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Dini H, Bruni LE, Ramsøy TZ, Calhoun VD, Sendi MSE. The overlap across psychotic disorders: A functional network connectivity analysis. Int J Psychophysiol 2024; 201:112354. [PMID: 38670348 PMCID: PMC11163820 DOI: 10.1016/j.ijpsycho.2024.112354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 03/20/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024]
Abstract
Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).
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Affiliation(s)
- Hossein Dini
- Augmented Cognition Lab, Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Luis E Bruni
- Augmented Cognition Lab, Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Thomas Z Ramsøy
- Department of Applied Neuroscience, Neurons Inc., Taastrup, Denmark; Faculty of Neuroscience, Singularity University, Santa Clara, CA, United States
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; Department of Electrical and Computer Engineering at, Georgia Institute of Technology, Atlanta, GA, United States; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Mohammad S E Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States; McLean Hospital and Harvard Medical School, Boston, MA, USA.
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2
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Novak L, Malinakova K, Trnka R, Mikoska P, Sverak T, Kiiski H, Tavel P, van Dijk JP. Neural bases of social deficits in ADHD: A systematic review. Does the Theory of Mind matter? Brain Res Bull 2024; 215:111011. [PMID: 38906229 DOI: 10.1016/j.brainresbull.2024.111011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/31/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
INTRODUCTION The Attention Deficit Hyperactivity Disorder (ADHD) causes serious interpersonal problems from childhood to adulthood, one of them being problematic social functioning. This phenomenon in ADHD should be associated with impairments in the Theory of Mind (ToM). Therefore, understanding the neural correlates of the ToM could be crucial for helping individuals with ADHD with their social functioning. Thus, we aimed to review published literature concerning neuroanatomical and functional correlates of ToM deficits in children and adolescents with ADHD. METHODS We reviewed studies published between 1970 and 2023. In accordance with PRISMA guidelines, after data from three databases were collected, two authors (LN and PM) independently screened all relevant records (n=638) and consequently, both authors did the data extraction. The quality of the included studies (n=5) was measured by a modified version of The Newcastle-Ottawa Scale and by measures specific for our study. This systematic review was registered on PROSPERO (CRD42020139847). RESULTS Results indicated that impairments in performing of the ToM tasks were negatively associated with the grey matter volume in the bilateral amygdala and hippocampus in both, ADHD and control group. In EEG studies, a significantly greater electrophysiological activity during ToM tasks was observed in the, frontal, temporal, parietal and occipital lobes in participants with ADHD as compared to healthy subjects. CONCLUSION More research is needed to explore the ToM deficits in children with ADHD. Future research might focus on the neural circuits associated with attention and inhibition, which deficits seems to contribute to the ToM deficits in children and adolescents with ADHD.
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Affiliation(s)
- Lukas Novak
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Department of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Klara Malinakova
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Radek Trnka
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Prague College of Psychosocial Studies, Prague, Czech Republic
| | - Petr Mikoska
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Tomas Sverak
- Department of Psychiatry, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Hanni Kiiski
- Trinity Institute of Neuroscience and School of Psychology, Trinity College Dublin, Ireland
| | - Peter Tavel
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Jitse P van Dijk
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Department of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Graduate School Kosice Institute for Society and Health, P.J. Safarik University in Kosice, Kosice, Slovak Republic
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Chen Y, Ma Y, Fan X, Lyu J, Yang R. Facial expression recognition ability and its neuropsychological mechanisms in children with attention deficit and hyperactive disorder. Zhejiang Da Xue Xue Bao Yi Xue Ban 2024; 53:254-260. [PMID: 38650447 PMCID: PMC11057990 DOI: 10.3724/zdxbyxb-2023-0390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 03/17/2024] [Indexed: 04/25/2024]
Abstract
Attention deficit and hyperactive disorder (ADHD) is a chronic neurodevelopmental disorder characterized by inattention, hyperactivity-impulsivity, and working memory deficits. Social dysfunction is one of the major challenges faced by children with ADHD. It has been found that children with ADHD can't perform as well as typically developing children on facial expression recognition (FER) tasks. Generally, children with ADHD have some difficulties in FER, while some studies suggest that they have no significant differences in accuracy of specific emotion recognition compared with typically developing children. The neuropsychological mechanisms underlying these difficulties are as follows. First, neuroanatomically. Compared to typically developing children, children with ADHD show smaller gray matter volume and surface area in the amygdala and medial prefrontal cortex regions, as well as reduced density and volume of axons/cells in certain frontal white matter fiber tracts. Second, neurophysiologically. Children with ADHD exhibit increased slow-wave activity in their electroencephalogram, and event-related potential studies reveal abnormalities in emotional regulation and responses to angry faces when facing facial stimuli. Third, psychologically. Psychosocial stressors may influence FER abilities in children with ADHD, and sleep deprivation in ADHD children may significantly increase their recognition threshold for negative expressions such as sadness and anger. This article reviews research progress over the past three years on FER abilities of children with ADHD, analyzing the FER deficit in children with ADHD from three dimensions: neuroanatomy, neurophysiology and psychology, aiming to provide new perspectives for further research and clinical treatment of ADHD.
