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Li F, Zhang S, Jiang L, Duan K, Feng R, Zhang Y, Zhang G, Zhang Y, Li P, Yao D, Xie J, Xu W, Xu P. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodyn 2024; 18:1033-1045. [PMID: 38826670 PMCID: PMC11143134 DOI: 10.1007/s11571-023-09962-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/24/2023] [Accepted: 03/17/2023] [Indexed: 06/04/2024] Open
Abstract
Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
| | - Shu Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Keyi Duan
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Rui Feng
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Yingli Zhang
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Gao Zhang
- The Preston Robert Tisch Brain Tumor Center, Department of Neurosurgery, Department of Pathology, Duke University School of Medicine, Durham, NC 27710 USA
| | - Yangsong Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001 China
| | - Jiang Xie
- Chengdu Third People’s Hospital, Affiliated Hospital of Southwest JiaoTong University Medical School, Chengdu, 610031 China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041 China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610042 China
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2
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Yang Y, Yang H, Yu C, Ni F, Yu T, Luo R. Alterations in the topological organization of the default-mode network in Tourette syndrome. BMC Neurol 2023; 23:390. [PMID: 37899454 PMCID: PMC10614376 DOI: 10.1186/s12883-023-03421-1] [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: 07/04/2023] [Accepted: 10/05/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND The exact pathophysiology of TS is still elusive. Previous studies have identified default mode networks (DMN) abnormalities in patients with TS. However, these literatures investigated the neural activity during the tic suppression, not a true resting-state. Therefore, this study aimed to reveal the neural mechanism of Tourette's syndrome (TS) from the perspective of topological organization and functional connectivity within the DMN by electroencephalography (EEG) in resting-state. METHODS The study was conducted by analyzing the EEG data of TS patients with graph theory approaches. Thirty children with TS and thirty healthy controls (HCs) were recruited, and all subjects underwent resting-state EEG data acquisition. Functional connectivity within the DMN was calculated, and network properties were measured. RESULTS A significantly lower connectivity in the neural activity of the TS patients in the β band was found between the bilateral posterior cingulate cortex/retrosplenial cortex (t = -3.02, p < 0.05). Compared to HCs, the TS patients' local topological properties (degree centrality) in the left temporal lobe in the γ band were changed, while the global topological properties (global efficiency and local efficiency) in DMN exhibited no significant differences. It was also demonstrated that the degree centrality of the left temporal lobe in the γ band was positively related to the Yale Global Tic Severity Scale scores (r = 0.369, p = 0.045). CONCLUSIONS The functional connectivity and topological properties of the DMN of TS patients were disrupted, and abnormal DMN topological property alterations might affect the severity of tic in TS patients. The abnormal topological properties of the DMN in TS patients may be due to abnormal functional connectivity alterations. The findings provide novel insight into the neural mechanism of TS patients.
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Affiliation(s)
- Yue Yang
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Hua Yang
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Chunmei Yu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Fang Ni
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Tao Yu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Rong Luo
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Sichuan University, Chengdu, 610041, China.
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3
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Previously Marzena Szkodo MOR, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni ML. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neurosci Biobehav Rev 2023; 145:105021. [PMID: 36581169 DOI: 10.1016/j.neubiorev.2022.105021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
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Affiliation(s)
| | - Martina Micai
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Maria Fazio
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale F. Stagno d'Alcontres, 31, 98166 Messina, Italy.
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
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4
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Duan K, Xie S, Zhang X, Xie X, Cui Y, Liu R, Xu J. Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT). Brain Sci 2023; 13:brainsci13020247. [PMID: 36831790 PMCID: PMC9954291 DOI: 10.3390/brainsci13020247] [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: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1-30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.
