Basal Ganglia Surface Morphology and the Effects of Stimulant Medications in Youth With Attention Deficit Hyperactivity Disorder
Publication: American Journal of Psychiatry
Abstract
Objective
Disturbances in the basal ganglia portions of cortico-striato-thalamo-cortical circuits likely contribute to the symptoms of attention deficit hyperactivity disorder (ADHD). The authors examined the morphologic features of the basal ganglia nuclei (caudate, putamen, and globus pallidus) in children with ADHD.
Method
A total of 104 individuals (combined-type ADHD patients: N=47; healthy comparison subjects: N=57), aged 7 to 18 years, were examined in a cross-sectional case-control study using anatomical magnetic resonance imaging. Conventional volumes and the surface morphology for the basal ganglia were measured.
Results
Overall volumes were significantly smaller only in the putamen. Analysis of the morphological surfaces revealed significant inward deformations in each of the three nuclei, localized primarily in portions of these nuclei that are components of limbic, associative, and sensorimotor pathways in the cortico-striato-thalamo-cortical circuits in which these nuclei reside. The more prominent these inward deformations were in the patient group, the more severe the ADHD symptoms. Surface analyses also demonstrated significant outward deformations of all basal ganglia nuclei in the ADHD children treated with stimulants compared with those ADHD youth who were untreated. These stimulant-associated enlargements were in locations similar to the reduced volumes detected in the ADHD group relative to the comparison group. The outward deformations associated with stimulant medications attenuated the statistical effects of the primary group comparisons.
Conclusions
These findings potentially represent evidence of anatomical dysregulation in the circuitry of the basal ganglia in children with ADHD and suggest that stimulants may normalize morphological features of the basal ganglia in children with the disorder.
The pathogenesis of attention deficit hyperactivity disorder (ADHD) is thought to involve anatomical and functional disturbances in cortico-striato-thalamo-cortical circuits. These circuits traverse portions of the frontal cortex and basal ganglia that support the learning of emotional responses to reinforcing stimuli, the regulation of attentional resources, and the programming of simple and complex motor behaviors. This pathophysiological model for ADHD is based as much on what is known of the neural bases of these processes from animal models (1) and neurobiological studies of healthy humans as it is on direct experimental evidence from youth who have ADHD (2). Indeed, findings from brain imaging studies of children with ADHD have been highly variable, with the preponderance of the largest and methodologically most rigorous studies indicating the presence of reduced volumes of the cerebral cortex (3), particularly of the lateral prefrontal cortex (4, 5), and reduced volumes of one or more basal ganglia nuclei (6–8) (caudate, putamen, or globus pallidus). With one exception (9), these studies have not examined the local volumes of basal ganglia nuclei to determine which portions of those nuclei, and by implication which portions of the cortico-striato-thalamo-cortical pathways, are involved in the pathogenesis of ADHD. None of these studies has yet reported the presence of significant effects of stimulant medications on basal ganglia morphology, despite the fact that stimulant medications are among the most robustly and most predictably helpful medications available for any neuropsychiatric illness.
Herein, we present a magnetic resonance imaging (MRI) study of the three basal ganglia nuclei—caudate, putamen, and globus pallidus—in children with ADHD (N=47) and healthy comparison subjects (N=57). We examined the conventional volumes of the basal ganglia and their detailed surface morphologic features. We also hypothesized that these measures would differ between youth with ADHD and comparison youth and that the morphological features of basal ganglia nuclei in ADHD youth treated with stimulants would differ from patients not taking stimulants.
Method
Further details of the recruitment, behavioral assessments, MRI pulse sequence, and image processing are described in the data supplement accompanying the online version of this article.
Participants
We acquired MRIs in 47 children with ADHD and 57 healthy comparison children, aged 7–18 years (Table 1). All patients met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV) (10), criteria for combined-type ADHD. Exclusion criteria for patients with ADHD were a history of obsessive-compulsive, bipolar, psychotic, anxiety, tic, conduct, or pervasive developmental disorders. In the ADHD group, a total of 23 (48.9%) patients had a co-occurring lifetime diagnosis of depression (N=12), oppositional defiant disorder (N=12), and/or specific developmental disorder (e.g., reading, mathematics, written expression, or motor coordination problems [N=7]).
We estimated symptom severity using the Barkley, DuPaul, and McMurray ADHD rating scale (11). We excluded comparison participants who had a history of medical, psychiatric, or neurologic disorders. Additional exclusion criteria for both diagnostic groups were any previous seizure, head trauma with loss of consciousness, current or previous substance abuse, or IQ <70. The Institutional Review Boards of Yale and Columbia Universities approved the study. Parents provided written informed consent, and participants provided verbal assent.
Surface Morphology
We evaluated the surface morphology of the basal ganglia by computing the signed Euclidean distance from each point on the surface of the basal ganglia nuclei of an individual participant to the corresponding point on the basal ganglia nuclei of a healthy comparison reference template. We encoded outward deformations using positive distances and inward deformations using negative distances.
