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. 2019 Jul 30;40(17):4877–4887. doi: 10.1002/hbm.24743

Disrupted brain functional networks in drug‐naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis

Ying Chen 1,2, Xiaoqi Huang 1, Min Wu 1, Kaiming Li 1, Xinyu Hu 1, Ping Jiang 1, Lizhou Chen 1, Ning He 2, Jing Dai 3, Song Wang 1, Manxi He 3, Lanting Guo 2,✉, John A Sweeney 1,4, Qiyong Gong 1,5,✉
PMCID: PMC6865592  PMID: 31361385

Abstract

Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting‐state functional magnetic resonance imaging was performed on 51 drug‐naïve children with ADHD and 55 age‐matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q < 0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p <0 .05). Alterations in network topologies in drug‐naïve ADHD patients indicate weaker small‐worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo‐fronto‐parietal attention network were associated with inhibitory control deficits.

Keywords: ADHD, brain functional networks, graph theory analysis, inhibitory cognitive control deficits, psychoradiology

1. INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). These symptoms, especially hyperactivity and impulsive behavior, are seen as manifestations of core impairments in the brain systems underlying inhibitory behavioral control (Chambers, Garavan, & Bellgrove, 2009; McAuley, Crosbie, Charach, & Schachar, 2014; Sergeant, Geurts, & Oosterlaan, 2002).

Prior ADHD neuroimaging studies have shown that the inhibitory behavioral control impairments in ADHD are related to regional abnormalities in the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), temporal/parietal areas, and basal ganglia (Chen et al., 2015; Chen et al., 2016; Lei et al., 2014; Lei, Du, et al., 2015; Lei, Li, et al., 2015; Li, He, et al., 2014; Li, Li, et al., 2014; Sun et al., 2018). Using a seed‐based functional connectivity analysis, we found that alterations in frontostriatal circuitry correlated with the degree of inhibitory executive dysfunction in ADHD (Li, He, et al., 2014; Li, Li, et al., 2014).

Recent studies have demonstrated that the human brain is composed of a complex, interacting network of widely distributed components (Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006; Salvador et al., 2005; Sporns, Chialvo, Kaiser, & Hilgetag, 2004). Examining the large‐scale functional network in ADHD patients may facilitate understanding of brain alterations in ADHD and their relation to inhibitory behavioral control deficits. This line of work has relied on graph theory approaches for exploring brain network organization in patients with ADHD and other neuropsychiatric disorders (Gong & He, 2015; Gong, Lui, & Sweeney, 2016; Lui, Zhou, Sweeney, & Gong, 2016). In graph theory, the brain is viewed as a graph constructed with edges representing connections among nodes that represent brain regions. A small‐world network describes the balance between information segregation and integration at the global level. Regionally specific properties, such as nodal efficiency, degree and betweenness, are examined to identify and evaluate the contributions of “hubs” that facilitate integrative processes (Bullmore & Sporns, 2009; Watts & Strogatz, 1998). Several studies have reported that disturbances in these networks contribute to the cognitive deficits and symptoms of psychiatric disorders (Achard et al., 2006; Barttfeld et al., 2011; He, Chen, & Evans, 2008; Liu et al., 2008).

The results of previous ADHD whole‐brain functional network studies using graph theory have been somewhat inconsistent (Ahmadlou, Adeli, & Adeli, 2012; Cao et al., 2013; Hong et al., 2014; Wang et al., 2009). For example, one study showed a shift toward regularization by increased segregation in children with ADHD (Wang et al., 2009), while another study reported decreased segregation, suggesting a shift toward randomization (Xia, Foxe, Sroubek, Branch, & Li, 2014). Those inconsistent results may be due to sample differences in age or illness severity, medication effects or psychiatric comorbidities (Noordermeer et al., 2017; Rubia, Alegria, & Brinson, 2014).

Previous fMRI studies have found that stimulant medication increased brain activation in inferior frontal cortex, insula, and caudate nucleus, which are key areas for effective cognitive control (Cubillo et al., 2014; Konrad, Neufang, Fink, & Herpertz‐Dahlmann, 2007). However, even patients not receiving medication therapy showed increased activation of brain regions important for cognitive control including the supplementary motor area and dorsal ACC relative to healthy controls (HCs) (Schweren et al., 2017). These key structures of the cognitive processing neurocircuitry also have been reported to show higher levels of activation in the presence of some psychiatric comorbidities (Cohn et al., 2013; Herpertz et al., 2008). Thus, psychoradiologic studies (Lui et al., 2016; Huang et al., 2019) of a relatively large group of drug‐naïve ADHD patients with limited comorbidities are needed to better define disorder‐related abnormalities in brain networks.