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5
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Chou CY, Agin-Liebes J, Kuo SH. Emerging therapies and recent advances for Tourette syndrome. Heliyon 2023; 9:e12874. [PMID: 36691528 PMCID: PMC9860289 DOI: 10.1016/j.heliyon.2023.e12874] [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: 09/13/2022] [Revised: 11/27/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023] Open
Abstract
Tourette syndrome is the most prevalent hyperkinetic movement disorder in children and can be highly disabling. While the pathomechanism of Tourette syndrome remains largely obscure, recent studies have greatly improved our knowledge about this disease, providing a new perspective in our understanding of this condition. Advances in electrophysiology and neuroimaging have elucidated that there is a reduction in frontal cortical volume and reduction of long rage connectivity to the frontal lobe from other parts of the brain. Several genes have also been identified to be associated with Tourette syndrome. Treatment of Tourette syndrome requires a multidisciplinary approach which includes behavioral and pharmacological therapy. In severe cases surgical therapy with deep brain stimulation may be warranted, though the optimal location for stimulation is still being investigated. Studies on alternative therapies including traditional Chinese medicine and neuromodulation, such as transcranial magnetic stimulation have shown promising results, but still are being used in an experimental basis. Several new therapies have also recently been tested in clinical trials. This review provides an overview of the latest findings with regards to genetics and neuroimaging for Tourette syndrome as well as an update on advanced therapeutics.
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Affiliation(s)
- Chih-Yi Chou
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University, New York, NY, USA
| | - Julian Agin-Liebes
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University, New York, NY, USA
| | - Sheng-Han Kuo
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University, New York, NY, USA
- Corresponding author. 650 West 168th Street, Room 305, New York, NY, 10032, USA. Fax: +(212) 305 1304.
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Peng Y, Huang Y, Chen B, He M, Jiang L, Li Y, Huang X, Pei C, Zhang S, Li C, Zhang X, Zhang T, Zheng Y, Yao D, Li F, Xu P. Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2577-2588. [PMID: 36044502 DOI: 10.1109/tnsre.2022.3203073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.
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7
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Zhang Y, Zhang S, Chen B, Jiang L, Li Y, Dong L, Feng R, Yao D, Li F, Xu P. Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1898-1907. [PMID: 35788457 DOI: 10.1109/tnsre.2022.3188564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (ADOS) scores, as well as with the typically developing (TD) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high- and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.
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8
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Lee I, Lee J, Lim MH, Kim KM. Comparison of Quantitative Electroencephalography between Tic Disorder and Attention-Deficit/Hyperactivity Disorder in Children. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2021; 19:739-750. [PMID: 34690129 PMCID: PMC8553536 DOI: 10.9758/cpn.2021.19.4.739] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 12/31/2022]
Abstract
Objective Attention-deficit/hyperactivity disorder (ADHD) and tic disorder (TD) are among the most common comorbid psychopathologies and have a shared genetic basis. The psychopathological and neurophysiological aspects of the mechanism underlying the comorbidity of both disorders have been investigated, but the pathophysiological aspects remain unclear. Therefore, this study aimed to compare the neurophysiological characteristics of ADHD with those of TD using resting-state electroencephalography and exact low-resolution brain electromagnetic tomography (eLORETA) analysis. Methods We performed eLORETA analysis based on the resting-state scalp-recorded electrical potential distribution in 34 children with ADHD and 21 age-matched children with TD. Between-group differences in electroencephalography (EEG) current source density in delta, theta, alpha, beta, and gamma bands were investigated in each cortical region. Results Compared with the TD group, the ADHD group showed significantly increased theta activity in the frontal region (superior frontal gyrus, t = 3.37, p < 0.05; medial frontal gyrus, t = 3.35, p < 0.05). In contrast, children with TD showed decreased posterior alpha activity than those with ADHD (precuneus, t = −3.40, p < 0.05; posterior cingulate gyrus, t = −3.38, p < 0.05). These findings were only significant when the eyes were closed. Conclusion Increased theta activity in the frontal region is a neurophysiological marker that can distinguish ADHD from TD. Also, reduced posterior alpha activity might represent aberrant inhibitory control. Further research needs to confirm these characteristics by simultaneously measuring EEG-functional magnetic resonance imaging.