We previously validated these procedures to identify known regional increases and decreases in volumes at the surface of brain structures (12). Procedures are detailed elsewhere (13–15) and summarized in the present study. We applied a two-step procedure for surface morphometry of the basal ganglia. First, we coregistered each participant's brain with the template brain using a similarity transformation. The parameters estimated for this transformation are global scaling, three translations, and three rotations of the brain. The translation and rotation parameters were applied to place the caudate, putamen, and globus pallidus into the template space. The global scaling parameters controlled for any scaling differences in these structures caused by differing whole-brain volumes. Second, we independently and rigidly coregistered each basal ganglia nucleus and then nonlinearly warped it to the exact size and shape of the corresponding nucleus in the template, allowing precise identification of corresponding points along the surface of these regions. Next, we unwarped each region while preserving the labels assigned to corresponding points on the surface of each region. We then computed the distance from each point on the surfaces of the basal ganglia nuclei for each participant to the corresponding point on the template. Therefore, at each point on the surface of a basal ganglia nucleus in the template brain, we had a set of 57 distances for the healthy comparison group and a set of 47 distances for the ADHD group. Finally, these two sets of distances were compared statistically to determine local regions of significant differences between the groups of participants (see discussion on template selection).
Template Selection
Conceivably, localization and interpretation of point correspondences may depend on the selected template (14). Therefore, we selected the template brain by a two-step process. First, we selected as a preliminary template the brain of the healthy participant that best represented, demographically, the healthy participants in the study. The remaining healthy subjects were coregistered to this preliminary template. We then computed the distances between the corresponding points on the surface of the basal ganglia nuclei (as detailed in the discussion on surface morphology). Second, we determined the final template by selecting the brain for which all points across the surface of the basal ganglia nuclei were closest to the average of the computed distances. The surface morphometry was then repeated for our entire cohort of healthy and affected participants using the final template brain.
We used a single representative brain as a template rather than an averaged brain because a single brain has well-defined tissue interfaces, such as CSF gray matter or gray-white matter interfaces. Averaging images for a template blurs these boundaries and increases registration errors that are subtle but important when distinguishing subtle effects across populations. In addition, precise surface morphometry requires a brain with smooth gray and white surfaces that are devoid of topological defects, which cannot be reconstructed by averaging brains from several participants.
Statistical Analysis
For conventional volumes, to test the a priori hypothesis that patients with ADHD have different volumes of basal ganglia nuclei relative to healthy comparison subjects, we calculated the statistical significance of the main effects of diagnosis (between-factor) and the interaction of diagnosis with the nucleus (within-factor) in a mixed-model analysis of covariance (PROC MIXED, SAS Institute, Inc., Cary, N.C.) with repeated measures over a spatial domain (caudate, putamen, and globus pallidus). Covariates included in the mixed model for assessment of conventional volumes were whole-brain volume (to control for scaling effects), age, sex, and hemisphere (left or right). To control for possible confounds, lifetime diagnoses of comorbid depression, oppositional defiant disorder, or specific developmental disorders (e.g., reading, mathematics, written expression, or motor coordination problems) were used in confirmatory models to assess the effects of these comorbid conditions. Statistically nonsignificant terms (p≥0.10) were eliminated from the final mixed model. The variables age and sex remained in all final statistical models because of the biological plausibility that these variables could influence the overall findings. All dependent measures were normally distributed, as assessed using the Kolmogorov-Smirnov test.
For analysis of surface features, the distances between each point on the surface of the basal ganglia for each participant and the corresponding point on the template basal ganglia were compared statistically between the two diagnostic groups using multiple linear regression while covarying for age and sex. Because global scaling parameters controlled for any scaling differences across brains, covarying for whole-brain volume was not necessary. We used the theory of Gaussian random fields to correct p values appropriately for the multiple comparisons performed across the basal ganglia surface (16).
We used two complementary approaches to test the hypothesis that the morphological features of basal ganglia nuclei in youth with ADHD who are currently treated with stimulant medication differ from the basal ganglia of patients not taking stimulant medications. First, we analyzed the effects of stimulant use in the ADHD group alone. For conventional volumes, we tested the main effect of stimulant use (between-subject factor) and the interaction of stimulant use with nuclei (within-subject factor) in a mixed-effects model (while covarying for age, sex, and whole-brain volume). For surface morphology, we tested the significance of stimulant use in a multiple linear regression model while covarying for age and sex. Second, we assessed the main effects of diagnosis (as detailed above) on surface features in the subgroup of ADHD youth who were taking stimulants (N=31) relative to healthy comparison subjects and in the subgroup of ADHD youth not taking stimulants (N=16) relative to healthy comparison subjects.
Symptom Severity
In the ADHD group, we explored correlations of basal ganglia nuclei volumes or surface features with symptom severity at the time of scanning, as measured with the Barkley, DuPaul, and McMurray ADHD rating scale (11).
Results
Effects of Diagnosis
The main effect of diagnosis on the conventional volumes of the basal ganglia nuclei was significant (F=8.30, df=1, 95, p=0.005 [Table 2]). The significant diagnosis-by-nucleus interaction demonstrated nucleus specificity in diagnosis effects (F=10.53, df=2, 204, p<0.0001). Least-square means between the diagnostic groups indicated that the contribution to group differences derived mainly from smaller putamen volumes in youth with ADHD relative to comparison subjects (4,966.7 mm3 [SD=74] versus 5,311.8 mm3 [SD=67], p<0.001). Overall volumes of the caudate (3,683.9 mm3 [SD=79] versus 3,808.3 mm3 [SD=71]) and globus pallidus (1,698.1 mm3 [SD=26] versus 1,785.0 mm3 [SD=23]) did not differ significantly across diagnostic groups.
