Neuroscience Letters 600 (2015) 45–49
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Research article
Neuroanatomical deficits correlate with executive dysfunction in boys with attention deficit hyperactivity disorder Ning He a,1 , Fei Li b,1 , Yuanyuan Li a , Lanting Guo a,∗ , Lizhou Chen b , Xiaoqi Huang b , Su Lui b , Qiyong Gong b a b
Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
h i g h l i g h t s • Neuroanatomical deficits in male drug-naive ADHD children without comorbidity. • Reduced frontal gray matter volumes in ADHD. • Structural deficits correlated with executive dysfunction in ADHD.
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Article history: Received 9 March 2015 Received in revised form 8 May 2015 Accepted 30 May 2015 Available online 3 June 2015 Keywords: Attention deficit hyperactivity disorder Children and adolescents Voxel-based morphometry Gray matter volume Executive function
a b s t r a c t Previous structural imaging studies have revealed gray matter volume abnormalities to reflect the etiology of attention deficit hyperactivity disorder (ADHD), however, which are confounded by age, medication and comorbidity and also ignore the core feature of brain structure in the executive impairments of ADHD. In the present study, we explored gray matter volume abnormalities in male children and adolescents with ADHD who were drug-naive and without comorbidities, and tried to connect structural data and behavioral executive dysfunction to provide more information regarding the brain–behavior relationships in ADHD. Seventy-two male subjects (37 patients and 35 controls) underwent three-dimensional high-resolution structural magnetic resonance imaging and executive function assessments, including the Stroop Color–Word Test and Wisconsin Card Sorting Test (WCST). Voxel-based morphometry with diffeomorphic anatomical registration through exponentiated Lie algebra was used to identify gray matter volume differences between the ADHD and controls. Correlation analyses were performed to identify neuroanatomical deficits that were associated with executive dysfunctions. Significantly reduced gray matter volumes were identified in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex of ADHD patients compared with controls (P < 0.05, corrected for family-wise errors). In patients group, the gray matter volumes of the right orbitofrontal cortex and left posterior midcingulate cortex were positively correlated with the completed categories on the WCST, and the gray matter volume of the left posterior midcingulate cortex was negatively correlated with the total and non-perseverative errors on the WCST (P < 0.05). The present findings show gray matter volume reductions in motor regions as well as the orbitofrontal and cingulate cortex; this evidence supports theories that suggest frontal abnormalities in children and adolescents with ADHD at early illness stage. The correlations between structural abnormalities and executive dysfunction suggest that neuroanatomical substrate deficits are implicated in the pathophysiology of ADHD. © 2015 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
Abbreviations: BA, brodmann area; MNI, Montreal Neurological Institute. ∗ Corresponding author at: Department of Psychiatry, West China Hospital of Sichuan University; No.37 Guo Xue Xiang, Chengdu, Sichuan 610041, China. Tel.: +86 2885422633/18980601720. E-mail address: [email protected] (L. Guo). 1 Drs. Ning He and Fei Li contributed equally to this work. http://dx.doi.org/10.1016/j.neulet.2015.05.062 0304-3940/© 2015 Elsevier Ireland Ltd. All rights reserved.
Attention deficit hyperactivity disorder (ADHD) is the most commonly diagnosed childhood-onset neuropsychiatric disorder; it is reported to have a worldwide prevalence of 5.3% in children and adolescents and is characterized by an age-inappropriate pattern of inattention, hyperactivity–impulsivity or both [1]. ADHD frequently occurs concomitantly with various externalizing and
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internalizing disorders [2] and is associated with impairments in several domains of executive function such as inhibitory control, cognitive flexibility and strategic planning [3]. Given ADHD’s burden on society, families and individual patients, a wealth of neuroimaging studies have been performed to clarify the causes and underlying mechanisms of ADHD. Initially, neuroimaging researchers embraced a prefrontalstriatal-cerebellar model of ADHD pathophysiology. However, subsequent functional magnetic resonance imaging (fMRI) findings have developed models of ADHD that consist of several different large-scale networks, such as attentional, visual, motor and defaultmode networks [4]. Structural MRI (sMRI) complements fMRI by assessing brain structures on a morphological level, finding the relevance of the abnormal brain structures to the pathophysiological processes that likely reflect the etiology of ADHD [5]. By surveying whole-brain structures utilizing a fully automated technique, voxel-based morphometry (VBM) allows for an unbiased assessment of both gray and white matter volumes separately [6]. Structural studies that utilized VBM to study children and adolescents with ADHD have revealed various abnormalities in almost all four lobes of the cerebrum as well as the cerebellum; additionally, abnormalities have been detected in several specific structures such as the basal ganglia, cingulate cortex and hippocampus [7,8]. However, several important discrepancies should be noted in the previous literature. For example, Wang et al. observed greater right occipital lobe volumes in ADHD patients [9] whereas volumetric reductions in this region were reported by Sasayama et