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Xu Y, Yu Z, Li Y, Liu Y, Li Y, Wang Y. Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108196. [PMID: 38678958 DOI: 10.1016/j.cmpb.2024.108196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/30/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND OBJECTIVE People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. METHODS This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. RESULTS Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. CONCLUSIONS This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.
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Affiliation(s)
- Yongjie Xu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zengjie Yu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yisheng Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuehan Liu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ye Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yishan Wang
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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Lanfranco RC, Dos Santos Sousa F, Wessel PM, Rivera-Rei Á, Bekinschtein TA, Lucero B, Canales-Johnson A, Huepe D. Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals. Cortex 2024; 174:201-214. [PMID: 38569258 DOI: 10.1016/j.cortex.2024.03.004] [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: 07/20/2023] [Revised: 01/19/2024] [Accepted: 03/05/2024] [Indexed: 04/05/2024]
Abstract
Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.
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Affiliation(s)
- Renzo C Lanfranco
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Center for Research in Cognition & Neurosciences, Université libre de Bruxelles, Brussels, Belgium
| | | | - Pierre Musa Wessel
- Department of Criminology, University of Cambridge, Cambridge, United Kingdom
| | - Álvaro Rivera-Rei
- Center for Social and Cognitive Neuroscience (SCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tristán A Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Boris Lucero
- The Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, Talca, Chile
| | - Andrés Canales-Johnson
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom; The Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, Talca, Chile.
| | - David Huepe
- Center for Social and Cognitive Neuroscience (SCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile.
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3
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Li J, Kong X, Sun L, Chen X, Ouyang G, Li X, Chen S. Identification of autism spectrum disorder based on electroencephalography: A systematic review. Comput Biol Med 2024; 170:108075. [PMID: 38301514 DOI: 10.1016/j.compbiomed.2024.108075] [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: 09/30/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.
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Affiliation(s)
- Jing Li
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Xiaoli Kong
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Linlin Sun
- Neuroscience Research Institute, Peking University, Beijing, 100191, China; Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China, Beijing, 100191, China
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing, 100120, China; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100032, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shengyong Chen
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
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4
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Ke SY, Wu H, Sun H, Zhou A, Liu J, Zheng X, Liu K, Westover MB, Xu H, Kong XJ. Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study. Front Neurosci 2024; 18:1330556. [PMID: 38332856 PMCID: PMC10850305 DOI: 10.3389/fnins.2024.1330556] [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: 10/31/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.
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Affiliation(s)
- Si Yang Ke
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
| | - Huiwen Wu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Haoqi Sun
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Aiqin Zhou
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Jianhua Liu
- Huangshi Maternity and Child Health Care Hospital, Huangshi, Hubei, China
| | - Xiaoyun Zheng
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Kevin Liu
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Haiqing Xu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Xue-jun Kong
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Beth Israel Deaconess Medical Center, Boston, MA, United States
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5
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Zhu G, Li Y, Wan L, Sun C, Liu X, Zhang J, Liang Y, Liu G, Yan H, Li R, Yang G. Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis. Cereb Cortex 2024; 34:bhad413. [PMID: 37950877 DOI: 10.1093/cercor/bhad413] [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: 09/21/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/13/2023] Open
Abstract
Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.
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Affiliation(s)
- Gang Zhu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yuhang Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau S.A.R., China
- Department of Psychology, Faculty of Social Sciences, University of Macau, Macau S.A.R., China
| | - Lin Wan
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Chunhua Sun
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xinting Liu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jing Zhang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yan Liang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guoyin Liu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Huimin Yan
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Rihui Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau S.A.R., China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau S.A.R., China
| | - Guang Yang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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6
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Kang J, Xie H, Mao W, Wu J, Li X, Geng X. EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study. Bioengineering (Basel) 2023; 10:1030. [PMID: 37760132 PMCID: PMC10525147 DOI: 10.3390/bioengineering10091030] [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: 07/10/2023] [Revised: 08/12/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series. We also developed a novel wavelet-based PLE method for the calculation of PLE at specific scales. Our findings indicated that the diversity of connectivity in ASD children was higher than that in typically developing children at Delta and Alpha frequency bands, both within brain regions and across hemispheric brain regions. These findings provide insight into the underlying neural mechanisms of ASD and suggest that PLE may be a useful tool for investigating brain connectivity in ASD.
