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hyperactivity disorder
Journal of Affective Disorders 250 (2019) 397–403
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Characterization of cortical and subcortical abnormalities in drug-naive boys with attention-deficit/hyperactivity disorder
T
Lu Lua,1, Zhang Lianqinga,1, Tang Shia, Bu Xuana, Chen Yinga, Hu Xinyua, Hu Xiaoxiaoa, ⁎ ⁎ Li Hailonga, Guo Lantingb, Sweeney John A.a,c, Gong Qiyonga, , Huang Xiaoqia, a
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China c Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA b
ARTICLE INFO
ABSTRACT
Keywords: ADHD MRI Drug-naive Attention network
Background: The current study was designed to investigate the anatomical differences in cortical and subcortical morphometry between drug-naive boys with attention-deficit/hyperactivity disorder (ADHD) and healthy controls (HCs) using three-dimensional T1-weighted imaging and to explore the effects of age on morphometric abnormalities. Methods: Fifty-three drug-naive boys with ADHD and 53 HCs underwent high-resolution anatomical magnetic resonance (MR) imaging using a 3-T MR scanner. The FreeSurfer image analysis suite was used to obtain measures of cortical volume, thickness, and surface area, as well as the volumes of 14 subcortical structures. Statistically significant differences in measures between children with ADHD and HCs were evaluated using a least general linear model, with the intracranial volume and age as covariates. Results: Compared to HCs, boys with ADHD exhibited an increased cortical volume in the left frontal eye field (FEF), a decreased surface area in the left ventral frontal cortex (VFC), and a decreased volume in the right putamen (cluster-wise p < 0.05; Monte Carlo-corrected). Moreover, we also observed age-related differences in FEF and VFC between groups. Limitations: The cross-sectional study design limited inferences about the effects of age on regions displaying morphometric differences. Conclusions: To our knowledge, this study is the first to characterize the cortical morphometry, including volume, thickness and surface area, of drug-naive boys with ADHD at the whole brain level; which provided detailed information about neuroanatomical alterations in attention systems beyond effects reported in previous studies at the lobe and sub-lobe levels. Based on our results, boys with ADHD presented significant alterations in cortical and subcortical morphology in several important nodes of the attention network.
Abbreviations: ADHD, attention-deficit/ hyperactivity disorder MR, magnetic resonance VFC, ventral frontal cortex FEF, frontal eye field MPRAG, magnetization prepared rapid gradient echo CPRS, Conners’ Parent Rating Scale ICV, intracranial volume ANCOVA, analysis of covariance DAN, dorsal attention network VAN, ventral attention network
1. Introduction Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder occurring childhood and is defined as age-inappropriate problems with inattention, impulsivity, and hyperactivity. It affects 5–7.1% of children and adolescents worldwide (Polanczyk et al., 2007; Willcutt, 2012) and is associated with poor educational, occupational, economic, and social outcomes, as well as higher criminality in adulthood (Klein et al., 2012). Given the high prevalence and substantial social burden of this disorder, ongoing and indispensable issues are to obtain an understanding of its neural
mechanism and to identify safe and effective treatments. Recently, neuroimaging has become an important tool to explore the structural and functional alterations in the cerebral cortex, with the hope of obtaining a better understanding of the biological mechanism of psychiatric disorders. Because the brain anatomy is the fundamental basis of cerebral function and tends to be more stable across brain development (Cao et al., 2016), numerous studies have been conducted on subjects with ADHD using automatic volumetric measurement tools, such as voxel-based morphometry. However, since the cortical volume is a product of cortical thickness and surface area, which have distinct genetic determinants (Panizzon et al., 2009), phylogeny (Rakic, 1988),
Corresponding authors. E-mail address: [email protected] (X. Huang). 1 Lu Lu and Lianqing Zhang contributed equally to this work. ⁎
https://doi.org/10.1016/j.jad.2019.03.048 Received 31 August 2018; Received in revised form 9 January 2019; Accepted 7 March 2019 Available online 08 March 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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and developmental trajectories (Armstrong et al., 1995), explorations of cortical volume, cortical thickness and surface area in a more comprehensive way to obtain a better understanding of the brain mechanism associated with the psychopathology of ADHD are worthwhile. To our knowledge, only two studies have examined those parameters in the same ADHD group. A study by Silk et al. (2016) reported smaller total cortical volume, surface area, and mean cortical thickness in children with ADHD compared to controls, and Wolosin et al. (2009) reported decreases in the cortical volume and surface area in all four lobes, while no significant differences in cortical thickness were found between subjects with ADHD and healthy controls (HCs). Nevertheless, these two studies examined the alterations in lobes and sub-lobes in which they were interested and did not report the exact locations of regions displaying deficits associated with ADHD. In addition to cortical alterations, evidence has also revealed important roles of subcortical structures, particularly the basal ganglia, in the pathophysiology of ADHD. For example, using the largest dataset to date, Hoogman et al. (2017) observed smaller volumes of the accumbens, amygdala, caudate, hippocampus and putamen in individuals with ADHD. However, due to the heterogeneity among studies and relatively small sample size, findings from previous studies have been quite variable. Furthermore, the patients recruited in previous studies often had comorbidities, took stimulant medications and were of both sexes. However, according to previous studies, patients with ADHD presenting with comorbidities, such as oppositional defiant disorder, simple phobia and dysthymic disorder, show a different cortical morphometry than patients with ADHD without comorbidities (Wolosin et al., 2009). In addition, compared to non-medicated patients with ADHD, medicated patients with ADHD have more normal volumes and/or morphology in ADHD-relevant brain regions, including the anterior cingulate cortex (Semrud-Clikeman et al., 2006), cerebellum (Bledsoe et al., 2009), and basal ganglia (Sobel et al., 2010). Finally, ADHD is diagnosed more frequently in males than in females (2–4 to 1), and evidence suggests that there are different underlying neuropathophysiological processes between boys and girls with ADHD (Davies, 2014; Qiu et al., 2009). Therefore, the recruited population in ADHD studies should be narrowed to determine the relevant alterations in brain morphometry without confounding from the aforementioned factors to improve our understanding of this disorder. Thus, in the current study, we recruited a relatively large sample of drug-naive boys with ADHD without comorbidities to examine the cortical and subcortical abnormalities associated with this disorder and to assess the effects of age on morphometric abnormalities between groups. Based on previous findings of impairments in the frontal-striatal circuit in children with ADHD, we hypothesized that the morphometry of frontal cortex and striatum would be changed and age would exert different effects on those morphometric abnormalities between the two groups.