Moreover, the associations between altered metrics of brain functional networks and the core behavioral inhibition deficits of ADHD need to be more systematically examined. The current psychoradiologic study applied graph theory approaches to investigate the topological architecture of intrinsic cerebral functional networks in drug‐naïve ADHD children without psychiatric comorbidities during resting‐state fMRI studies. We hypothesized that the topological organization of functional brain networks would be atypical in ADHD, and that identified network alterations would be related to inhibitory cognitive control deficits and symptom severity.

2. METHODS

2.1. Participants

This study was approved by the research ethics committee of West China Hospital of Sichuan University. Written informed consent was obtained from the parents/guardians of all participants, and assent was obtained from study participants prior to participation. For the ADHD group, 68 right‐handed participants with ADHD (age range 7–16 years, 11 girls and 57 boys) who were drug‐naïve and without psychiatric comorbidities were recruited from the Mental Health Center of West China Hospital of Sichuan University between June 2009 and July 2013. For the control group, 63 right‐handed health control (HC) subjects (9 girls and 54 boys) matched for age and educational level were recruited from the community.

ADHD diagnoses were confirmed by two experienced clinical psychiatrists (L.G., Y.L., N.H., or Y.C.) using the Structured Clinical Interview for DSM‐IV‐Patient Edition. The revised Conners' Parent Rating Scale (CPRS) and the Child Behavior Checklist (with ratings provided by participants' guardians) were used to characterize clinical features of ADHD (Conners, 1999). The following exclusion criteria were applied to all participants: (a) a history of conduct disorder, oppositional defiant disorder, Tourette's disorder, any psychotic or mood disorder, head trauma, neurologic disorder or neurosurgery; (b) a history of treatment with stimulants or other medications for symptoms of ADHD; and (c) left‐handedness, as assessed using Annett's Hand Preference Questionnaire. HC subjects were screened using the Structured Clinical Interview for DSM‐IV Non‐patient Edition. Control participants had no first‐degree relatives with a known history of psychiatric illness.

We used the Stroop color–word interference task to assess inhibitory cognitive control. One hundred and twelve words were presented on a card (21 × 29.7 cm2). Participants named the color of congruent words (red written in red) on the first card as quickly as possible. Next, participants named colors of the words printed in different colors (the word red written in blue) on the second card. The completion time of reading all words on the list was measured. No practice was permitted. The color–word interference time, the additional time required to complete the second task relative to the first task, was used as a measure of inhibitory cognitive control.

2.2. MRI data acquisition

All resting‐state fMRI data were acquired using a 3.0‐T MRI system (Trio, Siemens, Erlangen, Germany) with an eight‐channel phased‐array head coil. High resolution T1‐weighted anatomical images were acquired using a magnetization prepared rapid gradient echo sequence (TR = 1900 ms, TE = 2.5 ms, TI = 900 ms, flip angle = 9) with 256 × 256 matrix over a field of view of 256 × 256 mm and 176 sagittal slices of 1 mm thickness.

A gradient‐echo echo‐planar functional imaging sequence was used with the following parameters: repetition time, 2 s; echo time, 30 ms; flip angle, 90°; slice thickness, 5 mm with no slice gap; matrix size, 64 × 64; field of view, 240 × 240 mm2; and voxel size, 3.75 × 3.75 × 5 mm3. Each brain volume comprised 30 axial slices within 6 min and 50 s of acquisition, and each functional imaging session included 200 image volumes preceded by five dummy volumes. The subjects were asked to close their eyes, not think of anything in particular, without falling asleep (with compliance verified immediately after the experiment).

