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Original Investigation |

Developmentally Stable Whole-Brain Volume Reductions and Developmentally Sensitive Caudate and Putamen Volume Alterations in Those With Attention-Deficit/Hyperactivity Disorder and Their Unaffected Siblings FREE

Corina U. Greven, PhD1,2,3; Janita Bralten, Msc1,4; Maarten Mennes, PhD1; Laurence O’Dwyer, PhD1; Kimm J. E. van Hulzen, PhD4; Nanda Rommelse, PhD2,5; Lizanne J. S. Schweren, MSc6; Pieter J. Hoekstra, MD, PhD6; Catharina A. Hartman, PhD6; Dirk Heslenfeld, PhD7; Jaap Oosterlaan, PhD7; Stephen V. Faraone, PhD9; Barbara Franke, PhD4,5; Marcel P. Zwiers, PhD1,10; Alejandro Arias-Vasquez, PhD1,4,5; Jan K. Buitelaar, MD, PhD1,2
[+] Author Affiliations
1Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
2Karakter Child and Adolescent Psychiatry University Center, Nijmegen, the Netherlands
3Social, Genetic, and Developmental Psychiatry Center, Institute of Psychiatry, King’s College London, London, England
4Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
5Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
6Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
7Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, the Netherlands
9Department of Psychiatry, State University of New York Upstate Medical University, Syracuse
10Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
JAMA Psychiatry. 2015;72(5):490-499. doi:10.1001/jamapsychiatry.2014.3162.
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Importance  Attention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental disorder. It has been linked to reductions in total brain volume and subcortical abnormalities. However, owing to heterogeneity within and between studies and limited sample sizes, findings on the neuroanatomical substrates of ADHD have shown considerable variability. Moreover, it remains unclear whether neuroanatomical alterations linked to ADHD are also present in the unaffected siblings of those with ADHD.

Objective  To examine whether ADHD is linked to alterations in whole-brain and subcortical volumes and to study familial underpinnings of brain volumetric alterations in ADHD.

Design, Setting, and Participants  In this cross-sectional study, we included participants from the large and carefully phenotyped Dutch NeuroIMAGE sample (collected from September 2009-December 2012) consisting of 307 participants with ADHD, 169 of their unaffected siblings, and 196 typically developing control individuals (mean age, 17.21 years; age range, 8-30 years).

Main Outcomes and Measures  Whole-brain volumes (total brain and gray and white matter volumes) and volumes of subcortical regions (nucleus accumbens, amygdala, caudate nucleus, globus pallidus, hippocampus, putamen, thalamus, and brainstem) were derived from structural magnetic resonance imaging scans using automated tissue segmentation.

Results  Regression analyses revealed that relative to control individuals, participants with ADHD had a 2.5% smaller total brain (β = −31.92; 95% CI, −52.69 to −11.16; P = .0027) and a 3% smaller total gray matter volume (β = −22.51; 95% CI, −35.07 to −9.96; P = .0005), while total white matter volume was unaltered (β = −10.10; 95% CI, −20.73 to 0.53; P = .06). Unaffected siblings had total brain and total gray matter volumes intermediate to participants with ADHD and control individuals. Significant age-by-diagnosis interactions showed that older age was linked to smaller caudate (P < .001) and putamen (P = .01) volumes (both corrected for total brain volume) in control individuals, whereas age was unrelated to these volumes in participants with ADHD and their unaffected siblings. Attention-deficit/hyperactivity disorder was not significantly related to the other subcortical volumes.

Conclusions and Relevance  Global differences in gray matter volume may be due to alterations in the general mechanisms underlying normal brain development in ADHD. The age-by-diagnosis interaction in the caudate and putamen supports the relevance of different brain developmental trajectories in participants with ADHD vs control individuals and supports the role of subcortical basal ganglia alterations in the pathophysiology of ADHD. Alterations in total gray matter and caudate and putamen volumes in unaffected siblings suggest that these volumes are linked to familial risk for ADHD.

Figures in this Article

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity/impulsivity.1 Anatomical magnetic resonance imaging (MRI) studies have associated ADHD with a 3% to 5% smaller total brain size compared with control individuals.24 Meta-analyses have further documented smaller volumes in ADHD across several specific brain regions, most consistently in basal ganglia volumes: right globus pallidus and right putamen and caudate.58 These abnormalities are in accordance with a neurodevelopmental theory that hypothesizes ADHD to be caused by early-emerging persistent subcortical abnormalities.9

Existing brain volumetric studies in ADHD face some limitations. First, the child and adult literature have largely been kept separate, ignoring the transition from adolescence into early adulthood in studying age effects. Hyperactive/impulsive more than inattentive symptoms of ADHD tend to decrease with age.1012 Moreover, cross-sectional evidence suggests that some brain volumetric alterations observed in childhood may normalize with age.7,8 Second, findings on brain volumetric alterations in ADHD have been variable (eg, one meta-analysis reported that only 25% to 50% of included studies revealed similar results).8 Heterogeneity in sample composition within and between studies and small samples sizes may explain inconsistencies; individual studies included in meta-analyses ranged from 17 to 291 participants with ADHD and control individuals, with only 4 studies exceeding a sample size of 100.58 The use of a large well-defined sample has added value over and extends existing meta-analyses, as meta-analyses are limited by shortcomings of the individual studies. Third, ADHD is more common in boys than girls,1,13 and brain volumetric alterations in ADHD may be hemisphere specific.58 However, most studies used small samples to study sex and lateralization effects or revealed inconsistent findings.5,7,8,14 Finally, ADHD runs in families1517 and is heritable (76%18), and brain volumes are also heritable (total brain volume, 66%-97%19; subcortical volumes, 44%-88%20). Two studies reported alterations in prefrontal gray matter and occipital gray and white matter,4 as well as inferior frontal gyrus gray matter and inferior fronto-occipital fasciculus white matter21 not only in participants with ADHD, but also in their unaffected siblings relative to control individuals. Because first-degree relatives on average share 50% of their genetic information, as well as all familywide environmental influences, these findings suggest familial (ie, shared genetic and/or shared environmental) underpinnings to associations between ADHD and brain volumes. However, whether ADHD-brain associations are influenced by familial factors remains widely unexplored.

The present cross-sectional study addresses these shortcomings through studying the brain volumetric correlates of ADHD in a large well-characterized sample of adolescents and young adults with ADHD, their unaffected siblings, and typically developing control individuals. First, we investigated whether ADHD was linked to whole-brain and subcortical volumes and whether results were influenced by age, sex, or lateralization. Second, familial underpinnings of brain volumetric alterations in ADHD were examined.

