Brain functional networks and executive functions in children with attention- deficit/hyperactivity disorder

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We explored neural mechanisms through electroencephalography (EEG) studies of executive function and functional brain networks in children with ADHD. Executive function data were collected and resting-state EEG was measured in 84 children with ADHD and 84 healthy children. Functional connectivity was assessed across all scalp channels in five frequency bands. Brain networks were constructed, and relevant metrics were calculated using graph theory. Children with ADHD show varied executive function deficits. Connectivity in the frontal and parietal regions was reduced in both the eyes-open and eyes-closed states, particularly in the beta and gamma bands. Brain networks differed significantly in the beta band. Reduced characteristic path length (CPL) was seen in the eyes-closed state; global efficiency increased and CPL, clustering coefficient, and local efficiency decreased in the eyes-open state. Functional networks in children with ADHD correlate with executive function. Altered EEG connectivity and brain network topology may be underlying neural mechanisms of ADHD. Thus, EEG network dysfunction could be a potential biomarker or treatment target for future research. This study provides new insights into the underlying mechanisms of ADHD through EEG-based functional network analysis. attention deficit/hyperactivity disorder children electroencephalography brain networks executive functions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattentive, hyperactive, and impulsive behaviors that are inconsistent with age norms. The global prevalence of ADHD in children is approximately 5–6%, with a prevalence of 5.7% in Chinese children (Sehlin et al. 2018 ). ADHD is considered a multifactorial neurodevelopmental disorder, yet its exact etiology and mechanisms remain unclear. Current research suggests that ADHD is a cognitive-neurological disorder, with deficits in executive function thought to be its core deficits in pathogenesis (Faraone et al. 2015 ). Previous findings on functional and structural neuroimaging in ADHD support these claims; however, the neuroimaging results are complex and variable (Yasumura et al. 2019 ). Examining executive function deficits and neuroimaging characteristics in children with ADHD is critical for understanding the neuropathological mechanisms of ADHD. With the advantages of non-invasiveness, high temporal resolution, simplicity, and low cost, electroencephalography (EEG) measures the electrical activity generated by groups of neurons in the cerebral cortex with millisecond temporal resolution and has now become an important tool in the study of neurological and psychiatric disorders. It has been suggested that there may be different mechanisms in the central nervous system in the eyes-closed and eyes-open states (Barry et al. 2009 ). Therefore, in this study, EEG was investigated separately in the eyes-open and -closed states. Recent advances in graph theory have enabled researchers to characterize the topological properties of complex brain networks and provide explanations for the brain’s information processing and processing mechanisms. Barry et al. (Barry et al. 2009 ) found increased functional connectivity in the frontal lobes of patients with ADHD. Brain networks in ADHD may have problems transferring information between different neural regions (Dini et al. 2020 ), but they do not provide a clear explanation of the different network properties. The aim of this study was to reveal the possible abnormal neural mechanisms of ADHD by constructing functional brain networks and to explore the connection between abnormal brain network topological features and executive function. Material and Methods Participants This study enrolled 168 children aged 6–12 years, including 84 children with ADHD (8.88 ± 1.28 years) and 84 healthy children (9.29 ± 1.62 years), who attended West China Second University Hospital, Sichuan University from October 2022 to January 2023. Physicians diagnosed ADHD in children according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and categorized them into three subtypes: attention deficit (n = 36), mixed (n = 42), and hyperactive/impulsive (n = 6). Inclusion criteria consisted of a Draw-a-Person Test score ≥ 85 and right-handedness. All participants were asked to complete a health status questionnaire to assess whether they had attentional problems, neuropsychiatric disorders, or close relatives with an ADHD or attention-deficit disorder diagnosis. The patients were asked not to take any medication for at least 24 h before the experiment. Written informed consent was obtained from the parents of each participant, and the study was approved by the Human Research Ethics Committee of the West China Second University Hospital, Sichuan University. Executive function assessment The Behavior Rating Inventory of Executive Function-Parent Form, Second Edition (BRIEF2) is used to assess the executive functioning abilities, including behavioral, emotional, and cognitive aspects, of children and adolescents aged 5–18 years. It consists of three main domains and nine subscales with a total of 63 items. The three domains include the Behavioral Regulation Index (BRI), encompassing inhibition and self-monitoring; the Emotional Regulation Index (ERI), covering shifting and emotional control; and the Cognitive Regulation Index (CRI), involving planning/organization, organization of materials, initiation, task monitoring, and working memory. Higher scores on each factor of the BRIEF2 indicate greater difficulties with that specific aspect of executive functioning. EEG acquisition Participants were seated in a quiet, temperature-controlled room devoid of strong light for the EEG test. This test consisted of 3 min of resting-state EEG with eyes open and 3 min with eyes closed. Nineteen Ag/AgCl electrodes were placed according to the International 10–20 system: Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, Pz, P3, P4, T3, T4, T5, T6, O1, and O2. The sampling rate was set at 2000 Hz, with the impedance between the electrodes and the scalp maintained below 5 kΩ. Additionally, two reference electrodes (A1 and A2) were placed on both earlobes. During the eyes-open phase, participants were asked to fixate on a cross in the middle of a black screen for 3 min. Fifteen minutes of resting-state EEG data were recorded for each participant. Data analysis EEG data pre-processing The acquired EEG data were processed in MATLAB R2021a (MathWorks, Natick, MA) using EEGLAB v14.1.2. This study aimed to obtain reliable EEG signal segments for each participant. The procedure included downsampling to 512 Hz, averaging referencing, bandpass filtering from 0.5–70 Hz, 50-Hz notch filtering, 2-s data segmentation, baseline correction, and artifact removal by independent component analysis with a threshold set to ± 90 µV. The lengths of the EEG epochs free of artifacts varied from 10 to 25 s. Functional connectivity analysis In this study, the spectral coherence method was used to estimate functional connectivity. The spectral coherence of the EEG data at 19 electrode locations was calculated to obtain a 19×19 functional connectivity matrix. The coherence was calculated for each pair of electrodes in the delta, theta, alpha, beta, and gamma bands, thus reflecting the correlation information exchanged between the 19 electrodes. Network measures This study applied graph