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Affiliation(s)
- Ilju Lee
- Department of Psychology, College of Health Science, Dankook University, Cheonan, Korea
| | - Jiryun Lee
- Department of Psychiatry, Dankook University Hospital, Cheonan, Korea
| | - Myung Ho Lim
- Department of Psychology, College of Health Science, Dankook University, Cheonan, Korea
| | - Kyoung Min Kim
- Department of Psychiatry, Dankook University Hospital, Cheonan, Korea.,Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Korea
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9
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Li Y, Li F, Zheng H, Jiang L, Peng Y, Zhang Y, Yao D, Xu T, Yuan T, Xu P. Recognition of general anesthesia-induced loss of consciousness based on the spatial pattern of the brain networks. J Neural Eng 2021; 18. [PMID: 34534980 DOI: 10.1088/1741-2552/ac27fc] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Objective.Unconsciousness is a key feature related to general anesthesia (GA) but is difficult to be evaluated accurately by anesthesiologists clinically.Approach.To tracking the loss of consciousness (LOC) and recovery of consciousness (ROC) under GA, in this study, by investigating functional connectivity of the scalp electroencephalogram, we explore any potential difference in brain networks among anesthesia induction, anesthesia recovery, and the resting state.Main results.The results of this study demonstrated significant differences among the three periods, concerning the corresponding brain networks. In detail, the suppressed default mode network, as well as the prolonged characteristic path length and decreased clustering coefficient, during LOC was found in the alpha band, compared to the Resting and the ROC state. When to further identify the Resting and LOC states, the fused network topologies and properties achieved the highest accuracy of 95%, along with a sensitivity of 93.33% and a specificity of 96.67%.Significance.The findings of this study not only deepen our understanding of propofol-induced unconsciousness but also provide quantitative measurements subserving better anesthesia management.
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Affiliation(s)
- Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yangsong Zhang
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Tao Xu
- Department of Anesthesiology, Affiliated Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, People's Republic of China.,Department of Anesthesiology, Tongzhou People's Hospital, Nantong 226300, People's Republic of China
| | - Tifei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China.,Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226001, People's Republic of China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
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Abstract
Tic disorders and Tourette syndrome are the most common movement disorders in children and are characterized by movements or vocalizations. Clinically, Tourette syndrome is frequently associated with comorbid psychiatric symptoms. Although dysfunction of cortical–striatal–thalamic–cortical circuits with aberrant neurotransmitter function has been considered the proximate cause of tics, the mechanism underlying this association is unclear. Recently, many studies have been conducted to elucidate the epidemiology, clinical course, comorbid symptoms, and pathophysiology of tic disorders by using laboratory studies, neuroimaging, electrophysiological testing, environmental exposure, and genetic testing. In addition, many researchers have focused on treatment for tics, including behavioral therapy, pharmacological treatment, and surgical treatment. Here, we provide an overview of recent progress on Tourette syndrome.
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Affiliation(s)
- Keisuke Ueda
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kevin J Black
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
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11
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Rothenberger A, Heinrich H. Electrophysiology Echoes Brain Dynamics in Children and Adolescents With Tourette Syndrome-A Developmental Perspective. Front Neurol 2021; 12:587097. [PMID: 33658971 PMCID: PMC7917116 DOI: 10.3389/fneur.2021.587097] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 01/19/2021] [Indexed: 11/28/2022] Open
Abstract
The development of the complex clinical picture of motor and vocal tics in children and adolescents with Tourette syndrome (TS) must be paralleled by changes in the underlying pathophysiology. Electrophysiological methods such as EEG and event-related potentials (ERPs) are non-invasive, safe and easy to apply and thus seem to provide an adequate means to investigate brain dynamics during this brain maturational period. Also, electrophysiology is characterized by a high time resolution and can reflect motor, sensory and cognitive aspects as well as sleep behavior. Hence, this narrative review focuses on how electrophysiology echoes brain dynamics during development of youngsters with TS and might be useful for the treatment of tics. A comprehensive picture of developmental brain dynamics could be revealed showing that electrophysiological parameters evolve concurrently with clinical characteristics of TS. Specifically, evidence for a maturational delay of motor inhibition related to cortico-spinal hyper-excitability and brain mechanisms for its cognitive compensation could be shown. Moreover, deviant sleep parameters and probably a stronger perception-action binding were reported. For neuromodulatory treatments (e.g., neurofeedback; repetitive transcranial magnetic stimulation, rTMS/transcranial direct current stimulation, tDCS) targeting neuronal deficits and/or strengthening compensatory brain mechanisms, pilot studies support the possibility of positive effects regarding tic reduction. Finally, attention-deficit/hyperactivity disorder (ADHD), as a highly frequent co-existing disorder with TS, has to be considered when using and interpreting electrophysiological measures in TS. In conclusion, application of electrophysiology seems to be promising regarding clinical and research aspects in youngsters with TS.
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Affiliation(s)
- Aribert Rothenberger
- Clinic for Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Hartmut Heinrich
- neuroCare Group, Munich, Germany.,kbo-Heckscher-Klinikum, Munich, Germany.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
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