Sample

Participants came from the NeuroIMAGE project,22 a follow-up of the Dutch part of the International Multicenter ADHD Genetics (IMAGE) Study performed between 2003 and 2006.2326 The IMAGE Study recruited ADHD families with at least 1 child with combined subtype ADHD and at least 1 biological sibling (regardless of ADHD diagnosis) and control families with at least 1 child and 1 biological sibling with no formal or suspected ADHD diagnosis in first-degree family members. Inclusion criteria were age between 8 and 30 years; European white descent; intelligence quotient (IQ) greater than or equal to 70; and no diagnosis of autism, epilepsy, general learning difficulties, brain disorders, and known genetic disorders. Dutch participants of the IMAGE Study were invited for follow-up measurement and (re)assessed in the NeuroIMAGE Study (follow-up rate: ADHD families, 75.6%; control families, 75.1%; mean [SD] time between measurements, 5.9 [0.74] years). Inclusion criteria were largely consistent with the IMAGE Study, except that inclusion of any ADHD subtype was allowed. Following exclusion for contraindications to MRI scanning and quality control of MRI scans (described further on), the final sample consisted of 307 participants with ADHD, 169 unaffected siblings, and 196 control participants (siblings from 389 families) (Table 1). The group with ADHD (n = 307) included 128 siblings (only siblings from ADHD families where each sibling had an ADHD diagnosis), the unaffected siblings group (n = 169) included 53 siblings (only siblings from ADHD families without an ADHD diagnosis), and the control group (n = 196) included 142 siblings (only siblings from control families). Ethical approval for the current study was obtained from CMO Regio Arnhem-Nijmegen, and all participants provided written informed consent. For details on diagnostic assessment, see eAppendix 1 in the Supplement.

Table Graphic Jump LocationTable 1.  Participant Characteristics
Image Acquisition and Segmentation

Imaging was conducted at 2 locations (VU University Amsterdam, Amsterdam, and Radboud University Medical Center, Nijmegen) using 2 comparable 1.5-T scanners and scan protocols (Sonata and Avanto; Siemens) with the same product 8-channel head-coil and closely matched scan protocols.22 The protocol included 2 high-resolution T1-weighted magnetization-prepared rapid acquisition gradient echo anatomical scans (176 sagittal slices; repetition time = 2730 milliseconds; echo time = 2.95 milliseconds; inversion time = 1000 milliseconds; flip angle = 7°; GRAPPA (generalized autocalibrating partial parallel acquisition) 2; voxel size = 1.0 × 1.0 × 1.0 mm; field of view = 256 mm). Magnetic resonance imaging scans that yielded relevant incidental findings or in which manual ratings revealed poor quality or motion artifacts were excluded.28 In participants with 2 good-quality scans, volume estimates were averaged across scans (eTable 1 in the Supplement).

Whole-Brain Volumes

Normalization, bias correction, and segmentation into gray matter, white matter, and cerebrospinal fluid volumes were performed using the unified procedure of the VBM 8.1 toolbox (http://dbm.neuro.uni-jena.de/vbm/) in SPM (default settings). Total gray and white matter volumes were calculated by summation of their tissue probability maps. Total brain volume was the sum of total gray and white matter volumes.

Subcortical Volumes

Automated FIRST (FMRIB’s Integrated Registration and Segmentation Tool) subcortical segmentation was applied to estimate total, left, and right volumes of the amygdala, caudate nucleus, hippocampus, nucleus accumbens, globus pallidus, putamen, thalamus, and brainstem. Previous research has shown these volumes to be heritable.20 FIRST is part of FMRIB’s Software Library and performs registration and shape modeling of the just-mentioned regions in Montreal Neurological Institute 152 standard space.29 FIRST-based segmentation includes the hippocampus as a subcortical region, although it is not usually considered subcortical.29

Assessment of IQ and Medication Use

Participants’ full-scale IQ was estimated using the Vocabulary and Block Design subtests of the Wechsler Intelligence Scale for Children III or the Wechsler Adult Intelligence Scale III (for participants aged ≥17 years).22 Cumulative medication intake, calculated as treatment duration corrected for age multiplied by the mean daily dose in milligrams, was gathered from pharmacy transcripts and questionnaire reports (eAppendix 1 in the Supplement).

Analyses
Comparisons Between Participants With ADHD and Control Individuals

Brain volumetric measures were normally distributed and outliers more than 3 SDs greater than or less than the mean were removed. Overall, there were few outliers (1-5 individuals per volume). Associations between ADHD and brain volumes were examined using regression analyses that included brain volumes 1 by 1 as outcome measures, with scanner location (Amsterdam or Nijmegen), age, age squared, and sex as covariates.20,30,31 Total brain volume was included as an additional covariate for regressions of subcortical volumes to enable inferences about subcortical alterations unconfounded by total brain volume.32 The regression model included binary ADHD diagnosis (ADHD and control) as a main effect, as well as the 2-way interaction effects between binary ADHD diagnosis and age, age squared, and sex (ADHD by age; ADHD by age squared; and ADHD by sex). Interaction effects not reaching nominal significance (.05) were dropped from the final model. Centering of variables was used33 before creating interaction terms, and multicollineary statistics were examined. To correct for the nonindependence of the data of siblings in the ADHD and control groups (see Sample description), the correlation structure of the data was accounted for by calculating robust standard errors using the cluster command in Stata (StataCorp).34,35 This was merely a correction to account for the underlying assumption in regression that observations are independent and does not preclude examination of familiality effects underlying brain-ADHD associations.

Correction for Multiple Testing

A multiple-testing correction was applied, which adjusts correlated tests based on an effective number of independent comparisons36 derived from the eigenvalues of a correlation matrix between the included outcome measures adjusted for covariates. The multiple-testing adjusted P value was determined to be .003 (eTable 2 in the Supplement) and applied to analyses comparing participants with ADHD and control individuals. Any follow-up analyses of volumes surviving multiple-testing correction used the nominal significance level (.05) and should be seen as exploratory.

Comparisons of Unaffected Siblings With Participants With ADHD and Control Individuals

For volumes surviving multiple-testing correction, the previously mentioned regression models were repeated, replacing binary ADHD diagnosis with a 3-level group membership variable (ADHD, unaffected sibling, and control individual). All other model parameters were kept the same as in the analyses comparing participants with ADHD and control individuals and the cluster command, and the same covariates were used. Familiality was considered present if the unaffected siblings differed in brain volumes from control individuals but not from participants with ADHD or differed from both groups showing intermediate brain volumes.

Sensitivity Analyses

For volumes surviving multiple-testing correction, sensitivity analyses were conducted to examine potential effects of total brain volume, IQ, medication, scanner location, and sex distribution (eAppendix 2 in the Supplement).

Descriptives

Mean brain volumes corrected for covariates and for raw data are shown in Table 2 and eTable 3 in the Supplement, respectively. For mean volumes relating to the sensitivity analyses, see eTables 4 through 7 in the Supplement.