al. [10]. These inconsistent results may be the result of diversity in the samples, comorbidities and medications of the ADHD patients. In particular, previous research has suggested that ADHD with comorbidities displays core symptoms, clinical outcomes and treatment responses that are distinct from ADHD patients without comorbidities [11]. Therefore, it was unclear if ADHD or another comorbid disorders accounted for the observed structural differences. Likewise, the effect of medications on brain structures has also been demonstrated in the literature, suggesting stimulant medication may normalize such as anterior cingulate cortex volume [12]. Therefore, the existence of comorbidities and medications may confuse our understanding the primary pathophysiology of ADHD. As previously mentioned, patients with ADHD often exhibit deficits in executive function and these deficits have been reported in the neuropsychological literature [13]. Although, there is evidence supporting the notion that executive function plays a role in the expression of specific behaviors and psychiatric symptoms, it is unclear whether deficits in executive function in ADHD are directly related to structural neural alterations. A meta-analysis of fMRI literature on ADHD identified dysfunction in multiple neuronal systems that are involved in higher-level cognitive and sensorimotor functions [14]. However, it is poorly understood whether the brain regions with altered functional activity related to executive function represent structural abnormalities. Given that brain structure may be the neural substrate of executive function, it is necessary to explore the association between structural abnormalities in the brain and executive dysfunction in children and adolescents with ADHD [15]. The diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) is a relatively new VBM method with high regional sensitivity and provides an unbiased analysis to explore voxel-wise brain abnormalities automatically [16]. In the present study, we aimed to identify structural abnormalities in children and adolescents with ADHD who were drug-naive and without comorbidities, comparing them with healthy controls using the VBM approach based on DARTEL, which may help determine what is the nature of brain abnormalities in the pure form of untreated child ADHD, and whether the time of ADHD symptom onset is the critical period for the structure volume change. In addition, we also
aimed to determine if these structural alterations correlate with executive function deficits in ADHD, in order to provide important information relevant to models of pathogenesis. 2. Materials and methods 2.1. Participants This study was approved by the local ethics committee. All subjects were well informed about the goal and procedures of this experiment and were fully willing to participate in, and written informed consent was obtained from the guardians of all subjects. Three experienced child psychiatrists (L.G., Y.L., and N.H.) ascertained a diagnosis of ADHD using the Chinese modified version of Structured Clinical Interview for DSM-IV-TR Axis I disorders, Research Version (SCID-I, Patient Edition) [17]. Children with oppositional defiant disorder, Tourette’s disorder, conduct disorder or any other comorbid Axis I psychiatric disorder were excluded from the study. Details of other exclusion criteria are provided in the Supplementary data. The final study included 37 right-handed, drug-naive, male patients with ADHD (mean age, 9.9 years ± 2.4 [standard deviation]; age range, 7–16 years; 26 with the combined subtype and 11 with the inattentive subtype of ADHD). The healthy controls were recruited using advertisements in local schools. They were screened using the Chinese modified version of SCID-I (Non-patient Edition) to exclude any Axis I psychiatric diagnosis, and all of their first-degree relatives were free of psychiatric illness. Exclusion criteria for healthy controls were identical to the criteria used for ADHD patients. As a result, the healthy controls consisted of 35 right-handed males (mean age, 10.7 years ± 2.6; age range, 8–15 years). The mean IQ did not significantly differ between ADHD patients and controls (P > 0.05). 2.2. Behavioral measures and executive function tests Primary behavioral measures for all subjects consisted of the attention problem scores from the Child Behavioral Checklist (CBCL), Parent version [18] and the hyperactivity–impulsivity scores and hyperactivity index from the revised Conners’ Parent Rating Scale (CPRS) [19]. Executive function was measured using the modified Wisconsin Card Sorting Test (WCST) [20] and the Stroop Color–Word (Stroop-CW) Test [21] (details are provided in the Supplementary data). 2.3. sMRI data acquisition and preprocessing A daily quality assurance protocol was utilized to establish the stability of the MRI system using a water phantom. We acquired high-resolution T1-weighted images using a 3T MRI system (Trio; Siemens) with a 3-dimensional spoiled gradient-recalled imaging sequence, and the VBM analyses were performed using SPM8 (details are provided in the Supplementary data). 2.4. Statistical analysis Our analysis of gray matter volume differences between ADHD patients and healthy controls was performed voxel-by-voxel using a two-sample t-test in SPM8, with age and total brain volume as the covariates. The statistical cluster-level threshold was set at P < 0.05, with family-wise error correction. Then, Pearson’s correlation analyses were performed in SPSS 16.0 to assess the relationship between anatomical abnormalities and executive function controlling for age and total brain volume as confounding variables (P < 0.05, uncorrected) (details are provided in the Supplementary data).