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Affiliation(s)
- Jiannan Kang
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Hongxiang Xie
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Wenqin Mao
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Juanmei Wu
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
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7
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Kamarajan C, Pandey AK, Chorlian DB, Meyers JL, Kinreich S, Pandey G, Subbie-Saenz de Viteri S, Zhang J, Kuang W, Barr PB, Aliev F, Anokhin AP, Plawecki MH, Kuperman S, Almasy L, Merikangas A, Brislin SJ, Bauer L, Hesselbrock V, Chan G, Kramer J, Lai D, Hartz S, Bierut LJ, McCutcheon VV, Bucholz KK, Dick DM, Schuckit MA, Edenberg HJ, Porjesz B. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. Behav Sci (Basel) 2023; 13:bs13050427. [PMID: 37232664 DOI: 10.3390/bs13050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ashwini K Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Subbie-Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Peter B Barr
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrey P Anokhin
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | | | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Laura Almasy
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alison Merikangas
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah J Brislin
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Grace Chan
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Dongbing Lai
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sarah Hartz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Laura J Bierut
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego, CA 92103, USA
| | | | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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8
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Zheng J, Shao L, Yan Z, Lai X, Duan F. Study subnetwork developing pattern of autism children by non-negative matrix factorization. Comput Biol Med 2023; 158:106816. [PMID: 37003070 DOI: 10.1016/j.compbiomed.2023.106816] [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: 03/20/2022] [Revised: 03/08/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND As a developmental disorder, the brain networks of autism children show abnormal patterns compared with that of typically developing. The differences between them are not stable due to the developing progress of children. It has become a choice to study the differences of developing trajectories between autistic and typically developing children by investigating the change of each group respectively. Related researches studied the developing of brain network by analyzing the relationship between network indices of the entire or sub brain networks and the cognitive developing scores. METHODS As a matrix decomposition algorithm, non-negative matrix factorization (NMF) was applied to decompose the association matrices of brain networks. By NMF, we can obtain subnetworks in an unsupervised way. The association matrices of autism and control children were estimated by their magnetoencephalography data. NMF was applied to decompose the matrices to obtain common subnetworks of both groups. Then we calculated the expression of each subnetwork in each child's brain network by two indices, energy and entropy. The relationship between the expression and the cognitive and development indices were investigated. RESULTS We found a subnetwork with left lateralization pattern in α band showed different expression tendency in two groups. The expression indices of two groups were correlated with cognitive indices in autism and control group in an opposite way. In γ band, a subnetwork with strong connections on right hemisphere of brain showed a negative correlation between the expression indices and development indices in autism group. CONCLUSION NMF algorithm can effectively decompose brain network to meaningful subnetworks. The finding of α band subnetworks confirms the results of abnormal lateralization of autistic children mentioned in relevant studies. We assume the results of decrease of expression of the subnetwork may relate to the dysfunction of mirror neuron. The decrease expression of γ subnetwork of autism may be related to the weaken process of high-frequency neurons in the neurotrophic competition.
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Affiliation(s)
- JinLin Zheng
- College of Information Science and Engineering, Huaqiao University, 668 Jimei Road, Xiamen 361021, China
| | - LiCheng Shao
- College of Information Science and Engineering, Huaqiao University, 668 Jimei Road, Xiamen 361021, China
| | - Zheng Yan
- College of Information Science and Engineering, Huaqiao University, 668 Jimei Road, Xiamen 361021, China
| | - XiaoFei Lai
- College of Information Science and Engineering, Huaqiao University, 668 Jimei Road, Xiamen 361021, China
| | - Fang Duan
- College of Information Science and Engineering, Huaqiao University, 668 Jimei Road, Xiamen 361021, China.
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9
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Mafi M, Radfar S. High Dimensional Convolutional Neural Network for EEG Connectivity-Based Diagnosis of ADHD. J Biomed Phys Eng 2022; 12:645-654. [PMID: 36569562 PMCID: PMC9759645 DOI: 10.31661/jbpe.v0i0.2108-1380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 02/20/2022] [Indexed: 12/02/2022]
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification. Objective This study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals. Material and Methods In this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC) were considered connectivity measures. The neighborhood between EEG channels was arranged in a two-dimensional matrix for better representation. Four-dimensional (4D) and six-dimensional (6D) connectivity tensors were composed as model inputs. Two architectures were developed, one 4D and 6D CNN for SL and WC-based diagnosis of ADHD, respectively. Results A 5-fold cross-validation was utilized to assess developed models. The average accuracy of 98.56% for 4D CNN and 98.85% for 6D CNN in epoch-based classification were obtained. In the case of subject-based classification, the accuracy was 99.17% for both models. Conclusion Based on the evaluation metrics of the proposed models, ADHD children can be diagnosed and ADHD and normal children can be successfully distinguished.