Surface analyses demonstrated that the decrease in overall conventional volumes of the putamen in the ADHD group derived from marked inward deformations across multiple portions of its surface (Figure 1). In addition, inward deformations of several portions of the surfaces of the caudate and globus pallidus survived Gaussian random field correction, indicating the presence of regional volume reductions in the ADHD group that were not evident using measures of overall conventional volume (Figure 1). These inward deformations corresponded approximately to the ventral, anterior, and posterior aspects of these nuclei.
Figure 1. Main Effects of Diagnosis on Surface Morphologic Features of Basal Ganglia Nuclei in Youth With ADHD Relative to Healthy Comparison Subjectsa
aThe right and left caudate, putamen, and globus pallidus are displayed in rotational views and in their dorsal and ventral perspectives. Anterior (A), posterior (P), lateral (L), and medial (M) views of each nucleus are shown. The color bar indicates the significance value for group comparisons at each point on the surface. Green values represent statistically nonsignificant differences (p≥0.05) of the surface of the basal ganglia nuclei between groups. Yellow and red values (p<0.0001) represent outward deformations of the surfaces or local volume increases, whereas blue and purple represent inward deformations of the surfaces or local volume reductions (p<0.0001).
bThe theory of Gaussian random fields was used to correct p values appropriately for the multiple comparisons performed across the basal ganglia.
Effects of Stimulant Use
Within the ADHD group, analysis of conventional volumes in those who were and were not treated with stimulant medication did not discern significant effects of stimulant medication. Volumes of the caudate (3,681.0 mm3 [SD=92] versus 3,642.6 mm3 [SD=129]), putamen (4,989.1 mm3 [SD=101] versus 4,838.1 mm3 [SD=142]), and globus pallidus (1,728.3 mm3 [SD=32] versus 1,671 mm3 [SD=45]) did not differ significantly between ADHD youth receiving stimulants and those not receiving stimulants. However, surface analyses revealed significant localized outward deformations of the basal ganglia surface in ADHD youth treated with stimulants relative to those with untreated ADHD (Figure 2). In addition, we detected a statistical attenuation of the main effect of diagnosis when comparing the comparison group with individuals in the ADHD group treated with stimulants, whereas we detected a strengthening of the statistical significance of diagnosis when comparing the comparison group with those in the ADHD group who were untreated. These findings demonstrate the presence of exacerbated inward deformations of the basal ganglia surface in untreated youth with ADHD compared with treated youth with ADHD. Our findings did not change when excluding the five subjects with ADHD who were taking both stimulant and nonstimulant medications or when excluding from the subgroup of ADHD subjects not receiving stimulants the one youth who was not stimulant-naive.
Figure 2. Main Effects of Stimulant Use on Surface Morphologic Features of Basal Ganglia Nuclei in Youth With ADHD Relative to Healthy Comparison Subjectsa
aGaussian random field-corrected images are displayed in anterior (A), posterior (P), lateral (L), and medial (M) views, whereas uncorrected images are displayed in lateral and medial views only. The color bar indicates the p values associated with either the diagnosis term (left, right) or the stimulant term (center). The outward deformations in the basal ganglia of youth treated with stimulants compared with those untreated approximately align with the inward deformations detected in the overall main effects of diagnosis (see Figure 1). The statistical attenuation of the main effects of diagnosis, indicated by a less significant inward deformation on the surface of the basal ganglia in youth taking stimulants versus those not taking stimulants relative to comparison subjects suggests that a major component of the overall main effects of diagnosis (see Figure 1) was attenuated by the effects of stimulant medication on the morphological features of the basal ganglia. S=superior; I=inferior.
bThe theory of Gaussian random fields was used to correct p values appropriately for the multiple comparisons performed across the basal ganglia.
Correlations With Symptom Severity
Conventional volumes did not correlate significantly with measures of symptom severity in the ADHD group for the caudate (r=–0.04), putamen (r=0.08), or globus pallidus (r=0.05). Nevertheless, surface analyses revealed that ADHD symptom severity positively correlated with the magnitude of inward deformations (greater symptom severity correlated with larger inward deformations of the surface of the basal ganglia [Figure 3]) within several portions of the basal ganglia surfaces where those deformations were detected in the ADHD group (Figure 1).
Figure 3. Correlations of Symptom Severity With Surface Features of Basal Ganglia Nuclei in Youth With ADHDa
aThe image depicts the correlation of surface measures with total ADHD symptom severity. The color bar depicts the p value for the partial Pearson correlation coefficient (r), ranging from p<0.0001 in red (highly significant positive correlation) to p<0.0001 in purple (highly significant inverse correlations). Anterior (A), posterior (P), lateral (L), and medial (M) views of each nucleus are shown.
bThe theory of Gaussian random fields was used to correct p values appropriately for the multiple comparisons performed across the basal ganglia.
Possible Confounds
In the analyses for conventional volumes and surface features, we did not discern appreciable effects of comorbid illness (Table 2 [also see the data supplement]) or medication duration on our findings. The main effect of diagnosis was similar in an analysis of boys only. We also did not detect significant interactions of diagnosis with age or diagnosis with sex, indicating that the effect of diagnosis was stable across age and sex.