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3. Results The ADHD patients and healthy controls did not significantly differ with regard to age (P = 0.215), sex (all male subjects), handedness (all right-handed) and total brain volume (ADHD, 1425.9 ml ± 131.2; controls, 1421.5 ml ± 127.2; t = 0.144, df = 70, P = 0.886). Table S1 shows the results of the behavioral measures and executive function tests. As expected, patients with ADHD showed higher scores on attention problems on the CBCL (t = 6.931, df = 70, P < 0.001) and higher hyperactivity-impulsivity scores (t = 5.869, df = 70, P < 0.001) and hyperactivity index (t = 7.047, df = 63, P < 0.001) on the CPRS compared with healthy controls. ADHD patients also had poorer performance on executive function tests compared with healthy controls. On the Stroop-CW Test, ADHD patients achieved fewer numbers of correct responses (t = −5.562, df = 59, P < 0.001), more numbers of errors (t = 6.416, df = 63, P < 0.001) and corrections (t = 6.266, df = 43, P < 0.001), as well as longer total times (t = 5.681, df = 51, P < 0.001) compared with healthy controls. On the WCST, patients had fewer total correct (t = −3.178, df = 65, P = 0.002) and completed categories (t = −2.6, df = 70, P = 0.011), as well as more total errors (t = 2.68, df = 62, P = 0.009), perseverative errors (t = 2.309, df = 55, P = 0.025) and non-perseverative errors (t = 2.518, df = 65, P = 0.014) compared with healthy controls. Table 1 and Fig. 1A display the results of the VBM analysis. Compared to healthy controls, patients with ADHD showed reduced gray matter volume in the right orbitofrontal cortex (Brodmann area [BA] 11) (t = −4.98, df = 70, P = 0.006), right primary motor/premotor cortex (BA 4/6) (t = −4.72, df = 70, P = 0.029), left anterior cingulate cortex (BA 32) (t = −4.3, df = 70, P = 0.02) and left posterior midcingulate cortex (BA 24) (t = −4.9, df = 70, P = 0.001). In the ADHD group, gray matter volume in the right orbitofrontal cortex was positively correlated with the number of completed categories on the WCST (r = 0.287, N = 37, P = 0.042); gray matter volume in the left posterior midcingulate cortex was positively correlated with the number of completed categories on the WCST (r = 0.334, N = 37, P = 0.022) and negatively correlated with the total number of errors (r = −0.276, N = 37, P = 0.049) and the number of non-perseverative errors (r = −0.288, N = 37, P = 0.042) on the WCST (Fig. 1B and Table S2). No significant correlations in the healthy controls were identified between gray matter volumes in those brain regions and WCST scores (Table S2). And there also were no other significant correlations between gray matter volumes of the four regions and CBCL, CPRS and Stroop-CW in both groups (P > 0.05). 4. Discussion The present study explored alterations in the gray matter volume of children and adolescents with ADHD who were drugnaive and without comorbidities. Compared with healthy controls, patients with ADHD showed decreased gray matter volume in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex. Furthermore, in the ADHD group, reduced gray matter volumes in the right orbitofrontal cortex and left posterior midcingulate cortex correlated with executive dysfunction assessed using the WCST. Our findings correspond with the results of previous studies and highlight the role of motor regions, as well as the orbitofrontal and cingulate cortex in the pathophysiology of ADHD. The present study included drug-naive children and adolescents with ADHD who were without comorbidities; these criteria are important not only as a starting point for evaluating the progression of brain alterations and assessing brain anatomy before they can be influenced by potential confounding effects, but also as a technique to better specify the brain regions where anatomical changes are
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seen early in the course of illness to provide new information that is relevant to models of pathogenesis [15]. Furthermore, the effect of ADHD on structural abnormalities have often been interpreted in the context of disrupted maturational processes [22]. The delay of gray matter maturation in children with ADHD was reported to be most prominent in the prefrontal regions that are important for the control of cognitive process including attention and motor planning [22]; this developmental finding is consistent with our present results indicating reduced gray matter volume in ADHD patients, and the present changes in the brain of ADHD might aid in developing improved targets and timing of intervention for the future implications and directions. However, a previous meta-analysis found reductions in the gray matter volume of the basal ganglia in ADHD patients; however, the research study recruited both children and adults as well as subjects who used stimulant medications. Therefore, these reported basal ganglia abnormalities might, to some extent, indicate possible confounding effects for age, comorbidities and medications on gray matter volume in ADHD patients [7]. Consistent with previous study recruiting medication-naive right-handed male adolescents with ADHD reporting no significant group differences of gray matter volume in the basal ganglia [23], our results were not confounded by the stimulant medications as well. The orbitofrontal cortex has also been implicated in ADHD by other investigators, with studies reporting smaller gray matter volume [24] or reduced cortical thickness [25]. The orbitofrontal cortex plays a key role in modulating emotion and impulsive behavior and its dysfunction of this region may account for the impulsivity seen in ADHD [15]. However, the role of the orbitofrontal cortex in the executive function of ADHD patients has remained speculative because no direct correlation with neuropsychological evaluations has been provided by previous studies. In the present study, reduced gray matter volume in the orbitofrontal cortex of ADHD patients correlates with the number of completed categories on the WCST, a measure that reflects the functioning of cognitive flexibility [26]. Therefore, this finding elucidates the structural neural substrate for executive function by indicating that volume reduction in the orbitofrontal cortex may have an effect on ADHDrelated executive dysfunction. The anterior cingulate cortex has always been a topic of research on ADHD due to its important roles in symptom severity and emotional processing [4]. Unmedicated children and adolescents with ADHD have been previously reported to have a smaller volume of the anterior cingulate cortex compared with both chronically medicated ADHD children and healthy controls [12], as well as a reduced activation for inhibition in the anterior cingulate cortex [27]; these findings are in agreement with results of the present study indicating gray matter volume reductions in the anterior cingulate cortex of drug-naive ADHD children and adolescents. In present study, the regions with reduced gray matter volume within the anterior cingulate cortex were located anterior and ventral to the corpus callosum. Based on cytoarchitecture and connectivity study [28], the pregenual and subgenual anterior cingulate cortex are considered the affective subdivision, and the anterior midcingulate cortex is regarded as the cognitive subdivision [29]. This functional discrimination corresponds with our result showing that the reduced volume of the pregenual and subgenual anterior cingulate cortex did not significantly correlate with cognitive performance on the Stroop-CW test and WCST. According to the four region model proposed by Vogt, the region that is posterior to cingulate cortex and dorsal to the corpus callosum is the midcingulate cortex, which is predominantly involved in action execution and motor paradigms [28]. Previous studies have found decreased functional connectivity between the anterior and posterior cingulate cortex regions extending to the posterior midcingulate cortex [30]; correspondingly, the posterior midcingu-
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Table 1 Brain regions with reduced gray matter volume in ADHD patients compared to healthy controls. Brain region
BA
Right orbitofrontal cortex Right primary motor/premotor cortex Left anterior cingulate cortex Left posterior midcingulate cortex
11 4/6 32 24
*
MNI coordinates x
y
z
7 34 −3 −7
20 −12 42 −9
−30 61 3 47
Voxels
p-Value*
2952 1682 2255 3947
0.006 0.029 0.02 0.001
Cluster-level P < 0.05 after family-wise error corrected.
