Exploring the Brain Lateralization in ADHD Based on Variability of Resting-State fMRI Signal

Abstract

Objective: In this study, we investigate the brain lateralization in ADHD patients. Furthermore, we also explore the difference between male and female patients, and the difference among distinct ADHD subtypes, that is, ADHD–inattentive (ADHD-IA) and ADHD–combined (ADHD-C). Method: We employed the standard deviation to quantify the variability of resting-state functional magnetic resonance imaging (fMRI) signal and measure the lateralization index (LI). Results: ADHD patients showed significantly increased rightward lateralization in the inferior frontal gyrus (opercular), precuneus, and paracentral lobule, and decreased rightward lateralization in the insula. Compared with male patients, female patients showed significantly rightward lateralization in the putamen and lobule VII of cerebellar hemisphere. ADHD-C patients exhibited increased rightward lateralization in the inferior frontal gyrus (opercular), and decreased rightward lateralization in the inferior temporal gyrus, as compared with ADHD-IA. The LI was also found to be related to inattentive and hyper/impulsive scores. Conclusion: These key findings may aid in understanding the pathology of ADHD.

Keywords

ADHD brain lateralization standard deviation resting-sate fMRI

Introduction

The anatomy and function of human brain are differences for the left and right hemispheres, which is believed to reflect not only the emergence of language but also developmental and genetic factors (Toga & Thompson, 2003). Prior studies found that the left hemisphere is dominant in terms of language and handedness (Macneilage, 2013) and the right hemisphere regarded as being dominant for some nonverbal functions, such as spatial attention and the processing -of facial recognition (Cai, Der Haegen, & Brysbaert, 2013; Dundas, Plaut, & Behrmann, 2014; Toga & Thompson, 2003). Brain asymmetry is present at the structural and functional level, and there has been interest in examining possible disruption of typical patterns of brain lateralization in neurodevelopmental disorders (Rentería, 2012).

ADHD is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms. Although this disorder was discovered at the beginning of the 20th century (Oztoprak et al., 2017), the underlying mechanism of ADHD is still not completely understood (Riaz, Asad, Alonso, & Slabaugh, 2017). There is evidence showed that abnormal brain lateralization may be a core component underlying dysfunctions in ADHD (Hale et al., 2009).

In the past several years, many researchers focused on investigating the brain lateralization in ADHD. For example, Sheppard et al. (Sheppard, Bradshaw, Mattingley, & Lee, 1999) found children with ADHD bisect lines with a rightward bias, indicating a “neglect” of the left half of space. They inferred that ADHD may involve abnormal right hemisphere processing. Mohamed and colleagues (Mohamed, Börger, Geuze, & Jj, 2015) reported that ADHD participants with high level of inattention symptoms close to or above the clinical cutoff have a reduced right hemisphere processing in the shape physical–identity condition. However, there is still lack of functional analysis studiesto explore the brain lateralization in ADHD.

Brain laterality differences between the genders have also been reported and may underlie gender differences in cognitive styles (Proustlima et al., 2008). For example, the females’ overall linguistic advantage over males may reflect stronger leftward lateralization of the language networks, whereas the males’ spatial skill advantage over females may reflect stronger rightward lateralization of visuospatial networks (Clements et al., 2006). Gender differences in the brain function lateralization may also lead to differences in the incidence of brain diseases such as autism spectrum disorder (Baroncohen, Knickmeyer, & Belmonte, 2005) and schizophrenia (Narr et al., 2001). Few of previous studies focused on investigating the difference of brain lateralization between male and female ADHD patients.

In addition, according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994; Hennes & Rodes, 1994), patients with ADHD can be grouped into three clinical subtypes: ADHD–inattentive (ADHD-IA), ADHD–hyperactive/impulsive (ADHD-HI), and ADHD–combined (ADHD-C). Recent studies have reported that patients with different ADHD subtypes showed distinct learning problems (Barnard, Stevens, To, Lan, & Mulsow, 2010). For example, ADHD-IA patients have a difficult time focusing on a given task, ADHD-HI patients tend to interrupt or intrude on others, and ADHD-C patients suffer from issues of both ADHD-IA and ADHD-HI patients (Park, Kim, Seo, Lee, & Park, 2015). However, it is still unclear whether the different functions among ADHD subtypes correlated with brain lateralization.

