Gray Matter Increase in Motor Cortex in Pediatric ADHD: A Voxel-Based Morphometry Study

Abstract

Objective: Several studies report that ADHD is associated with reduced gray matter (GM), whereas others report no differences in GM volume between ADHD patients and controls, and some even report more GM volume in individuals with ADHD. These conflicting findings suggest that reduced GM is not a universal finding in ADHD, and that more research is needed to delineate with greater accuracy the range of GM alterations. Method: The present study aimed to identify GM alterations in ADHD using pediatric templates. 19 drug-naïve ADHD patients and 18 controls, all aged 7 to 14 years, were scanned using magnetic resonance imaging. Results: Relative to the controls, the ADHD patients had more GM, predominantly in the precentral and supplementary motor areas. Moreover, there were positive correlations between GM volume in these areas and ADHD scale scores. Conclusion: The clinical and pathophysiological significance of increased GM in the motor areas remains to be elucidated by additional research.

Keywords

ADHD voxel-based morphometry supplementary motor area precentral gyrus Brodmann area 6

Introduction

ADHD is characterized by inattention, and/or hyperactivity and impulsivity (Diagnostic and Statistical Manual of Mental Disorders [5th ed.; DSM-5]; American Psychiatric Association [APA], 2013). It is among the most prevalent neurodevelopmental disorders in children and young adolescents (Willcutt, 2012). Following the emergence of theories of the neurological bases of attention (Mesulam, 1990; Posner & Petersen, 1989), ADHD has been increasingly recognized as a neurobiological disorder, and a number of models have attempted to explain ADHD symptoms as indications of neuropsychological deficits due to impairment in the brain’s cognitive networks.

Among these models, the executive dysfunction theory (EDT) proposes that impaired executive functions, such as planning, sequencing, reasoning, holding attention to a task, and inhibition of inappropriate and selection of appropriate behaviors, could explain the symptoms of ADHD (Barkley, 1997). In addition, earlier cross-sectional studies have shown that children with ADHD have executive dysfunction of moderate effect size (see Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005, for a review). In terms of the brain regions associated with EDT, there are expected to be distinct structural abnormalities in the executive networks in cases of ADHD, particularly in the prefrontal cortex and frontostriatal networks.

According to the literature, the most widely used method for structural neuroimaging is voxel-based morphometry (VBM), which facilitates investigation of gray matter (GM) and white matter (WM) differences in the brain, based on statistical parametric mapping. For children, meta-analyses reported reduced right globus pallidus, putamen, cerebellum, anterior cingulate cortex (ACC), and caudate volumes with ACC volume reduction being more pronounced in untreated children (Frodl & Skokauskas, 2012; Valera, Faraone, Murray, & Seidman, 2007). However, a meta-analytic study on adult ADHD showed bilateral GM reductions in ACC (Frodl & Skokauskas, 2012). In addition to that, other studies reported GM reductions in dorsolateral prefrontal cortex (Biederman et al., 2008), inferior parietal lobule (Seidman et al., 2011), inferior frontal gyrus (Depue, Burgess, Bidwell, Willcutt, & Banich, 2010), caudate (Montes et al., 2010; Proal et al., 2011), cerebellum, anterior cingulate cortex (Amico, Stauber, Koutsouleris, & Frodl, 2011; Biederman et al., 2008), left orbito-frontal cortex, and amygdala (Frodl et al., 2010) in adults with ADHD.

Although these findings support EDT, some inconsistent findings such as increased GM volume in children with ADHD were also reported (Baumeister & Hawkins, 2001). For instance, Seidman et al. (2011) reported increased GM volume in the dorsolateral prefrontal cortex, and Sowell et al. (2003) noted prominent increases in GM in the posterior temporal and bilateral inferior parietal regions in children with ADHD. These GM increases were attributed to a number of factors, including the effects of medications, developmental differences, or abnormal network activity in young ADHD patients, and comorbid mood disorders (Seidman et al., 2011).