(Table 1). ADHD diagnoses were confirmed by at least two experienced psychiatrists (L.G. and Y.C., with 28 and 5 years of experience in clinical psychiatry, respectively) using the Chinese version of the Structured Clinical Interview for Diagnostic and Statistical Manual 4 Text Revision Axis I Disorders (SCID-I, patient edition) (Bizzi and Schiller, 1970). Patients with oppositional defiant disorder, conduct disorder, Tourette disorder or any other Axis I psychiatric comorbid disorders were excluded. Other exclusion criteria were a full-scale IQ less than 90 based on an age-appropriate Wechsler Intelligence Scale for Children (Chinese revision); current use of or a history of taking psychotropic medications; left-handedness, as assessed by the Annett Hand Preference Questionnaire (Annett, 1970); any previous history of head trauma, psychosurgery, or substantial physical illness; and standard MR scanning contraindications. The revised Conners’ Parent Rating Scale (CPRS) was used to measure the behavioral problems of patients with ADHD and included 6 factors: conduct problems, study problems, psychosomatic, impulsivity-hyperactivity, anxiety and the hyperactivity index (Conners et al., 1998). HCs were also screened using the SCID-I (nonpatient edition) to exclude any Axis I psychiatric diagnosis. Other exclusion criteria were the same as those used for patients with ADHD. The study was approved by the Research Ethics Committee of West China Hospital of Sichuan University, and written informed consent was obtained from all participants. 2.2. Magnetic resonance imaging (MRI) data acquisition and preprocessing Participants were scanned using a 3-T MR scanner (Trio; Siemens, Erlangen, Germany) with an eight-channel phased-array head coil. Scanner noise was attenuated with earplugs, and head motion was restricted by placing foam padding around the head. High-resolution anatomical MR images were obtained with a magnetization prepared rapid gradient echo (MPRAG) sequence with the following parameters: repetition time/echo time, 1900/2.5 msec; inversion time, 900 msec; flip angle, 9°; matrix, 256 × 256; field of view, 256 × 256 mm; number of sagittal sections, 176; and section thickness, 1 mm. The FreeSurfer image analysis suite (http://ftp.nmr.mgh.harvard. edu/), the reliability of which has been validated against a histological analysis of postmortem brains and manual measurements (Rosas et al., 2002; Salat et al., 2004), was used to generate a cortical surface model and subsequently provide measures of cortical volume, cortical thickness and surface area at each vertex. Briefly, the imaging data were processed by performing a visual inspection of the data for motion artifacts, transformation to the Talairach space, intensity normalization, skull stripping and segmentation of the subcortical white matter and gray matter volumetric structures (Fischl et al., 2002, 2004), tessellation of the gray matter and white matter boundaries, automated topology correction (Fischl et al., 2001; Segonne et al., 2007) and surface deformation following intensity gradients to optimally place the gray/ white and gray/ cerebrospinal fluid borders at the locations with the greatest shifts in signal intensity (Dale et al., 1999; Fischl and Dale, 2000). Cortical volumes were measured based on the cortical parcellations, which were based on the cortical folding patterns (Fischl et al., 2004). Cortical thickness was quantified at each surface location or vertex as the distance (in mm) from the gray/white boundary to the pial surface (Fischl and Dale, 2000). The surface area was obtained by assigning an area to each vertex equal to the average of its surrounding triangles. When the vertex areas were summed over all vertices, the total was equal to the sum of the areas of the triangles. Vertex-level cortical volume, cortical thickness, and surface area values were obtained and mapped onto the normalized cortical surface, which was smoothed with a 10-mm full width at half maximum kernel. We extracted the volumes of several subcortical structures, including the bilateral thalamus, caudate, putamen, pallidum, hippocampus, amygdala and nucleus accumbens, using the FreeSurfer
2. Methods 2.1. Participants Patients with ADHD were recruited at the Mental Health Center, West China Hospital of Sichuan University from June 2009 to December 2011. Seventy-nine boys participated in this study, 26 of whom were excluded for the following reasons: inability to remain in the scanner (7), excessive motion artifacts (13), and inability to complete the executive function tests (6). Eighty-one healthy male controls were recruited via advertisements placed in local schools, 28 of whom were excluded for the following reasons: older age (7), inability to remain in the scanner (2), excessive motion artifacts (10), and inability to complete the executive function tests (9). Finally, 106 boys ranging in age from 7 to 16 participated in this study, including 53 patients with ADHD and 53 age-, gender-, handedness- and IQ-matched HCs 398
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Table 1 Demographics and clinical characteristics of patients with ADHD and HCs.
Age, years Gender (male/female) Handedness (R/L) IQ scores Conners’ Parent Rating Scale Conduct problem Study problem Psychosomatic Hyperactivity–impulsivity Anxiety Hyperactivity index Intracranial volume
Patients with ADHD
HCs
Statistics
df
P Value
10.43 (2.14) 53/0 53/0 104.0 (5.1)
10.85 (2.32) 53/0 53/0 107.6 (6.6)
-0.956 – – −2.03
104 – – 104
0.413 – – 0.06
11.70 (7.55) 7.47 (2.86) 1.13 (1.36) 5.40 (3.12) 1.66 (1.71) 12.91 (6.16) 1,493,509.39
5.34 (4.47) 3.23 (2.52) 0.51 (1.14) 2.23 (1.97) 1.64 (1.76) 5.43 (4.57) 1,479,104.47
5.279 8.114 2.558 6.254 0.056 7.094 0.532
104 104 104 104 104 104 104
< 0.001 < 0.001 0.012 < 0.001 0.955 < 0.001 0.596
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; R, right; L, left; df, degrees of freedom. Notes: Data are presented as means and standard deviation.
automatic segmentation pipeline (Fischl et al., 2002).
3.2. Age-related differences in cortical and subcortical morphometric abnormalities
2.3. Statistical analysis
In the FEF, a significant negative correlation was observed between age and cortical volume in the ADHD group (r = −0.281, p = 0.043), whereas in HCs, a significant positive correlation was observed (r = 0.293, p = 0.035). A direct statistical comparison of correlation coefficients between two groups established a significantly weaker correlation between age and the cortical volume of the FEF in children with ADHD than in HCs (Z = 2.953, p < 0.05; Fig. 1B). According to the results of the subgroup analysis, patients with ADHD displayed a significantly larger cortical volume for the FEF during childhood (T = 2.32, p = 0.02; Fig. 1C), while an insignificantly smaller volume was observed in adolescence (T = −1.14, p = 0.27; Fig. 1C). In the VFC, a significant positive correlation between age and surface area was observed in patients with ADHD (r = 0.308, p = 0.027), but a trend toward a negative correlation was identified in HCs (r = −0.042, p = 0.765). A direct statistical comparison of correlation coefficients between the two groups established a significantly different correlation between age and surface area of the VFC in children with ADHD than in HCs (Z = 1.802, p < 0.05; Fig. 2B). Based on the results of the sub-group analysis, the surface area of the VFC was not different between two groups during childhood (T = −0.53, p = 0.60; Fig. 2C) or adolescence (T = 1.48, p = 0.16; Fig. 2C). A significant correlation between the subcortical volume in the right putamen and age was not observed in either group (ADHD r = −0.092, p = 0.518; HC r = −0.141, p = 0.320). Significant differences in their correlation coefficients were not observed between the patients with ADHD and HCs (Z = 0. 248, p > 0.05).
