  JDJames  RSEbens  CLWalter  JMZijdenbos  AEvans  ACGiedd  JNRapoport  JL Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 2002;2881740- 1748
PubMed Link to Article
Durston  SHulshoff Pol  HESchnack  HGBuitelaar  JKSteenhuis  MPMinderaa  RBKahn  RSvan Engeland  H Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry 2004;43332- 340
PubMed Link to Article
Filipek  PASemrud-Clikeman  MSteingard  RJRenshaw  PFKennedy  DNBiederman  J Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls. Neurology 1997;48589- 601
PubMed Link to Article
Mostofsky  SHCooper  KLKates  WRDenckla  MBKaufmann  WE Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biol Psychiatry 2002;52785- 794
PubMed Link to Article
Hill  DEYeo  RACampbell  RAHart  BVigil  JBrooks  W Magnetic resonance imaging correlates of attention-deficit/hyperactivity disorder in children. Neuropsychology 2003;17496- 506
PubMed Link to Article
Bush  GFrazier  JARauch  SLSeidman  LJWhalen  PJJenike  MARosen  BRBiederman  J Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop. Biol Psychiatry 1999;451542- 1552
PubMed Link to Article
Booth  JRBurman  DMeyer  JLei  ZTrommer  BLDavenport  NDLi  WParrish  TBGitelman  DRMesulam  MM Larger deficits in brain networks selective for response inhibition than for visual selective attention in ADHD. J Child Psychol Psychiatry 2005;4694- 111
PubMed Link to Article
Durston  STottenham  NTThomas  KMDavidson  MCEigsti  IMYang  YUlug  AMCasey  BJ Differential patterns of striatal activation in young children with and without ADHD. Biol Psychiatry 2003;53871- 878
PubMed Link to Article
Schulz  KPFan  JTang  CYNewcorn  JHBuchsbaum  MSCheung  AMHalperin  JM Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related FMRI study. Am J Psychiatry 2004;1611650- 1657
PubMed Link to Article
Ernst  MKimes  ASLondon  EDMatochik  JAEldreth  DTata  SContoreggi  CLeff  MBolla  K Neural substrates of decision making in adults with attention deficit hyperactivity disorder. Am J Psychiatry 2003;1601061- 1070
PubMed Link to Article
Rubia  KOvermeyer  STaylor  EBrammer  MWilliams  SCSimmons  ABullmore  ET Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. Am J Psychiatry 1999;156891- 896
PubMed
Langleben  DDActon  PDAustin  GElman  IKrikorian  GMonterosso  JRPortnoy  ORidlehuber  HWStrauss  HW Effects of methylphenidate discontinuation on cerebral blood flow in prepubescent boys with attention deficit hyperactivity disorder. J Nucl Med 2002;431624- 1629
PubMed
Mesulam  MM Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci 1999;3541325- 1346[published correction appears in Philos Trans R Soc Lond B Biol Sci. 1999;354:2083]
PubMed Link to Article
Posner  MIPetersen  SE The attention system of the human brain. Annu Rev Neurosci 1990;1325- 42
PubMed Link to Article
Ernst  MCohen  RMLiebenauer  LLJons  PHZametkin  AJ Cerebral glucose metabolism in adolescent girls with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1997;361399- 1406
PubMed Link to Article
Vaidya  CJBunge  SADudukovic  NMZalecki  CAElliott  GRGabrieli  JD Altered neural substrates of cognitive control in childhood ADHD: evidence from functional magnetic resonance imaging. Am J Psychiatry 2005;1621605- 1613
PubMed Link to Article
Mannuzza  SKlein  RGBonagura  NMalloy  PGiampino  TLAddalli  KA Hyperactive boys almost grown up, V: replication of psychiatric status. Arch Gen Psychiatry 1991;4877- 83
PubMed Link to Article
Kabani  NLe Goualher  GMacDonald  DEvans  AC Measurement of cortical thickness using an automated 3-D algorithm: a validation study. Neuroimage 2001;13375- 380
PubMed Link to Article
Lerch  JPEvans  AC Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage 2005;24163- 173
PubMed Link to Article
Sowell  ERThompson  PMLeonard  CMWelcome  SEKan  EToga  AW Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 2004;248223- 8231