Table Graphic Jump LocationTable 2.  Participants With ADHD, Unaffected Siblings, and Control Individuals
Comparisons Between Participants With ADHD and Control Individuals
Whole-Brain Volumes

Compared with control individuals, participants with ADHD had smaller total brain (β = −31.92; 95% CI, −52.69 to −11.16; P = .0027) and total gray matter (β = −22.51; 95% CI, −35.07 to −9.96; P = .0005) volumes. Total brain volume was 2.5% (32 mL) smaller and total gray matter volume was 3% (22 mL) smaller (Figure 1; Table 3). No significant group differences were found for total white matter volume (β = −10.10; 95% CI, −20.73 to 0.53; P = .06). None of the interactions between ADHD diagnosis and sex, age, and age squared on whole-brain volumes were significant (Table 3). Hence, differences between participants with ADHD and control individuals in total brain and gray matter volumes were stable across age and sex.

Place holder to copy figure label and caption
Figure 1.
Mean Total Brain and Total Gray Matter Volumes in Participants With Attention-Deficit/Hyperactivity Disorder, Unaffected Siblings, and Control Individuals

Siblings indicate the unaffected siblings of participants with attention-deficit/hyperactivity disorder. The means are based on estimated marginal means corrected for age, age squared, sex, and scanner location. The error bars represent standard errors.

Graphic Jump Location
Table Graphic Jump LocationTable 3.  Regression of Binary ADHD Diagnosis on Brain Volumesa
Subcortical Volumes

Main effects of binary ADHD diagnosis on subcortical volumes were nonsignificant (Table 3). However, the interaction between binary ADHD diagnosis and age on total, left, and right caudate and putamen volumes was significant (total: P = .0004; left: P = .0003; and right: P = .0035 for caudate) (total: P = .0011; left: P < .0004; and right: P = .0018 for putamen) (Table 3). Because results were similar for left, right, and total volumes, subsequent results are described only for total caudate and putamen volumes. Post hoc analyses revealed that older age was related to smaller total caudate (P < .001) and putamen (P = .01) volumes in control individuals but not in participants with ADHD (caudate: P = .82; putamen: P = .16). Dividing the sample into 3 roughly equal age bands (younger: ≤15 years; middle: >15-≤22 years, and older: >22 years) revealed that relative to control individuals, participants with ADHD had significantly smaller total caudate (0.33 mL; 3.9%; P = .04) and total putamen (0.36 mL; 3.3%; P = .0061) volumes in the younger group, had nonsignificantly larger total caudate volume (0.04 mL; 0.5%; P = .67) and significantly larger putamen volumes in the middle group (0.26 mL; 2.4%; P = .02), and had significantly larger total caudate (0.87 mL; 12.0%; P = .0088) and total putamen (0.89 mL; 8.6%; P = .0038) volumes in the older group (eTable 8 and the eFigure in the Supplement). No significant interaction effects were present for the other subcortical volumes.

Comparisons of Unaffected Siblings With Participants With ADHD and Control Individuals
Whole-Brain Volumes

Compared with participants with ADHD, unaffected siblings had a 1.6% (20 mL; P = .04) larger total brain volume and a 1.8% (13 mL; P = .03) larger total gray matter volume (Tables 2 and 3). Compared with control individuals, unaffected siblings had nonsignificantly smaller total brain (0.9%; 12 mL; P = .32) and total gray matter (1.3%; 10 mL; P = .18) volumes. Nonetheless, a linear trend was present in these volumes across the 3 groups (total brain: P = .0018; total gray matter: P < .0003; eAppendix 3 in the Supplement), indicating that unaffected siblings had total brain and gray matter volumes intermediate to participants with ADHD and control individuals.

Subcortical Volumes

The interaction between group membership unaffected siblings vs control individuals and age was a significant predictor of caudate and putamen volumes (total caudate: P = .05; total putamen: P = .0086); however, the interaction between group membership unaffected sibling vs ADHD and age was not significant (total caudate: P = .15; total putamen: P = .48) (Figure 2). This suggests that the unaffected siblings differed from control individuals but not from participants with ADHD. As was the case for participants with ADHD, age was not significantly related to caudate and putamen volumes in unaffected siblings (total caudate: P = .09; total putamen: P = .47).

Place holder to copy figure label and caption
Figure 2.
Group-by-Age Interaction on Caudate Nucleus Volume (A) and Putamen Volume (B)

Siblings indicate the unaffected siblings of participants with attention-deficit/hyperactivity disorder. The same pattern of results was found for total, left, and right volumes. The lines represent the regression lines, with the shading representing the 95% CIs.

Graphic Jump Location
Sensitivity Analyses

Findings were robust across sensitivity analyses (eAppendix 2 in the Supplement).

We found that ADHD was linked to a 2.5% smaller total brain volume relative to control individuals, driven by a 3% smaller total gray matter volume. Moreover, ADHD was linked to alterations in caudate and putamen volumes, which were developmentally sensitive. Unaffected siblings showed a pattern of results intermediate to participants with ADHD and control individuals. In this large sample, neurobiological alterations associated with ADHD were neither sex nor hemisphere specific.

Globalized differences between participants with ADHD and control individuals in gray matter volume across the brain could indicate alterations to general mechanisms underlying normal brain development such as those concerning neuron number (eg, neurogenesis and naturally occurring neuron death) and neuronal migration.37,38 This is in line with results from genome-wide association and bioinformatics analyses that implicated abnormal neuronal migration and directed neurite outgrowth in ADHD.39,40 Such mechanisms could be probed further through studying post-mortem brain tissue or animal models. Smaller total brain and gray matter volumes in participants with ADHD, relative to control individuals, were found at different ages, consistent with previous research that related smaller total brain size to ADHD in children and adolescents,2,4 as well as in adults.41