Group differences in cortical volume, cortical thickness and surface area were examined on the surface maps in a vertex by vertex manner using QDEC software (https://surfer.nmr.mgh.harvard.edu/fswiki/ Qdec) by employing a general linear model with the intracranial volume (ICV) and age as covariates. The Monte Carlo simulation was performed to control for multiple comparisons (10,000 iterations, cluster-forming p < 0.05, cluster-wise corrected p < 0.05). Analyses of differences in subcortical volumes were assessed using a multivariate analysis of covariance (MANCOVA) with ICV and age as covariates. To examine potential differences in the effects of age on morphometric abnormalities between groups, we first extracted the average data from regions with significant group differences for each subject. Since the effects of age on the human brain during this period follow a linear trajectory (O'Donnell et al., 2005; Shaw et al., 2008), we performed a partial correlation analysis to determine the effect of age on abnormal cortical and subcortical regions after controlling for ICV for each group separately. Fisher's z’ transformation was used to perform a direct statistical comparison between correlation coefficients obtained from the two groups to determine whether age-related differences existed between patients and HCs. Furthermore, we performed a subgroup analysis by dividing the age distribution into children and adolescents (7–12 and 13–16 years, respectively). Pearson's partial correlation analyses were also conducted to identify relationships between behavioral problems and anatomical measures that displayed abnormalities in patients with ADHD, after controlling for ICV and age.
3.3. Clinical correlation analysis No significant correlations were observed between behavioral problems measured using the CPRS and any cerebral morphometric abnormalities in the ADHD group.
3. Results 3.1. Group differences in cortical and subcortical morphometry
4. Discussion
Compared with HCs, children with ADHD exhibited an increased cortical volume in the left frontal eye field (FEF), a decreased cortical surface area in the left ventral frontal cortex (VFC) (p < 0.05, Monte Carlo-corrected; Table 2 and Figs. 1A and 2A). A significant difference in cortical thickness was not detected between the ADHD group and the HC group. Compared with HCs, children with ADHD also displayed a reduced volume of subcortical structures in the right putamen (F = 4.491, p = 0.037; Table 3). Significant differences in other nuclei were not observed between the two groups.
In the current study, we used multiple morphometric measurements to characterize anatomical differences in both cortical and subcortical morphometry between drug-naive boys with ADHD and HCs and to explore the age-related differences in the regions displaying morphometric abnormalities. Consistent with our hypothesis, boys with ADHD showed altered cortical and subcortical morphologies in several important nodes of the attention network. Specifically, an increased cortical volume was observed in the left FEF, a decreased surface area was detected in the left VFC, and a reduced subcortical volume was observed in the right putamen. In addition, we also identified significant 399
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Table 2 Brain regions displaying differences in the surface area and cortical volume in patients with ADHD compared to HCs.
Surface area ADHD < HCs Cortical volume ADHD > HCs
Size (mm2)
df
-log10(p)
3.1
−1285.87
102
−3.336
28.6
815.62
102
2.877
Brain region
Talairach coordinates X Y
Z
Left ventral frontal cortex
−29.4
29.6
Left frontal eye field
−42.2
22.4
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls; df, degrees of freedom. Negative values for -log10(p) represent a decreased surface area in patients with ADHD, and positive values for -log10(p) represent an increased cortical volume in patients with ADHD.
age-related differences in these regions between the two groups, implying an abnormality in the neural developmental trajectory of these regions in patients with ADHD. The FEF is a frontal node of the dorsal attention network (DAN) that is involved in voluntary (top-down) orienting attention and shows increased activity after the presentation of cues indicating where, when, or to what subjects should direct their attention. Neurocognitive studies of the function of the attention network in children with ADHD did not report significant differences in orienting attention between the ADHD and typically developing groups, although this behavior tended to be impaired (Adolfsdottir et al., 2008; Booth et al., 2007; Konrad et al., 2006; Mullane et al., 2011). In the present study, we observed an increased cortical volume of the FEF in drug-naive boys with ADHD. Based on this finding, an increase in the volume of this brain region may compensate for the top-down orienting attention deficit and maintain it at the normal level in children with ADHD. The VFC is a frontal node of the ventral attention network (VAN) whose main function is to reorient attention toward salient stimuli and to maintain an optically alert attentional state; the activity of the VFC increases upon the detection of salient targets, particularly when these targets appear in unexpected locations (Fox et al., 2006; Posner and Petersen, 1990). Studies from cognitive neuroscience have revealed impairments in alerting attention in children with ADHD, indicating a lower general level of alertness in patients (Corkum and Siegel, 1993; Losier et al., 1996; Mullane et al., 2011). Our study revealed decreased surface area in the VFC in children with ADHD, which was consistent with previous fMRI studies showing decreased amplitude low-frequency fluctuation in VFC and decreased connectivity between the VFC and basal ganglia in patients with ADHD during attention conditions (Cortese et al., 2012; Rubia et al., 2009). Taking these findings together, we speculated that structural and functional alterations in the VFC may partially account for the low alerting attention in children with ADHD. Many previous morphometric studies and meta-analyses have revealed that the putamen is one of the primary structures involved in
ADHD (Ellison-Wright et al., 2008; Greven et al., 2015; Nakao et al., 2011; Qiu et al., 2009; Shaw et al., 2014; Valera et al., 2007). Consistent with these findings, we also observed a reduced volume of the right putamen in children with ADHD in the current study. Previous studies examining the attention network in children with ADHD suggested that executive attention is the most significant deficit among alerting, orienting and executive attention (Konrad et al., 2006; Mullane et al., 2011). As shown in the study by Konrad et al. (2006) children with ADHD show decreased neural activity in the right putamen during the conflict condition, indicating that the putamen is an important node of the executive attention system. Based on the aforementioned analysis, we postulate that the decreased volume of the putamen might contribute to deficits in planning, control, and execution of behaviors that are characteristic clinical features of ADHD. In addition to the regional brain abnormalities discussed above, we also identified significantly different age-related morphometric changes in the FEF and VFC between HCs and children with ADHD. By further dividing the age distribution into two stages, children and adolescents, we revealed that morphometric abnormalities occurring in children would change to the opposite direction in adolescents. Similar to many previous studies, our analyses stratified by age indicated that ADHD is a disorder characterized by an altered developmental pattern, and that pattern will enable children with ADHD to progressively catch up with normally developing children. Moreover, our finding that the decreased surface area of the VFC increased with age in children with ADHD is similar to the developmental delay revealed by previous study using cortex thickness as a measurement (Shaw et al., 2007). Previous functional MRI studies have indicated the activation of VFC increased progressively with age during the interference inhibition task (Eden and Moats, 2002), and greater involvement of the VFC was associated with better executive performance. Our result further revealed that the surface area of the VFC increased progressively with age in children with ADHD, which may also contribute to the neural mechanism underlying the maturation of executive attention performance from
Fig. 1. A. Increased cortical volume of the left FEF in patients with ADHD compared with HCs (p < 0.05, Monte Carlo-corrected). B. Scatter plot of age-related differences in cortical volume of the left FEF in patients with ADHD and HCs. C. Bar chart depicting the results of the subgroup analysis of cortical volume of the left FEF (❉, p < 0.05). Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls. 400
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Fig. 2. A. Decreased surface area of left VFC in patients with ADHD compared with HCs (p < 0.05, Monte Carlo-corrected). B. Scatter plot of age-related differences in the surface area of the left VFC in patients with ADHD and HCs. C. Bar chart showing the results of the subgroup analysis of the surface area of the left VFC. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls.