PubMed Link to Article
Lerch  JPPruessner  JCZijdenbos  AHampel  HTeipel  SJEvans  A Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. Cereb Cortex 2005;15995- 1001
PubMed Link to Article
Sowell  ERThompson  PMWelcome  SEHenkenius  ALToga  AWPeterson  BS Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. Lancet 2003;3621699- 1707
PubMed Link to Article
Reich  W Diagnostic Interview for Children and Adolescents (DICA). J Am Acad Child Adolesc Psychiatry 2000;3959- 66
PubMed Link to Article
Werry  JSSprague  RLCohen  MN Conners' Teacher Rating Scale for use in drug studies with children: an empirical study. J Abnorm Child Psychol 1975;3217- 229
PubMed Link to Article
Ward  MFWender  PHReimherr  FW The Wender Utah Rating Scale: an aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. Am J Psychiatry 1993;150885- 890[published correction appears in Am J Psychiatry. 1993;150:1280]
PubMed
Shaffer  DGould  MSBrasic  JAmbrosini  PFisher  PBird  HAluwahlia  S A Children's Global Assessment Scale (CGAS). Arch Gen Psychiatry 1983;401228- 1231
PubMed Link to Article
Sled  JGZijdenbos  APEvans  AC A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;1787- 97
PubMed Link to Article
Zijdenbos  APForghani  REvans  AC Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 2002;211280- 1291
PubMed Link to Article
MacDonald  DKabani  NAvis  DEvans  AC Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 2000;12340- 356
PubMed Link to Article
Pinheiro  JCBates  DM Mixed-Effects Models in S and S-PLUS.  New York, NY Springer-Verlag NY Inc2000;
Genovese  CRLazar  NANichols  T Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002;15870- 878
PubMed Link to Article
Keselman  HJCribbie  RHolland  B Controlling the rate of type I error over a large set of statistical tests. Br J Math Stat Psychol 2002;5527- 39
PubMed Link to Article
Venables  WNRipley  BD Modern Applied Statistics With S. 4th New York, NY Springer-Verlag NY Inc2002;
Hastie  TTibshirani  RFriedman  J The Elements of Statistical Learning.  New York, NY Springer-Verlag NY Inc2001;
Overmeyer  SBullmore  ETSuckling  JSimmons  AWilliams  SCSantosh  PJTaylor  E Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychol Med 2001;311425- 1435
PubMed Link to Article
Kates  WRFrederikse  MMostofsky  SHFolley  BSCooper  KMazur-Hopkins  PKofman  OSinger  HSDenckla  MBPearlson  GDKaufmann  WE MRI parcellation of the frontal lobe in boys with attention deficit hyperactivity disorder or Tourette syndrome. Psychiatry Res 2002;11663- 81
PubMed Link to Article
Moll  GHHeinrich  HTrott  GEWirth  SBock  NRothenberger  A Children with comorbid attention-deficit-hyperactivity disorder and tic disorder: evidence for additive inhibitory deficits within the motor system. Ann Neurol 2001;49393- 396
PubMed Link to Article
Schweitzer  JBFaber  TLGrafton  STTune  LEHoffman  JMKilts  CD Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. Am J Psychiatry 2000;157278- 280
PubMed Link to Article
Tamm  LMenon  VRingel  JReiss  AL Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2004;431430- 1440
PubMed Link to Article
Kaya  GCPekcanlar  ABekis  RAda  EMiral  SEmiroglu  NDurak  H Technetium-99m HMPAO brain SPECT in children with attention deficit hyperactivity disorder. Ann Nucl Med 2002;16527- 531
PubMed Link to Article
Diamond  A Normal development of prefrontal cortex from birth to young adulthood: cognitive functions, anatomy and biochemistry. In:Knight  SAed.The Frontal Lobes. London, England Oxford University Press2002;
Casey  BJGiedd  JNThomas  KM Structural and functional brain development and its relation to cognitive development. Biol Psychol 2000;54241- 257
PubMed Link to Article
Carver  ACLivesey  DJCharles  M Age related changes in inhibitory control as measured by stop signal task performance. Int J Neurosci 2001;10743- 61
PubMed Link to Article