theory to model the brain as a network with nodes and edges. Nineteen electrode locations were defined as nodes, and the connections between these nodes were considered the edges of the brain network. Local efficiency (LE), clustering coefficients (CCs), characteristic path lengths (CPLs), and global efficiency (GE) network metrics were measured. LE reflects the ability of local brain networks to transmit information to each other. GE represents the efficiency of information exchange in a network and is used to measure the efficiency of information transfer between all nodes in the network. The CC measures the level of a network community, which is an important statistical feature of complex networks. The CPL is the most commonly used measure of functional integration and plays an important role in the transmission of information through networks. Statistical analysis The EEG data were analyzed using SPSS 23.0 (IBM, Armonk, NY). Data were tested for normality using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Measures that conformed to normality were described as mean ± standard deviation, while those that did not conform to normal distribution were described as median ± interquartile range. Grouped or paired t-tests were used for measures that were normally or approximately normally distributed, and non-parametric rank-sum tests were used for measures that were not normally distributed. The chi-squared test was used for between-group comparisons of categorical information. The Bonferroni correction was applied for multiple hypothesis testing. Partial correlation analysis was used to analyze the correlation between executive function and topological features of the brain network. Results Comparison of demographic characteristics Demographic information for all participant subgroups is presented in Table 1 . There were more males in the ADHD group than in the healthy control (HC) group ( P < 0.001), but the age difference was not statistically significant. Table 1 Participant demographics ADHD HC P Number 84 84 Age(years) 8.88 ± 1.28 9.29 ± 1.62 0.069 Sex(M/F) 69/15 42/42 < 0.001 a Values are mean ± standard deviation. HC: healthy control; ADHD: attention-deficit/hyperactivity disorder; M/F: male/female. Clinical features and executive functions Comparison of executive functions The comparison of the factors of BRIEF2 between children with ADHD and HCs is shown in Table 2 . Children with ADHD had higher scores on all factors compared with HCs ( P < 0.05). Table 2 BRIEF2 in the ADHD and control groups BRIEF2 ADHD(n = 84) HC(n = 84) P Inhibition 58.1190 ± 1.0262 44.5119 ± 0.7181 <0.0001 Self-monitoring 64.1429 ± 0.9706 50.1071 ± 0.9665 <0.0001 Shifting 55.7976 ± 0.9602 47.0952 ± 0.7927 <0.0001 Emotional control 56.2738 ± 1.1603 46.5238 ± 0.7986 <0.0001 Initiation 59.7619 ± 0.9739 48.5357 ± 0.8637 <0.0001 Working memory 66.9167 ± 0.8666 50.1190 ± 0.8956 <0.0001 Planning/organization 62.6667 ± 0.8422 49.3571 ± 0.9048 <0.0001 Task monitoring 65.2381 ± 0.9387 52.0595 ± 0.9515 <0.0001 Organization of materials 57.7262 ± 0.9204 46.2143 ± 0.6890 <0.0001 Behavioral Regulation Index 62.1071 ± 0.9278 46.5357 ± 0.8324 <0.0001 Emotional Regulation Index 57.1190 ± 1.0258 46.0952 ± 0.9797 <0.0001 Cognitive Regulation Index 63.4167 ± 0.8617 49.2738 ± 0.8493 <0.0001 Overall score 65.2024 ± 0.9567 48.7738 ± 1.0184 <0.0001 b BRIEF2: Behavior Rating Inventory of the Executive Function-Parent Form, Second Edition; HC: healthy controls; ADHD: attention-deficit/hyperactivity disorder. Functional Connectivity This study assessed the possible functional connectivity between ADHD and HC groups using a coherence analysis across five frequency bands in different brain regions. In the eyes-closed state, the ADHD group had significantly reduced connectivity in the frontal (Fz-F3, Fz-F4, Fz-Cz, and F4-C4 channels) and parietal (Pz-P3 and Pz-P4 channels) regions in the beta band, as well as significantly reduced connectivity in the frontal (Fz-F3, Fz-F4, F3-C3, and F4-C4 channels) region in the gamma band compared with the HC group. There was no significant difference in the functional connectivity between the two groups in the delta, theta, and alpha frequency bands ( P > 0.05, Bonferroni-corrected) (Fig. 1 ). In the eyes-open state, the ADHD group had significantly lower connectivity in the frontal (Fz-F3, Fz-F4 channels) and parietal (Pz-P3, Pz-P4, P3-C4 channels) regions in the beta band, as well as significantly reduced connectivity in the frontal (Fz-F3, Fz-F4 channels) region in the gamma band, when compared with the HC group. However, there was no significant difference in functional connectivity between the ADHD and HC groups in the delta, theta, or alpha bands ( P > 0.05, Bonferroni-corrected). Graph Theory To investigate whether there are differences in brain network topological features between different brain regions, the present study quantified and compared changes in brain network topological features between children with ADHD and healthy children. In the beta band in the eyes-closed state, with a threshold range of 0.16–0.5, a comparison of the four network parameters between the children with ADHD and the HC group revealed that the CPL was reduced in the ADHD group ( P 0.05, Bonferroni-corrected) (Fig. 7). In the gamma band, with a threshold range of 0.16–0.29, no significant differences in GE, CPL, CC, and LE were observed in the ADHD group compared with the HC group ( P > 0.05, Bonferroni-corrected) (Fig. 2 ). In the beta band of the eyes-open state, the thresholds ranged from 0.16–0.5, and a comparison of the four network parameters between the children with ADHD and the HC group showed an increase in GE and a decrease in CPL, CC, and LE in the ADHD group ( P < 0.05, Bonferroni-corrected), with a significant difference in the thresholds from 0.16 to 0.36 (Fig. 9). In the gamma band, with thresholds ranging from 0.16 to 0.30, no significant differences in GE, CPL, CC, and LE were observed in the ADHD group compared with the HC group ( P > 0.05, Bonferroni-corrected) (Fig. 3 ). Correlation of executive function with topological features in children with ADHD In the beta band in the eyes-closed state, anxiety in the BRIEF2 was positively correlated with LE ( r = 0.233, P = 0.033). In the gamma band, emotional control in BRIEF2 was positively correlated with CC and LE ( r = 0.230, P = 0.035; r = 0.252, P = 0.021). The CRI was positively correlated with the CC and LE ( r = 0.222, P = 0.042 and r = 0.241, P = 0.027, respectively) (Fig. 4 ). In the beta band in the eyes-open state, emotional control in BRIEF2 was positively correlated with LE ( r = 0.252, P = 0.021). In the gamma band, inhibition in the BRIEF2 was negatively correlated with CC and LE ( r =-0.260, P = 0.017; r =-0.233, P = 0.033) (Fig. 5 ). Discussion Executive functions are a series of interrelated higher-order cognitive processes that control goal-directed behavior and problem-solving (Miyake et al. 2000 ). This study found that children with ADHD showed more executive functioning deficits, thus supporting Barkley’s model (Kofler et al. 2010 ). Executive dysfunction may represent an important target for preventing functional impairment in multiple domains in children with ADHD. Therefore, we investigated the neuropathological mechanisms underlying ADHD using EEG. The study of functional brain connectivity contributes to our understanding of the neural mechanisms of ADHD and can reveal patterns of brain network connectivity that are dysregulated in patients with ADHD. In the present study, we found