It is clear that the available body of structural neuroimaging data cannot yield a unified model for understanding ADHD, and that more studies that aim to determine the factors associated with these inconsistencies across studies are required. In addition, it should be noted that ADHD is a very heterogeneous disorder, and that various subgroups with distinct neuropsychological and neurobiological profiles may exist.

Another issue that has been attributed to the inconsistencies across studies is differences in methodology (i.e., automated VBM, optimized VBM, surface density techniques, or cortical thickness measurement; Valera et al., 2007). Importantly, the majority of the structural neuroimaging studies on ADHD were conducted with children, but greater part of these studies did not use pediatric templates or tissue probability maps (TPMs; Villemonteix, De Brito, Kavec, et al., 2015; Villemonteix, De Brito, Slama, et al., 2015; Yang et al., 2008), which can result in tissue misclassification, problems with registration and spatial normalization, and erroneous VBM results (Wilke, Schmithorst, & Holland, 2003); therefore, studies employing more precise procedures for determining structural abnormalities can help resolve the above-mentioned inconsistencies and clearly delineate which brain regions are affected in ADHD and how.

The present study aimed to identify structural GM alterations in ADHD using specific TPMs and pediatric templates. Previous VBM studies on ADHD have primarily focused on GM reductions in ADHD patients; however, ADHD is a neurodevelopmental disorder—not a neurodegenerative disease—and we hypothesized that in addition to reduced GM an increase in GM might be associated with ADHD, as earlier studies have reported that only a subset of individuals with ADHD have impaired executive function (Willcutt et al., 2005), and that including individuals with different neuropsychological profiles might show that there are GM alterations in brain regions beyond the prefrontal cortex.

Materials and Method

Participants

The ADHD group included 19 children (14 males and 5 females) aged 7 to 14 years (M_age = 10.32 ± 1.95 years) that met Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria for ADHD. The diagnosis of ADHD was confirmed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL; Kaufman et al., 1997). All ADHD patients were drug naïve and were recruited from a tertiary referral center. The control group included 18 children (12 males and 6 females) aged 6 to 14 years (M_age = 10.17 ± 2.04 years) with a negative history of neurological and psychiatric disease.

All participants were evaluated using the Wechsler Intelligence Scale for Children–Revised (WISC-R; Wechsler, 1974), and their parents completed the Conners’ Parent Rating Scale (Dereboy, Şenol, Şener, & Dereboy, 2006) and the DSM-IV-Based Behavior Disorders Screening and Rating Scale (BDSRS) in Turkish (Ercan et al., 2001). Children in the control group who had more than five symptoms based on one subscale of the BDSRS were excluded from the study.

The study protocol was approved by the local Ethics Committee and was performed in accordance with the Declaration of Helsinki. All the participants and families were informed about the study procedures, and the parents provided written informed consent.

Data Acquisition and Preparation

Whole brain images were acquired using a 1.5 Tesla Philips Achieva magnetic resonance imaging (MRI) scanner (Philips Medical Systems, Best, the Netherlands) and a SENSE 8-channel head coil at NPIstanbul-Uskudar University, Istanbul, Turkey. High-resolution T1-weighted images were acquired using the 3D magnetization-prepared rapid acquisition gradient echo (MPRAGE) method (Mugler & Brookeman, 1990). The imaging parameters used were as follows: acquisition matrix: 256 × 256 × 140; TR: 2.8 s; TE: 4.0 s; flip angle: 8°; field of view: 240 mm: coronal slices: 135; voxel size: 0.937 × 0.937 × 1.2 mm resolution.