2.3. Data preprocessing

We used the Data Processing Assistant for Resting‐State fMRI (DPARSF, http://restfmri.net/forum/DPARSF) software to preprocess functional images, which is based on the statistical parametric mapping software and the Resting‐State fMRI Data Analysis Toolkit. Functional images were corrected for intra‐volume acquisition time delays and head motion, spatially normalized to a 3 × 3 × 3 mm3 Montreal Neurological Institute 152 template, and linearly detrended and temporally band‐pass filtered (0.01–0.08 Hz) to remove low‐frequency drift and high‐frequency physiological noise. Finally, the global signal intensity, white matter signal, cerebrospinal fluid signal, and motion parameters (three translational and three rotational parameters) were regressed out before statistical analyses. Twenty‐five children (17 patients and 8 HCs) were excluded because of head motion greater than 1.5 mm translation or 1.5° in any direction. MRI imaging data from 51 patients with ADHD (7 girls and 44 boys, 26 patients were diagnosed with the combined subtype and 25 patients were diagnosed with the inattentive subtype) and 55 control subjects (6 girls and 49 boys) were included in the analysis. We also further examined the Frame‐wise displacement (FD) and D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels (DVARS) head motion profiles (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012; Tao et al., 2017) in the remaining participants and found no significant differences between the ADHD and HC groups (Table S2).

2.4. Functional network construction

2.4.1. Node definition

Networks were constructed using the GRETNA software (http://www.nitrc.org/projects/gretna). A network was constructed with edges representing connections among nodes and with nodes representing brain regions. The automated anatomical labeling (AAL) atlas (Tzourio‐Mazoyer; Table S1), which is the most widely used atlas in the graph theory literature, was used to divide the whole brain into 90 cortical and subcortical regions of interest. Each region was regarded as a network node.

2.4.2. Edge definition

To define network edges, the mean time series of each region was acquired, and partial correlations correcting for the effects of the remaining 88 regions were computed between the mean time series for all pairs of nodes (representing their conditional dependences). This process produced a 90 × 90 partial correlation matrix for each subject, which was converted into a binary matrix (reflecting significant or nonsignificant associations) using a predefined threshold (described below; Zhang et al., 2011).

2.5. Network analysis

2.5.1. Threshold selection

We applied a range of sparsity thresholds (S) to the correlation matrices. The minimum and maximum values of S used were established, ensuring that the thresholded networks were estimable for the small‐worldness scalar, and that the small‐world index was larger than 1.0 (Zhang et al., 2011). Our threshold range was 0.10 < S < 0.34, with an interval of 0.01. This thresholding strategy produced networks that could estimate small‐worldness with sparse properties and the minimum number of spurious edges (Zhang et al., 2011). The area under the curve (AUC), which reflected measures across the sparsity parameter S, was calculated for each network metric to provide a summary scalar for each parameter reflecting the topological organization of the brain networks.

2.5.2. Small‐world properties and network efficiency

We examined the topologic properties of brain networks at both the global and nodal levels. Three global‐level metrics were included (Watts & Strogatz, 1998): segregation of networks, including the clustering coefficient (Cp), normalized clustering coefficient (γ), and local efficiency (Eloc); integration of networks, including the characteristic path length (Lp), normalized characteristic path length (λ), and global efficiency (Eglob); and small‐world parameters (σ). The nodal‐level properties included nodal efficiency, nodal degree, and betweenness centrality.

2.6. Statistical analysis

2.6.1. Differences in demographic and clinical variables

A two‐sample t test was used to analyze differences in age, Stroop test performance and behavioral measures, and a chi‐square test was used to examine the gender distribution between the two groups in SPSS 16.0.

2.6.2. Differences in network metrics

Nonparametric permutation tests using MATLAB language were used to assess differences in the AUC between the ADHD and control groups for each metric (small‐world, network efficiency, and regional centrality measures). For each permutation, all values were randomly reallocated into two groups, and the mean differences were recalculated between the two randomized groups for each network metric. This randomization procedure was repeated 10,000 times, and the 95th percentile of each distribution was used as the critical value for significance testing. To address the problem of multiple comparisons, the p values of comparisons of all network metrics were corrected by the Benjamini Hochberg false discovery rate (FDR q value<.05). (Genovese, Lazar, & Nichols, 2002). Age, gender, and head motion (FD and DVARS) were also controlled. We also used multiple linear regression methods to establish the model with group, age, gender, head motion, and group*age as independent variables, and the network metrics as dependent variables (Supporting Information, Statistical analysis). In addition, the differences in network metrics between the boys in each participant group were analyzed (Supporting Information).