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Affiliation(s)
- Yi Chen
- Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Ye Ma
- Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xiaoli Fan
- Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Jiamin Lyu
- Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Rongwang Yang
- Department of Psychology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China.
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Sanchis J, García-Ponsoda S, Teruel MA, Trujillo J, Song IY. A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning. Heliyon 2024; 10:e26028. [PMID: 38379973 PMCID: PMC10877365 DOI: 10.1016/j.heliyon.2024.e26028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/10/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024] Open
Abstract
Objective Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field. Therefore, the primary objective of this work is to present a study to find the subset of channels or brain regions that best classify ADHD vs Typically Developing children by means of Electroencephalograms (EEG). Methods To achieve this goal, we present a novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning (DL). First, we perform a filtering and artefact removal process on the EEG signal. Then we generate different subsets of EEG channels depending on their location on the scalp (hemispheres, lobes, sets of lobes and single channels) and using backward and forward stepwise feature selection methods. Finally, we feed the DL neural network with each set, and compute the f 1 -score. Results and conclusions Based on the obtained results, the Frontal Lobe (FL) (0.8081 f 1 -score) and the Left Hemisphere (LH) (0.8056 f 1 -score) provide more significant information detecting individuals with ADHD, than using the entire set of EEG Channels (0.8067 f 1 -score). However, when combining the Temporal, Parietal and Occipital Lobes (TL, PL, OL), better results (0.8097 f 1 -score) were obtained compared with using only the FL and LH subsets. The best performance was obtained using Feature Selection Methods. In the case of the Backward Stepwise Feature Selection method, a combination of 14 EEG channels yielded a 0.8281 f 1 -score. Similarly, using the Forward Stepwise Feature Selection method, a combination of 11 EEG channels yielded a 0.8271 f 1 -score. These findings hold significant value for physicians in the quest to better understand the underlying causes of ADHD.
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Affiliation(s)
- Javier Sanchis
- Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Carretera de San Vicente del Raspeig, s/n, San Vicente del Raspeig, 03690, Spain
| | - Sandra García-Ponsoda
- Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Carretera de San Vicente del Raspeig, s/n, San Vicente del Raspeig, 03690, Spain
- ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, 46022, Valencia, Spain
| | - Miguel A. Teruel
- Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Carretera de San Vicente del Raspeig, s/n, San Vicente del Raspeig, 03690, Spain
- Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - Juan Trujillo
- Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Carretera de San Vicente del Raspeig, s/n, San Vicente del Raspeig, 03690, Spain
- Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - Il-Yeol Song
- College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, USA
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Dini H, Simonetti A, Bruni LE. Exploring the Neural Processes behind Narrative Engagement: An EEG Study. eNeuro 2023; 10:ENEURO.0484-22.2023. [PMID: 37460223 DOI: 10.1523/eneuro.0484-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 06/02/2023] [Accepted: 06/10/2023] [Indexed: 07/20/2023] Open
Abstract
Past cognitive neuroscience studies using naturalistic stimuli have considered narratives holistically and focused on cognitive processes. In this study, we incorporated the narrative structure, the dramatic arc, as an object of investigation, to examine how engagement levels fluctuate across a narrative-aligned dramatic arc. We explored the possibility of predicting self-reported engagement ratings from neural activity and investigated the idiosyncratic effects of each phase of the dramatic arc on brain responses as well as the relationship between engagement and brain responses. We presented a movie excerpt following the six-phase narrative arc structure to female and male participants while collecting EEG signals. We then asked this group of participants to recall the excerpt, another group to segment the video based on the dramatic arc model, and a third to rate their engagement levels while watching the movie. The results showed that the self-reported engagement ratings followed the pattern of the narrative dramatic arc. Moreover, while EEG amplitude could not predict group-averaged engagement ratings, other features comprising dynamic intersubject correlation (dISC), including certain frequency bands, dynamic functional connectivity patterns and graph features were able to achieve this. Furthermore, neural activity in the last two phases of the dramatic arc significantly predicted engagement patterns. This study is the first to explore the cognitive processes behind the dramatic arc and its phases. By demonstrating how neural activity predicts self-reported engagement, which itself aligns with the narrative structure, this study provides insights on the interrelationships between narrative structure, neural responses, and viewer engagement.