Data Analysis of Imaging Data

Preprocessing

Statistical Parametric Mapping software (SPM8; Wellcome Trust Centre for Neuroimaging, London, http://www.fil.ion.ucl.ac.uk/spm/software/spm8/) and VBM8 Toolbox were used for VBM analysis (VBM8; http://dbm.neuro.uni-jena.de/vbm/). Pediatric template and TPMs specific for this study sample’s age and gender composition were created using Template-O-Matic (TOM8; http://dbm.neuro.uni-jena.de/software/tom/; Wilke, Holland, Altaye, & Gaser, 2008). Sample-specific TPMs and templates were used to prevent tissue misclassifications that can arise due to analyzing a pediatric sample based on adult parameters. This issue is critically important, as Yoon et al. (2009) reported that age-specific template use significantly increases the accuracy of cortical thickness, cortical surface area, cortical folding, GM volume, and cerebrospinal fluid volume measurements.

Before preprocessing, each image was checked for artifacts and misalignments. Two participants with motion artifacts were excluded from the sample, and misaligned images were fitted to the template data created using TOM8 by rotating 20° and translating 5 cm. During preprocessing, an optimized VBM procedure was used to improve image registration and segmentation. Native structural T1 volumes were segmented into GM, WM, and cerebrospinal fluid using the TPMs created with TOM8. Using diffeomorphic anatomical registration via the exponentiated Lie algebra (DARTEL) algorithm, a study-specific DARTEL template was created from the segmented images. Segmented images were then normalized to the study-specific DARTEL template that was created in the previous step. Spatially normalized images were modulated using the “preserve amount” option. The modulated GM images were then smoothed with a Gaussian kernel of 8 mm full-width at half maximum to increase the signal-to-noise ratio.

Statistical analysis

The spatially normalized modulated smoothed images were checked for sample homogeneity, and no outlier images were detected. Differences in classified tissue volume size between the ADHD and control groups were analyzed using a two-sample t test, for which age and gender of each participant were included in the statistical model as a nuisance variable; absolute threshold masking with a threshold of 0.2 was applied. The ADHD and control groups were compared as ADHD group > control group and control group > ADHD group.

Statistical significance thresholds were applied at the voxel level (p < .001, uncorrected), and the cluster threshold was set at 20 voxels. Using region of interest (ROI) analysis via the toolbox MarsBar (Brett, Anton, Valabregue, & Poline, 2002), mean GM volumes for the regions showing ADHD group–control group differences were extracted. To obtain an unbiased estimate of GM volume, ROI masks were selected from the automated anatomical labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002).

The independent samples t test was used to evaluate group differences in total WISC-R IQ and ADHD symptom scores. Pearson’s bivariate correlation analysis was used to define the relationship between IQ/symptom scores and GM volume. All statistical analyses were performed using IBM SPSS Statistics v.22.0 for Windows (IBM Corp., Armonk, NY).

Results

Independent samples t-test results showed that there was not a significant difference in total WISC-R IQ or subtest scores between the ADHD and control groups (Table 1). As expected, the ADHD group had significantly higher scores than the control group on Conners’ and BDSRS scales (p < .01; Table 1).

Table 1.

T Test Results for WISC-R, ADHD Scales, and Their Subtest and Subscale Scores in the ADHD and Control Groups.

	M	SD	t	df	p
WISC-R Verbal
ADHD	107.84	18.02	−2.020	35	.051
Controls	118.22	12.59	−2.020	35	.051
WISC-R Performance
ADHD	117.53	21.88	−0.005	35	.996
Controls	117.56	17.05	−0.005	35	.996
WISC-R Total
ADHD	113.53	20.76	−1.019	35	.315
Controls	119.67	15.32	−1.019	35	.315
Conners’ Conduct
ADHD	9.58	6.90	4.146	35	.000
Controls	2.39	2.62	4.146	35	.000
Conners’ Hyperactivity
ADHD	6.32	2.56	3.138	35	.003
Controls	3.61	2.68	3.138	35	.003
Conners’ Learning
ADHD	7.00	2.60	7.246	35	.000
Controls	1.83	1.58	7.246	35	.000
BDSRS Inattention
ADHD	17.74	6.12	9.770	35	.000
Controls	2.67	2.38	9.770	35	.000
BDSRS Hyperactivity
ADHD	13.05	6.39	5.793	35	.000
Controls	3.61	2.70	5.793	35	.000
BDSRS ODD
ADHD	7.95	6.01	2.858	35	.007
Controls	3.50	2.79	2.858	35	.007