children to adolescents. Similar to the study by Wolosin et al. (2009), we did not observe any significant differences in cortical thickness between children with ADHD and HCs, which differed from most previous studies (Shaw et al., 2006; Yang et al., 2015). We postulated that this discrepancy may be due to the differences in study populations and sample size. For example, many previous studies had included individuals with ADHD presenting with comorbid disorders (Shaw et al., 2006), such as conduct disorder, learning disorder and anxiety disorder, among others. However, all children with ADHD enrolled in our current study were screened to exclude such comorbidities. Moreover, as the cortex morphometry develops with aging, different age ranges may also explain the differences between our results and previous studies. Finally, cortical volume is a 3-dimensional measure, surface area is a 2-dimensional measure, and cortical thickness is a 1-dimensional measure. In contrast to cortical volume and surface area, cortical thickness is measured at tens of thousands of vertices along the surface. A difference in cortical thickness may therefore be inherently difficult to detect and may require a larger sample size. Several limitations in our study must be recognized. First, the crosssectional design of our study limits inferences about the age-related trajectory of morphometric abnormalities. Hypotheses about
development must be confirmed by longitudinal studies in the future. Second, the strict inclusion criteria resulted in our subjects being medication-naive boys with ADHD who were right-handed and did not present with comorbidities. Although this highly homogenous population will improve our understanding of the brain pathology that is directly relevant to “pure” ADHD and not confounded by other factors, it limits the generalizability of our findings to a broader range of patients with ADHD, such as girls with ADHD and patients who are left-handed or have comorbidities. Future studies may benefit from exploring the effects of gender, handedness and comorbidities on the cortical morphometry to deepen our knowledge about the involvement of those factors in ADHD. Third, the current study was a structural MRI study lacking data on attentional functions; future studies should combine attention tasks, such as the attention network test, with structural and functional MRI data to verify our explanation of the relationship between MRI alterations and attention functions in patients with ADHD. 5. Conclusions In the current study, we investigated the characteristic abnormalities of cortical and subcortical structures in drug-naive boys with ADHD and explored the age-related differences in the regions
Table 3 Volumes of subcortical gray matter structures in patents with ADHD and HCs. Subcortical area Thalamus Left Right Caudate Left Right Putamen Left Right Pallidum Left Right Hippocampus Left Right Amygdala Left Right Accumbens area Left Right
Volume in patients with ADHD (mm3)
Volume in HCs (mm3)
F Value
df
P Value
8398.0 (8242.4–8553.6) 7608.0 (7469.2–7746.8)
8348.7 (8193.1–8504.3) 7526.2 (7387.4–7665.0)
0.196 0.677
102 102
0.659 0.413
3838.1 (3699.6–3976.6) 3802.5 (3675.2–3929.8)
3814.6 (3676.1–3953.1) 3872.7 (3745.4–4000.0)
0.056 0.592
102 102
0.813 0.443
6274.4 (6055.1–6439.6) 6281.9 (6126.2–6437.7)
6451.1 (6258.9–6643.4) 6518.2 (6362.5–6674.0)
2.191 4.491
102 102
0.142 0.037
1645.3 (1580.8–1709.9) 1667.6 (1626.0–1709.3)
1620.0 (1555.5–1684.5) 1705.3 (1663.7–1747.0)
0.301 1.598
102 102
0.585 0.209
4435.6 (4323.5–4547.8) 4569.1 (4460.3–4677.9)
4502.9 (4390.7–4615.1) 4675.4 (4566.6–4784.1)
0.702 1.862
102 102
0.404 0.175
1664.3 (1617.0–1711.5) 1782.3 (1727.6–1836.9)
1716.2 (1668.9–1763.4) 1847.5 (1792.8–1902.2)
2.353 2.776
102 102
0.128 0.099
671.7 (640.0–703.5) 680.9 (655.9–705.8)
684.8 (653.0–716.5) 708.2 (683.3–733.1)
0.328 2.352
102 102
0.568 0.128
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls; df, degrees of freedom. Notes: Data are presented as means and 95% confidence intervals; Bold, p < 0.05. 401
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displaying morphometric abnormalities. Based on our results, ADHD was implicated in anatomical deficits in several important nodes of the dorsal and ventral attention networks associated with alerting, orienting and executive attention. Furthermore, the normal age-related morphometric development was disrupted in boys with ADHD. To our knowledge, this study is the first to characterize the cortical morphometry, including volume, thickness and surface area, in drug-naive boys with ADHD at the whole-brain level, thus providing detailed information about neuroanatomical alterations in attention systems beyond effects reported in previous studies at the lobe and sublobe levels.
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Declaration of interest The authors report no financial or other relationships relevant to the subject of this article. Contributors All authors reviewed the manuscript and approved the final article. Lu L and Lianqing Z designed the study, performed the experiments and wrote the article. Xuan B, Ying C and Lanting G collected the clinical data. Xinyu H, Xiaoxiao H and Hailong L collected the imaging data and contributed to the analysis. John A. S made an important contribution to statistical analysis and interpretation of results. Xiaoqi H and Qiyong G critically revised the manuscript for important intellectual content. Xiaoqi H obtained funding. Role of funding source This study was supported by grant 81671669 from the National Natural Science Foundation. Acknowledgements We would like to thank the patients and their families for their participation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.03.048. References Adolfsdottir, S., Sorensen, L., Lundervold, A.J., 2008. The attention network test: a characteristic pattern of deficits in children with ADHD. Behav. Brain Funct. 4, 9. Annett, M., 1970. A classification of hand preference by association analysis. Br. J. Psychol. 61, 303. Armstrong, E., Schleicher, A., Omran, H., Curtis, M., Zilles, K., 1995. The ontogeny of human gyrification. Cereb. Cortex 5, 56–63. Bizzi, E., Schiller, P.H., 1970. Single unit activity in the frontal eye fields of unanesthetized monkeys during eye and head movement. Exp. Brain Res. 10, 150–158. Bledsoe, J., Semrud-Clikeman, M., Pliszka, S.R., 2009. A magnetic resonance imaging study of the cerebellar vermis in chronically treated and treatment-naive children with attention-deficit/hyperactivity disorder combined type. Biol. Psychiatry 65, 620–624. Booth, J.E., Carlson, C.L., Tucker, D.M., 2007. Performance on a neurocognitive measure of alerting differentiates ADHD combined and inattentive subtypes: a preliminary report. Arch. Clin. Neuropsychol. 22, 423–432. Cao, M., Huang, H., Peng, Y., Dong, Q., He, Y., 2016. Toward developmental connectomics of the human brain. Front. Neuroanat. 10, 25. Conners, C.K., Sitarenios, G., Parker, J.D.A., Epstein, J.N., 1998. The revised Conners' Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. J. Abnorm. Child Psychol. 26, 257–268. Corkum, P.V., Siegel, L.S., 1993. Is the continuous performance task a valuable research tool for use with children with attention-deficit-hyperactivity disorder. J. Child Psychol. Psychiatry 34, 1217–1239. Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M.P., Castellanos, F.X., 2012. Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am. J. Psychiatry 169, 1038–1055. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194.