2.6.3. Relationships between network measures and clinical variables

Exploratory partial correlation analysis was used to assess associations of global and regional network measurements with clinical symptoms and Stroop scores using age and gender as covariates (p values corrected by the FDR q value <0.05). Linear regression analyses were used to evaluate relations between age and brain metrics (p values corrected by FDR q value <0.05) in regions where group differences were detected, and the regression coefficients of the two participant groups were compared. The statistical analysis was performed with the SPSS software, version 16.0.

3. RESULTS

3.1. ADHD‐related alterations in inhibitory cognitive control

Demographic variables, including age and gender, were not significantly different between the two groups (p = 0.100, 0.441, respectively). Compared with the control subjects, children with ADHD had more incorrect responses and corrected errors and longer color–word interference times on the Stroop test (all p < 0.001; Table 1).

Table 1.

Demographic, cognitive and clinical characteristics of the study participants

ADHD group (n = 51) Control group (n = 55) t/X 2 p
Age (years) 10.0 ± 2.39 10.8 ± 2.1 2.38 0.100
Gender (female/male) 7/44 6/49 0.77 0.441
Stroop color and word Test
Color–word interference time (s) 172.74 ± 75.85 97.77 ± 33.11 6.67 <0.001
Revised Conners' Parent Rating Scale hyperactivity index 13.0 ± 6.6 5.4 ± 4.7 6.90 <0.001
Child Behavior Checklist attention problem scores 8.9 ± 3.1 3.7 ± 2.9 8.87 <0.001
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Abbreviation: ADHD, attention deficit hyperactivity disorder.

3.2. ADHD‐related alterations in small‐world properties

Figure 1 shows the small‐world properties of the functional brain networks of both the ADHD patients and controls in the 0.01–0.34 range of cost thresholds, with γ = Cp/Cr > 1 (Cr: clustering coefficient of a random network) and λ = Lp/Lr ≈ 1 (Lr: characteristic path length of a random network). However, compared with the controls, the children with ADHD exhibited an increased characteristic path length Lp (FDR q = 0.01) but no significant differences in the clustering coefficient Cp, normalized clustering coefficient γ, normalized characteristic path length λ, or small‐worldness σ. With respect to network efficiency, both global efficiency (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) were lower in the ADHD group (Figure 2).

Figure 1.

Figure 1

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Small‐world properties are demonstrated in the functional brain networks of attention deficit hyperactivity disorder (ADHD) patients and control subjects in the 0.01–0.34 range of cost thresholds, with normalized clustering coefficients (Cp) (γ) > 1 (a) and normalized path lengths (Lp) (λ) ≈ 1 (b) [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2.

Figure 2

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Comparisons of control and ADHD groups in characteristic path length (Lp) (a), global efficiency (Eglob) (b), local efficiency (Eloc) (c), and nodal efficiency in the left inferior frontal gyrus (IFG.L) (d) and left anterior cingulate cortex (ACC.L) (e). ACC.L, left anterior cingulate cortex; ADHD, attention deficit hyperactivity disorder; IFG.L, left inferior frontal gyrus [Color figure can be viewed at http://wileyonlinelibrary.com]

3.3. ADHD‐related alterations in regional nodal characteristics

For nodal metrics, compared to controls, the ADHD group exhibited decreased nodal efficiency in the left IFG and the left ACC (FDR both q < 0.05; Figures 2 and 3). No significant differences between the groups were observed in nodal degree or betweenness centrality.

Figure 3.

Figure 3

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Brain regions with abnormal nodal centralities in brain functional networks of ADHD patients. ACC.L, left anterior cingulate cortex; ADHD, attention deficit hyperactivity disorder; IFG.L, left inferior frontal gyrus

3.4. Associations between clinical variables and graph measures

Among the ADHD patients, older age was significantly correlated with decreased global efficiency (β = −0.32, FDR q = 0.028), reduced local efficiency (β = −0.45, FDR q = 0.005), and increased characteristic path length (β = 0.32, FDR q = 0.029) and nodal efficiency in the left ACC (β = 0.37, FDR q = 0.018). In contrast, in the control group, local efficiency (β =0 .31, p = 0.02, uncorrected) continued to increase with age, whereas other global and nodal properties showed no significant associations with age. Age‐related trends of local efficiency (β = −1.73, FDR q < 0.001) and characteristic path length (β = 0.88, p = 0.047, uncorrected) were significantly different between the two groups (Figure 4 and Table 2); that is, local efficiency decreased with age in the ADHD subjects but increased with age in the controls, and the characteristic path length increased with age in the ADHD subjects, with no significant change with age in the controls.