Note. WISC-R = Wechsler Intelligence Scale for Children–Revised; Conners’ Conduct = Conduct Problems subscale; Conners’ Hyperactivity = Hyperactivity subscale; Conners’ Learning = Learning Problems subscale; BDSRS Inattention = Inattention subscale; BDSRS hyperact = Hyperactivity subscale; BDSRS ODD = Oppositional Defiant Disorder subscale.

At the uncorrected p-level threshold of .001, GM volume was higher in the ADHD group in seven clusters (see Table 2). However, the control group > ADHD group comparison did not show a significant cluster. When cluster-level correction based on the expected number of voxels per cluster according to smoothness of the data was calculated using SPM8 (k = 140, p = .029 uncorrected), only one cluster remained significant (right precentral gyrus: t = 4.92, x = 30, y = −7, and z = 69; superior frontal gyrus: t = 4.31, x = 46, y = −1, and z = 57; middle frontal gyrus: t = 3.91, x = 45, y = 0, and z = 46; Figure 1).

Table 2.

GM Volume in the ADHD and Control Groups.

Clusters	Voxels	BA	Brain region	t	Z	MNI coordinates
Clusters	Voxels	BA	Brain region	t	Z	x	y	z
Cluster 1	700	6	Right superior frontal gyrus	4.92	4.26	30	−7	69
		6	Right middle frontal gyrus	4.31	3.83	46	−1	57
		6	Right precentral gyrus	3.91	3.54	45	0	46
Cluster 2	92	4	Left supplementary motor area	4.27	3.8	−6	−19	66
Cluster 3	82	4	Left postcentral gyrus	3.93	3.55	−26	−28	70
Cluster 3		5	Left precentral gyrus	3.4	3.14	−26	−37	72
Cluster 4	26	19	Right middle occipital gyrus	3.86	3.5	33	−76	4
Cluster 5	57	6	Left superior frontal gyrus	3.67	3.36	−17	12	58
Cluster 6	20	6	Right supplementary motor area	3.56	3.27	6	−10	64
Cluster 7	35	19	Left cuneus	3.54	3.25	−14	−84	37

Note. Cluster threshold set at 20 voxels using p < .001. GM = gray matter; MNI: Montreal Neurological Institute; BA: Brodmann Area.

There was no significant correlation between GM regional volumes and behavioral measures in the control group or ADHD group. When both groups were combined, there was a significantly positive correlation between Conners’ Hyperactivity subscale score, and GM volume in the right supplementary motor area, left paracentral lobule, and left precentral gyrus (Table 3). The BDSRS Inattention subscale score was positively correlated with GM volume in the right precentral gyrus and supplementary motor area, and the left paracentral lobule and superior frontal gyrus. There was a significantly positive correlation between the BDSRS Hyperactivity subscale score and GM volume in the right precentral gyrus, supplementary motor area, and the left precentral gyrus.

Table 3.

Correlations Between WISC-R, ADHD Scales, and Their Subscale Scores, and GM Volume in the Total Study Population.