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Characterization of cortical and subcortical abnormalities in drug-naive boys with attention-deficit/hyperactivity disorder
T
Lu Lua,1, Zhang Lianqinga,1, Tang Shia, Bu Xuana, Chen Yinga, Hu Xinyua, Hu Xiaoxiaoa, ⁎ ⁎ Li Hailonga, Guo Lantingb, Sweeney John A.a,c, Gong Qiyonga, , Huang Xiaoqia, a
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China c Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA b
ARTICLE INFO
ABSTRACT
Keywords: ADHD MRI Drug-naive Attention network
Background: The current study was designed to investigate the anatomical differences in cortical and subcortical morphometry between drug-naive boys with attention-deficit/hyperactivity disorder (ADHD) and healthy controls (HCs) using three-dimensional T1-weighted imaging and to explore the effects of age on morphometric abnormalities. Methods: Fifty-three drug-naive boys with ADHD and 53 HCs underwent high-resolution anatomical magnetic resonance (MR) imaging using a 3-T MR scanner. The FreeSurfer image analysis suite was used to obtain measures of cortical volume, thickness, and surface area, as well as the volumes of 14 subcortical structures. Statistically significant differences in measures between children with ADHD and HCs were evaluated using a least general linear model, with the intracranial volume and age as covariates. Results: Compared to HCs, boys with ADHD exhibited an increased cortical volume in the left frontal eye field (FEF), a decreased surface area in the left ventral frontal cortex (VFC), and a decreased volume in the right putamen (cluster-wise p < 0.05; Monte Carlo-corrected). Moreover, we also observed age-related differences in FEF and VFC between groups. Limitations: The cross-sectional study design limited inferences about the effects of age on regions displaying morphometric differences. Conclusions: To our knowledge, this study is the first to characterize the cortical morphometry, including volume, thickness and surface area, of drug-naive boys with ADHD at the whole brain level; which provided detailed information about neuroanatomical alterations in attention systems beyond effects reported in previous studies at the lobe and sub-lobe levels. Based on our results, boys with ADHD presented significant alterations in cortical and subcortical morphology in several important nodes of the attention network.
Abbreviations: ADHD, attention-deficit/ hyperactivity disorder MR, magnetic resonance VFC, ventral frontal cortex FEF, frontal eye field MPRAG, magnetization prepared rapid gradient echo CPRS, Conners’ Parent Rating Scale ICV, intracranial volume ANCOVA, analysis of covariance DAN, dorsal attention network VAN, ventral attention network
1. Introduction Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder occurring childhood and is defined as age-inappropriate problems with inattention, impulsivity, and hyperactivity. It affects 5–7.1% of children and adolescents worldwide (Polanczyk et al., 2007; Willcutt, 2012) and is associated with poor educational, occupational, economic, and social outcomes, as well as higher criminality in adulthood (Klein et al., 2012). Given the high prevalence and substantial social burden of this disorder, ongoing and indispensable issues are to obtain an understanding of its neural
mechanism and to identify safe and effective treatments. Recently, neuroimaging has become an important tool to explore the structural and functional alterations in the cerebral cortex, with the hope of obtaining a better understanding of the biological mechanism of psychiatric disorders. Because the brain anatomy is the fundamental basis of cerebral function and tends to be more stable across brain development (Cao et al., 2016), numerous studies have been conducted on subjects with ADHD using automatic volumetric measurement tools, such as voxel-based morphometry. However, since the cortical volume is a product of cortical thickness and surface area, which have distinct genetic determinants (Panizzon et al., 2009), phylogeny (Rakic, 1988),
Corresponding authors. E-mail address: [email protected] (X. Huang). 1 Lu Lu and Lianqing Zhang contributed equally to this work. ⁎
https://doi.org/10.1016/j.jad.2019.03.048 Received 31 August 2018; Received in revised form 9 January 2019; Accepted 7 March 2019 Available online 08 March 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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and developmental trajectories (Armstrong et al., 1995), explorations of cortical volume, cortical thickness and surface area in a more comprehensive way to obtain a better understanding of the brain mechanism associated with the psychopathology of ADHD are worthwhile. To our knowledge, only two studies have examined those parameters in the same ADHD group. A study by Silk et al. (2016) reported smaller total cortical volume, surface area, and mean cortical thickness in children with ADHD compared to controls, and Wolosin et al. (2009) reported decreases in the cortical volume and surface area in all four lobes, while no significant differences in cortical thickness were found between subjects with ADHD and healthy controls (HCs). Nevertheless, these two studies examined the alterations in lobes and sub-lobes in which they were interested and did not report the exact locations of regions displaying deficits associated with ADHD. In addition to cortical alterations, evidence has also revealed important roles of subcortical structures, particularly the basal ganglia, in the pathophysiology of ADHD. For example, using the largest dataset to date, Hoogman et al. (2017) observed smaller volumes of the accumbens, amygdala, caudate, hippocampus and putamen in individuals with ADHD. However, due to the heterogeneity among studies and relatively small sample size, findings from previous studies have been quite variable. Furthermore, the patients recruited in previous studies often had comorbidities, took stimulant medications and were of both sexes. However, according to previous studies, patients with ADHD presenting with comorbidities, such as oppositional defiant disorder, simple phobia and dysthymic disorder, show a different cortical morphometry than patients with ADHD without comorbidities (Wolosin et al., 2009). In addition, compared to non-medicated patients with ADHD, medicated patients with ADHD have more normal volumes and/or morphology in ADHD-relevant brain regions, including the anterior cingulate cortex (Semrud-Clikeman et al., 2006), cerebellum (Bledsoe et al., 2009), and basal ganglia (Sobel et al., 2010). Finally, ADHD is diagnosed more frequently in males than in females (2–4 to 1), and evidence suggests that there are different underlying neuropathophysiological processes between boys and girls with ADHD (Davies, 2014; Qiu et al., 2009). Therefore, the recruited population in ADHD studies should be narrowed to determine the relevant alterations in brain morphometry without confounding from the aforementioned factors to improve our understanding of this disorder. Thus, in the current study, we recruited a relatively large sample of drug-naive boys with ADHD without comorbidities to examine the cortical and subcortical abnormalities associated with this disorder and to assess the effects of age on morphometric abnormalities between groups. Based on previous findings of impairments in the frontal-striatal circuit in children with ADHD, we hypothesized that the morphometry of frontal cortex and striatum would be changed and age would exert different effects on those morphometric abnormalities between the two groups.