Figure 4.

Figure 4

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Comparison of age‐related trend differences of local efficiency (Eloc) (a) and characteristic path length (Lp) (b) between ADHD and HC. For attention deficit hyperactivity disorder (ADHD), the Eloc decreased with age and the Lp increased with age, while for healthy control (HC), the Eloc increased along with age and the Lp was not correlated with age. The age‐related differences of Eloc and Lp between ADHD and HC were significant (p <0.05) [Color figure can be viewed at http://wileyonlinelibrary.com]

Table 2.

Age‐related changes in brain networks in the ADHD and control groups

Metrics Regression for the ADHD group Regression for the control group Regression for group comparison
β p/q β p β p/q
Global efficiency −0.32 0.028* −0.023 0.870 −0.668 0.128
Local efficiency −0.446 0.005* 0.314 0.02 −1.734 <0.001*
Characteristic path length 0.318 0.029* −0.061 0.661 0.883 0.047
Nodal efficiency in ACC.L 0.373 0.018* 0.015 0.913 0.786 0.069
Nodal efficiency in IFG.L −0.031 0.821 −0.1 0.486 −0.15 0.735
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Note: *Corrected by the FDR q value <0.05. Abbreviations: ACC.L, left anterior cingulate cortex; ADHD, attention deficit hyperactivity disorder; IFG.L, left inferior frontal gyrus.

Exploratory partial correlation analysis examining relationships between the global topological structure of functional brain networks and Stroop test performance in the ADHD patients and HCs controlling for age and gender revealed that the color–word interference time in the Stroop test was negatively associated with global efficiency (r = −0.45, FDR q = 0.009) and local efficiency (r = −0.37, FDR q = 0.027) and was positively associated with characteristic path length (r = 0.44, FDR q = 0.009). With respect to behavioral problems, the hyperactivity index of CPRS was negatively correlated with the nodal efficiency of the left IFG (r = −0.20, p = 0.045, uncorrected; Figure 5). No significant correlations were observed in the HCs (p > 0.05).

Figure 5.

Figure 5

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Partial correlation analysis of the global topological architecture of functional brain networks and cognitive function in ADHD patients. The color–word interference times of the Stroop test were negatively associated with global efficiency (Eglob) (a) and local efficiency (Eloc) (b) but were positively associated with path length (Lp) (c). The hyperactivity index of the Conners' Parent Rating Scale was negatively correlated with nodal efficiency of the left inferior frontal gyrus (IFG) (d) [Color figure can be viewed at http://wileyonlinelibrary.com]

4. DISCUSSION

This is the first study to investigate system‐level neural mechanisms of inhibitory response defects in drug‐naïve ADHD patients without significant psychiatric comorbidities. Although the networks in both groups followed a small‐world organization across a range of thresholds, relative to control subjects, ADHD children demonstrated reduced integration and segregated network organization. In addition to our global topology findings, we also found decreased nodal efficiency in frontal lobe regions important for attention and inhibitory behavioral control. Importantly, altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD which establishes a clinical relevance of the observed neural network alterations. Thus, the network‐level aberrations identified in the present study in the absence of potential confound of medications and psychiatric comorbidities provide novel insights into the pathophysiology of inhibitory control deficits in ADHD.

The balance between segregation and integration is an important attribute of small‐world networks. Given our findings of both lower segregation and integration in the ADHD subjects compared to controls, the functional networks in the ADHD patients appeared to tend toward “weaker small‐worldization,” with decreased integration and segregation (Suo et al., 2018; Wang, Yuan, Bai, You, & Zhang, 2016; Zhu et al., 2016). These findings indicate that the balance of brain network organization has shifted in a high‐cost, low‐profit manner (Strogatz, 2001).

Alterations of small‐world topological properties have also been reported in three previous functional connectome studies of ADHD children (Tao et al., 2017; Wang et al., 2009; Xia et al., 2014), but the results were not fully consistent. The results of a smaller‐sample study using drug‐naïve ADHD children were consistent with our results in showing a decreased global efficiency in ADHD (Tao et al., 2017). However, in the other two studies using drug treated samples, no significant changes in global efficiency in ADHD were found, and opposite changes in local efficiency were observed. Compared with HCs, one study reported an increase in local efficiency (Wang et al., 2009) while the other study reported a decrease in local efficiency in ADHD (Xia et al., 2014).