Discussion
Analyses of overall conventional volumes detected differences between ADHD youth and healthy comparison subjects only in the putamen. Surface analyses demonstrated that this reduced volume derived from local inward deformations in most portions of the surface of the putamen, and they further identified significant inward deformations in the caudate and globus pallidus. Many of the locations of these inward deformations in the ADHD group corresponded with locations where overall symptom severity correlated significantly with local inward deformations, with more pronounced inward deformations accompanying more severe symptoms. Surface analyses also demonstrated that outward deformations of these nuclei were associated with ADHD youth treated with stimulants relative to those who were untreated at the time of the scan. These outward deformations were in locations similar to those where inward deformations were detected in ADHD youth relative to comparison subjects.
The decreased conventional volume of the putamen in the ADHD group is consistent with findings from previous anatomical imaging studies of ADHD (6–8). In addition, the similarity in conventional volumes of the caudate across groups corroborates the findings from several other studies that also controlled for scaling effects by covarying for whole-brain volume (3, 17–19). Our finding that globus pallidus volumes were similar between diagnostic groups conflicts with findings of reduced globus pallidus volumes reported in a study of a demographically similar cohort (18), although our improved signal-to-noise characteristics (accomplished with two signal averages during image acquisition compared with one in prior studies) and our better spatial resolution (1.2 mm slice thickness in the present study compared with 2 mm) likely improved the precision and accuracy of our anatomical measurements. Finally, the findings of our analyses comparing surface morphology across groups are consistent with those from a study of ADHD youth that employed similar analytic procedures (9). That study reported reductions in overall conventional volumes across all three basal ganglia nuclei, as well as inward deformations across the surfaces of all three nuclei, particularly in the anterior portions and midbodies of each structure. The similarity of results across studies corroborates the existence of structural disturbances within the basal ganglia of individuals with ADHD.
Our surface analyses provide a degree of spatial detail of the basal ganglia nuclei that overall conventional volume measures cannot provide, permitting us to identify highly localized anatomical disturbances in these nuclei and allowing us to infer the presence of corresponding disturbances in the anatomical pathways that contain those regional abnormalities in youth with ADHD. Neuroanatomical tracing studies in animals (20–25), as well as diffusion tensor imaging (26) and functional connectivity studies (27) in humans, suggest that ventral, anterior, and posterior portions of the putamen, caudate, and globus pallidus can be partitioned topographically into limbic, associative, and sensorimotor systems, respectively, based on the topographically organized input to these nuclei from cortical and subcortical areas and on the topographically organized projections that these nuclei send back to cortical areas via the thalamus.
We detected inward deformations located in the limbic (primarily ventral) portions of the basal ganglia nuclei in ADHD youth, consistent with prior studies of persons with ADHD that report abnormalities in limbic structures such as the orbital frontal cortex and amygdala (14, 28). The limbic portions of the putamen, caudate, and globus pallidus connect anatomically and interact functionally with the orbital frontal cortex, amygdala, and nucleus accumbens to form the distributed limbic neural circuit that guides reinforcement-based learning (the acquisition and selection of appropriate behavior) (29–32). Therefore, morphological aberrations in the limbic circuits that support reinforcement learning (33–35) may account for the difficulties that ADHD youth have with delayed gratification and with selecting inappropriate behaviors in a given environmental context. The inward deformations that were located generally in the associative (anterior) portions of the basal ganglia nuclei, combined with a prior report of thinning in the lateral prefrontal cortex in ADHD youth (5), may represent an altered associative neural circuit in persons with ADHD. Given that the associative portions of the basal ganglia nuclei and the lateral prefrontal cortex together guide executive functioning (36–38), the behavioral consequence of an altered neural circuit for associative learning likely includes impaired executive functioning, which is arguably the hallmark of ADHD (39–41). The inward deformations located generally in the sensorimotor (primarily posterior) portions of the basal ganglia nuclei accord well with other reports of anatomical abnormalities in the sensorimotor cortices of ADHD youth (42, 43). The sensorimotor portions of the basal ganglia, together with the sensorimotor cortex, drive motor learning and control (44–48). Deficits in the sensorimotor neural circuit may underlie the dysfunction in fine and gross motor control and the impairments in learning and execution of complex motor behaviors that are characteristic of ADHD (39, 49–53).
Although the neurobiological mechanisms that cause inward deformations of the basal ganglia in ADHD youth are unknown, one possibility is that dopamine dysfunction in ADHD may alter local cytoarchitecture within basal ganglia nuclei. Several candidate genes related to dopamine neurotransmission have been associated with ADHD (54). One of these, the dopamine-transporter gene, removes dopamine from the synaptic cleft through a reuptake mechanism (55). Abnormally high levels of the dopamine-transporter gene reported in persons with ADHD (56–59) may diminish concentrations of synaptic dopamine. The basal ganglia may be particularly sensitive to diminished levels of dopamine, since basal ganglia nuclei receive substantial projections from midbrain-dopamine afferents (60) and contain the largest relative concentration of the dopamine-transporter gene in the brain (61, 62). Indeed, prior studies of the histological features of the basal ganglia in animals (63–66) and humans (64, 67, 68) suggest that deficits in dopamine concentrations produce anatomical alterations in basal ganglia neurons, including reductions in the number of synapses, decreases in the density of dendrite spines, and decreases in dendritic arborization and length. These cellular changes, when affecting large-scale populations of neurons in the basal ganglia, could produce the local volume reductions of the basal ganglia that we detected in the ADHD group. Moreover, this putative mechanism could also account for the location of the most prominent volume reductions in the putamen, which is the largest recipient of midbrain-dopamine afferents of all the basal ganglia nuclei (62, 69, 70). An exquisite modulation of the basal ganglia by dopamine supports the acquisition and execution of a broad range of behavioral actions that are orchestrated by limbic, associative, and sensorimotor basal ganglia circuits (71–73). Thus, the local volume reductions that span the limbic, associative, and sensorimotor portions of basal ganglia nuclei may be associated with the heterogeneous symptoms of ADHD, which include context inappropriate behaviors, deficits in working memory, and impaired motor control (35, 74–76).