Fig. 1. (A) Compared to healthy controls, patients with ADHD showed reduced gray matter volume (cool color) in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex (cluster-level P < 0.05 after family-wise error corrected). (B) Significant correlations between gray matter volume in the right orbitofrontal cortex and left posterior midcingulate cortex and scores of executive function assessed with the Wisconsin Card Sorting Test in patients with ADHD (P < 0.05, uncorrected). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
late cortex interacts with the dorsal part of the posterior cingulate cortex [28]. And it is of interest that reduced volume of posterior midcingulate cortex was also shown in previous work with medication-naive adult ADHD [5]. This finding combined with our present results might indicate the syndromatic continuity between pediatric and adult ADHD at the neural structural level. Additionally, the functional abnormalities of the posterior midcingulate cortex have been found to correlate with the extent of executive dysfunction in other brain disorders [31]. Our study found that volume reductions in the posterior midcingulate cortex were associated with the cognitive domain assessments utilizing the WCST in ADHD patients, but not in healthy controls; this finding draws attention to a possible anatomical substrate of ADHD expressed by gray matter volumetric abnormalities, indicating that smaller gray matter volumes of the posterior midcingulate cortex may predict poorer executive function in ADHD patients. Our investigation of the primary motor and premotor cortex indicated that structural motor areas are involved in the deficits seen in ADHD. Both regions are located in the posterior portion of the frontal lobe and work in association with other motor areas,
such as the supplementary motor area and posterior parietal cortex, to plan and execute movements. Patients with ADHD, especially children and adolescents, often have age-inappropriate motor difficulties including timed repetitive and sequential movements, motor overflow and difficulties with balance [32]. Functional studies have revealed anomalous activations within motor areas in patients with ADHD at a resting state or when performing tasks that require response inhibition or motor control [33,34]. Previous structural study has also found reduced volume of motor areas in ADHD patients [35]. The results of our study are consistent with these previous findings; gray matter volume reductions in the primary motor/premotor cortex of patients with ADHD may demonstrate a critical role of motor structure deficits in the pathophysiology of ADHD. Several limitations should have been considered in the current study. Although we only recruited boys and enhanced the homogeneity of the sample that based on the higher prevalence in boys for ADHD [15], it limits the generalizability to females with ADHD. It should be noted that larger sample sizes are generally required to confirm the robustness of differences seen on
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neuroimaging and to explore the gender differences in the neuroanatomy; additionally, longitudinal studies are needed in the future to monitor the dynamic structural changes that occur in patients with ADHD. Secondly, although the results of correlation analyses did not survive multiple comparison test, we are reluctant to completely reject our hypotheses for the correlations on account of the exploratory results observed and the previous researches supporting the hypothesis. 5. Conclusions In conclusion, the findings of the present study demonstrate regional gray matter volume deficits in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex in children and adolescents with ADHD who were drug-naive and without comorbidities at early illness stage. The correlations between the structural abnormalities and executive dysfunction suggest that neuroanatomical substrate deficits underlie the pathophysiology of ADHD. Conflict of interest All the authors report no financial relationships with commercial interests. Acknowledgments This study was supported by the National Natural Science Foundation of China (Grants No. 30970903 and 81401396) and the International Postdoctoral Exchange Fellowship Program 2014 by the Office of China Postdoctoral Council (The approval document number: No.29 Document of OCPC, 2014. Certificate of Financial Support number: No. 20140058). Preliminary data for this article were collected at the Department of Psychiatry and Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.05. 062 References [1] G. Polanczyk, M.S. de Lima, B.L. Horta, J. Biederman, L.A. Rohde, The worldwide prevalence of ADHD: a systematic review and metaregression analysis, Am. J. Psychiatry 164 (6) (2007) 942–948. [2] K. Konrad, S.B. Eickhoff, Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder, Hum. Brain Mapp. 31 (6) (2010) 904–916. [3] F. Li, N. He, Y. Li, L. Chen, X. Huang, S. Lui, L. Guo, G.J. Kemp, Q. Gong, Intrinsic brain abnormalities in attention deficit hyperactivity disorder: a resting-state functional MR imaging study, Radiology 272 (2) (2014) 514–523. [4] F.X. Castellanos, E. Proal, Large-scale brain systems in ADHD: beyond the prefrontal–striatal model, Trends Cognit. Sci. 16 (1) (2012) 17–26. [5] N. Makris, L. Liang, J. Biederman, E.M. Valera, A.B. Brown, C. Petty, T.J. Spencer, S.V. Faraone, L.J. Seidman, Toward defining the neural substrates of ADHD: a controlled structural MRI study in medication-naive adults, J. Atten. Disord. (2013), Nov 4. [Epub ahead of print]. [6] J. Ashburner, K.J. Friston, Voxel-based morphometry the methods, Neuroimage 11 (6 Pt 1) (2000) 805–821. [7] T. Nakao, J. Radua, K. Rubia, D. Mataix-Cols, Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication, Am. J. Psychiatry 168 (11) (2011) 1154–1163. [8] T. Frodl, N. Skokauskas, Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects, Acta Psychiatr. Scand. 125 (2) (2012) 114–126. [9] J. Wang, T. Jiang, Q. Cao, Y. Wang, Characterizing anatomic differences in boys with attention-deficit/hyperactivity disorder with the use of deformation-based morphometry, AJNR Am. J. Neuroradiol. 28 (3) (2007) 543–547.