that children with ADHD had diminished connectivity between the frontal and parietal regions in the beta band and between the frontal regions in the gamma band in both the closed- and open-eye states compared with the HC group. Robbie et al. ( 2016 ) found that children with ADHD display reduced coherence in different regions of the cerebral hemispheres. The executive control network, comprising the supraparietal and prefrontal lobes, thalamus, and striatum, is a hotspot in the study of functional brain networks in ADHD. Previous structural magnetic resonance imaging (MRI) studies have found reduced frontal and dorsolateral prefrontal volumes and significantly thinner cortices in patients with ADHD, which have been associated with the severity of hyperactivity/impulsivity and cognitive deficits (Shaw et al. 2011 ; Proal et al. 2011 ). Additionally, functional MRI (fMRI) studies have shown reduced activation of the superior parietal gyrus and left dorsolateral prefrontal cortical regions of the brain in children with ADHD when performing attention-related vigilance tasks (Christakou et al. 2013 ). Similarly, fMRI studies have demonstrated reduced activation of the superior parietal gyrus and left dorsolateral prefrontal cortical regions of the brain in children with ADHD when performing inhibitory control tasks (Christakou et al. 2013 ). Children with ADHD show hypoactivation of the left superior frontal lobe when performing tasks of inhibitory control (Hwang et al. 2019 ). We suggest that executive function and attention dysfunction in children with ADHD may be related to abnormal functional connectivity between the aforementioned brain regions. Children with ADHD have reduced activation of their executive control networks in both task and resting states and can experience functional impairments in impulsivity, oppositional behavior, response inhibition, and attentional control. Patients with ADHD have abnormally low presynaptic dopamine stores in the prefrontal cortex, severely impairing attentional function, cognitive processes, and working memory (Kollins and Adcock 2014 ). The prefrontal-cerebellar circuit undergoes core dysfunction in patients with ADHD, and its functional connectivity may underlie the neural basis of multidimensional behavioral deficits closely related to the manifestation of syndromic symptoms (Durston et al. 2011 ). Bakhshi et al. ( 2022 ) found that the frontocerebellar circuits of patients with ADHD had different ratios of choline/creatine and glutamate/creatine and that alterations in frontal-cerebellar metabolites may be related to cognitive and behavioral deficits. Altered EEG coherence suggests that functional connectivity between brain regions can provide important information about brain activation and neuropsychological features. In addition to prefrontal cortical activation, coherent patterns in frontal-parietal regions play a crucial role in executive function (Van Son et al. 2019 ). Many studies, including fMRI and EEG, have emphasized that the most common cognitive deficits in patients with ADHD are related to frontal or parietal cortical dysfunction and that abnormal EEG may be a good predictor of cognitive impairment in ADHD (Hoogman et al. 2017 ). The present study found reduced functional connectivity in the frontoparietal regions of the beta and gamma frequency bands, suggesting that this may be related to executive function abnormalities in children with ADHD. The neural mechanisms in patients with ADHD may involve localized functional abnormalities in the frontal and parietal brain regions. A reduction in internal connectivity between the frontal and parietal regions may be the neurological basis for the symptoms of ADHD and abnormalities in executive functions. We analyzed resting-state EEG data from patients with ADHD and healthy children and used graph theory to help understand the possible neural mechanisms of ADHD from the perspective of functional networks, contributing to the understanding of the pathogenesis of ADHD. In the present study, we found diminished frontal- and parietal-dominated connectivity only in the beta and gamma bands when comparing the ADHD and HC groups, and abnormal alterations in network parameter analyses were found only in the beta band. In children with ADHD, CPL decreased in the eyes-closed state, whereas the GE increased and the CPL, CC, and LE decreased in the eyes-open state. Brain networks in children with ADHD have problems transferring information between different neural regions, and the beta band better reflects the differences between children with ADHD and HCs and may be the best EEG sub-band to investigate connectivity impairments in ADHD (Michelini et al. 2019 ). In this study, CPL was lower in the ADHD group than in the HC group, suggesting that information is more aggregated in the brains of children with ADHD than in healthy children and that this aggregation impedes the easy transfer of information (Dini et al. 2020 ). Patients with ADHD are more likely to have elevated GE in early childhood, and an elevated GE reflects overactive functional integration, which may disrupt the transfer of information across the entire brain and impede complex cognitive functions (Ma et al. 2018 ; Furlong et al. 2021 ). A higher CC indicates a more complex information exchange between brain regions (Fornito et al. 2015), which may lead to increased susceptibility to ADHD. This study found reduced CC in children with ADHD, which may be related to the heterogeneity and variability of brain activity in these children. The reduced LE in children with ADHD in this study suggests reduced functional separation of local brain regions. In complex networks, shorter path lengths are not necessarily advantageous, and lower CC and CPL imply a faster transition to network randomness (De Haan et al. 2009 ). There is a correlation between executive function and topological properties in patients with ADHD (Zhou et al. 2023 ), and these properties may reflect differences in neural network patterns between the disorders. In this study, anxiety in BRIEF2 and LE were positively correlated in the beta band with eyes closed, and emotional control and CRI were positively correlated with both CC and LE in the gamma band. In the beta band of the eyes-open state, emotional control in BRIEF2 was positively correlated with CC and LE. It has been suggested that the more pronounced the executive function symptoms in children with ADHD, the lower the ability of brain networks to transmit information to each other. Previous studies have shown that 35% of children with ADHD exhibit extensive deficits in multiple domains of executive functioning, and 89% of children with ADHD exhibit objectively defined impairments in at least one executive function, findings that suggest substantial heterogeneity in executive functioning deficits in individuals with ADHD (Kofler et al. 2019 ). Cognitive function is an important intermediate phenotype connecting the brain and behavioral performance and is a potential factor explaining the heterogeneity of ADHD (Tripp and Wickens 2009 ). Executive function impairments have been linked to underlying neural mechanisms. A meta-analysis of task-state MRI involving executive function showed that patients with ADHD exhibited reduced activation of the frontoparietal network (Cortese et al. 2012 ). Our study did have some limitations: Although this study had a large sample size, the number of female patients included was still small. Future studies need to increase the sample size of female