If local volume reductions of the basal ganglia in persons with ADHD reflect the morphological modifications that occur in response to a deficit in dopamine, then the local volume increases of basal ganglia in youth with ADHD who are treated with stimulants may represent the morphological changes that occur in response to the relative improvement in dopamine levels that the stimulants produce. Indeed, previous imaging studies in youth with ADHD demonstrate an association between stimulant treatment and normalized gray matter volume in various brain regions (77–79). Stimulants bind to and block the dopamine transporter gene, effectively increasing synaptic and extracellular dopamine levels (80, 81). Therefore, stimulants may alleviate the deleterious cellular effects that the deficit of dopamine has on target basal ganglia neurons in untreated persons with ADHD. Although the exact anatomical modifications produced by a stimulant-induced increase in dopamine in humans are unknown, animal studies suggest that stimulants induce changes in gene expression and dendritic architecture within the basal ganglia in a direction opposite of that seen with deficient dopamine (82–86). Thus, the significant local volume reductions of the basal ganglia in ADHD youth not taking stimulants and the attenuation of these reductions in those youth receiving stimulants may reflect the architectural modifications that occur in response to deficient and relatively corrected dopamine concentrations, respectively.
Limitations
Although we cannot exclude entirely the possibility that co-occurring illnesses, sex, or age effects influenced our findings, including these effects as covariates in our statistical models and conducting separate analyses of ADHD subgroups indicated that these effects did not appreciably affect our findings. In addition, the cross-sectional design of this study precludes strong inferences that the morphological abnormalities of the basal ganglia represented either the causes or consequences of ADHD. The cross-sectional design similarly precludes strong inferences that the seemingly morphological normalizing effects of stimulant medications represented the direct effects of these medications on the basal ganglia or indirect influences from their effects on regions that are connected with the basal ganglia or even some sort of ascertainment bias related to unknown clinical features that determined which participants were or were not taking stimulant medications. Longitudinal imaging studies of ADHD youth that are combined with randomized studies of stimulant medications are needed to identify more conclusively the causal effects that ADHD and stimulant medications have on the morphology of basal ganglia nuclei.
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References
1.
Sagvolden T, Russell VA, Aase H, Johansen EB, Farshbaf M: Rodent models of attention-deficit/hyperactivity disorder. Biol Psychiatry 2005; 57:1239–1247
2.
Plessen K, Peterson BS: The neurobiology of impulsivity and self-regulatory control in children with attention-deficit/hyperactivity disorder, in Neurobiology of Mental Illness, 3rd ed. Edited by, Charney D, Nestler EJ. Oxford, England, Oxford University Press, 2008, pp 1129–1152
3.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS, Blumenthal JD, James RS, Ebens CL, Walter JM, Zijdenbos A, Evans AC, Giedd JN, Rapoport JL: Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 2002; 288:1740–1748
4.
Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, Giedd J, Castellanos FX, Rapoport J: Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2006; 63:540–549
5.
Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW, Peterson BS: Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. Lancet 2003; 362:1699–1707
6.
McAlonan GM, Cheung V, Cheung C, Chua SE, Murphy DG, Suckling J, Tai KS, Yip LK, Leung P, Ho TP: Mapping brain structure in attention deficit-hyperactivity disorder: a voxel-based MRI study of regional grey and white matter volume. Psychiatry Res 2007; 154:171–180
7.
Overmeyer S, Bullmore ET, Suckling J, Simmons A, Williams SC, Santosh PJ, Taylor E: Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychol Med 2001; 31:1425–1435
8.
Wang J, Jiang T, Cao Q, Wang Y: Characterizing anatomic differences in boys with attention-deficit/hyperactivity disorder with the use of deformation-based morphometry. AJNR Am J Neuroradiol 2007; 28:543–547
9.
Qiu A, Crocetti D, Adler M, Mahone EM, Denckla MB, Miller MI, Mostofsky SH: Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. Am J Psychiatry 2009; 166:74–82
10.
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4th ed (DSM–IV). Washington, DC, American Psychiatric Publishing, 1994
11.
Barkley RA, DuPaul GJ, McMurray MB: Attention deficit disorder with and without hyperactivity: clinical response to three dose levels of methylphenidate. Pediatrics 1991; 87:519–531
12.
Bansal R, Staib LH, Whiteman R, Wang YM, Peterson BS: ROC-based assessments of 3D cortical surface-matching algorithms. Neuroimage 2005; 24:150–162
13.
Bansal R, Staib LH, Xu D, Zhu H, Peterson PS: Statistical analyses of brain surfaces using Gaussian random fields on 2-D manifolds. IEEE Trans Med Imaging 2007; 26:46–57
14.
Plessen KJ, Bansal R, Zhu H, Whiteman R, Amat J, Quackenbush GA, Martin L, Durkin K, Blair C, Royal J, Hugdahl K, Peterson BS: Hippocampus and amygdala morphology in attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2006; 63:795–807
15.