Medication effects may be a major contributor to these inconsistent findings. Methylphenidate and the nonstimulant atomoxetine are first‐line pharmacologic treatments for ADHD (Hanwella, Senanayake, & de Silva, 2011). At therapeutic doses, methylphenidate blocks 60–70% of dopamine transporters in the striatum (Volkow, Fowler, Ding, Wang, & Gatley, 1998) and 70–80% of norepinephrine transporters in other brain regions such as the frontal lobes (Hannestad et al., 2010). Atomoxetine at therapeutic doses robustly occupies noradrenaline transport sites in the anterior cingulate, thalamus, locus ceruleus, and cerebellum (Gallezot et al., 2011). In previously medicated ADHD children, methylphenidate has been shown to upregulate activation in the frontal, anterior cingulate, and striatal areas during an inhibition task and to upregulate or normalize fronto‐striatal and temporo‐parietal activation during other cognitive tasks (Cubillo et al., 2014; Rubia et al., 2014). Therefore, drug treatment may contribute to differences in findings regarding the topological properties of brain functional networks in ADHD.

In addition, statistical methodologies across studies may also contribute to the variability of study findings. When a single threshold has been selected for comparisons of small‐world parameters, the results can be inconsistent because values of network metrics change with sparsity thresholds. Computing the AUC for each network metric as done in the present study is independent of a single threshold, and thus provide a more robust index for the detection of topological alterations in brain disorders (Lei, Du, et al., 2015; Lei, Li, et al., 2015; Zhang et al., 2011).

Inhibitory behavioral dysfunction is a core deficit in ADHD. In the current study, we found that decreased segregation and integration were correlated with the ability to inhibit prepotent response tendencies in ADHD, indicating that altered topological organization of the brain connectome may cause difficulty in the efficient prevention of inappropriate behaviors as observed clinically as behavioral hyperactivity and impulsivity in ADHD. Several investigators have demonstrated that ADHD individuals exhibit abnormal patterns of neural connectivity during execution of behavioral inhibition tasks (Cortese et al., 2012; Hart, Radua, Nakao, Mataix‐Cols, & Rubia, 2013). While these alterations are consistent with present findings, they have typically been supported by analyses of single brain regions or task‐specific analyses. Our study examining whole brain networks in resting‐state functional data extends the prior literature by providing a system‐level understanding of the brain function alterations in ADHD and of the neural mechanisms of inhibitory behavioral control deficits that are a primary feature of the disorder.

In addition, we found different age‐related trends for path length and local efficiency between the ADHD and NC groups, suggesting that both segregation and integration of the brain functional network may decline or fail to develop appropriately in untreated ADHD patients during adolescence. These results are consistent with the view of ADHD as a disorder of abnormal brain maturational trajectory mainly affecting components of networks supporting cognitive control (Marcos‐Vidal et al., 2018; Shaw et al., 2013). While the mechanism of this effect is uncertain, decades of research have documented the involvement of neurotrophic polymorphisms in ADHD (Forero, Arboleda, Vasquez, & Arboleda, 2009), including monoamine polymorphisms that have been shown to be involved in ADHD and to play roles in modeling brain function connectivity during development (Ko et al., 2018; Nymberg et al., 2013). Environmental and epigenetic factors may also play an important role in the altered neural development observed in ADHD. Evidence from mouse models suggests that learning and social behavior can alter gene expression events that regulate the long‐term plasticity of the brain (Williamson, Franks, & Curley, 2016) and can evoke plastic changes in connections between cognitive brain regions (Cardoso, Teles, & Oliveira, 2015). The interplay between genes and environment may impact neural development in ways that have consequences for large‐scale connectivity alterations in ADHD.