The caudate and putamen play important roles in several basal ganglia–thalamocortical circuits42 involved in motor control and learning, as well as selecting and enabling cognitive, executive, and emotional programs.43 Several of these processes (eg, motor functions, reward processing, and cognitive and attentional control) are impaired in ADHD.9,26 We found that older age was significantly related to smaller caudate and putamen volumes in control individuals, in line with previous studies in typically developing individuals reporting a decline in basal ganglia volumes across childhood development.30,44 In contrast, in participants with ADHD, age was not significantly related to caudate and putamen volumes. As a result, participants with ADHD had smaller caudate and putamen volumes than control individuals in childhood and early adolescence (aged 8-15 years); these differences diminished in midadolescence to late adolescence. Previous studies also found smaller, or different-shaped, basal ganglia volumes in ADHD58,45,46; further, the different developmental trajectories in participants with ADHD and control individuals may provide a potential explanation for previous studies suggesting that alterations in basal ganglia volumes in ADHD normalize with age.2,7,8 By early adulthood (aged 22-30 years), ADHD was related to larger caudate and putamen volumes relative to control individuals, inconsistent with previous studies in adults with ADHD showing smaller basal ganglia volumes14,47,48 or no differences.4951 Most adult studies included older participants than the current study, potentially explaining this inconsistency. Delays in developmental trajectories of the caudate and putamen30,44 in ADHD may potentially explain why larger volumes observed in the current study are no longer observed in studies on older samples. Alternatively, the larger volumes in early adulthood may represent overcompensatory mechanisms or could be related to reduced neuronal pruning. In a longitudinal study of children and adolescents (aged 4-19 years), participants with ADHD had smaller basal ganglia volumes and total surface areas compared with control participants; however, inconsistent with our results, these differences were fixed over time.52 Only in a small region involved in reward processing, the ventral striatal surface area, did developmental trajectories differ between participants with ADHD and control participants.52 Hence, a next step to extend the present findings would be to study the mechanisms underlying the age-by-diagnosis interaction effects in caudate and putamen volumes in a longitudinal design and to apply complementary methods in the same participants (eg, surface area and voxel-based morphometry) to reveal findings that may be undetectable by the volumetric technique used here. Overall, our results were consistent with the hypothesis that subcortical alterations are key in the pathophysiology of ADHD.9

Findings in the unaffected siblings provided evidence that total brain, gray matter, caudate, and putamen volumes are linked to familial risk for ADHD. As familial underpinnings of ADHD are thought to be largely genetic,17,53 it is plausible that genetic mechanisms may underlie the reported ADHD-brain associations, creating possible new targets for molecular genetic research. Given the small percentage of volume difference and additional challenges added by age-dependent effects in the caudate and putamen, multisite international consortia comprising large data sets from different age groups are necessary in such gene-identification efforts.

Although effect sizes were small, effects were robust across sensitivity analyses. The only exception was that the intermediate position of unaffected siblings for total brain and gray matter volumes was carried by only 1 of our scanner locations (Nijmegen). Hence, familiality with regard to total brain and gray matter volumes should be interpreted with some caution. When IQ was included as a covariate, alterations in total brain and gray matter volumes were attenuated but remained. There is an active debate whether studies on ADHD should adjust for IQ group differences54 because IQ is likely to share meaningful variance with ADHD.55 Therefore, including IQ as a covariate may lead to overcorrection. Hence, we report findings with and without adjustment for IQ. Two cross-sectional meta-analyses and a review, based on binary medication use or percentage treated, concluded that ADHD medication may decrease structural brain alterations in participants with ADHD.7,8,56 In contrast, a longitudinal study found no significant association between using/not using medication or the proportion of time taking medication and developmental trajectories of basal ganglia.52 The present study is in line with the latter results, as we found no association between cumulative medication intake and brain volumes in participants with ADHD. Hence, our findings do not support a normalizing effect of stimulant medication on structural brain alterations. Nonetheless, medication effects need to be explored more fully longitudinally before drawing firm conclusions (eg, through studying age at medication initiation, long- vs short-acting stimulants, and stimulants vs nonstimulants).

A major innovation of this study was the inclusion of unaffected siblings, allowing the study of familial effects, the use of a large sample, and the use of a more precise measure of medication (cumulative medication intake) for which we had complete data. The present sample followed a naturalistic design, allowing greater generalization to families with ADHD as a whole, in contrast to the common approach to use a matched-sample design. Nonetheless, our findings were also robust when groups were matched for sex. Further, our sample included adolescents and young adults, which are an underrepresented age group because most studies have focused either on child or adult samples. Therefore, the current study contributes to fill several gaps in the literature and helps to investigate the influence of age on brain volumetric alterations in ADHD.

Naturally, this study also comes with limitations. First, the current study was cross sectional. Longitudinal follow-up is underway. Second, because most of this sample was adolescent, age-dependent results in early adulthood should be interpreted with some caution. Third, although we had clear reasons to focus on total and subcortical volumes, there are other relevant brain regions such as the cerebellum and frontal areas.2,4,9 Finally, the FIRST algorithm used to segment the subcortical volumes comes with limitations29; nonetheless, it is more objective than manual segmentation methods.

Our cross-sectional findings point to the relevance of different trajectories of brain development in ADHD vs control participants that are influenced by familial factors. Our results also support the role of subcortical basal ganglia alterations as key to understanding the pathophysiology of ADHD.

Submitted for Publication: June 7, 2014; final revision received December 10, 2014; accepted December 12, 2014.

Corresponding Author: Corina U. Greven, PhD, Donders Institute for Brain, Cognition, and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Internal Post 204, Nijmegen, the Netherlands (corina.greven@donders.ru.nl).

Published Online: March 18, 2015. doi:10.1001/jamapsychiatry.2014.3162.

Author Contributions: Dr Greven and Ms Bralten had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Greven and Ms Bralten were joint first authors.

Study concept and design: Greven, Bralten, Rommelse, Hartman, Heslenfeld, Oosterlaan, Faraone, Franke, Zwiers, Arias-Vasquez, Buitelaar.

Acquisition, analysis, or interpretation of data: Greven, Bralten, Mennes, O’Dwyer, van Hulzen, Rommelse, Schweren, Hoekstra, Hartman, Oosterlaan, Faraone, Franke, Zwiers, Arias-Vasquez, Buitelaar.

Drafting of the manuscript: Greven, Bralten, Mennes, Rommelse, Arias-Vasquez, Buitelaar.

Critical revision of the manuscript for important intellectual content: Greven, Bralten, Mennes, O’Dwyer, van Hulzen, Schweren, Hoekstra, Hartman, Heslenfeld, Oosterlaan, Faraone, Franke, Zwiers, Arias-Vasquez, Buitelaar.

Statistical analysis: Greven, Bralten, Mennes, van Hulzen, Schweren, Zwiers, Arias-Vasquez.

Obtained funding: Heslenfeld, Oosterlaan, Faraone, Franke, Buitelaar.

Administrative, technical, or material support: O’Dwyer, Rommelse, Heslenfeld, Oosterlaan, Zwiers.

Study supervision: Hoekstra, Oosterlaan, Faraone, Franke, Arias-Vasquez, Buitelaar.

Conflict of Interest Disclosures: Dr Hoekstra has received honoraria from Eli Lilly and Shire and an unrestricted research grant from Shire. Dr Oosterlaan has received an unrestricted grant from Shire. Dr Faraone has received consulting income and/or research support from Shire, Otsuka Pharmaceutical, and Alcobra Pharma. He has also received consulting fees from, was on advisory boards for, or participated in continuing medical education programs sponsored by Shire, Ortho-McNeil-Janssen Pharmaceuticals, Novartis, Pfizer, and Eli Lilly. Dr Faraone receives royalties from books published by Guilford Press, Straight Talk About Your Child’s Mental Health, and Oxford University Press, Schizophrenia: The Facts. Dr Buitelaar has been a consultant to/member of advisory boards of and/or a speaker for Janssen-Cilag BV, Eli Lilly, Bristol-Myers Squibb, Schering Plough, UCB, Shire, Novartis, and Servier. No other disclosures were reported.