Therefore, in this study, we employed the standard deviation (SD) to quantify the variability of the resting-state functional magnetic resonance imaging (rs-fMRI) signal and measure the lateralization index (LI). The SD has been used to explore the brain lateralization in schizophrenia, and achieved promising results (Xie et al., 2018). We hypothesized the following:

Hypothesis 1: Different brain lateralization can be observed between ADHD patients and controls.

Hypothesis 2: Male and female patients showed distinct brain lateralization.

Hypothesis 3: There exists significantly different brain lateralization among different ADHD subtypes.

We hope this work can help us more in understanding the pathology of ADHD.

Materials and Method

Participants

We collected 202 individuals with ADHD (162 male and 40 female) and 225 matched typically developing controls (TDCs, 123 male and 102 female) in this study. Data were downloaded from the ADHD-200 database (http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html). These participants were recruited from three centers: Kennedy Krieger Institute (KKI), New York University Child Study Center (NYU), and Peking University (PK). Detailed information of enrolled participants is summarized in Table 1.

Table 1.

Demographics and Clinical Characteristics.

Variabilities	ADHD	TDC	p
Age (years)	11.52 ± 2.44	11.52 ± 2.40	.9962
Verbal IQ	90.50 ± 40.40	100.82 ± 39.06	.0076
Performance IQ	104.56 ± 15.92	110.86 ± 14.79	<.001
Full IQ	104.28 ± 13.75	113.41 ± 13.34	<.001
ADHD index	64.42 ± 13.36	37.79 ± 10.40	<.001
Inattentive	55.99 ± 21.96	32.01 ± 15.95	<.001
Hyper/impulsive	52.81 ± 24.40	31.71 ± 17.33	<.001

Note. Data are expressed as M ± SD. TDC = typically developing control.

Data Acquisition

During acquisition of the rs-fMRI, participants were instructed to relax, to think of nothing, and to stay awake. In KKI, participants were asked to keep their eyes open and fixate on a center cross; in NYU, participants were instructed to close their eyes; and participants in PK were asked to keep their eyes either open or closed. Table 2 summarizes the scanning parameters of the data used in this study.

Table 2.

The Scanning Parameters of the Data Used in This Study.

Center	KKI	NYU	PK1	PK2	PK3
TR (ms)	2,500	2,000	2,000	2,000	2,000
TE (ms)	30	15	30	30	30
Flip angle (°)	75	90	90	90	90
Volumes	124	176	236	236	236
Slices	47	33	33	33	30
Slice thickness (mm)	3.0	4.0	3.5	3.0	4.5

Note. KKI = Kennedy Krieger Institute; NYU = New York University Child Study Center; PK = Peking University; TR = repetition time; TE = echo time.

Image Preprocessing

All fMRI image preprocessing was carried out using the Data Processing Assistant for Resting-Sate fMRI (DPARSF) toolbox (Yan & Zang, 2010). To ensure steady-state longitudinal magnetization, the first 10 images of each participant were removed. The remaining functional images had slice-timing correction done for different signal acquisition between each slice and motion effects. The corrected images were then normalized to the standard Montreal Neurological Institute (MNI) space. All normalized images smoothed using a 4-mm full-width at half maximum Gaussian kernel. Then, a band-pass filter (0.01-0.08 Hz) was applied to reduce the effect of low-frequency drifts and high-frequency noise. Finally, the nuisance covariates, including six head motion parameters, white matter signals, and cerebrospinal fluid were regressed for the next analysis. The automated anatomical labeling (AAL) template (Tzourio-Mazoyer et al., 2002) was used to divide the regressed rs-fMRI images into 116 regions of interest (ROIs).

LI of SD of the rs-fMRI Signal

We first estimated the SD of rs-fMRI signals in every gray matter voxel, and then obtained the average of SD in ROIs based on AAL template. The LI based on SD is defined as (Xie et al., 2018)

LI = \frac{SDL - SDR}{SDL + SDR},

where SDL and SDR are the SD of the left and the corresponding right hemisphere, respectively. The LI allows us to examine difference between the left and right hemispheres, with positive LI indicating leftward asymmetry and negative LI indicating rightward asymmetry.