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Research article
Neuroanatomical deficits correlate with executive dysfunction in boys with attention deficit hyperactivity disorder Ning He a,1 , Fei Li b,1 , Yuanyuan Li a , Lanting Guo a,∗ , Lizhou Chen b , Xiaoqi Huang b , Su Lui b , Qiyong Gong b a b
Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
h i g h l i g h t s • Neuroanatomical deficits in male drug-naive ADHD children without comorbidity. • Reduced frontal gray matter volumes in ADHD. • Structural deficits correlated with executive dysfunction in ADHD.
a r t i c l e
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Article history: Received 9 March 2015 Received in revised form 8 May 2015 Accepted 30 May 2015 Available online 3 June 2015 Keywords: Attention deficit hyperactivity disorder Children and adolescents Voxel-based morphometry Gray matter volume Executive function
a b s t r a c t Previous structural imaging studies have revealed gray matter volume abnormalities to reflect the etiology of attention deficit hyperactivity disorder (ADHD), however, which are confounded by age, medication and comorbidity and also ignore the core feature of brain structure in the executive impairments of ADHD. In the present study, we explored gray matter volume abnormalities in male children and adolescents with ADHD who were drug-naive and without comorbidities, and tried to connect structural data and behavioral executive dysfunction to provide more information regarding the brain–behavior relationships in ADHD. Seventy-two male subjects (37 patients and 35 controls) underwent three-dimensional high-resolution structural magnetic resonance imaging and executive function assessments, including the Stroop Color–Word Test and Wisconsin Card Sorting Test (WCST). Voxel-based morphometry with diffeomorphic anatomical registration through exponentiated Lie algebra was used to identify gray matter volume differences between the ADHD and controls. Correlation analyses were performed to identify neuroanatomical deficits that were associated with executive dysfunctions. Significantly reduced gray matter volumes were identified in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex of ADHD patients compared with controls (P < 0.05, corrected for family-wise errors). In patients group, the gray matter volumes of the right orbitofrontal cortex and left posterior midcingulate cortex were positively correlated with the completed categories on the WCST, and the gray matter volume of the left posterior midcingulate cortex was negatively correlated with the total and non-perseverative errors on the WCST (P < 0.05). The present findings show gray matter volume reductions in motor regions as well as the orbitofrontal and cingulate cortex; this evidence supports theories that suggest frontal abnormalities in children and adolescents with ADHD at early illness stage. The correlations between structural abnormalities and executive dysfunction suggest that neuroanatomical substrate deficits are implicated in the pathophysiology of ADHD. © 2015 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
Abbreviations: BA, brodmann area; MNI, Montreal Neurological Institute. ∗ Corresponding author at: Department of Psychiatry, West China Hospital of Sichuan University; No.37 Guo Xue Xiang, Chengdu, Sichuan 610041, China. Tel.: +86 2885422633/18980601720. E-mail address: [email protected] (L. Guo). 1 Drs. Ning He and Fei Li contributed equally to this work. http://dx.doi.org/10.1016/j.neulet.2015.05.062 0304-3940/© 2015 Elsevier Ireland Ltd. All rights reserved.
Attention deficit hyperactivity disorder (ADHD) is the most commonly diagnosed childhood-onset neuropsychiatric disorder; it is reported to have a worldwide prevalence of 5.3% in children and adolescents and is characterized by an age-inappropriate pattern of inattention, hyperactivity–impulsivity or both [1]. ADHD frequently occurs concomitantly with various externalizing and
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internalizing disorders [2] and is associated with impairments in several domains of executive function such as inhibitory control, cognitive flexibility and strategic planning [3]. Given ADHD’s burden on society, families and individual patients, a wealth of neuroimaging studies have been performed to clarify the causes and underlying mechanisms of ADHD. Initially, neuroimaging researchers embraced a prefrontalstriatal-cerebellar model of ADHD pathophysiology. However, subsequent functional magnetic resonance imaging (fMRI) findings have developed models of ADHD that consist of several different large-scale networks, such as attentional, visual, motor and defaultmode networks [4]. Structural MRI (sMRI) complements fMRI by assessing brain structures on a morphological level, finding the relevance of the abnormal brain structures to the pathophysiological processes that likely reflect the etiology of ADHD [5]. By surveying whole-brain structures utilizing a fully automated technique, voxel-based morphometry (VBM) allows for an unbiased assessment of both gray and white matter volumes separately [6]. Structural studies that utilized VBM to study children and adolescents with ADHD have revealed various abnormalities in almost all four lobes of the cerebrum as well as the cerebellum; additionally, abnormalities have been detected in several specific structures such as the basal ganglia, cingulate cortex and hippocampus [7,8]. However, several important discrepancies should be noted in the previous literature. For example, Wang et al. observed greater right occipital lobe volumes in ADHD patients [9] whereas volumetric reductions in this region were reported by Sasayama et al. [10]. These