		Right frontal supplementary	Right frontal middle	Right precentral	Right occipital middle	Right supplementary motor area	Left paracentral lobule	Left supplementary motor area	Left postcentral	Left precentral	Left frontal supplementary	Left occipital superior	Left cuneus
WISC-R Verbal	PC	−.157	−.213	−.166	−.178	−.206	−.045	−.192	.018	−.098	−.316	−.055	.009
WISC-R Perf.	PC	−.209	−.239	−.006	−.191	−.042	.213	−.052	.063	−.098	−.260	−.032	.011
WISC-R Total	PC	−.201	−.245	−.094	−.200	−.129	.095	−.129	.045	−.109	−.311	−.049	.007
Conners’ Conduct	PC	.068	.050	.233	.150	.100	.262	−.017	.158	.123	.107	.083	.154
Conners’ Hyperactivity	PC	.206	.212	.296	.170	.349*	.327*	.302	.315	.334*	.235	.079	.136
Conners’ Learning	PC	.162	.155	.249	.295	.229	.216	.185	.204	.138	.219	.171	.205
BDSRS Inattention	PC	.278	.271	.396*	.313	.347*	.359*	.282	.176	.253	.324	.285	.298
BDSRS Hyper.	PC	.296	.261	.385*	.234	.329*	.290	.308	.199	.358*	.303	.115	.160
BDSRS ODD	PC	.267	.193	.277	.127	.182	.206	.115	.099	.265	.252	.012	.164

Note. WISC-R = Wechsler Intelligence Scale for Children–Revised; GM = gray matter; PC = Pearson’s correlation; Conners’ Conduct = Conduct Problems subscale; Conners’ Hyperactivity = Hyperactivity subscale; Conners’ Learning = Learning Problems subscale; BDSRS Inattention = Inattention subscale; BDSRS Hyper. = Hyperactivity subscale; BDSRS ODD = Oppositional Defiant Disorder subscale.

p ≤ .05.

Figure 1.

Greater GM volumes in ADHD patients compared with controls.

Discussion

The present findings show that GM volume in ADHD patients was increased in seven clusters, including the right precentral, supplementary, middle and superior frontal, and occipital gyrus, and the left supplementary motor area, cuneus, precentral, postcentral, and superior frontal gyrus. Moreover, there were significant positive correlations between GM volume in these areas and BDSRS scores.

Interestingly, increases in GM were predominantly observed in the Brodmann area 6 of the cortex, which is associated with the motor circuits (e.g., precentral gyrus and supplementary motor area). These areas are involved in planning complex and coordinated movements (Freund, 1989). An earlier morphometric study also reported increased central sulcus cortical thickness in children with ADHD (Li, Wang, Li, Li, & Li, 2015). Although there have been other VBM studies that reported GM volume (Carmona et al., 2005) and cortical thickness reductions (Hoekzema et al., 2012; Narr et al., 2009; Shaw et al., 2006) in the brain’s motor regions in ADHD, the present findings and those of Li et al. (2015) indicate that decreased GM volume in motor regions is not a consistent finding in ADHD patients. Li et al. (2015) posited that increased GM volume in the motor region mediates hyperactivity and talkativeness in children with ADHD. The significant correlations between GM volume in these areas and symptom scores in the present study lend support to this notion. It is also possible that increased GM volume in ADHD is due to reduced synaptic pruning during brain maturation (Sowell et al., 2003).

It is possible that the differences between the present findings and those of earlier studies are because the present study’s ADHD patients had a relatively high mean IQ (113.53), and IQ did not differ significantly between the ADHD and control groups. Pediatric ADHD patients typically have lower IQ than controls, as reported in a meta-analysis of 137 studies (Frazier, Demaree, & Youngstrom, 2004), and differences in IQ between ADHD and controls have been reported in previous VBM studies (Cao et al., 2010; Pironti et al., 2014; Proal et al., 2011). The similarity in IQ in the present study’s ADHD and controls groups might be why reduced GM was not observed in the ADHD group.

Another factor that may explain the heterogeneity among VBM studies in ADHD is the mean age of the study sample. For instance, Shaw et al. (2007) reported that the patients with ADHD had a delay in cortical maturation in childhood, and the children with and without ADHD reach the peak frontal GM thickness at different ages. In line with this maturation delay hypothesis, a meta-analysis of VBM studies in ADHD showed that the differences between ADHD patients and healthy controls tended to diminish over time from childhood to adulthood; however, the untreated adults with ADHD have less GM in the ACC (Frodl & Skokauskas, 2012). However, Castellanos et al. (2002) conducted a study with 152 children/adolescents with ADHD and 139 healthy controls. They showed that volumetric alterations in total and regional cerebral measures and in the cerebellum persisted with increasing age. Interestingly, another study reported only cerebellar volume reductions in drug-naïve adults with ADHD (Makris et al., 2015). All in all, the VBM results indicate that as ADHD is a developmental disorder, one should expect a spectrum of GM alterations as a function of age rather than static and consistent findings. More specifically, frontostriatal alterations that characterize pediatric ADHD tend to normalize over time, while the GM reductions in ACC and cerebellum persist into adulthood.