Statistical analysis was conducted using MATLAB. For the SD and lateralization index, the standard t-test was performed to evaluate the difference between ADHD patients and controls. A value of p<0.05 was considered to be significant difference. In particular, the false discovery rate (FDR) correlation was empolyed to assess statistical significance with multiple comparisons.

Within the ADHD group, linear regression models were used to explore the relationship between the LI and clinical characteristics, that is, inattentive and hyper/impulsive scores.

Results

Participants’ Demographic Information

As shown in Table 1, there was no significant difference between the groups in age but showed great difference in terms of IQ, ADHD index, inattentive, and hyper/impulsive scores.

Comparison of LI Between ADHD Patients and Controls

Table 3 lists the difference in the LI between ADHD patients and controls in corresponding AAL brain area. ADHD patients showed significantly increased leftward lateralization in the olfactory and lobule III of cerebellar hemisphere, and increased rightward lateralization in the inferior frontal gyrus (opercular), precuneus, and paracentral lobule, compared with controls. In contrast, patients with ADHD exhibited a significantly decreased rightward lateralization in the insula. In addition, we observed that ADHD patients displayed significant changes from rightward to leftward lateralization in the orbitofrontal cortex (superior), orbitofrontal cortex (middle), postcentral gyrus, whereas significant changes from leftward to rightward lateralization were also observed in the posterior cingulate gyrus and hippocampus.

Table 3.

Regions Showing Significant Changes in Lateralization Index Between Hemispheres.

Region	ADHD	TDC	p
Orbitofrontal cortex (superior)	0.0170	−0.0100	.0306
Orbitofrontal cortex (middle)	0.0035	−0.0332	.0049
Inferior frontal gyrus (opercular)	−0.0254	−0.0038	.0316
Olfactory	0.1238	0.1011	.0419
Insula	−0.0302	−0.0433	.0425
Posterior cingulate gyrus	−0.0120	0.0222	<.001
Hippocampus	−0.0180	0.0065	.0152
Postcentral gyrus	0.0145	−0.0039	.0333
Precuneus	−0.0543	−0.0376	.0057
Paracentral lobule	−0.1160	−0.0922	.0247
Lobule III of cerebellar hemisphere	0.0328	0.0035	.0238

Note. TDC = typically developing control.

Comparison of LI Between Male and Female Patients

Table 4 presents the difference in the LI between male and female patients in the corresponding brain area. Male and female patients showed the same brain lateralization in the superior frontal gyrus (dorsal), cuneus, postcentral gyrus, paracentral lobule, and lobule VIII of cerebellar hemisphere, and displayed different brain lateralization in the putamen and lobule VII of cerebellar hemisphere. For example, male patients exhibited significantly leftward lateralization in the putamen and lobule VII of cerebellar hemisphere, whereas the female patients showed significantly rightward lateralization in these two regions.

Table 4.

Regions Showing Significant Changes in Lateralization Index Between Male and Female Patients.

Region	Male	Female	p
Superior frontal gyrus (dorsal)	0.0141	0.0507	.010
Cuneus	0.0358	0.0050	.0070
Postcentral gyrus	0.0083	0.0398	.0320
Paracentral lobule	−0.1260	−0.0757	.0131
Putamen	0.0198	−0.0079	.0085
Lobule VII of cerebellar hemisphere	0.0070	−0.0416	.0161
Lobule VIII of cerebellar hemisphere	0.0407	0.0036	.0268

Comparison of LI Among Different Subtypes

Given the limited number of ADHD-HI patients, in this subsection, we just explore the LI in ADHD-IA and ADHD-C patients. As we can see from Table 5, ADHD-C patients exhibited increased rightward lateralization in the inferior frontal gyrus (opercular), and decreased rightward lateralization in the inferior temporal gyrus and leftward lateralization in the inferior frontal gyrus (triangular), as compared with ADHD-IA. In addition, ADHD-C patients displayed significant changes from rightward to leftward lateralization in the middle cingulate gyrus and supramarginal gyrus.

Table 5.

Regions Showing Significant Changes in Lateralization Index Among Different ADHD Subtypes.

Region	ADHD-C	ADHD-IA	p
Inferior frontal gyrus (opercular)	−0.0430	−0.0031	.0050
Inferior frontal gyrus (triangular)	0.0247	0.0509	.0142
Middle cingulate gyrus	0.0175	−0.0014	.0279
Supramarginal gyrus	0.0237	−0.0196	.0058
Inferior temporal gyrus	−0.0590	−0.0808	.0466

Note. ADHD-C = ADHD–combined; ADHD-IA = ADHD–inattentive.