inconsistent results may be the result of diversity in the samples, comorbidities and medications of the ADHD patients. In particular, previous research has suggested that ADHD with comorbidities displays core symptoms, clinical outcomes and treatment responses that are distinct from ADHD patients without comorbidities [11]. Therefore, it was unclear if ADHD or another comorbid disorders accounted for the observed structural differences. Likewise, the effect of medications on brain structures has also been demonstrated in the literature, suggesting stimulant medication may normalize such as anterior cingulate cortex volume [12]. Therefore, the existence of comorbidities and medications may confuse our understanding the primary pathophysiology of ADHD. As previously mentioned, patients with ADHD often exhibit deficits in executive function and these deficits have been reported in the neuropsychological literature [13]. Although, there is evidence supporting the notion that executive function plays a role in the expression of specific behaviors and psychiatric symptoms, it is unclear whether deficits in executive function in ADHD are directly related to structural neural alterations. A meta-analysis of fMRI literature on ADHD identified dysfunction in multiple neuronal systems that are involved in higher-level cognitive and sensorimotor functions [14]. However, it is poorly understood whether the brain regions with altered functional activity related to executive function represent structural abnormalities. Given that brain structure may be the neural substrate of executive function, it is necessary to explore the association between structural abnormalities in the brain and executive dysfunction in children and adolescents with ADHD [15]. The diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) is a relatively new VBM method with high regional sensitivity and provides an unbiased analysis to explore voxel-wise brain abnormalities automatically [16]. In the present study, we aimed to identify structural abnormalities in children and adolescents with ADHD who were drug-naive and without comorbidities, comparing them with healthy controls using the VBM approach based on DARTEL, which may help determine what is the nature of brain abnormalities in the pure form of untreated child ADHD, and whether the time of ADHD symptom onset is the critical period for the structure volume change. In addition, we also
aimed to determine if these structural alterations correlate with executive function deficits in ADHD, in order to provide important information relevant to models of pathogenesis. 2. Materials and methods 2.1. Participants This study was approved by the local ethics committee. All subjects were well informed about the goal and procedures of this experiment and were fully willing to participate in, and written informed consent was obtained from the guardians of all subjects. Three experienced child psychiatrists (L.G., Y.L., and N.H.) ascertained a diagnosis of ADHD using the Chinese modified version of Structured Clinical Interview for DSM-IV-TR Axis I disorders, Research Version (SCID-I, Patient Edition) [17]. Children with oppositional defiant disorder, Tourette’s disorder, conduct disorder or any other comorbid Axis I psychiatric disorder were excluded from the study. Details of other exclusion criteria are provided in the Supplementary data. The final study included 37 right-handed, drug-naive, male patients with ADHD (mean age, 9.9 years ± 2.4 [standard deviation]; age range, 7–16 years; 26 with the combined subtype and 11 with the inattentive subtype of ADHD). The healthy controls were recruited using advertisements in local schools. They were screened using the Chinese modified version of SCID-I (Non-patient Edition) to exclude any Axis I psychiatric diagnosis, and all of their first-degree relatives were free of psychiatric illness. Exclusion criteria for healthy controls were identical to the criteria used for ADHD patients. As a result, the healthy controls consisted of 35 right-handed males (mean age, 10.7 years ± 2.6; age range, 8–15 years). The mean IQ did not significantly differ between ADHD patients and controls (P > 0.05). 2.2. Behavioral measures and executive function tests Primary behavioral measures for all subjects consisted of the attention problem scores from the Child Behavioral Checklist (CBCL), Parent version [18] and the hyperactivity–impulsivity scores and hyperactivity index from the revised Conners’ Parent Rating Scale (CPRS) [19]. Executive function was measured using the modified Wisconsin Card Sorting Test (WCST) [20] and the Stroop Color–Word (Stroop-CW) Test [21] (details are provided in the Supplementary data). 2.3. sMRI data acquisition and preprocessing A daily quality assurance protocol was utilized to establish the stability of the MRI system using a water phantom. We acquired high-resolution T1-weighted images using a 3T MRI system (Trio; Siemens) with a 3-dimensional spoiled gradient-recalled imaging sequence, and the VBM analyses were performed using SPM8 (details are provided in the Supplementary data). 2.4. Statistical analysis Our analysis of gray matter volume differences between ADHD patients and healthy controls was performed voxel-by-voxel using a two-sample t-test in SPM8, with age and total brain volume as the covariates. The statistical cluster-level threshold was set at P < 0.05, with family-wise error correction. Then, Pearson’s correlation analyses were performed in SPSS 16.0 to assess the relationship between anatomical abnormalities and executive function controlling for age and total brain volume as confounding variables (P < 0.05, uncorrected) (details are provided in the Supplementary data).