Funding/Support: This work was supported by National Institutes of Health grant R01MH62873 (Dr Faraone), NWO Large Investment grant 1750102007010 (Dr Buitelaar), NWO Brain & Cognition grant 433-09-242 (Dr Buitelaar), and grants from Radboud University Medical Center, University Medical Center Groningen, Accare, and VU University Amsterdam. Dr Faraone has received research support from the National Institutes of Health.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Previous Presentations: This study was presented at the 25th Annual Convention of the Association for Psychological Science, May 24, 2013, Washington, DC; the 23rd Eunethydis ADHD Network Meeting, October 6, 2013, Prague, Czech Republic; and the 3rd Eunethydis International Conference on ADHD, May 22, 2014, Istanbul, Turkey.

Additional Contributions: We acknowledge the Department of Pediatrics of the VU University Medical Center for the opportunity to use the mock scanner for preparation of our participants.

Polanczyk  G, Rohde  LA.  Epidemiology of attention-deficit/hyperactivity disorder across the lifespan. Curr Opin Psychiatry. 2007;20(4):386-392.
PubMed   |  Link to Article
Castellanos  FX, Lee  PP, Sharp  W,  et al.  Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA. 2002;288(14):1740-1748.
PubMed   |  Link to Article
Castellanos  FX, Giedd  JN, Marsh  WL,  et al.  Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. Arch Gen Psychiatry. 1996;53(7):607-616.
PubMed   |  Link to Article
Durston  S, Hulshoff Pol  HE, Schnack  HG,  et al.  Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry. 2004;43(3):332-340.
PubMed   |  Link to Article
Valera  EM, Faraone  SV, Murray  KE, Seidman  LJ.  Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2007;61(12):1361-1369.
PubMed   |  Link to Article
Ellison-Wright  I, Ellison-Wright  Z, Bullmore  E.  Structural brain change in attention deficit hyperactivity disorder identified by meta-analysis. BMC Psychiatry. 2008;8(51):51.
PubMed   |  Link to Article
Nakao  T, Radua  J, Rubia  K, Mataix-Cols  D.  Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. Am J Psychiatry. 2011;168(11):1154-1163.
PubMed   |  Link to Article
Frodl  T, Skokauskas  N.  Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatr Scand. 2012;125(2):114-126.
PubMed   |  Link to Article
Halperin  JM, Schulz  KP.  Revisiting the role of the prefrontal cortex in the pathophysiology of attention-deficit/hyperactivity disorder. Psychol Bull. 2006;132(4):560-581.
PubMed   |  Link to Article
Biederman  J, Mick  E, Faraone  SV.  Age-dependent decline of symptoms of attention deficit hyperactivity disorder: impact of remission definition and symptom type. Am J Psychiatry. 2000;157(5):816-818.
PubMed   |  Link to Article
Greven  CU, Asherson  P, Rijsdijk  FV, Plomin  R.  A longitudinal twin study on the association between inattentive and hyperactive-impulsive ADHD symptoms. J Abnorm Child Psychol. 2011;39(5):623-632.
PubMed   |  Link to Article
Larsson  H, Lichtenstein  P, Larsson  JO.  Genetic contributions to the development of ADHD subtypes from childhood to adolescence. J Am Acad Child Adolesc Psychiatry. 2006;45(8):973-981.
PubMed   |  Link to Article
Greven  CU, Rijsdijk  FV, Plomin  R.  A twin study of ADHD symptoms in early adolescence: hyperactivity-impulsivity and inattentiveness show substantial genetic overlap but also genetic specificity. J Abnorm Child Psychol. 2011;39(2):265-275.
PubMed   |  Link to Article
Onnink  AM, Zwiers  MP, Hoogman  M,  et al.  Brain alterations in adult ADHD: effects of gender, treatment and comorbid depression. Eur Neuropsychopharmacol. 2014;24(3):397-409.
PubMed   |  Link to Article
Levy  F, Hay  DA, Bennett  KS.  Genetics of attention deficit hyperactivity disorder: a current review and future prospects. Int J Disabil Dev Educ. 2006;53(1):5-20.
Link to Article
Faraone  SV, Biederman  J, Mick  E,  et al.  Family study of girls with attention deficit hyperactivity disorder. Am J Psychiatry. 2000;157(7):1077-1083.
PubMed   |  Link to Article
Sprich  S, Biederman  J, Crawford  MH, Mundy  E, Faraone  SV.  Adoptive and biological families of children and adolescents with ADHD. J Am Acad Child Adolesc Psychiatry. 2000;39(11):1432-1437.
PubMed   |  Link to Article
Faraone  SV, Mick  E.  Molecular genetics of attention deficit hyperactivity disorder. Psychiatr Clin North Am. 2010;33(1):159-180.
PubMed   |  Link to Article
Peper  JS, Brouwer  RM, Boomsma  DI, Kahn  RS, Hulshoff Pol  HE.  Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum Brain Mapp. 2007;28(6):464-473.
PubMed   |  Link to Article
den Braber  A, Bohlken  MM, Brouwer  RM,  et al.  Heritability of subcortical brain measures: a perspective for future genome-wide association studies. Neuroimage. 2013;83:98-102.
PubMed   |  Link to Article
Pironti  VA, Lai  MC, Müller  U,  et al.  Neuroanatomical abnormalities and cognitive impairments are shared by adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives. Biol Psychiatry. 2014;76(8):639-647.
PubMed   |  Link to Article
von Rhein  D, Mennes  M, van Ewijk  H,  et al.  The NeuroIMAGE Study: a prospective phenotypic, cognitive, genetic and MRI study in children with attention-deficit/hyperactivity disorder: design and descriptives [published online July 11, 2014]. Eur Child Adolesc Psychiatry.
PubMed
Müller  UC, Asherson  P, Banaschewski  T,  et al.  The impact of study design and diagnostic approach in a large multi-centre ADHD study, part 1: ADHD symptom patterns. BMC Psychiatry. 2011;11(54):54.
PubMed   |  Link to Article
Müller  UC, Asherson  P, Banaschewski  T,  et al.  The impact of study design and diagnostic approach in a large multi-centre ADHD study, part 2: dimensional measures of psychopathology and intelligence. BMC Psychiatry. 2011;11(55):55.
PubMed   |  Link to Article
Nijmeijer  JS, Hoekstra  PJ, Minderaa  RB,  et al.  PDD symptoms in ADHD: an independent familial trait? J Abnorm Child Psychol. 2009;37(3):443-453.
PubMed   |  Link to Article