patients to help compare differences in EEG characteristics between sexes. EEG signals have a high temporal resolution, but their spatial resolution is limited. In the future, multiple functional brain imaging techniques can be jointly applied to combine the high spatial resolution of brain imaging with the high temporal resolution of EEG signals to obtain more reliable neural network features. In summary, we identified local functional abnormalities in the frontal and parietal brain regions in the pathogenesis of ADHD in children based on EEG data through the construction of a functional brain network. Children’s brain networks have problems transmitting information between different neural regions, which may lead to the development of the disease, with the beta band reflecting differences between the two groups of children. Impairment in the executive function of children with ADHD reflects underlying neurological mechanisms. Our findings offer potential for identifying neural markers of ADHD through EEG network analysis. Future studies are required to investigate the clinical utility of this approach for diagnosis and to develop targeted interventions aimed at improving information flow within these dysfunctional networks in ADHD. Declarations Declaration of Competing Interest None. Funding This work was supported by the National Key R&D Program of China (No. 2021YFC1005305) and Sichuan Provincial Department of Science and Technology Project(No.2023NSFSC1492). Author Contribution HY analyzed the data and wrote the draft of the article. YY and AW supported data analysis. 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Neuropharmacology 57:579–589. https://doi.org/10.1016/j.neuropharm.2009.07.026 Van Son D, De Rover M, De Blasio FM, van der Does W, Barry RJ, Putman P (2019) Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network. Ann N Y Acad Sci 1452:52–64. https://doi.org/10.1111/nyas.14180 Yasumura A, Omori M, Fukuda A, Takahashi J, Yasumura Y, Nakagawa E et al (2019) Age-related differences in frontal lobe function in children with ADHD. Brain Dev 41:577–586. https://doi.org/10.1016/j.braindev.2019.03.006 Zhou J, Duan J, Liu X, Wang Y, Zheng J, Tang L et al (2023) Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects. https://doi.org/10.1007/s00787-023-02314-5 . Eur Child Adolesc Psychiatry Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4278865","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293724527,"identity":"afa65eda-7bdf-4cb2-a701-13314e9dda7d","order_by":0,"name":"Hua Yang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Yang","suffix":""},{"id":293724529,"identity":"b3f8f5f6-0570-4721-9167-776303a37858","order_by":1,"name":"Yue Yang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yang","suffix":""},{"id":293724531,"identity":"494d1cf5-e31e-40ff-bc89-b24e5472c322","order_by":2,"name":"Anqi Wang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"Wang","suffix":""},{"id":293724533,"identity":"1e7dc31b-dae9-46f9-add4-c6550ff2dc91","order_by":3,"name":"Jie Yang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":293724535,"identity":"9cbc4f0b-8b0b-4db6-8936-af368304df92","order_by":4,"name":"Xiaowen Yang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Yang","suffix":""},{"id":293724536,"identity":"c56a5697-82a6-4066-98d7-4de3751c4a4e","order_by":5,"name":"Jielan Zhou","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jielan","middleName":"","lastName":"Zhou","suffix":""},{"id":293724537,"identity":"705dff25-d077-4285-b422-5de8a1bf80da","order_by":6,"name":"Tao Yu","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yu","suffix":""},{"id":293724538,"identity":"7e27db40-518f-49c7-95b8-51591aaf1050","order_by":7,"name":"Hao Liu","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Liu","suffix":""},{"id":293724539,"identity":"d48c61fa-51ae-4c41-859c-7a470f1d0f2c","order_by":8,"name":"Rong Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACPjBZAeFIEKWFDUyeIVkLYxtJWqSPP/zMO69O3uAA88HbPAx2eYS18OUYS/NuO2y44QBbsjUPQ3IxYS08PGzMvNsOJBgc4DGT5mE4kNhAWAv7M2beOXVALfzfiNXCYMbM28AMsoWNWC08xpJzjh02nHmYzdhyjkEyYS38POwPP7ypqZPnO9788MabCjvCWhCAGUQYEK9+FIyCUTAKRgEeAABOLy6gDouPCgAAAABJRU5ErkJggg==","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-04-17 02:44:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4278865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4278865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55381325,"identity":"ccb1d46b-5322-4ef5-9810-d27445a99e60","added_by":"auto","created_at":"2024-04-26 13:50:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":563929,"visible":true,"origin":"","legend":"\u003cp\u003eCoherence connectivity matrix for the two groups in the eyes-closed state and eyes-open state. Note: The horizontal and vertical axes represent the nodes of the 19 scalp electrodes, with yellow representing increased inter-nodal connectivity and blue representing decreased inter-nodal connectivity\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/a4f3e1f8860448f223994ad8.jpg"},{"id":55381324,"identity":"c86c657f-5b13-41c8-9ea6-553c8d041712","added_by":"auto","created_at":"2024-04-26 13:50:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244535,"visible":true,"origin":"","legend":"\u003cp\u003eTopological features of brain networks in two groups of children in the beta and gamma bands during the eyes-closed state\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/b7f2a16bc8070fd3d8c96b5a.jpg"},{"id":55381326,"identity":"558ffbe4-04d9-4e7c-8abb-5bdc8dfc366f","added_by":"auto","created_at":"2024-04-26 13:50:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247546,"visible":true,"origin":"","legend":"\u003cp\u003eTopological features of brain networks in two groups of children in the beta and gamma bands during the eyes-open state\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/7cb698c30e5af4d096bbb2e3.jpg"},{"id":55381327,"identity":"2d7e0691-fa9e-4aba-ace5-cc410a7e2a4e","added_by":"auto","created_at":"2024-04-26 13:50:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171079,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between executive function and topological feature in the beta and gamma bands of children with ADHD in the eyes-closed state. ADHD, attention-deficit/hyperactivity disorder\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/e28dc5985eca01a553d1653f.jpg"},{"id":55381328,"identity":"fd2e8108-c8f1-426b-9a16-e610f865bb6c","added_by":"auto","created_at":"2024-04-26 13:50:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93970,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between executive function and topological feature in the beta and gamma bands of children with ADHD in the eyes-open state. ADHD, attention-deficit/hyperactivity disorder\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/ac469840d5f6c3da0f1cafcc.jpg"},{"id":57196634,"identity":"602709f7-ffe2-4275-bf98-dbf405899c44","added_by":"auto","created_at":"2024-05-27 08:52:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1771389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4278865/v1/6ba96767-164c-45af-9da8-08b63b12f9c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain functional networks and executive functions in children with attention- deficit/hyperactivity disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattentive, hyperactive, and impulsive behaviors that are inconsistent with age norms. The global prevalence of ADHD in children is approximately 5\u0026ndash;6%, with a prevalence of 5.7% in Chinese children (Sehlin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). ADHD is considered a multifactorial neurodevelopmental disorder, yet its exact etiology and mechanisms remain unclear. Current research suggests that ADHD is a cognitive-neurological disorder, with deficits in executive function thought to be its core deficits in pathogenesis (Faraone et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Previous findings on functional and structural neuroimaging in ADHD support these claims; however, the neuroimaging results are complex and variable (Yasumura et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Examining executive function deficits and neuroimaging characteristics in children with ADHD is critical for understanding the neuropathological mechanisms of ADHD.