In addition to inclusion of clinical and control groups with comparable IQ, the present study has a number of strengths. It should also be noted that the ADHD group was comprised of drug-naïve patients, which rules out regional brain differences due to medication. Thusly, an important contribution of this study is its investigation of GM volume in drug-naïve children and adolescents with ADHD, as findings from two recent meta-regression studies suggest that stimulant use normalizes GM abnormalities in the caudate nucleus, the ACC, and the amygdala (Frodl & Skokauskas, 2012; Nakao, Radua, Rubia, & Mataix-Cols, 2011). Another important contribution the present study makes to the literature is its use of optimized VBM preprocessing and the creation of a study-specific pediatric template for preventing tissue misclassification, and registration and spatially normalization errors. Wilke et al. (2003) suggest that significant tissue misclassification can occur when analyzing pediatric data based on adult templates. Yoon et al. (2009) compared VBM findings in a pediatric sample using pediatric and adult templates, and reported less cortical thickness, larger cortical surface area, a higher degree of cortical folding, increased GM volume, and decreased cerebrospinal fluid volume when VBM preprocessing was performed with a pediatric template, as opposed to adult template-based brain images. In addition, they showed that the degree of deformation during non-linear spatial normalization was significantly reduced when using the pediatric template. In terms of limitations, the present study included moderately sized samples in both groups, which might have negatively affected the study’s statistical power.

In conclusion, the present findings show that ADHD is not necessarily associated with reduced GM volume and that the clinical significance of increased GM volume in motor areas remains to be explained. In addition, we recommend that future studies focus on analyzing pediatric ADHD samples with more sophisticated methods, taking into account differences between pediatric and adult brains.

Footnotes

Acknowledgements

We thank Ayse Imir, Sedat Aydin and Oznur Karadeniz for their help during data collection.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK; Project No.: 114C150).

Author Biographies

Bernis Sutcubasi Kaya is a PhD student in neuroscience. Her research interests include ADHD, autism spectrum disorders, electroencephalography and neuroimaging.

Baris Metin is an associate professor of neurology and has a PhD in psychology. He is conducting research on ADHD and neuroimaging of neuropsychiatric disorders.

Zeynep Cubukcuoglu Tas is a Child and Adolocent Psychiatrist and is currently working as an Assistant Professor at İzmir University Department of Child and Adolocent Psychiatry. She completed her residency training in Germany, LVR Clinics- Bedburg-Hau. As a clinician her special area of interest is the neuropsychology of autism and ADHD.

Ayse Buyukaslan, MD, is a Child and Adolescent Psychiatrist working at Medeniyet University in Istanbul,Turkey. She conducted research on emotional and social intelligence in children with ADHD.

Aysegul Soysal, MSc, is a clinical psychologist working mainly with children and adolescents with ADHD. Her research interests are in the following areas: neuroimaging, ADHD and neuropsychology.

Deniz Hatiloglu is the head of radiology department at NeuroPsychiatry Istanbul Hospital and is an assistant professor teaching in Imaging Techniques and Radiotherapy programs at Üsküdar University. Her research interest is neuroradiology.

Nevzat Tarhan is a professor of psychiatry. He is also the founder and the president of Uskudar University. His research interests include neuroimaging of psychiatric disorders, applying brain mapping techniques for developing biomarkers, precision medicine and transcranial magnetic stimuliation.

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