(Table 1). ADHD diagnoses were confirmed by at least two experienced psychiatrists (L.G. and Y.C., with 28 and 5 years of experience in clinical psychiatry, respectively) using the Chinese version of the Structured Clinical Interview for Diagnostic and Statistical Manual 4 Text Revision Axis I Disorders (SCID-I, patient edition) (Bizzi and Schiller, 1970). Patients with oppositional defiant disorder, conduct disorder, Tourette disorder or any other Axis I psychiatric comorbid disorders were excluded. Other exclusion criteria were a full-scale IQ less than 90 based on an age-appropriate Wechsler Intelligence Scale for Children (Chinese revision); current use of or a history of taking psychotropic medications; left-handedness, as assessed by the Annett Hand Preference Questionnaire (Annett, 1970); any previous history of head trauma, psychosurgery, or substantial physical illness; and standard MR scanning contraindications. The revised Conners’ Parent Rating Scale (CPRS) was used to measure the behavioral problems of patients with ADHD and included 6 factors: conduct problems, study problems, psychosomatic, impulsivity-hyperactivity, anxiety and the hyperactivity index (Conners et al., 1998). HCs were also screened using the SCID-I (nonpatient edition) to exclude any Axis I psychiatric diagnosis. Other exclusion criteria were the same as those used for patients with ADHD. The study was approved by the Research Ethics Committee of West China Hospital of Sichuan University, and written informed consent was obtained from all participants. 2.2. Magnetic resonance imaging (MRI) data acquisition and preprocessing Participants were scanned using a 3-T MR scanner (Trio; Siemens, Erlangen, Germany) with an eight-channel phased-array head coil. Scanner noise was attenuated with earplugs, and head motion was restricted by placing foam padding around the head. High-resolution anatomical MR images were obtained with a magnetization prepared rapid gradient echo (MPRAG) sequence with the following parameters: repetition time/echo time, 1900/2.5 msec; inversion time, 900 msec; flip angle, 9°; matrix, 256 × 256; field of view, 256 × 256 mm; number of sagittal sections, 176; and section thickness, 1 mm. The FreeSurfer image analysis suite (http://ftp.nmr.mgh.harvard. edu/), the reliability of which has been validated against a histological analysis of postmortem brains and manual measurements (Rosas et al., 2002; Salat et al., 2004), was used to generate a cortical surface model and subsequently provide measures of cortical volume, cortical thickness and surface area at each vertex. Briefly, the imaging data were processed by performing a visual inspection of the data for motion artifacts, transformation to the Talairach space, intensity normalization, skull stripping and segmentation of the subcortical white matter and gray matter volumetric structures (Fischl et al., 2002, 2004), tessellation of the gray matter and white matter boundaries, automated topology correction (Fischl et al., 2001; Segonne et al., 2007) and surface deformation following intensity gradients to optimally place the gray/ white and gray/ cerebrospinal fluid borders at the locations with the greatest shifts in signal intensity (Dale et al., 1999; Fischl and Dale, 2000). Cortical volumes were measured based on the cortical parcellations, which were based on the cortical folding patterns (Fischl et al., 2004). Cortical thickness was quantified at each surface location or vertex as the distance (in mm) from the gray/white boundary to the pial surface (Fischl and Dale, 2000). The surface area was obtained by assigning an area to each vertex equal to the average of its surrounding triangles. When the vertex areas were summed over all vertices, the total was equal to the sum of the areas of the triangles. Vertex-level cortical volume, cortical thickness, and surface area values were obtained and mapped onto the normalized cortical surface, which was smoothed with a 10-mm full width at half maximum kernel. We extracted the volumes of several subcortical structures, including the bilateral thalamus, caudate, putamen, pallidum, hippocampus, amygdala and nucleus accumbens, using the FreeSurfer
2. Methods 2.1. Participants Patients with ADHD were recruited at the Mental Health Center, West China Hospital of Sichuan University from June 2009 to December 2011. Seventy-nine boys participated in this study, 26 of whom were excluded for the following reasons: inability to remain in the scanner (7), excessive motion artifacts (13), and inability to complete the executive function tests (6). Eighty-one healthy male controls were recruited via advertisements placed in local schools, 28 of whom were excluded for the following reasons: older age (7), inability to remain in the scanner (2), excessive motion artifacts (10), and inability to complete the executive function tests (9). Finally, 106 boys ranging in age from 7 to 16 participated in this study, including 53 patients with ADHD and 53 age-, gender-, handedness- and IQ-matched HCs 398
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Table 1 Demographics and clinical characteristics of patients with ADHD and HCs.
Age, years Gender (male/female) Handedness (R/L) IQ scores Conners’ Parent Rating Scale Conduct problem Study problem Psychosomatic Hyperactivity–impulsivity Anxiety Hyperactivity index Intracranial volume
Patients with ADHD
HCs
Statistics
df
P Value
10.43 (2.14) 53/0 53/0 104.0 (5.1)
10.85 (2.32) 53/0 53/0 107.6 (6.6)
-0.956 – – −2.03
104 – – 104
0.413 – – 0.06
11.70 (7.55) 7.47 (2.86) 1.13 (1.36) 5.40 (3.12) 1.66 (1.71) 12.91 (6.16) 1,493,509.39
5.34 (4.47) 3.23 (2.52) 0.51 (1.14) 2.23 (1.97) 1.64 (1.76) 5.43 (4.57) 1,479,104.47
5.279 8.114 2.558 6.254 0.056 7.094 0.532
104 104 104 104 104 104 104
< 0.001 < 0.001 0.012 < 0.001 0.955 < 0.001 0.596
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; R, right; L, left; df, degrees of freedom. Notes: Data are presented as means and standard deviation.
automatic segmentation pipeline (Fischl et al., 2002).
3.2. Age-related differences in cortical and subcortical morphometric abnormalities
2.3. Statistical analysis
In the FEF, a significant negative correlation was observed between age and cortical volume in the ADHD group (r = −0.281, p = 0.043), whereas in HCs, a significant positive correlation was observed (r = 0.293, p = 0.035). A direct statistical comparison of correlation coefficients between two groups established a significantly weaker correlation between age and the cortical volume of the FEF in children with ADHD than in HCs (Z = 2.953, p < 0.05; Fig. 1B). According to the results of the subgroup analysis, patients with ADHD displayed a significantly larger cortical volume for the FEF during childhood (T = 2.32, p = 0.02; Fig. 1C), while an insignificantly smaller volume was observed in adolescence (T = −1.14, p = 0.27; Fig. 1C). In the VFC, a significant positive correlation between age and surface area was observed in patients with ADHD (r = 0.308, p = 0.027), but a trend toward a negative correlation was identified in HCs (r = −0.042, p = 0.765). A direct statistical comparison of correlation coefficients between the two groups established a significantly different correlation between age and surface area of the VFC in children with ADHD than in HCs (Z = 1.802, p < 0.05; Fig. 2B). Based on the results of the sub-group analysis, the surface area of the VFC was not different between two groups during childhood (T = −0.53, p = 0.60; Fig. 2C) or adolescence (T = 1.48, p = 0.16; Fig. 2C). A significant correlation between the subcortical volume in the right putamen and age was not observed in either group (ADHD r = −0.092, p = 0.518; HC r = −0.141, p = 0.320). Significant differences in their correlation coefficients were not observed between the patients with ADHD and HCs (Z = 0. 248, p > 0.05).