\u003c/p\u003e \u003cp\u003eWith the advantages of non-invasiveness, high temporal resolution, simplicity, and low cost, electroencephalography (EEG) measures the electrical activity generated by groups of neurons in the cerebral cortex with millisecond temporal resolution and has now become an important tool in the study of neurological and psychiatric disorders. It has been suggested that there may be different mechanisms in the central nervous system in the eyes-closed and eyes-open states (Barry et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, in this study, EEG was investigated separately in the eyes-open and -closed states.\u003c/p\u003e \u003cp\u003eRecent advances in graph theory have enabled researchers to characterize the topological properties of complex brain networks and provide explanations for the brain\u0026rsquo;s information processing and processing mechanisms. Barry et al. (Barry et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found increased functional connectivity in the frontal lobes of patients with ADHD. Brain networks in ADHD may have problems transferring information between different neural regions (Dini et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but they do not provide a clear explanation of the different network properties. The aim of this study was to reveal the possible abnormal neural mechanisms of ADHD by constructing functional brain networks and to explore the connection between abnormal brain network topological features and executive function.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study enrolled 168 children aged 6\u0026ndash;12 years, including 84 children with ADHD (8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28 years) and 84 healthy children (9.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years), who attended West China Second University Hospital, Sichuan University from October 2022 to January 2023. Physicians diagnosed ADHD in children according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and categorized them into three subtypes: attention deficit (n\u0026thinsp;=\u0026thinsp;36), mixed (n\u0026thinsp;=\u0026thinsp;42), and hyperactive/impulsive (n\u0026thinsp;=\u0026thinsp;6). Inclusion criteria consisted of a Draw-a-Person Test score\u0026thinsp;\u0026ge;\u0026thinsp;85 and right-handedness. All participants were asked to complete a health status questionnaire to assess whether they had attentional problems, neuropsychiatric disorders, or close relatives with an ADHD or attention-deficit disorder diagnosis. The patients were asked not to take any medication for at least 24 h before the experiment. Written informed consent was obtained from the parents of each participant, and the study was approved by the Human Research Ethics Committee of the West China Second University Hospital, Sichuan University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExecutive function assessment\u003c/h2\u003e \u003cp\u003eThe Behavior Rating Inventory of Executive Function-Parent Form, Second Edition (BRIEF2) is used to assess the executive functioning abilities, including behavioral, emotional, and cognitive aspects, of children and adolescents aged 5\u0026ndash;18 years. It consists of three main domains and nine subscales with a total of 63 items. The three domains include the Behavioral Regulation Index (BRI), encompassing inhibition and self-monitoring; the Emotional Regulation Index (ERI), covering shifting and emotional control; and the Cognitive Regulation Index (CRI), involving planning/organization, organization of materials, initiation, task monitoring, and working memory. Higher scores on each factor of the BRIEF2 indicate greater difficulties with that specific aspect of executive functioning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEEG acquisition\u003c/h2\u003e \u003cp\u003eParticipants were seated in a quiet, temperature-controlled room devoid of strong light for the EEG test. This test consisted of 3 min of resting-state EEG with eyes open and 3 min with eyes closed. Nineteen Ag/AgCl electrodes were placed according to the International 10\u0026ndash;20 system: Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, Pz, P3, P4, T3, T4, T5, T6, O1, and O2. The sampling rate was set at 2000 Hz, with the impedance between the electrodes and the scalp maintained below 5 kΩ. Additionally, two reference electrodes (A1 and A2) were placed on both earlobes. During the eyes-open phase, participants were asked to fixate on a cross in the middle of a black screen for 3 min. Fifteen minutes of resting-state EEG data were recorded for each participant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eEEG data pre-processing\u003c/h2\u003e \u003cp\u003eThe acquired EEG data were processed in MATLAB R2021a (MathWorks, Natick, MA) using EEGLAB v14.1.2. This study aimed to obtain reliable EEG signal segments for each participant. The procedure included downsampling to 512 Hz, averaging referencing, bandpass filtering from 0.5\u0026ndash;70 Hz, 50-Hz notch filtering, 2-s data segmentation, baseline correction, and artifact removal by independent component analysis with a threshold set to \u0026plusmn;\u0026thinsp;90 \u0026micro;V. The lengths of the EEG epochs free of artifacts varied from 10 to 25 s.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional connectivity analysis\u003c/h2\u003e \u003cp\u003eIn this study, the spectral coherence method was used to estimate functional connectivity. The spectral coherence of the EEG data at 19 electrode locations was calculated to obtain a 19\u0026times;19 functional connectivity matrix. The coherence was calculated for each pair of electrodes in the delta, theta, alpha, beta, and gamma bands, thus reflecting the correlation information exchanged between the 19 electrodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNetwork measures\u003c/h2\u003e \u003cp\u003eThis study applied graph theory to model the brain as a network with nodes and edges. Nineteen electrode locations were defined as nodes, and the connections between these nodes were considered the edges of the brain network. Local efficiency (LE), clustering coefficients (CCs), characteristic path lengths (CPLs), and global efficiency (GE) network metrics were measured. LE reflects the ability of local brain networks to transmit information to each other. GE represents the efficiency of information exchange in a network and is used to measure the efficiency of information transfer between all nodes in the network. The CC measures the level of a network community, which is an important statistical feature of complex networks. The CPL is the most commonly used measure of functional integration and plays an important role in the transmission of information through networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe EEG data were analyzed using SPSS 23.0 (IBM, Armonk, NY). Data were tested for normality using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests. Measures that conformed to normality were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while those that did not conform to normal distribution were described as median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range. Grouped or paired t-tests were used for measures that were normally or approximately normally distributed, and non-parametric rank-sum tests were used for measures that were not normally distributed. The chi-squared test was used for between-group comparisons of categorical information. The Bonferroni correction was applied for multiple hypothesis testing. Partial correlation analysis was used to analyze the correlation between executive function and topological features of the brain network.