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Affiliation(s)
- Hossein Dini
- The Augmented Cognition Lab, Aalborg University, Copenhagen 2450, Denmark
| | - Aline Simonetti
- Department of Marketing and Market Research, University of Valencia, Valencia 46022, Spain
| | - Luis Emilio Bruni
- The Augmented Cognition Lab, Aalborg University, Copenhagen 2450, Denmark
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Gong L, Li M, Zhang T, Chen W. EEG emotion recognition using attention-based convolutional transformer neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Chen H, Yang Y, Odisho D, Wu S, Yi C, Oliver BG. Can biomarkers be used to diagnose attention deficit hyperactivity disorder? Front Psychiatry 2023; 14:1026616. [PMID: 36970271 PMCID: PMC10030688 DOI: 10.3389/fpsyt.2023.1026616] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 02/14/2023] [Indexed: 03/10/2023] Open
Abstract
Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms “ADHD,” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro” were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies.
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Affiliation(s)
- Hui Chen
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia
| | - Yang Yang
- Research Centre, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Diana Odisho
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia
| | - Siqi Wu
- Research Centre, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Chenju Yi
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- Research Centre, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
- Shenzhen Key Laboratory of Chinese Medicine Active Substance Screening and Translational Research, Shenzhen, China
- *Correspondence: Chenju Yi,
| | - Brian G. Oliver
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia
- Respiratory Cellular and Molecular Biology, Woolcock Institute of Medical Research, The University of Sydney, Glebe, NSW, Australia
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Investigation of phase synchronization in functional brain networks of children with ADHD using nonlinear recurrence measure. J Theor Biol 2023; 560:111381. [PMID: 36528091 DOI: 10.1016/j.jtbi.2022.111381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/24/2022] [Accepted: 12/11/2022] [Indexed: 12/16/2022]
Abstract
Measuring the phase synchronization between different brain regions in functional brain networks is a common approach to investigate many psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD). The emotional processing deficit in ADHD children is one of the main obstacles in their social interactions. In this study, the nonlinear Correlation between Probability of Recurrences (CPR) method is used for the first time to construct functional brain networks of 22 boys with ADHD and 22 healthy ones during watching four visual-emotional stimuli types. Topological features of brain networks, including shortest path length, clustering coefficient, and nodes strengths, are investigated in groups of ADHD and healthy. The results indicate a significantly (P-Values < 0.01) greater average clustering coefficient and lower shortest path length in the brain networks of ADHD individuals than the healthy ones. Accordingly, in the ADHD brain networks, the information exchange in both local and global scales is abnormally more than the healthy ones, leading to a hyper-synchronization in this group. The topological alterations of ADHD brain networks are mainly observed in the brain's frontal and occipital lobes, indicating impaired brain function of this group in emotional and visual processing. This survey demonstrates that the CPR method can be a good candidate for distinguishing the phase interactions of ADHD and healthy brain networks. Therefore, this study can contribute to further insights into the nonlinear dynamics analysis of brain networks in ADHD individuals.