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3. Results The ADHD patients and healthy controls did not significantly differ with regard to age (P = 0.215), sex (all male subjects), handedness (all right-handed) and total brain volume (ADHD, 1425.9 ml ± 131.2; controls, 1421.5 ml ± 127.2; t = 0.144, df = 70, P = 0.886). Table S1 shows the results of the behavioral measures and executive function tests. As expected, patients with ADHD showed higher scores on attention problems on the CBCL (t = 6.931, df = 70, P < 0.001) and higher hyperactivity-impulsivity scores (t = 5.869, df = 70, P < 0.001) and hyperactivity index (t = 7.047, df = 63, P < 0.001) on the CPRS compared with healthy controls. ADHD patients also had poorer performance on executive function tests compared with healthy controls. On the Stroop-CW Test, ADHD patients achieved fewer numbers of correct responses (t = −5.562, df = 59, P < 0.001), more numbers of errors (t = 6.416, df = 63, P < 0.001) and corrections (t = 6.266, df = 43, P < 0.001), as well as longer total times (t = 5.681, df = 51, P < 0.001) compared with healthy controls. On the WCST, patients had fewer total correct (t = −3.178, df = 65, P = 0.002) and completed categories (t = −2.6, df = 70, P = 0.011), as well as more total errors (t = 2.68, df = 62, P = 0.009), perseverative errors (t = 2.309, df = 55, P = 0.025) and non-perseverative errors (t = 2.518, df = 65, P = 0.014) compared with healthy controls. Table 1 and Fig. 1A display the results of the VBM analysis. Compared to healthy controls, patients with ADHD showed reduced gray matter volume in the right orbitofrontal cortex (Brodmann area [BA] 11) (t = −4.98, df = 70, P = 0.006), right primary motor/premotor cortex (BA 4/6) (t = −4.72, df = 70, P = 0.029), left anterior cingulate cortex (BA 32) (t = −4.3, df = 70, P = 0.02) and left posterior midcingulate cortex (BA 24) (t = −4.9, df = 70, P = 0.001). In the ADHD group, gray matter volume in the right orbitofrontal cortex was positively correlated with the number of completed categories on the WCST (r = 0.287, N = 37, P = 0.042); gray matter volume in the left posterior midcingulate cortex was positively correlated with the number of completed categories on the WCST (r = 0.334, N = 37, P = 0.022) and negatively correlated with the total number of errors (r = −0.276, N = 37, P = 0.049) and the number of non-perseverative errors (r = −0.288, N = 37, P = 0.042) on the WCST (Fig. 1B and Table S2). No significant correlations in the healthy controls were identified between gray matter volumes in those brain regions and WCST scores (Table S2). And there also were no other significant correlations between gray matter volumes of the four regions and CBCL, CPRS and Stroop-CW in both groups (P > 0.05). 4. Discussion The present study explored alterations in the gray matter volume of children and adolescents with ADHD who were drugnaive and without comorbidities. Compared with healthy controls, patients with ADHD showed decreased gray matter volume in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex. Furthermore, in the ADHD group, reduced gray matter volumes in the right orbitofrontal cortex and left posterior midcingulate cortex correlated with executive dysfunction assessed using the WCST. Our findings correspond with the results of previous studies and highlight the role of motor regions, as well as the orbitofrontal and cingulate cortex in the pathophysiology of ADHD. The present study included drug-naive children and adolescents with ADHD who were without comorbidities; these criteria are important not only as a starting point for evaluating the progression of brain alterations and assessing brain anatomy before they can be influenced by potential confounding effects, but also as a technique to better specify the brain regions where anatomical changes are
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seen early in the course of illness to provide new information that is relevant to models of pathogenesis [15]. Furthermore, the effect of ADHD on structural abnormalities have often been interpreted in the context of disrupted maturational processes [22]. The delay of gray matter maturation in children with ADHD was reported to be most prominent in the prefrontal regions that are important for the control of cognitive process including attention and motor planning [22]; this developmental finding is consistent with our present results indicating reduced gray matter volume in ADHD patients, and the present changes in the brain of ADHD might aid in developing improved targets and timing of intervention for the future implications and directions. However, a previous meta-analysis found reductions in the gray matter volume of the basal ganglia in ADHD patients; however, the research study recruited both children and adults as well as subjects who used stimulant medications. Therefore, these reported basal ganglia abnormalities might, to some extent, indicate possible confounding effects for age, comorbidities and medications on gray matter volume in ADHD patients [7]. Consistent with previous study recruiting medication-naive right-handed male adolescents with ADHD reporting no significant group differences of gray matter volume in the basal ganglia [23], our results were not confounded by the stimulant medications as well. The orbitofrontal cortex has also been implicated in ADHD by other investigators, with studies reporting smaller gray matter volume [24] or reduced cortical thickness [25]. The orbitofrontal cortex plays a key role in modulating emotion and impulsive behavior and its dysfunction of this region may account for the impulsivity seen in ADHD [15]. However, the role of the orbitofrontal cortex in the executive function of ADHD patients has remained speculative because no direct correlation with neuropsychological evaluations has been provided by previous studies. In the present study, reduced gray matter volume in the orbitofrontal cortex of ADHD patients correlates with the number of completed categories on the WCST, a measure that reflects the functioning of cognitive flexibility [26]. Therefore, this finding elucidates the structural neural substrate for executive function by indicating that volume reduction in the orbitofrontal cortex may have an effect on ADHDrelated executive dysfunction. The anterior cingulate cortex has always been a topic of research on ADHD due to its important roles in symptom severity and emotional processing [4]. Unmedicated children and adolescents with ADHD have been previously reported to have a smaller volume of the anterior cingulate cortex compared with both chronically medicated ADHD children and healthy controls [12], as well as a reduced activation for inhibition in the anterior cingulate cortex [27]; these findings are in agreement with results of the present study indicating gray matter volume reductions in the anterior cingulate cortex of drug-naive ADHD children and adolescents. In present study, the regions with reduced gray matter volume within the anterior cingulate cortex were located anterior and ventral to the corpus callosum. Based on cytoarchitecture and connectivity study [28], the pregenual and subgenual anterior cingulate cortex are considered the affective subdivision, and the anterior midcingulate cortex is regarded as the cognitive subdivision [29]. This functional discrimination corresponds with our result showing that the reduced volume of the pregenual and subgenual anterior cingulate cortex did not significantly correlate with cognitive performance on the Stroop-CW test and WCST. According to the four region model proposed by Vogt, the region that is posterior to cingulate cortex and dorsal to the corpus callosum is the midcingulate cortex, which is predominantly involved in action execution and motor paradigms [28]. Previous studies have found decreased functional connectivity between the anterior and posterior cingulate cortex regions extending to the posterior midcingulate cortex [30]; correspondingly, the posterior midcingu-
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Table 1 Brain regions with reduced gray matter volume in ADHD patients compared to healthy controls. Brain region
BA
Right orbitofrontal cortex Right primary motor/premotor cortex Left anterior cingulate cortex Left posterior midcingulate cortex
11 4/6 32 24
*
MNI coordinates x
y
z
7 34 −3 −7
20 −12 42 −9
−30 61 3 47
Voxels
p-Value*
2952 1682 2255 3947
0.006 0.029 0.02 0.001
Cluster-level P < 0.05 after family-wise error corrected.