Rommelse  NN, Altink  ME, Martin  NC,  et al.  Neuropsychological measures probably facilitate heritability research of ADHD. Arch Clin Neuropsychol. 2008;23(5):579-591.
PubMed   |  Link to Article
Conners  CK, Sitarenios  G, Parker  JD, Epstein  JN.  The revised Conners’ Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. J Abnorm Child Psychol. 1998;26(4):257-268.
PubMed   |  Link to Article
Blumenthal  JD, Zijdenbos  A, Molloy  E, Giedd  JN.  Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage. 2002;16(1):89-92.
PubMed   |  Link to Article
Patenaude  B, Smith  SM, Kennedy  DN, Jenkinson  M.  A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56(3):907-922.
PubMed   |  Link to Article
Brain Development Cooperative Group.  Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development. Cereb Cortex. 2012;22(1):1-12.
PubMed   |  Link to Article
Reiss  AL, Abrams  MT, Singer  HS, Ross  JL, Denckla  MB.  Brain development, gender and IQ in children: a volumetric imaging study. Brain. 1996;119(pt 5):1763-1774.
PubMed   |  Link to Article
Mechelli  A, Price  CJ, Friston  KJ, Ashburner  J.  Voxel-based morphometry of the human brain: methods and applications. Curr Med Imaging Rev.2005;1(2):105-113.
Link to Article
Bradley  RA, Srivastava  SS.  Correlation in polynomial regression. Am Stat. 1979;33(1):11-14. doi:10.2307/2683059.
StataCorp. Stata Statistical Software: Release 10. College Station, TX: StataCorp; 2007.
Williams  RL.  A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56(2):645-646.
PubMed   |  Link to Article
Li  J, Ji  L.  Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb). 2005;95(3):221-227.
PubMed   |  Link to Article
Kwan  KY, Sestan  N, Anton  ES.  Transcriptional co-regulation of neuronal migration and laminar identity in the neocortex. Development. 2012;139(9):1535-1546.
PubMed   |  Link to Article
Kuan  CY, Roth  KA, Flavell  RA, Rakic  P.  Mechanisms of programmed cell death in the developing brain. Trends Neurosci. 2000;23(7):291-297.
PubMed   |  Link to Article
Poelmans  G, Pauls  DL, Buitelaar  JK, Franke  B.  Integrated genome-wide association study findings: identification of a neurodevelopmental network for attention deficit hyperactivity disorder. Am J Psychiatry. 2011;168(4):365-377.
PubMed   |  Link to Article
Franke  B, Neale  BM, Faraone  SV.  Genome-wide association studies in ADHD. Hum Genet. 2009;126(1):13-50.
PubMed   |  Link to Article
Hoogman  M, Rijpkema  M, Janss  L,  et al.  Current self-reported symptoms of attention deficit/hyperactivity disorder are associated with total brain volume in healthy adults. PLoS One. 2012;7(2):e31273.
PubMed   |  Link to Article
Alexander  GE, DeLong  MR, Strick  PL.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357-381.
PubMed   |  Link to Article
Ring  HA, Serra-Mestres  J.  Neuropsychiatry of the basal ganglia. J Neurol Neurosurg Psychiatry. 2002;72(1):12-21.
PubMed   |  Link to Article
Goddings  AL, Mills  KL, Clasen  LS, Giedd  JN, Viner  RM, Blakemore  SJ.  The influence of puberty on subcortical brain development. Neuroimage. 2014;88:242-251.
PubMed   |  Link to Article
Qiu  A, Crocetti  D, Adler  M,  et al.  Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. Am J Psychiatry. 2009;166(1):74-82.
PubMed   |  Link to Article
Sobel  LJ, Bansal  R, Maia  TV,  et al.  Basal ganglia surface morphology and the effects of stimulant medications in youth with attention deficit hyperactivity disorder. Am J Psychiatry. 2010;167(8):977-986.
PubMed   |  Link to Article
Almeida Montes  LG, Ricardo-Garcell  J, Barajas De La Torre  LB,  et al.  Clinical correlations of grey matter reductions in the caudate nucleus of adults with attention deficit hyperactivity disorder. J Psychiatry Neurosci. 2010;35(4):238-246.
PubMed   |  Link to Article
Seidman  LJ, Biederman  J, Liang  L,  et al.  Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biol Psychiatry. 2011;69(9):857-866.
PubMed   |  Link to Article
Ahrendts  J, Rüsch  N, Wilke  M,  et al.  Visual cortex abnormalities in adults with ADHD: a structural MRI study. World J Biol Psychiatry. 2011;12(4):260-270.
PubMed   |  Link to Article
Depue  BE, Burgess  GC, Bidwell  LC, Willcutt  EG, Banich  MT.  Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Res. 2010;182(3):231-237.
PubMed   |  Link to Article
Seidman  LJ, Valera  EM, Makris  N,  et al.  Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biol Psychiatry. 2006;60(10):1071-1080.
PubMed   |  Link to Article
Shaw  P, De Rossi  P, Watson  B,  et al.  Mapping the development of the basal ganglia in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2014;53(7):780-789.e11.
PubMed   |  Link to Article
Faraone  SV, Perlis  RH, Doyle  AE,  et al.  Molecular genetics of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2005;57(11):1313-1323.
PubMed   |  Link to Article
Dennis  M, Francis  DJ, Cirino  PT, Schachar  R, Barnes  MA, Fletcher  JM.  Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. J Int Neuropsychol Soc. 2009;15(3):331-343.
PubMed   |  Link to Article
Rommelse  NN, Altink  ME, Oosterlaan  J, Buschgens  CJ, Buitelaar  J, Sergeant  JA.  Support for an independent familial segregation of executive and intelligence endophenotypes in ADHD families. Psychol Med. 2008;38(11):1595-1606.
PubMed   |  Link to Article
Spencer  TJ, Brown  A, Seidman  LJ,  et al.  Effect of psychostimulants on brain structure and function in ADHD: a qualitative literature review of magnetic resonance imaging-based neuroimaging studies. J Clin Psychiatry. 2013;74(9):902-917.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Mean Total Brain and Total Gray Matter Volumes in Participants With Attention-Deficit/Hyperactivity Disorder, Unaffected Siblings, and Control Individuals

Siblings indicate the unaffected siblings of participants with attention-deficit/hyperactivity disorder. The means are based on estimated marginal means corrected for age, age squared, sex, and scanner location. The error bars represent standard errors.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Group-by-Age Interaction on Caudate Nucleus Volume (A) and Putamen Volume (B)