Peterson BS, Choi HA, Hao X, Amat JA, Zhu H, Whiteman R, Liu J, Xu D, Bansal R: Morphologic features of the amygdala and hippocampus in children and adults with Tourette syndrome. Arch Gen Psychiatry 2007; 64:1281–1291
16.
Taylor JE, Robert JA: Euler characteristics for Gaussian fields on manifolds. Ann Probab 2003; 31:533–563
17.
Aylward EH, Reiss AL, Reader MJ, Singer HS, Brown JE, Denckla MB: Basal ganglia volumes in children with attention-deficit hyperactivity disorder. J Child Neurol 1996; 11:112–115
18.
Castellanos FX, Giedd JN, Marsh WL, Hamburger SD, Vaituzis AC, Dickstein DP, Sarfatti SE, Vauss YC, Snell JW, Lange N, Kaysen D, Krain AL, Ritchie GF, Rajapakse JC, Rapoport JL: Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. Arch Gen Psychiatry 1996; 53:607–616
19.
Castellanos FX, Giedd JN, Berquin PC, Walter JM, Sharp W, Tran T, Vaituzis AC, Blumenthal JD, Nelson J, Bastain TM, Zijdenbos A, Evans AC, Rapoport JL: Quantitative brain magnetic resonance imaging in girls with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2001; 58:289–295
20.
Parent A, Hazrati LN: Functional anatomy of the basal ganglia, I: the cortico-basal ganglia-thalamo-cortical loop. Brain Res Brain Res Rev 1995; 20:91–127
21.
Selemon LD, Goldman-Rakic PS: Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey. J Neurosci 1985; 5:776–794
22.
Nakano K, Kayahara T, Tsutsumi T, Ushiro H: Neural circuits and functional organization of the striatum. J Neurol 2000; 247(suppl 5):V1–V15
23.
Haber SN: The primate basal ganglia: parallel and integrative networks. J Chem Neuroanat 2003; 26:317–330
24.
Alexander GE, DeLong MR, Strick PL: Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 1986; 9:357–381
25.
Morel A, Loup F, Magnin M, Jeanmonod D: Neurochemical organization of the human basal ganglia: anatomofunctional territories defined by the distributions of calcium-binding proteins and SMI-32. J Comp Neurol 2002; 443:86–103
26.
Lehericy S, Ducros M, Van de Moortele PF, Francois C, Thivard L, Poupon C, Swindale N, Ugurbil K, Kim DS: Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Ann Neurol 2004; 55:522–529
27.
Postuma RB, Dagher A: Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cereb Cortex 2006; 16:1508–1521
28.
Hesslinger B, Tebartz van Elst L, Thiel T, Haegele K, Hennig J, Ebert D: Frontoorbital volume reductions in adult patients with attention deficit hyperactivity disorder. Neurosci Lett 2002; 328:319–321
29.
Hollerman JR, Tremblay L, Schultz W: Involvement of basal ganglia and orbitofrontal cortex in goal-directed behavior. Prog Brain Res 2000; 126:193–215
30.
Baxter MG, Murray EA: The amygdala and reward. Nat Rev Neurosci 2002; 3:563–573
31.
O'Doherty JP, Deichmann R, Critchley HD, Dolan RJ: Neural responses during anticipation of a primary taste reward. Neuron 2002; 33:815–826
32.
Pasupathy A, Miller EK: Different time courses of learning-related activity in the prefrontal cortex and striatum. Nature 2005; 433:873–876
33.
Aase H, Sagvolden T: Infrequent, but not frequent, reinforcers produce more variable responding and deficient sustained attention in young children with attention-deficit/hyperactivity disorder (ADHD). J Child Psychol Psychiatry 2006; 47:457–471
34.
Aase H, Meyer A, Sagvolden T: Moment-to-moment dynamics of ADHD behaviour in South African children. Behav Brain Funct 2006; 2:11
35.
Frank MJ, Santamaria A, O'Reilly RC, Willcutt E: Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder. Neuropsychopharmacology 2007; 32:1583–1599
36.
Rao SM, Mayer AR, Harrington DL: The evolution of brain activation during temporal processing. Nat Neurosci 2001; 4:317–323
37.
Tanaka SC, Doya K, Okada G, Ueda K, Okamoto Y, Yamawaki S: Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nat Neurosci 2004; 7:887–893
38.
McNab F, Klingberg T: Prefrontal cortex and basal ganglia control access to working memory. Nat Neurosci 2008; 11:103–107
39.
Barkley RA: Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 1997; 121:65–94
40.
Toplak ME, Dockstader C, Tannock R: Temporal information processing in ADHD: findings to date and new methods. J Neurosci Methods 2006; 151:15–29
41.
Swanson JM, Kinsbourne M, Nigg J, Lanphear B, Stefanatos GA, Volkow N, Taylor E, Casey BJ, Castellanos FX, Wadhwa PD: Etiologic subtypes of attention-deficit/hyperactivity disorder: brain imaging, molecular genetic and environmental factors and the dopamine hypothesis. Neuropsychol Rev 2007; 17:39–59
42.
Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M, Tomas J, Raheb C, Gispert JD, Batlle S, Bulbena A: Global and regional gray matter reductions in ADHD: a voxel-based morphometric study. Neurosci Lett 2005; 389:88–93
43.
Mostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE: Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biol Psychiatry 2002; 52:785–794
44.