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Ansarinasab S, Parastesh F, Ghassemi F, Rajagopal K, Jafari S, Ghosh D. Synchronization in functional brain networks of children suffering from ADHD based on Hindmarsh-Rose neuronal model. Comput Biol Med 2022. [DOI: 10.1016/j.compbiomed.2022.106461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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10
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Liu X, Sun L, Zhang D, Wang S, Hu S, Fang B, Yan G, Sui G, Huang Q, Wang S. Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder. Clin EEG Neurosci 2022; 53:399-405. [PMID: 35257602 DOI: 10.1177/15500594221086195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In cognitive neuroscience, there is an increasing interest in identifying and understanding the synchronization of distinct neural oscillations with different frequencies that might support dynamic communication within the brain. This study explored the cross-frequency phase-amplitude coupling brain network characteristics of resting-state electroencephalograms between 30 children with attention-deficit/hyperactivity disorder (ADHD) and 30 age-matched typically developing children. Compared with control group, children with ADHD show increased coupling intensity and altered distribution patterns of dominant paired channels, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. Regarding graph theory properties, the characteristic path length, the mean clustering coefficient, the global efficiency, and the mean local efficiency significant difference in many cross-frequency coupling networks, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. The area under the receiver operating characteristic curve (AUC) in low-frequency coupling with a high-gamma frequency was larger than that in coupling with low-gamma frequency (AUC values of δ-γL, θ-γL, α-γL, βL-γL, βH-γL, δ-γH, θ-γH, α-γH, βL-γH, and βH-γH were 0.794, 0.722, 0.666, 0.570, 0.881, 0.992, 0.998, 0.998, 0.989, and 0.974, respectively). These findings demonstrate altered coupling intensity and disrupted topological organization of coupling networks, support the altered brain network theory in children with ADHD. The coupling intensity and graph theory properties of low-frequency coupling with high-gamma frequency were promising resting-state electroencephalogram biomarkers of ADHD in children.
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Affiliation(s)
- Xingping Liu
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
| | - Ling Sun
- Department of Child and Adolescent Psychology, 194039Tianjin Anding Hospital, Tianjin 300222, P.R.China
| | - Dujuan Zhang
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
| | - Shanshan Wang
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
| | - Shengjing Hu
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
| | - Bei Fang
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
| | - Guoli Yan
- Department of Child and Adolescent Psychology, 194039Tianjin Anding Hospital, Tianjin 300222, P.R.China
| | - Guanghong Sui
- Department of Child and Adolescent Psychology, 194039Tianjin Anding Hospital, Tianjin 300222, P.R.China
| | - Qiangwei Huang
- Department of Child and Adolescent Psychology, 194039Tianjin Anding Hospital, Tianjin 300222, P.R.China
| | - Suogang Wang
- School of Biomedical Engineering and Technology, 12610Tianjin Medical University, Tianjin 300070, P.R.China
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Functional neuronal networks reveal emotional processing differences in children with ADHD. Cogn Neurodyn 2022; 16:91-100. [PMID: 35126772 PMCID: PMC8807801 DOI: 10.1007/s11571-021-09699-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/02/2021] [Accepted: 07/07/2021] [Indexed: 02/03/2023] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that, in addition to inattention, excessive activity, or impulsivity, makes it difficult for children to process facial emotions and thus to interact with their peers. Here we analyze neuronal networks of children with this disorder by means of the phase-locking value (PLV) method. In particular, we determine the level of phase synchronization between 62 EEG channels of 22 healthy boys and 22 boys with ADHD, recorder whilst observing facial emotions of anger, happiness, neutrality, and sadness. We construct neuronal networks based on the gamma sub-band, which according to previous studies, shows the highest response to emotional stimuli. We find that the functional connectivity of the frontal and occipital lobes in the ADHD group is significantly (P-value < 0.01) higher than in the healthy group. More functional connectivity in these lobes shows more phase synchronization between the neurons of these brain regions, representing some problems in the brain emotional processing center in the ADHD group. The shortest path lengths in these lobes are also significantly (P-value < 0.01) higher in the ADHD group than in the healthy group. This result indicates less efficiency of information transmission and segregation in occipital and frontal lobes of ADHD neuronal networks, responsible for visual and emotional processing in the brain, respectively. We hope that our approach will help obtain further insights into ADHD with methods of network science.
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