Siblings indicate the unaffected siblings of participants with attention-deficit/hyperactivity disorder. The same pattern of results was found for total, left, and right volumes. The lines represent the regression lines, with the shading representing the 95% CIs.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Participant Characteristics
Table Graphic Jump LocationTable 2.  Participants With ADHD, Unaffected Siblings, and Control Individuals
Table Graphic Jump LocationTable 3.  Regression of Binary ADHD Diagnosis on Brain Volumesa

References

Polanczyk  G, Rohde  LA.  Epidemiology of attention-deficit/hyperactivity disorder across the lifespan. Curr Opin Psychiatry. 2007;20(4):386-392.
PubMed   |  Link to Article
Castellanos  FX, Lee  PP, Sharp  W,  et al.  Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA. 2002;288(14):1740-1748.
PubMed   |  Link to Article
Castellanos  FX, Giedd  JN, Marsh  WL,  et al.  Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. Arch Gen Psychiatry. 1996;53(7):607-616.
PubMed   |  Link to Article
Durston  S, Hulshoff Pol  HE, Schnack  HG,  et al.  Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry. 2004;43(3):332-340.
PubMed   |  Link to Article
Valera  EM, Faraone  SV, Murray  KE, Seidman  LJ.  Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2007;61(12):1361-1369.
PubMed   |  Link to Article
Ellison-Wright  I, Ellison-Wright  Z, Bullmore  E.  Structural brain change in attention deficit hyperactivity disorder identified by meta-analysis. BMC Psychiatry. 2008;8(51):51.
PubMed   |  Link to Article
Nakao  T, Radua  J, Rubia  K, Mataix-Cols  D.  Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. Am J Psychiatry. 2011;168(11):1154-1163.
PubMed   |  Link to Article
Frodl  T, Skokauskas  N.  Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatr Scand. 2012;125(2):114-126.
PubMed   |  Link to Article
Halperin  JM, Schulz  KP.  Revisiting the role of the prefrontal cortex in the pathophysiology of attention-deficit/hyperactivity disorder. Psychol Bull. 2006;132(4):560-581.
PubMed   |  Link to Article
Biederman  J, Mick  E, Faraone  SV.  Age-dependent decline of symptoms of attention deficit hyperactivity disorder: impact of remission definition and symptom type. Am J Psychiatry. 2000;157(5):816-818.
PubMed   |  Link to Article
Greven  CU, Asherson  P, Rijsdijk  FV, Plomin  R.  A longitudinal twin study on the association between inattentive and hyperactive-impulsive ADHD symptoms. J Abnorm Child Psychol. 2011;39(5):623-632.
PubMed   |  Link to Article
Larsson  H, Lichtenstein  P, Larsson  JO.  Genetic contributions to the development of ADHD subtypes from childhood to adolescence. J Am Acad Child Adolesc Psychiatry. 2006;45(8):973-981.
PubMed   |  Link to Article
Greven  CU, Rijsdijk  FV, Plomin  R.  A twin study of ADHD symptoms in early adolescence: hyperactivity-impulsivity and inattentiveness show substantial genetic overlap but also genetic specificity. J Abnorm Child Psychol. 2011;39(2):265-275.
PubMed   |  Link to Article
Onnink  AM, Zwiers  MP, Hoogman  M,  et al.  Brain alterations in adult ADHD: effects of gender, treatment and comorbid depression. Eur Neuropsychopharmacol. 2014;24(3):397-409.
PubMed   |  Link to Article
Levy  F, Hay  DA, Bennett  KS.  Genetics of attention deficit hyperactivity disorder: a current review and future prospects. Int J Disabil Dev Educ. 2006;53(1):5-20.
Link to Article
Faraone  SV, Biederman  J, Mick  E,  et al.  Family study of girls with attention deficit hyperactivity disorder. Am J Psychiatry. 2000;157(7):1077-1083.
PubMed   |  Link to Article
Sprich  S, Biederman  J, Crawford  MH, Mundy  E, Faraone  SV.  Adoptive and biological families of children and adolescents with ADHD. J Am Acad Child Adolesc Psychiatry. 2000;39(11):1432-1437.
PubMed   |  Link to Article
Faraone  SV, Mick  E.  Molecular genetics of attention deficit hyperactivity disorder. Psychiatr Clin North Am. 2010;33(1):159-180.
PubMed   |  Link to Article
Peper  JS, Brouwer  RM, Boomsma  DI, Kahn  RS, Hulshoff Pol  HE.  Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum Brain Mapp. 2007;28(6):464-473.
PubMed   |  Link to Article
den Braber  A, Bohlken  MM, Brouwer  RM,  et al.  Heritability of subcortical brain measures: a perspective for future genome-wide association studies. Neuroimage. 2013;83:98-102.
PubMed   |  Link to Article
Pironti  VA, Lai  MC, Müller  U,  et al.  Neuroanatomical abnormalities and cognitive impairments are shared by adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives. Biol Psychiatry. 2014;76(8):639-647.
PubMed   |  Link to Article
von Rhein  D, Mennes  M, van Ewijk  H,  et al.  The NeuroIMAGE Study: a prospective phenotypic, cognitive, genetic and MRI study in children with attention-deficit/hyperactivity disorder: design and descriptives [published online July 11, 2014]. Eur Child Adolesc Psychiatry.
PubMed
Müller  UC, Asherson  P, Banaschewski  T,  et al.  The impact of study design and diagnostic approach in a large multi-centre ADHD study, part 1: ADHD symptom patterns. BMC Psychiatry. 2011;11(54):54.
PubMed   |  Link to Article
Müller  UC, Asherson  P, Banaschewski  T,  et al.  The impact of study design and diagnostic approach in a large multi-centre ADHD study, part 2: dimensional measures of psychopathology and intelligence. BMC Psychiatry. 2011;11(55):55.
PubMed   |  Link to Article
Nijmeijer  JS, Hoekstra  PJ, Minderaa  RB,  et al.  PDD symptoms in ADHD: an independent familial trait? J Abnorm Child Psychol. 2009;37(3):443-453.
PubMed   |  Link to Article
Rommelse  NN, Altink  ME, Martin  NC,  et al.  Neuropsychological measures probably facilitate heritability research of ADHD. Arch Clin Neuropsychol. 2008;23(5):579-591.
PubMed   |  Link to Article
Conners  CK, Sitarenios  G, Parker  JD, Epstein  JN.  The revised Conners’ Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. J Abnorm Child Psychol. 1998;26(4):257-268.
PubMed   |  Link to Article
Blumenthal  JD, Zijdenbos  A, Molloy  E, Giedd  JN.  Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage. 2002;16(1):89-92.
PubMed   |  Link to Article
Patenaude  B, Smith  SM, Kennedy  DN, Jenkinson  M.  A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56(3):907-922.
PubMed   |  Link to Article
Brain Development Cooperative Group.  Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development. Cereb Cortex. 2012;22(1):1-12.
PubMed   |  Link to Article
Reiss  AL, Abrams  MT, Singer  HS, Ross  JL, Denckla  MB.  Brain development, gender and IQ in children: a volumetric imaging study. Brain. 1996;119(pt 5):1763-1774.
PubMed   |  Link to Article
Mechelli  A, Price  CJ, Friston  KJ, Ashburner  J.  Voxel-based morphometry of the human brain: methods and applications. Curr Med Imaging Rev.2005;1(2):105-113.
Link to Article
Bradley  RA, Srivastava  SS.  Correlation in polynomial regression. Am Stat. 1979;33(1):11-14. doi:10.2307/2683059.
StataCorp. Stata Statistical Software: Release 10. College Station, TX: StataCorp; 2007.
Williams  RL.  A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56(2):645-646.
PubMed   |  Link to Article
Li  J, Ji  L.  Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb). 2005;95(3):221-227.
PubMed   |  Link to Article
Kwan  KY, Sestan  N, Anton  ES.  Transcriptional co-regulation of neuronal migration and laminar identity in the neocortex. Development. 2012;139(9):1535-1546.
PubMed   |  Link to Article
Kuan  CY, Roth  KA, Flavell  RA, Rakic  P.  Mechanisms of programmed cell death in the developing brain. Trends Neurosci. 2000;23(7):291-297.
PubMed   |  Link to Article
Poelmans  G, Pauls  DL, Buitelaar  JK, Franke  B.  Integrated genome-wide association study findings: identification of a neurodevelopmental network for attention deficit hyperactivity disorder. Am J Psychiatry. 2011;168(4):365-377.
PubMed   |  Link to Article
Franke  B, Neale  BM, Faraone  SV.  Genome-wide association studies in ADHD. Hum Genet. 2009;126(1):13-50.
PubMed   |  Link to Article
Hoogman  M, Rijpkema  M, Janss  L,  et al.  Current self-reported symptoms of attention deficit/hyperactivity disorder are associated with total brain volume in healthy adults. PLoS One. 2012;7(2):e31273.
PubMed   |  Link to Article
Alexander  GE, DeLong  MR, Strick  PL.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357-381.
PubMed   |  Link to Article
Ring  HA, Serra-Mestres  J.  Neuropsychiatry of the basal ganglia. J Neurol Neurosurg Psychiatry. 2002;72(1):12-21.
PubMed   |  Link to Article
Goddings  AL, Mills  KL, Clasen  LS, Giedd  JN, Viner  RM, Blakemore  SJ.  The influence of puberty on subcortical brain development. Neuroimage. 2014;88:242-251.
PubMed   |  Link to Article
Qiu  A, Crocetti  D, Adler  M,  et al.  Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. Am J Psychiatry. 2009;166(1):74-82.
PubMed   |  Link to Article
Sobel  LJ, Bansal  R, Maia  TV,  et al.  Basal ganglia surface morphology and the effects of stimulant medications in youth with attention deficit hyperactivity disorder. Am J Psychiatry. 2010;167(8):977-986.
PubMed   |  Link to Article
Almeida Montes  LG, Ricardo-Garcell  J, Barajas De La Torre  LB,  et al.  Clinical correlations of grey matter reductions in the caudate nucleus of adults with attention deficit hyperactivity disorder. J Psychiatry Neurosci. 2010;35(4):238-246.
PubMed   |  Link to Article
Seidman  LJ, Biederman  J, Liang  L,  et al.  Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biol Psychiatry. 2011;69(9):857-866.
PubMed   |  Link to Article
Ahrendts  J, Rüsch  N, Wilke  M,  et al.  Visual cortex abnormalities in adults with ADHD: a structural MRI study. World J Biol Psychiatry. 2011;12(4):260-270.
PubMed   |  Link to Article
Depue  BE, Burgess  GC, Bidwell  LC, Willcutt  EG, Banich  MT.  Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Res. 2010;182(3):231-237.
PubMed   |  Link to Article
Seidman  LJ, Valera  EM, Makris  N,  et al.  Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biol Psychiatry. 2006;60(10):1071-1080.
PubMed   |  Link to Article
Shaw  P, De Rossi  P, Watson  B,  et al.  Mapping the development of the basal ganglia in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2014;53(7):780-789.e11.
PubMed   |  Link to Article
Faraone  SV, Perlis  RH, Doyle  AE,  et al.  Molecular genetics of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2005;57(11):1313-1323.
PubMed   |  Link to Article
Dennis  M, Francis  DJ, Cirino  PT, Schachar  R, Barnes  MA, Fletcher  JM.  Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. J Int Neuropsychol Soc. 2009;15(3):331-343.
PubMed   |  Link to Article
Rommelse  NN, Altink  ME, Oosterlaan  J, Buschgens  CJ, Buitelaar  J, Sergeant  JA.  Support for an independent familial segregation of executive and intelligence endophenotypes in ADHD families. Psychol Med. 2008;38(11):1595-1606.
PubMed   |  Link to Article
Spencer  TJ, Brown  A, Seidman  LJ,  et al.  Effect of psychostimulants on brain structure and function in ADHD: a qualitative literature review of magnetic resonance imaging-based neuroimaging studies. J Clin Psychiatry. 2013;74(9):902-917.
PubMed   |  Link to Article