Graybiel AM, Aosaki T, Flaherty AW, Kimura M: The basal ganglia and adaptive motor control. Science 1994; 265:1826–1831
45.
Jueptner M, Frith CD, Brooks DJ, Frackowiak RS, Passingham RE: Anatomy of motor learning, II: subcortical structures and learning by trial and error. J Neurophysiol 1997; 77:1325–1337
46.
Gerardin E, Pochon JB, Poline JB, Tremblay L, Van de Moortele PF, Levy R, Dubois B, Le Bihan D, Lehericy S: Distinct striatal regions support movement selection, preparation and execution. Neuroreport 2004; 15:2327–2331
47.
Lehericy S, Benali H, Van de Moortele PF, Pelegrini-Issac M, Waechter T, Ugurbil K, Doyon J: Distinct basal ganglia territories are engaged in early and advanced motor sequence learning. Proc Natl Acad Sci U S A 2005; 102:12566–12571
48.
Poldrack RA, Sabb FW, Foerde K, Tom SM, Asarnow RF, Bookheimer SY, Knowlton BJ: The neural correlates of motor skill automaticity. J Neurosci 2005; 25:5356–5364
49.
Pitcher TM, Piek JP, Hay DA: Fine and gross motor ability in males with ADHD. Dev Med Child Neurol 2003; 45:525–535
50.
Piek JP, Pitcher TM, Hay DA: Motor coordination and kinaesthesis in boys with attention deficit-hyperactivity disorder. Dev Med Child Neurol 1999; 41:159–165
51.
Fliers E, Rommelse N, Vermeulen SH, Altink M, Buschgens CJ, Faraone SV, Sergeant JA, Franke B, Buitelaar JK: Motor coordination problems in children and adolescents with ADHD rated by parents and teachers: effects of age and gender. J Neural Transm 2008; 115:211–220
52.
Mostofsky SH, Rimrodt SL, Schafer JG, Boyce A, Goldberg MC, Pekar JJ, Denckla MB: Atypical motor and sensory cortex activation in attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study of simple sequential finger tapping. Biol Psychiatry 2006; 59:48–56
53.
Dockstader C, Gaetz W, Cheyne D, Wang F, Castellanos FX, Tannock R: MEG event-related desynchronization and synchronization deficits during basic somatosensory processing in individuals with ADHD. Behav Brain Funct 2008; 4:8
54.
Bobb AJ, Castellanos FX, Addington AM, Rapoport JL: Molecular genetic studies of ADHD: 1991 to 2004. Am J Med Genet B Neuropsychiatr Genet 2005; 132:109–125
55.
Hitri A, Hurd YL, Wyatt RJ, Deutsch SI: Molecular, functional and biochemical characteristics of the dopamine transporter: regional differences and clinical relevance. Clin Neuropharmacol 1994; 17:1–22
56.
Dougherty DD, Bonab AA, Spencer TJ, Rauch SL, Madras BK, Fischman AJ: Dopamine transporter density in patients with attention deficit hyperactivity disorder. Lancet 1999; 354:2132–2133
57.
Krause KH, Dresel SH, Krause J, Kung HF, Tatsch K: Increased striatal dopamine transporter in adult patients with attention deficit hyperactivity disorder: effects of methylphenidate as measured by single photon emission computed tomography. Neurosci Lett 2000; 285:107–110
58.
Dresel S, Krause J, Krause KH, LaFougere C, Brinkbaumer K, Kung HF, Hahn K, Tatsch K: Attention deficit hyperactivity disorder: binding of [99mTc]TRODAT-1 to the dopamine transporter before and after methylphenidate treatment. Eur J Nucl Med 2000; 27:1518–1524
59.
Cheon KA, Ryu YH, Kim YK, Namkoong K, Kim CH, Lee JD: Dopamine transporter density in the basal ganglia assessed with [123I]IPT SPET in children with attention deficit hyperactivity disorder. Eur J Nucl Med Mol Imaging 2003; 30:306–311
60.
Smith Y, Kieval JZ: Anatomy of the dopamine system in the basal ganglia. Trends Neurosci 2000; 23(suppl 10):S28–S33
61.
Donnan GA, Kaczmarczyk SJ, McKenzie JS, Kalnins RM, Chilco PJ, Mendelsohn FA: Catecholamine uptake sites in mouse brain: distribution determined by quantitative [3H]mazindol autoradiography. Brain Res 1989; 504:64–71
62.
Donnan GA, Kaczmarczyk SJ, Paxinos G, Chilco PJ, Kalnins RM, Woodhouse DG, Mendelsohn FA: Distribution of catecholamine uptake sites in human brain as determined by quantitative [3H] mazindol autoradiography. J Comp Neurol 1991; 304:419–434
63.
Solis O, Limon DI, Flores-Hernandez J, Flores G: Alterations in dendritic morphology of the prefrontal cortical and striatum neurons in the unilateral 6-OHDA-rat model of Parkinson's disease. Synapse 2007; 61:450–458
64.
Jan C, Francois C, Tande D, Yelnik J, Tremblay L, Agid Y, Hirsch E: Dopaminergic innervation of the pallidum in the normal state, in MPTP-treated monkeys and in Parkinsonian patients. Eur J Neurosci 2000; 12:4525–4535
65.
Mounayar S, Boulet S, Tande D, Jan C, Pessiglione M, Hirsch EC, Feger J, Savasta M, Francois C, Tremblay L: A new model to study compensatory mechanisms in MPTP-treated monkeys exhibiting recovery. Brain 2007; 130:2898–2914
66.