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of demographic characteristics\u003c/h2\u003e \u003cp\u003eDemographic information for all participant subgroups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were more males in the ADHD group than in the healthy control (HC) group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the age difference was not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. HC: healthy control; ADHD: attention-deficit/hyperactivity disorder; M/F: male/female.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical features and executive functions\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eComparison of executive functions\u003c/h2\u003e \u003cp\u003eThe comparison of the factors of BRIEF2 between children with ADHD and HCs is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Children with ADHD had higher scores on all factors compared with HCs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBRIEF2 in the ADHD and control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRIEF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHD(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.1190\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.5119\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.1429\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.1071\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShifting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.7976\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e47.0952\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.2738\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.5238\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.7619\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.5357\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.9167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.1190\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning/organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.6667\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e49.3571\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.2381\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e52.0595\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganization of materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.7262\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.2143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Regulation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.1071\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.5357\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional Regulation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.1190\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.0952\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Regulation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.4167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e49.2738\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.2024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.7738\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eBRIEF2: Behavior Rating Inventory of the Executive Function-Parent Form, Second Edition; HC: healthy controls; ADHD: attention-deficit/hyperactivity disorder.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Connectivity\u003c/h2\u003e \u003cp\u003eThis study assessed the possible functional connectivity between ADHD and HC groups using a coherence analysis across five frequency bands in different brain regions. In the eyes-closed state, the ADHD group had significantly reduced connectivity in the frontal (Fz-F3, Fz-F4, Fz-Cz, and F4-C4 channels) and parietal (Pz-P3 and Pz-P4 channels) regions in the beta band, as well as significantly reduced connectivity in the frontal (Fz-F3, Fz-F4, F3-C3, and F4-C4 channels) region in the gamma band compared with the HC group. There was no significant difference in the functional connectivity between the two groups in the delta, theta, and alpha frequency bands (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Bonferroni-corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the eyes-open state, the ADHD group had significantly lower connectivity in the frontal (Fz-F3, Fz-F4 channels) and parietal (Pz-P3, Pz-P4, P3-C4 channels) regions in the beta band, as well as significantly reduced connectivity in the frontal (Fz-F3, Fz-F4 channels) region in the gamma band, when compared with the HC group. However, there was no significant difference in functional connectivity between the ADHD and HC groups in the delta, theta, or alpha bands (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Bonferroni-corrected).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGraph Theory\u003c/h2\u003e \u003cp\u003eTo investigate whether there are differences in brain network topological features between different brain regions, the present study quantified and compared changes in brain network topological features between children with ADHD and healthy children. In the beta band in the eyes-closed state, with a threshold range of 0.16\u0026ndash;0.5, a comparison of the four network parameters between the children with ADHD and the HC group revealed that the CPL was reduced in the ADHD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Bonferroni-corrected). Furthermore, thresholds in the range of 0.16\u0026ndash;0.36 yielded significantly different results, while GE, CC, and LE were not significantly different between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Bonferroni-corrected) (Fig.\u0026nbsp;7). In the gamma band, with a threshold range of 0.16\u0026ndash;0.29, no significant differences in GE, CPL, CC, and LE were observed in the ADHD group compared with the HC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Bonferroni-corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the beta band of the eyes-open state, the thresholds ranged from 0.16\u0026ndash;0.5, and a comparison of the four network parameters between the children with ADHD and the HC group showed an increase in GE and a decrease in CPL, CC, and LE in the ADHD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Bonferroni-corrected), with a significant difference in the thresholds from 0.16 to 0.36 (Fig.