Correlation Between LI and Clinical Features in ADHD Patients

We conducted the Pearson correlation analysis between LI of these significant brain regions (as shown in Table 3) and clinical characteristics in ADHD group. From Table 6, we can see that LI of orbitofrontal cortex (superior), orbitofrontal cortex (middle), and olfactory exhibited significantly positive correlation with both inattentive and hyper/impulsive scores, whereas the LI of inferior frontal gyrus (opercular) and precuneus displayed significantly negative association with both inattentive and hyper/impulsive scores.

Table 6.

Correlation Between LI and Clinical Features in ADHD Patients.

Region	Inattentive hyper/impulsive
Region	r	p	r	p
Orbitofrontal cortex (superior)	.1746	.0129	.2027	.0038
Orbitofrontal cortex (middle)	.1733	.0136	.2041	.0036
Inferior frontal gyrus (opercular)	−.2458	<.001	−.2589	<.001
Olfactory	.2426	<.001	.2565	<.001
Insula	.0054	.9387	−.0196	.7814
Posterior cingulate gyrus	−.0081	.9090	.0169	.8108
Hippocampus	−.0268	.7046	−.0642	.3642
Postcentral gyrus	.0393	.5786	.0149	.8335
Precuneus	−.1936	.0058	−.1506	.0324
Paracentral lobule	.0533	.4516	.0032	.9639
Lobule III of cerebellar hemisphere	.0663	.3485	.0503	.4770

Note. LI = lateralization index.

Discussion

A variety of cognitive studies have suggested that ADHD is associated with differences in brain lateralization. In this work, we applied the SD as a measure to explore the brain lateralization of ADHD. The crucial findings that have emerged from this work are that (a) ADHD patients showed significantly increased leftward lateralization in the olfactory and lobule III of cerebellar hemisphere, and increased rightward lateralization in the inferior frontal gyrus (opercular), precuneus, and paracentral lobule, compared with controls. In contrast, patients with ADHD exhibited a significantly decreased rightward lateralization in the insula. (b) Male and female patients showed different brain lateralization in the putamen and lobule VII of cerebellar hemisphere; (c) compared with ADHD-IA patients, individuals with ADHD-C displayed significant changes from rightward to leftward lateralization in the middle cingulate gyrus and supramarginal gyrus; (d) abnormal LI found in ADHD patients was related to the inattentive and hyper/impulsive scores.

A greater right hemisphere change was found in ADHD patients in this work. Prior works have reported that right hemisphere plays a key role in spatial attention, such as shifting attention (Corbetta, Miezin, Shulman, & Petersen, 1993) and sustaining attention (Pardo, Fox, & Raichle, 1991). A core symptom of ADHD patients is inattention, hence, abnormal brain signal variability in the right hemisphere may underlie, at least in part, the inattention of patients with ADHD.

In children and adolescents, ADHD is more commonly diagnosed in males, with the sex ratios ranging from 2:1 to 10:1 (Mowlem et al., 2018). However, it is unknown whether the abnormal sex ratios correlated with brain lateralization. In this study, we explored the brain lateralization in male and female ADHD patients. Significantly different lateralization between male and female patients was found in the putamen and lobule VII of cerebellar hemisphere. In males, leftward lateralization was appeared in these two regions, whereas the rightward lateralization was exhibited in females in these regions. Meta-analyses tend to show less severe symptoms in females versus males with ADHD identified from nonreferred, community populations, but similar levels in clinically ascertained samples—with the exception of inattention for which females had higher ratings in the more recent meta-analysis (Gaub & Carlson, 1997b; Gershon, 2002). Study on the children with ADHD reported that girls with ADHD were more likely to have predominantly inattentive types of ADHD than boys (Biederman et al., 2002). This may partially explain why female patients showed rightward lateralization in the two regions.