Correspondence

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Supplement.

eAppendix 1. Diagnostic Assessment of ADHD and Assessment of Medication Use in the NeuroIMAGE Sample

eReferences

eAppendix 2. Sensitivity Analyses Examining Potential Effects of Total Brain Volume, IQ, Medication, Scanner Location and Gender Distribution

eAppendix 3. Comparisons of Unaffected Siblings With Participants With ADHD and Controls on Total Brain and Total Gray Matter Volumes: Post Hoc Linear Trend Analyses

eTable 1. Number of Scans Used

eTable 2. Correlation Matrix of Brain Volumetric Outcome Measures (Whole Brain and Subcortical); Eigenvalues of Correlation Matrix Shown on Diagonal

eTable 3. Mean Brain Volumes in mL (± SE) Without Adjustment For Covariates

eTable 4. Mean Brain Volumes in mL (± SE) Without Total Brain Volume as a Covariate

eTable 5. Mean Brain Volumes in mL (± SE) With IQ as a Covariate

eTable 6. Mean Brain Volumes in mL (± SE) Stratified by Scanner Location (Amsterdam, Nijmegen)

eTable 7. Mean Brain Volumes in mL (± SE) Based on Sex-Matched Groups

eTable 8. Mean Brain Volumes in mL (± SE) Stratified by Age Group

eFigure. Mean Total Caudate and Putamen Volumes (in mL) Stratified by Age Group

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