Reduced nodal efficiency in ADHD was observed in the left ACC and IFG, which are key structures of the cingulo‐fronto‐parietal attention network, which is crucial to inhibitory behavioral control (Gasquoine, 2013). The ACC, especially its dorsal aspect, has dense reciprocal connections with prefrontal cortex, reflecting its central role in cognition, including error detection and conflict monitoring (Bush, Luu, & Posner, 2000; Swick & Turken, 2002). Many imaging studies have reported hypoactivation of the ACC in ADHD during several cognitive inhibition tasks, including Go/No‐Go tasks and Stroop tasks (Bledsoe, Semrud‐Clikeman, & Pliszka, 2013; Bush et al., 1999; Spinelli et al., 2011). Network‐based statistical analysis revealed that the left ACC had reduced functionally connectivity with multiple other brain regions in ADHD children, and that this alteration contributed to the impulsivity component feature of ADHD (Zhan, Liu, Wu, Gao, & Li, 2017). In addition to ADHD in childhood, abnormalities of the ACC have also been found in adults (Frodl & Skokauskas, 2012). Our observation that the nodal efficiency of the ACC increased with age in ADHD may be consistent with a reduced severity of ADHD features through the transition from adolescence to adulthood. Future longitudinal studies following up ADHD patients will be crucial to define the functional developmental trajectories of the ACC in ADHD and its behavioral significance.

Reduced nodal efficiency in the left IFG correlated with greater behavioral hyperactivity on the CPRS. The IFG region is believed to represent a core component of inhibitory behavioral processes and emotion control (Aron, Robbins, & Poldrack, 2004; Pavuluri & Sweeney, 2008). In particular, the left IFG has been shown to be important in the ability to monitor interference or the selection of relevant information (Hirshorn & Thompson‐Schill, 2006). Previous studies have reported hypoactivation of the left IFG in adults with ADHD during error signaling following failed inhibition of prepotent behaviors (Vasic et al., 2014). Thus, the decreased efficiency in the left IFG may contribute to reduced inhibitory behavioral control, especially in context with increased emotional arousal.

5. LIMITATIONS

Certain limitations exist in this clinical study. First, although we excluded ADHD subjects with comorbidities to better identify ADHD‐specific brain alterations, our study design limits the generalization of our findings to ADHD patients with more diverse and complicated behavioral problems. Also, our sample was predominantly male so that generalization to females needs to be established in future studies. Second, while we recruited patients who were untreated to identify brain alterations intrinsic to ADHD, whether the alterations that we identified would persist or would be diminished by short‐ or longer‐term treatment remains to be determined. Third, while age‐related effects are of interest in examining neurodevelopmental alterations, longitudinal studies are needed to evaluate the emergence and course of brain maturational abnormalities. Fourth, we used the AAL 90 template, which has been applied in several recent studies to investigate functional connectivity. However, different templates may have an impact on our results. Future studies might explore the impact of other available templates for evaluating the integrity of functional network architectures in ADHD.

6. CONCLUSIONS

In summary, the current study explored brain functional networks in drug‐naïve ADHD children and adolescents. The small‐world architecture in the brain networks of ADHD patients exhibited lower global efficiency, reduced local efficiency, and a longer characteristic path length. These findings reveal dysfunction of segregated and integrated brain connectivity known to be involved in information transfer in functional brain networks. These alterations were clinically relevant, being related to illness‐defining behavioral characteristics of hyperactivity and reduced behavioral control.

Supporting information

Appendix S1 Supporting Information.

Click here for additional data file. (698.2KB, docx)

ACKNOWLEDGMENTS

This study was supported by the National Natural Science Foundation (Grant Nos. 81801358, 81621003, 81761128023, 81220108013, 81227002, 81030027, 81671669, and 81371536), the Humboldt Foundation, and the Program for Changjiang Scholars and Innovative Research Teams in Universities of China (PCSIRT, Grant No. IRT16R52). Dr. Gong also acknowledges support from his Changjiang Scholar Professorship Award of China (Award No. T2014190) and the American CMB Distinguished Professorship Award (Award No. F510000/G16916411) administered by the Institute of International Education, USA.

Chen Y, Huang X, Wu M, et al. Disrupted brain functional networks in drug‐naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis. Hum Brain Mapp. 2019;40:4877–4887. 10.1002/hbm.24743

Drs. Ying Chen and Xiaoqi Huang contributed equally to the study.

Funding information Changjiang Scholar Professorship Award of China, Grant/Award Number: T2014190; American CMB Distinguished Professorship Award, Grant/Award Number: F510000/G16916411; National Natural Science Foundation of China, Grant/Award Numbers: 81801358, 81621003, 81761128023, 81220108013, 81227002, 81030027, 81671669, 81371536; Program for Changjiang Scholars and Innovative Research Teams in Universities of China, Grant/Award Number: IRT16R52

Contributor Information

Lanting Guo, Email: guolanting@sina.com.

Qiyong Gong, Email: qiyonggong@hmrrc.org.cn.

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