Group differences in cortical volume, cortical thickness and surface area were examined on the surface maps in a vertex by vertex manner using QDEC software (https://surfer.nmr.mgh.harvard.edu/fswiki/ Qdec) by employing a general linear model with the intracranial volume (ICV) and age as covariates. The Monte Carlo simulation was performed to control for multiple comparisons (10,000 iterations, cluster-forming p < 0.05, cluster-wise corrected p < 0.05). Analyses of differences in subcortical volumes were assessed using a multivariate analysis of covariance (MANCOVA) with ICV and age as covariates. To examine potential differences in the effects of age on morphometric abnormalities between groups, we first extracted the average data from regions with significant group differences for each subject. Since the effects of age on the human brain during this period follow a linear trajectory (O'Donnell et al., 2005; Shaw et al., 2008), we performed a partial correlation analysis to determine the effect of age on abnormal cortical and subcortical regions after controlling for ICV for each group separately. Fisher's z’ transformation was used to perform a direct statistical comparison between correlation coefficients obtained from the two groups to determine whether age-related differences existed between patients and HCs. Furthermore, we performed a subgroup analysis by dividing the age distribution into children and adolescents (7–12 and 13–16 years, respectively). Pearson's partial correlation analyses were also conducted to identify relationships between behavioral problems and anatomical measures that displayed abnormalities in patients with ADHD, after controlling for ICV and age.
3.3. Clinical correlation analysis No significant correlations were observed between behavioral problems measured using the CPRS and any cerebral morphometric abnormalities in the ADHD group.
3. Results 3.1. Group differences in cortical and subcortical morphometry
4. Discussion
Compared with HCs, children with ADHD exhibited an increased cortical volume in the left frontal eye field (FEF), a decreased cortical surface area in the left ventral frontal cortex (VFC) (p < 0.05, Monte Carlo-corrected; Table 2 and Figs. 1A and 2A). A significant difference in cortical thickness was not detected between the ADHD group and the HC group. Compared with HCs, children with ADHD also displayed a reduced volume of subcortical structures in the right putamen (F = 4.491, p = 0.037; Table 3). Significant differences in other nuclei were not observed between the two groups.
In the current study, we used multiple morphometric measurements to characterize anatomical differences in both cortical and subcortical morphometry between drug-naive boys with ADHD and HCs and to explore the age-related differences in the regions displaying morphometric abnormalities. Consistent with our hypothesis, boys with ADHD showed altered cortical and subcortical morphologies in several important nodes of the attention network. Specifically, an increased cortical volume was observed in the left FEF, a decreased surface area was detected in the left VFC, and a reduced subcortical volume was observed in the right putamen. In addition, we also identified significant 399
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Table 2 Brain regions displaying differences in the surface area and cortical volume in patients with ADHD compared to HCs.
Surface area ADHD < HCs Cortical volume ADHD > HCs
Size (mm2)
df
-log10(p)
3.1
−1285.87
102
−3.336
28.6
815.62
102
2.877
Brain region
Talairach coordinates X Y
Z
Left ventral frontal cortex
−29.4
29.6
Left frontal eye field
−42.2
22.4
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls; df, degrees of freedom. Negative values for -log10(p) represent a decreased surface area in patients with ADHD, and positive values for -log10(p) represent an increased cortical volume in patients with ADHD.
age-related differences in these regions between the two groups, implying an abnormality in the neural developmental trajectory of these regions in patients with ADHD. The FEF is a frontal node of the dorsal attention network (DAN) that is involved in voluntary (top-down) orienting attention and shows increased activity after the presentation of cues indicating where, when, or to what subjects should direct their attention. Neurocognitive studies of the function of the attention network in children with ADHD did not report significant differences in orienting attention between the ADHD and typically developing groups, although this behavior tended to be impaired (Adolfsdottir et al., 2008; Booth et al., 2007; Konrad et al., 2006; Mullane et al., 2011). In the present study, we observed an increased cortical volume of the FEF in drug-naive boys with ADHD. Based on this finding, an increase in the volume of this brain region may compensate for the top-down orienting attention deficit and maintain it at the normal level in children with ADHD. The VFC is a frontal node of the ventral attention network (VAN) whose main function is to reorient attention toward salient stimuli and to maintain an optically alert attentional state; the activity of the VFC increases upon the detection of salient targets, particularly when these targets appear in unexpected locations (Fox et al., 2006; Posner and Petersen, 1990). Studies from cognitive neuroscience have revealed impairments in alerting attention in children with ADHD, indicating a lower general level of alertness in patients (Corkum and Siegel, 1993; Losier et al., 1996; Mullane et al., 2011). Our study revealed decreased surface area in the VFC in children with ADHD, which was consistent with previous fMRI studies showing decreased amplitude low-frequency fluctuation in VFC and decreased connectivity between the VFC and basal ganglia in patients with ADHD during attention conditions (Cortese et al., 2012; Rubia et al., 2009). Taking these findings together, we speculated that structural and functional alterations in the VFC may partially account for the low alerting attention in children with ADHD. Many previous morphometric studies and meta-analyses have revealed that the putamen is one of the primary structures involved in
ADHD (Ellison-Wright et al., 2008; Greven et al., 2015; Nakao et al., 2011; Qiu et al., 2009; Shaw et al., 2014; Valera et al., 2007). Consistent with these findings, we also observed a reduced volume of the right putamen in children with ADHD in the current study. Previous studies examining the attention network in children with ADHD suggested that executive attention is the most significant deficit among alerting, orienting and executive attention (Konrad et al., 2006; Mullane et al., 2011). As shown in the study by Konrad et al. (2006) children with ADHD show decreased neural activity in the right putamen during the conflict condition, indicating that the putamen is an important node of the executive attention system. Based on the aforementioned analysis, we postulate that the decreased volume of the putamen might contribute to deficits in planning, control, and execution of behaviors that are characteristic clinical features of ADHD. In addition to the regional brain abnormalities discussed above, we also identified significantly different age-related morphometric changes in the FEF and VFC between HCs and children with ADHD. By further dividing the age distribution into two stages, children and adolescents, we revealed that morphometric abnormalities occurring in children would change to the opposite direction in adolescents. Similar to many previous studies, our analyses stratified by age indicated that ADHD is a disorder characterized by an altered developmental pattern, and that pattern will enable children with ADHD to progressively catch up with normally developing children. Moreover, our finding that the decreased surface area of the VFC increased with age in children with ADHD is similar to the developmental delay revealed by previous study using cortex thickness as a measurement (Shaw et al., 2007). Previous functional MRI studies have indicated the activation of VFC increased progressively with age during the interference inhibition task (Eden and Moats, 2002), and greater involvement of the VFC was associated with better executive performance. Our result further revealed that the surface area of the VFC increased progressively with age in children with ADHD, which may also contribute to the neural mechanism underlying the maturation of executive attention performance from
Fig. 1. A. Increased cortical volume of the left FEF in patients with ADHD compared with HCs (p < 0.05, Monte Carlo-corrected). B. Scatter plot of age-related differences in cortical volume of the left FEF in patients with ADHD and HCs. C. Bar chart depicting the results of the subgroup analysis of cortical volume of the left FEF (❉, p < 0.05). Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls. 400
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Fig. 2. A. Decreased surface area of left VFC in patients with ADHD compared with HCs (p < 0.05, Monte Carlo-corrected). B. Scatter plot of age-related differences in the surface area of the left VFC in patients with ADHD and HCs. C. Bar chart showing the results of the subgroup analysis of the surface area of the left VFC. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls.