\u0026nbsp;9). In the gamma band, with thresholds ranging from 0.16 to 0.30, no significant differences in GE, CPL, CC, and LE were observed in the ADHD group compared with the HC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Bonferroni-corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of executive function with topological features in children with ADHD\u003c/h2\u003e \u003cp\u003eIn the beta band in the eyes-closed state, anxiety in the BRIEF2 was positively correlated with LE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.233, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). In the gamma band, emotional control in BRIEF2 was positively correlated with CC and LE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.230, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.252, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). The CRI was positively correlated with the CC and LE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.222, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042 and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.241, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the beta band in the eyes-open state, emotional control in BRIEF2 was positively correlated with LE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.252, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). In the gamma band, inhibition in the BRIEF2 was negatively correlated with CC and LE (\u003cem\u003er\u003c/em\u003e=-0.260, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017; \u003cem\u003er\u003c/em\u003e=-0.233, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExecutive functions are a series of interrelated higher-order cognitive processes that control goal-directed behavior and problem-solving (Miyake et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This study found that children with ADHD showed more executive functioning deficits, thus supporting Barkley\u0026rsquo;s model (Kofler et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Executive dysfunction may represent an important target for preventing functional impairment in multiple domains in children with ADHD. Therefore, we investigated the neuropathological mechanisms underlying ADHD using EEG.\u003c/p\u003e \u003cp\u003eThe study of functional brain connectivity contributes to our understanding of the neural mechanisms of ADHD and can reveal patterns of brain network connectivity that are dysregulated in patients with ADHD. In the present study, we found that children with ADHD had diminished connectivity between the frontal and parietal regions in the beta band and between the frontal regions in the gamma band in both the closed- and open-eye states compared with the HC group. Robbie et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that children with ADHD display reduced coherence in different regions of the cerebral hemispheres. The executive control network, comprising the supraparietal and prefrontal lobes, thalamus, and striatum, is a hotspot in the study of functional brain networks in ADHD. Previous structural magnetic resonance imaging (MRI) studies have found reduced frontal and dorsolateral prefrontal volumes and significantly thinner cortices in patients with ADHD, which have been associated with the severity of hyperactivity/impulsivity and cognitive deficits (Shaw et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Proal et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, functional MRI (fMRI) studies have shown reduced activation of the superior parietal gyrus and left dorsolateral prefrontal cortical regions of the brain in children with ADHD when performing attention-related vigilance tasks (Christakou et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, fMRI studies have demonstrated reduced activation of the superior parietal gyrus and left dorsolateral prefrontal cortical regions of the brain in children with ADHD when performing inhibitory control tasks (Christakou et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Children with ADHD show hypoactivation of the left superior frontal lobe when performing tasks of inhibitory control (Hwang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We suggest that executive function and attention dysfunction in children with ADHD may be related to abnormal functional connectivity between the aforementioned brain regions. Children with ADHD have reduced activation of their executive control networks in both task and resting states and can experience functional impairments in impulsivity, oppositional behavior, response inhibition, and attentional control. Patients with ADHD have abnormally low presynaptic dopamine stores in the prefrontal cortex, severely impairing attentional function, cognitive processes, and working memory (Kollins and Adcock \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The prefrontal-cerebellar circuit undergoes core dysfunction in patients with ADHD, and its functional connectivity may underlie the neural basis of multidimensional behavioral deficits closely related to the manifestation of syndromic symptoms (Durston et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Bakhshi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that the frontocerebellar circuits of patients with ADHD had different ratios of choline/creatine and glutamate/creatine and that alterations in frontal-cerebellar metabolites may be related to cognitive and behavioral deficits. Altered EEG coherence suggests that functional connectivity between brain regions can provide important information about brain activation and neuropsychological features. In addition to prefrontal cortical activation, coherent patterns in frontal-parietal regions play a crucial role in executive function (Van Son et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many studies, including fMRI and EEG, have emphasized that the most common cognitive deficits in patients with ADHD are related to frontal or parietal cortical dysfunction and that abnormal EEG may be a good predictor of cognitive impairment in ADHD (Hoogman et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The present study found reduced functional connectivity in the frontoparietal regions of the beta and gamma frequency bands, suggesting that this may be related to executive function abnormalities in children with ADHD. The neural mechanisms in patients with ADHD may involve localized functional abnormalities in the frontal and parietal brain regions. A reduction in internal connectivity between the frontal and parietal regions may be the neurological basis for the symptoms of ADHD and abnormalities in executive functions.\u003c/p\u003e \u003cp\u003eWe analyzed resting-state EEG data from patients with ADHD and healthy children and used graph theory to help understand the possible neural mechanisms of ADHD from the perspective of functional networks, contributing to the understanding of the pathogenesis of ADHD. In the present study, we found diminished frontal- and parietal-dominated connectivity only in the beta and gamma bands when comparing the ADHD and HC groups, and abnormal alterations in network parameter analyses were found only in the beta band. In children with ADHD, CPL decreased in the eyes-closed state, whereas the GE increased and the CPL, CC, and LE decreased in the eyes-open state. Brain networks in children with ADHD have problems transferring information between different neural regions, and the beta band better reflects the differences between children with ADHD and HCs and may be the best EEG sub-band to investigate connectivity impairments in ADHD (Michelini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, CPL was lower in the ADHD group than in the HC group, suggesting that information is more aggregated in the brains of children with ADHD than in healthy children and that this aggregation impedes the easy transfer of information (Dini et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Patients