Fig. 1. (A) Compared to healthy controls, patients with ADHD showed reduced gray matter volume (cool color) in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex (cluster-level P < 0.05 after family-wise error corrected). (B) Significant correlations between gray matter volume in the right orbitofrontal cortex and left posterior midcingulate cortex and scores of executive function assessed with the Wisconsin Card Sorting Test in patients with ADHD (P < 0.05, uncorrected). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
late cortex interacts with the dorsal part of the posterior cingulate cortex [28]. And it is of interest that reduced volume of posterior midcingulate cortex was also shown in previous work with medication-naive adult ADHD [5]. This finding combined with our present results might indicate the syndromatic continuity between pediatric and adult ADHD at the neural structural level. Additionally, the functional abnormalities of the posterior midcingulate cortex have been found to correlate with the extent of executive dysfunction in other brain disorders [31]. Our study found that volume reductions in the posterior midcingulate cortex were associated with the cognitive domain assessments utilizing the WCST in ADHD patients, but not in healthy controls; this finding draws attention to a possible anatomical substrate of ADHD expressed by gray matter volumetric abnormalities, indicating that smaller gray matter volumes of the posterior midcingulate cortex may predict poorer executive function in ADHD patients. Our investigation of the primary motor and premotor cortex indicated that structural motor areas are involved in the deficits seen in ADHD. Both regions are located in the posterior portion of the frontal lobe and work in association with other motor areas,
such as the supplementary motor area and posterior parietal cortex, to plan and execute movements. Patients with ADHD, especially children and adolescents, often have age-inappropriate motor difficulties including timed repetitive and sequential movements, motor overflow and difficulties with balance [32]. Functional studies have revealed anomalous activations within motor areas in patients with ADHD at a resting state or when performing tasks that require response inhibition or motor control [33,34]. Previous structural study has also found reduced volume of motor areas in ADHD patients [35]. The results of our study are consistent with these previous findings; gray matter volume reductions in the primary motor/premotor cortex of patients with ADHD may demonstrate a critical role of motor structure deficits in the pathophysiology of ADHD. Several limitations should have been considered in the current study. Although we only recruited boys and enhanced the homogeneity of the sample that based on the higher prevalence in boys for ADHD [15], it limits the generalizability to females with ADHD. It should be noted that larger sample sizes are generally required to confirm the robustness of differences seen on
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neuroimaging and to explore the gender differences in the neuroanatomy; additionally, longitudinal studies are needed in the future to monitor the dynamic structural changes that occur in patients with ADHD. Secondly, although the results of correlation analyses did not survive multiple comparison test, we are reluctant to completely reject our hypotheses for the correlations on account of the exploratory results observed and the previous researches supporting the hypothesis. 5. Conclusions In conclusion, the findings of the present study demonstrate regional gray matter volume deficits in the right orbitofrontal cortex, right primary motor/premotor cortex, left anterior cingulate cortex and left posterior midcingulate cortex in children and adolescents with ADHD who were drug-naive and without comorbidities at early illness stage. The correlations between the structural abnormalities and executive dysfunction suggest that neuroanatomical substrate deficits underlie the pathophysiology of ADHD. Conflict of interest All the authors report no financial relationships with commercial interests. Acknowledgments This study was supported by the National Natural Science Foundation of China (Grants No. 30970903 and 81401396) and the International Postdoctoral Exchange Fellowship Program 2014 by the Office of China Postdoctoral Council (The approval document number: No.29 Document of OCPC, 2014. Certificate of Financial Support number: No. 20140058). Preliminary data for this article were collected at the Department of Psychiatry and Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.05. 062 References [1] G. Polanczyk, M.S. de Lima, B.L. Horta, J. Biederman, L.A. Rohde, The worldwide prevalence of ADHD: a systematic review and metaregression analysis, Am. J. Psychiatry 164 (6) (2007) 942–948. [2] K. Konrad, S.B. Eickhoff, Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder, Hum. Brain Mapp. 31 (6) (2010) 904–916. [3] F. Li, N. He, Y. Li, L. Chen, X. Huang, S. Lui, L. Guo, G.J. Kemp, Q. Gong, Intrinsic brain abnormalities in attention deficit hyperactivity disorder: a resting-state functional MR imaging study, Radiology 272 (2) (2014) 514–523. [4] F.X. Castellanos, E. Proal, Large-scale brain systems in ADHD: beyond the prefrontal–striatal model, Trends Cognit. Sci. 16 (1) (2012) 17–26. [5] N. Makris, L. Liang, J. Biederman, E.M. Valera, A.B. Brown, C. Petty, T.J. Spencer, S.V. Faraone, L.J. Seidman, Toward defining the neural substrates of ADHD: a controlled structural MRI study in medication-naive adults, J. Atten. Disord. (2013), Nov 4. [Epub ahead of print]. [6] J. Ashburner, K.J. Friston, Voxel-based morphometry the methods, Neuroimage 11 (6 Pt 1) (2000) 805–821. [7] T. Nakao, J. Radua, K. Rubia, D. Mataix-Cols, Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication, Am. J. Psychiatry 168 (11) (2011) 1154–1163. [8] T. Frodl, N. Skokauskas, Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects, Acta Psychiatr. Scand. 125 (2) (2012) 114–126. [9] J. Wang, T. Jiang, Q. Cao, Y. Wang, Characterizing anatomic differences in boys with attention-deficit/hyperactivity disorder with the use of deformation-based morphometry, AJNR Am. J. Neuroradiol. 28 (3) (2007) 543–547.
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