children to adolescents. Similar to the study by Wolosin et al. (2009), we did not observe any significant differences in cortical thickness between children with ADHD and HCs, which differed from most previous studies (Shaw et al., 2006; Yang et al., 2015). We postulated that this discrepancy may be due to the differences in study populations and sample size. For example, many previous studies had included individuals with ADHD presenting with comorbid disorders (Shaw et al., 2006), such as conduct disorder, learning disorder and anxiety disorder, among others. However, all children with ADHD enrolled in our current study were screened to exclude such comorbidities. Moreover, as the cortex morphometry develops with aging, different age ranges may also explain the differences between our results and previous studies. Finally, cortical volume is a 3-dimensional measure, surface area is a 2-dimensional measure, and cortical thickness is a 1-dimensional measure. In contrast to cortical volume and surface area, cortical thickness is measured at tens of thousands of vertices along the surface. A difference in cortical thickness may therefore be inherently difficult to detect and may require a larger sample size. Several limitations in our study must be recognized. First, the crosssectional design of our study limits inferences about the age-related trajectory of morphometric abnormalities. Hypotheses about
development must be confirmed by longitudinal studies in the future. Second, the strict inclusion criteria resulted in our subjects being medication-naive boys with ADHD who were right-handed and did not present with comorbidities. Although this highly homogenous population will improve our understanding of the brain pathology that is directly relevant to “pure” ADHD and not confounded by other factors, it limits the generalizability of our findings to a broader range of patients with ADHD, such as girls with ADHD and patients who are left-handed or have comorbidities. Future studies may benefit from exploring the effects of gender, handedness and comorbidities on the cortical morphometry to deepen our knowledge about the involvement of those factors in ADHD. Third, the current study was a structural MRI study lacking data on attentional functions; future studies should combine attention tasks, such as the attention network test, with structural and functional MRI data to verify our explanation of the relationship between MRI alterations and attention functions in patients with ADHD. 5. Conclusions In the current study, we investigated the characteristic abnormalities of cortical and subcortical structures in drug-naive boys with ADHD and explored the age-related differences in the regions
Table 3 Volumes of subcortical gray matter structures in patents with ADHD and HCs. Subcortical area Thalamus Left Right Caudate Left Right Putamen Left Right Pallidum Left Right Hippocampus Left Right Amygdala Left Right Accumbens area Left Right
Volume in patients with ADHD (mm3)
Volume in HCs (mm3)
F Value
df
P Value
8398.0 (8242.4–8553.6) 7608.0 (7469.2–7746.8)
8348.7 (8193.1–8504.3) 7526.2 (7387.4–7665.0)
0.196 0.677
102 102
0.659 0.413
3838.1 (3699.6–3976.6) 3802.5 (3675.2–3929.8)
3814.6 (3676.1–3953.1) 3872.7 (3745.4–4000.0)
0.056 0.592
102 102
0.813 0.443
6274.4 (6055.1–6439.6) 6281.9 (6126.2–6437.7)
6451.1 (6258.9–6643.4) 6518.2 (6362.5–6674.0)
2.191 4.491
102 102
0.142 0.037
1645.3 (1580.8–1709.9) 1667.6 (1626.0–1709.3)
1620.0 (1555.5–1684.5) 1705.3 (1663.7–1747.0)
0.301 1.598
102 102
0.585 0.209
4435.6 (4323.5–4547.8) 4569.1 (4460.3–4677.9)
4502.9 (4390.7–4615.1) 4675.4 (4566.6–4784.1)
0.702 1.862
102 102
0.404 0.175
1664.3 (1617.0–1711.5) 1782.3 (1727.6–1836.9)
1716.2 (1668.9–1763.4) 1847.5 (1792.8–1902.2)
2.353 2.776
102 102
0.128 0.099
671.7 (640.0–703.5) 680.9 (655.9–705.8)
684.8 (653.0–716.5) 708.2 (683.3–733.1)
0.328 2.352
102 102
0.568 0.128
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; HCs, healthy controls; df, degrees of freedom. Notes: Data are presented as means and 95% confidence intervals; Bold, p < 0.05. 401
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displaying morphometric abnormalities. Based on our results, ADHD was implicated in anatomical deficits in several important nodes of the dorsal and ventral attention networks associated with alerting, orienting and executive attention. Furthermore, the normal age-related morphometric development was disrupted in boys with ADHD. To our knowledge, this study is the first to characterize the cortical morphometry, including volume, thickness and surface area, in drug-naive boys with ADHD at the whole-brain level, thus providing detailed information about neuroanatomical alterations in attention systems beyond effects reported in previous studies at the lobe and sublobe levels.
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Declaration of interest The authors report no financial or other relationships relevant to the subject of this article. Contributors All authors reviewed the manuscript and approved the final article. Lu L and Lianqing Z designed the study, performed the experiments and wrote the article. Xuan B, Ying C and Lanting G collected the clinical data. Xinyu H, Xiaoxiao H and Hailong L collected the imaging data and contributed to the analysis. John A. S made an important contribution to statistical analysis and interpretation of results. Xiaoqi H and Qiyong G critically revised the manuscript for important intellectual content. Xiaoqi H obtained funding. Role of funding source This study was supported by grant 81671669 from the National Natural Science Foundation. Acknowledgements We would like to thank the patients and their families for their participation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.03.048. References Adolfsdottir, S., Sorensen, L., Lundervold, A.J., 2008. The attention network test: a characteristic pattern of deficits in children with ADHD. Behav. Brain Funct. 4, 9. Annett, M., 1970. A classification of hand preference by association analysis. Br. J. Psychol. 61, 303. Armstrong, E., Schleicher, A., Omran, H., Curtis, M., Zilles, K., 1995. The ontogeny of human gyrification. Cereb. Cortex 5, 56–63. Bizzi, E., Schiller, P.H., 1970. Single unit activity in the frontal eye fields of unanesthetized monkeys during eye and head movement. Exp. Brain Res. 10, 150–158. Bledsoe, J., Semrud-Clikeman, M., Pliszka, S.R., 2009. A magnetic resonance imaging study of the cerebellar vermis in chronically treated and treatment-naive children with attention-deficit/hyperactivity disorder combined type. Biol. Psychiatry 65, 620–624. Booth, J.E., Carlson, C.L., Tucker, D.M., 2007. Performance on a neurocognitive measure of alerting differentiates ADHD combined and inattentive subtypes: a preliminary report. Arch. Clin. Neuropsychol. 22, 423–432. Cao, M., Huang, H., Peng, Y., Dong, Q., He, Y., 2016. Toward developmental connectomics of the human brain. Front. Neuroanat. 10, 25. Conners, C.K., Sitarenios, G., Parker, J.D.A., Epstein, J.N., 1998. The revised Conners' Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. J. Abnorm. Child Psychol. 26, 257–268. Corkum, P.V., Siegel, L.S., 1993. Is the continuous performance task a valuable research tool for use with children with attention-deficit-hyperactivity disorder. J. Child Psychol. Psychiatry 34, 1217–1239. Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M.P., Castellanos, F.X., 2012. Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am. J. Psychiatry 169, 1038–1055. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194.
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