with ADHD are more likely to have elevated GE in early childhood, and an elevated GE reflects overactive functional integration, which may disrupt the transfer of information across the entire brain and impede complex cognitive functions (Ma et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Furlong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A higher CC indicates a more complex information exchange between brain regions (Fornito et al. 2015), which may lead to increased susceptibility to ADHD. This study found reduced CC in children with ADHD, which may be related to the heterogeneity and variability of brain activity in these children. The reduced LE in children with ADHD in this study suggests reduced functional separation of local brain regions. In complex networks, shorter path lengths are not necessarily advantageous, and lower CC and CPL imply a faster transition to network randomness (De Haan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a correlation between executive function and topological properties in patients with ADHD (Zhou et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and these properties may reflect differences in neural network patterns between the disorders. In this study, anxiety in BRIEF2 and LE were positively correlated in the beta band with eyes closed, and emotional control and CRI were positively correlated with both CC and LE in the gamma band. In the beta band of the eyes-open state, emotional control in BRIEF2 was positively correlated with CC and LE. It has been suggested that the more pronounced the executive function symptoms in children with ADHD, the lower the ability of brain networks to transmit information to each other. Previous studies have shown that 35% of children with ADHD exhibit extensive deficits in multiple domains of executive functioning, and 89% of children with ADHD exhibit objectively defined impairments in at least one executive function, findings that suggest substantial heterogeneity in executive functioning deficits in individuals with ADHD (Kofler et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cognitive function is an important intermediate phenotype connecting the brain and behavioral performance and is a potential factor explaining the heterogeneity of ADHD (Tripp and Wickens \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Executive function impairments have been linked to underlying neural mechanisms. A meta-analysis of task-state MRI involving executive function showed that patients with ADHD exhibited reduced activation of the frontoparietal network (Cortese et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study did have some limitations: Although this study had a large sample size, the number of female patients included was still small. Future studies need to increase the sample size of female patients to help compare differences in EEG characteristics between sexes. EEG signals have a high temporal resolution, but their spatial resolution is limited. In the future, multiple functional brain imaging techniques can be jointly applied to combine the high spatial resolution of brain imaging with the high temporal resolution of EEG signals to obtain more reliable neural network features.\u003c/p\u003e \u003cp\u003eIn summary, we identified local functional abnormalities in the frontal and parietal brain regions in the pathogenesis of ADHD in children based on EEG data through the construction of a functional brain network. Children\u0026rsquo;s brain networks have problems transmitting information between different neural regions, which may lead to the development of the disease, with the beta band reflecting differences between the two groups of children. Impairment in the executive function of children with ADHD reflects underlying neurological mechanisms. Our findings offer potential for identifying neural markers of ADHD through EEG network analysis. Future studies are required to investigate the clinical utility of this approach for diagnosis and to develop targeted interventions aimed at improving information flow within these dysfunctional networks in ADHD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (No. 2021YFC1005305) and Sichuan Provincial Department of Science and Technology Project(No.2023NSFSC1492).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHY analyzed the data and wrote the draft of the article. YY and AW supported data analysis. JY and XY supported the study design. JZ, TY and HL helped to revise the research report. RL contributed significantly to the editing and review of the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (No. 2021YFC1005305) and Sichuan Provincial Department of Science and Technology Project（No.2023NSFSC1492）.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBakhshi S, Tehrani-Doost M, Batouli SAH (2022) Evaluation of fronto-cerebellar neurometabolites in youth with ADHD compared to the healthy group and their associations with cognitive and behavioral characteristics: a proton magnetic spectroscopy study. 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Eur Child Adolesc Psychiatry\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"attention deficit/hyperactivity disorder, children, electroencephalography, brain networks, executive functions","lastPublishedDoi":"10.21203/rs.3.rs-4278865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4278865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain region dysfunctions associated with executive function abnormalities may contribute to attention-deficit/hyperactivity disorder (ADHD) pathogenesis. We explored neural mechanisms through electroencephalography (EEG) studies of executive function and functional brain networks in children with ADHD. Executive function data were collected and resting-state EEG was measured in 84 children with ADHD and 84 healthy children. Functional connectivity was assessed across all scalp channels in five frequency bands. Brain networks were constructed, and relevant metrics were calculated using graph theory. Children with ADHD show varied executive function deficits. Connectivity in the frontal and parietal regions was reduced in both the eyes-open and eyes-closed states, particularly in the beta and gamma bands. Brain networks differed significantly in the beta band. Reduced characteristic path length (CPL) was seen in the eyes-closed state; global efficiency increased and CPL, clustering coefficient, and local efficiency decreased in the eyes-open state. Functional networks in children with ADHD correlate with executive function. Altered EEG connectivity and brain network topology may be underlying neural mechanisms of ADHD. Thus, EEG network dysfunction could be a potential biomarker or treatment target for future research. This study provides new insights into the underlying mechanisms of ADHD through EEG-based functional network analysis.\u003c/p\u003e","manuscriptTitle":"Brain functional networks and executive functions in children with attention- deficit/hyperactivity disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 13:50:12","doi":"10.21203/rs.3.rs-4278865/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77be58bf-849e-4fb2-9a9b-bebeda54199a","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T08:44:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 13:50:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4278865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4278865","identity":"rs-4278865","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}