Day M, Wang Z, Ding J, An X, Ingham CA, Shering AF, Wokosin D, Ilijic E, Sun Z, Sampson AR, Mugnaini E, Deutch AY, Sesack SR, Arbuthnott GW, Surmeier DJ: Selective elimination of glutamatergic synapses on striatopallidal neurons in Parkinson disease models. Nat Neurosci 2006; 9:251–259
67.
O'Neill J, Schuff N, Marks WJ, Feiwell R, Aminoff MJ, Weiner MW: Quantitative 1H magnetic resonance spectroscopy and MRI of Parkinson's disease. Mov Disord 2002; 17:917–927
68.
Geng DY, Li YX, Zee CS: Magnetic resonance imaging-based volumetric analysis of basal ganglia nuclei and substantia nigra in patients with Parkinson's disease. Neurosurgery 2006; 58:256–262
69.
Madras BK, Gracz LM, Fahey MA, Elmaleh D, Meltzer PC, Liang AY, Stopa EG, Babich J, Fischman AJ: Altropane, a SPECT or PET imaging probe for dopamine neurons, III: human dopamine transporter in postmortem normal and Parkinson's diseased brain. Synapse 1998; 29:116–127
70.
Miller GW, Staley JK, Heilman CJ, Perez JT, Mash DC, Rye DB, Levey AI: Immunochemical analysis of dopamine transporter protein in Parkinson's disease. Ann Neurol 1997; 41:530–539
71.
Graybiel AM: Basal ganglia: input, neural activity, and relation to the cortex. Curr Opin Neurobiol 1991; 1:644–651
72.
Schultz W, Dayan P, Montague PR: A neural substrate of prediction and reward. Science 1997; 275:1593–1599
73.
Wickens JR, Horvitz JC, Costa RM, Killcross S: Dopaminergic mechanisms in actions and habits. J Neurosci 2007; 27:8181–8183
74.
Tripp G, Wickens JR: Dopamine transfer deficit: a neurobiological theory of altered reinforcement mechanisms in ADHD. J Child Psychol Psychiatry 2008; 49:691–704
75.
Sagvolden T, Johansen EB, Aase H, Russell VA: A dynamic developmental theory of attention-deficit/hyperactivity disorder (ADHD) predominantly hyperactive/impulsive and combined subtypes. Behav Brain Sci 2005; 28:397–419; discussion 419–468
76.
Williams ZM, Eskandar EN: Selective enhancement of associative learning by microstimulation of the anterior caudate. Nat Neurosci 2006; 9:562–568
77.
Shaw P, Sharp WS, Morrison M, Eckstrand K, Greenstein DK, Clasen LS, Evans AC, Rapoport JL: Psychostimulant treatment and the developing cortex in attention deficit hyperactivity disorder. Am J Psychiatry 2009; 166:58–63
78.
Semrud-Clikeman M, Pliszka SR, Lancaster J, Liotti M: Volumetric MRI differences in treatment-naïve vs chronically treated children with ADHD. Neurology 2006; 67:1023–1027
79.
Bledsoe J, Semrud-Clikeman M, Pliszka SR: A magnetic resonance imaging study of the cerebellar vermis in chronically treated and treatment-naïve children with attention-deficit/hyperactivity disorder combined type. Biol Psychiatry 2009; 65:620–624
80.
Volkow ND, Wang GJ, Fowler JS, Ding YS: Imaging the effects of methylphenidate on brain dopamine: new model on its therapeutic actions for attention-deficit/hyperactivity disorder. Biol Psychiatry 2005; 57:1410–1415
81.
Schiffer WK, Volkow ND, Fowler JS, Alexoff DL, Logan J, Dewey SL: Therapeutic doses of amphetamine or methylphenidate differentially increase synaptic and extracellular dopamine. Synapse 2006; 59:243–251
82.
Abrous DN, Desjardins S, Sorin B, Hancock D, Le Moal M, Herman JP: Changes in striatal immediate early gene expression following neonatal dopaminergic lesion and effects of intrastriatal dopaminergic transplants. Neuroscience 1996; 73:145–159
83.
Yano M, Beverley JA, Steiner H: Inhibition of methylphenidate-induced gene expression in the striatum by local blockade of D1 dopamine receptors: interhemispheric effects. Neuroscience 2006; 140:699–709
84.
Mazei MS, Pluto CP, Kirkbride B, Pehek EA: Effects of catecholamine uptake blockers in the caudate-putamen and subregions of the medial prefrontal cortex of the rat. Brain Res 2002; 936:58–67
85.
Li Y, Kolb B, Robinson TE: The location of persistent amphetamine-induced changes in the density of dendritic spines on medium spiny neurons in the nucleus accumbens and caudate-putamen. Neuropsychopharmacology 2003; 28:1082–1085
86.
Robinson TE, Kolb B: Persistent structural modifications in nucleus accumbens and prefrontal cortex neurons produced by previous experience with amphetamine. J Neurosci 1997; 17:8491–8497
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Received: 3 September 2009
Accepted: 11 January 2010
Published online: 1 August 2010
Published in print: August 2010
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Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
Supported in part by National Institute of Mental Health grants MH-59139, MH-068318, and K02-74677; a grant from the Tourette Syndrome Association; and a grant from the Klingenstein Third Generation Research Foundation.
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