Tipper  SPBourque  TAAnderson  SHBrehaut  JC Mechanisms of attention: a developmental study. J Exp Child Psychol 1989;48353- 378
PubMed Link to Article
van der Molen  MW Developmental changes in inhibitory processing: evidence from psychophysiological measures. Biol Psychol 2000;54207- 239
PubMed Link to Article
Rueda  MRFan  JMcCandliss  BDHalparin  JDGruber  DBLercari  LPPosner  MI Development of attentional networks in childhood. Neuropsychologia 2004;421029- 1040
PubMed Link to Article
Rubia  KOvermeyer  STaylor  EBrammer  MWilliams  SCSimmons  AAndrew  CBullmore  ET Functional frontalisation with age: mapping neurodevelopmental trajectories with fMRI. Neurosci Biobehav Rev 2000;2413- 19
PubMed Link to Article
Bunge  SADudukovic  NMThomason  MEVaidya  CJGabrieli  JD Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron 2002;33301- 311
PubMed Link to Article
Durston  SThomas  KMYang  YUlug  AMZimmerman  RDCasey  BJ A neural basis for the development of inhibitory control. Dev Sci 2002;5F9- F16
Link to Article
Tamm  LMenon  VReiss  AL Maturation of brain function associated with response inhibition. J Am Acad Child Adolesc Psychiatry 2002;411231- 1238
PubMed Link to Article
Luna  BThulborn  KRMunoz  DPMerriam  EPGarver  KEMinshew  NJKeshavan  MSGenovese  CREddy  WFSweeney  JA Maturation of widely distributed brain function subserves cognitive development. Neuroimage 2001;13786- 793
PubMed Link to Article
Casey  BJTrainor  RJOrendi  JLSchubert  ABNystrom  LEGiedd  JCastellanos  FXHaxby  JNoll  DCCohen  JDForman  SDDahl  RERapoport  JL A developmental functional MRI study of prefrontal activation during performance of a Go-No-Go task. J Cogn Neurosci 1997;9835- 847
Link to Article
Konrad  KNeufang  SThiel  CSpecht  KHanisch  CFan  JHerpertz-Dahlmann  BFink  GR Development of attentional networks: an fMRI study with children and adults. Neuroimage 2005;28429- 439
PubMed Link to Article
Casey  BJCastellanos  FXGiedd  JNMarsh  WLHamburger  SDSchubert  ABVauss  YCVaituzis  ACDickstein  DPSarfatti  SERapoport  JL Implication of right frontostriatal circuitry in response inhibition and attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1997;36374- 383
PubMed Link to Article
Semrud-Clikeman  MSteingard  RJFilipek  PBiederman  JBekken  KRenshaw  PF Using MRI to examine brain-behavior relationships in males with attention deficit disorder with hyperactivity. J Am Acad Child Adolesc Psychiatry 2000;39477- 484
PubMed Link to Article
Zametkin  AJLiebenauer  LLFitzgerald  GAKing  ACMinkunas  DVHerscovitch  PYamada  EMCohen  RM Brain metabolism in teenagers with attention-deficit hyperactivity disorder. Arch Gen Psychiatry 1993;50333- 340
PubMed Link to Article
Kim  YHGitelman  DRNobre  ACParrish  TBLaBar  KSMesulam  MM The large-scale neural network for spatial attention displays multifunctional overlap but differential asymmetry. Neuroimage 1999;9269- 277
PubMed Link to Article
Mesulam  MMNobre  ACKim  YHParrish  TBGitelman  DR Heterogeneity of cingulate contributions to spatial attention. Neuroimage 2001;131065- 1072
PubMed Link to Article
Small  DMGitelman  DRGregory  MDNobre  ACParrish  TBMesulam  MM The posterior cingulate and medial prefrontal cortex mediate the anticipatory allocation of spatial attention. Neuroimage 2003;18633- 641
PubMed Link to Article
Huttenlocher  PRDabholkar  AS Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 1997;387167- 178
PubMed Link to Article
Benes  FMTurtle  MKhan  YFarol  P Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch Gen Psychiatry 1994;51477- 484
PubMed Link to Article
Yakovlev  PILecours  AR The myelinogenetic cycles of regional maturation of the brain. In:Minokowski  Aed.Regional Development of the Brain in Early Life. Oxford, England Blackwell Scientific1967;

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