Patients with ADHD-IA and ADHD-C exhibited significantly different brain lateralization in the middle cingulate gyrus and supramarginal gyrus. For example, ADHD-IA patients showed rightward lateralization in the two regions, whereas patients with ADHD-C displayed significantly leftward predilection in these regions. Patients with ADHD-C are believed to be at higher risk of conduct problems (Milich, Balentine, & Lynam, 2010), whereas those with ADHD-IA are at higher risk of learning disabilities, anxiety, and depression (Gaub & Carlson, 1997a). The cingulate gyrus is a cortical area of mixed cytoarchitectonics that links to the limbic system and neocortex (Mesulam, 2000). The subcomponents of the cingulate gyrus serve a range of functions, including emotional, cognitive and attentional, nociceptive, and motor processing (Drevets, 2000; Kim et al., 1999; Mesulam, Nobre, Kim, Parrish, & Gitelman, 2001). The different brain lateralization in cingulate gyrus may relate to the symptoms of different ADHD subtypes.

Moreover, we further investigated the relationship between LI of significant brain regions and clinical features in ADHD patients. We observed that LI of orbitofrontal cortex (superior), orbitofrontal cortex (middle), olfactory, inferior frontal gyrus (opercular), and precuneus keep a close relationship with both inattentive and hyper/impulsive scores. The orbitofrontal cortex controls emotional, motivational behaviors, and inhibits socially inappropriate impulsive behaviors, which are impaired in ADHD (Itami & Uno, 2002). Recent ADHD research has focused on olfactory dysfunctions as a disorder characteristics (Weiland et al., 2011). For example, Romanos et al. (2008) reported improved odor sensitivity in children with ADHD. Karsz and colleagues (2008) found deficits in odor identification in ADHD children compared with healthy controls. The precunes has been suggested to be an important structure for the integration of mental processing through its role in cognitive processes (Zhong et al., 2017). The precuneus is a prominent node in the default mode network (DMN) that has been receiving increasing attention in the ADHD research. For example, in Castellanos et al. (2008), the researchers found decreased functional connectivity between the precuneus and other DMN regions in adults with ADHD.

Multi-center study is a hot topic in recent years, in this work, we collected participants from three different sites. The reason of no -additional operation for different data sets is that we employed the SD as the measure of brain signal variability, which is just depended on the blood-oxygenation-level dependent (BOLD) signal.

Although we employed multicenter data to explore the brain lateralization of ADHD, and some promising findings were observed, there are still several limitations that should be noticed. First, the data were collected from different centers, the scanners and scan parameters of these data were different, which may limit the findings of our study. For example, different scan time may influence the amount of information included in the brain signal, which may affect the investigation of brain lateralization. Second, we applied an ROI approach to investigate the lateralization of brain signal variability. The AAL template was used to divide the whole brain into 116 regions. However, previous studies have reported that different parcellation schemes may generate different results (Jie, Wee, Shen, & Zhang, 2016). Hence, further studies should be performed to determine which parcellation strategy is more appropriate in ADHD study. Third, we did not evaluate the influence of patients’ age, previous study reported the male-to-female is smaller in adult samples than in children and adolescent participants (Biederman, Faraonea, Monuteauxa, Boberb, & Elizabeth, 2004). Therefore, further exploring the relationship between brain lateralization and age in ADHD group is our future work.

Conclusion

To our knowledge, this is the first work to investigate the brain lateralization in ADHD based on the variability of rs-fMRI signal. We observed different brain lateralization between ADHD patients and controls. In addition, different brain lateralization was also presented in male and female patients, and ADHD-IA and ADHD-C patients. Moreover, we also found abnormal brain lateralization associated with clinical features of ADHD patients. This may help us more understanding the pathology of ADHD.

Footnotes

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 work was supported by the National Science Fund of China under Grant Numbers U1713208 and 61472187, the 973 Program Number 2014CB349303, and Program for Changjiang Scholars.

Author Biographies

Hongliang Zou received his BS and M.S. degrees in 2012 and 2015, respectively, and he is currently pursuing his PhD degree in the School of Computer Science and Engineering, Nanjing University of Science and Technology (NUST). His current research interests include pattern recognition, machine learning, bioinformatics and medical image analysis.

Jian Yang received the PhD degree from Nanjing University of Science and Technology (NUST), on the subject of pattern recognition and intelligence systems in 2002. Now, he is a Chang-Jiang professor in the School of Computer Science and Technology of NUST. His research interests include pattern recognition, computer vision and machine learning.

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