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Affiliation(s)
- Majid Mafi
- PhD, Biomedical Engineering Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Shokoufeh Radfar
- PhD, Department of Psychiatry, Behavioural Sciences Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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10
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Portnova G, Nekrashevich M, Morozova M, Martynova O, Sharaev M. New approaches to Clinical Electroencephalography analysis in typically developing children and children with autism. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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11
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Genetic mutation of TRPV2 induces anxiety by decreasing GABA-B R2 expression in hippocampus. Biochem Biophys Res Commun 2022; 620:135-142. [DOI: 10.1016/j.bbrc.2022.06.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/23/2022] [Indexed: 11/22/2022]
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12
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Garcés P, Baumeister S, Mason L, Chatham CH, Holiga S, Dukart J, Jones EJH, Banaschewski T, Baron-Cohen S, Bölte S, Buitelaar JK, Durston S, Oranje B, Persico AM, Beckmann CF, Bougeron T, Dell'Acqua F, Ecker C, Moessnang C, Charman T, Tillmann J, Murphy DGM, Johnson M, Loth E, Brandeis D, Hipp JF. Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis. Mol Autism 2022; 13:22. [PMID: 35585637 PMCID: PMC9118870 DOI: 10.1186/s13229-022-00500-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/06/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.
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Affiliation(s)
- Pilar Garcés
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland.
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Luke Mason
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Christopher H Chatham
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Stefan Holiga
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Emily J H Jones
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Simon Baron-Cohen
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, UK
| | - Sven Bölte
- Department of Women's and Children's Health, Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Karolinska Institutet and Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Sarah Durston
- Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bob Oranje
- Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Antonio M Persico
- Interdepartmental Program "Autism 0-90", "G. Martino" University Hospital, University of Messina, Messina, Italy
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Thomas Bougeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | - Flavio Dell'Acqua
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Christine Ecker
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Carolin Moessnang
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Tony Charman
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Julian Tillmann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Mark Johnson
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Eva Loth
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Joerg F Hipp
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
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The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits. Brain Sci 2021; 11:brainsci11111443. [PMID: 34827442 PMCID: PMC8615575 DOI: 10.3390/brainsci11111443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/14/2021] [Accepted: 10/27/2021] [Indexed: 01/06/2023] Open
Abstract
Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.
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14
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The Comparison of Quantitative Electroencephalography of Neural Connections between Children aged 6 to 13 years with Autism Spectrum Disorder and Typically Developing Children. JOURNAL OF COGNITIVE PSYCHOLOGY 2021. [DOI: 10.52547/jcp.9.3.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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15
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Milovanovic M, Grujicic R. Electroencephalography in Assessment of Autism Spectrum Disorders: A Review. Front Psychiatry 2021; 12:686021. [PMID: 34658944 PMCID: PMC8511396 DOI: 10.3389/fpsyt.2021.686021] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/30/2021] [Indexed: 01/01/2023] Open
Abstract
Electroencephalography (EEG) can further out our understanding of autistic spectrum disorders (ASD) neurophysiology. Epilepsy and ASD comorbidity range between 5 and 46%, but its temporal relationship, causal mechanisms and interplay with intellectual disability are still unknown. Epileptiform discharges with or without seizures go as high as 60%, and associate with epileptic encephalopathies, conceptual term suggesting that epileptic activity can lead to cognitive and behavioral impairment beyond the underlying pathology. Seizures and ASD may be the result of similar mechanisms, such as abnormalities in GABAergic fibers or GABA receptor function. Epilepsy and ASD are caused by a number of genetic disorders and variations that induce such dysregulation. Similarly, initial epilepsy may influence synaptic plasticity and cortical connection, predisposing a growing brain to cognitive delays and behavioral abnormalities. The quantitative EEG techniques could be a useful tool in detecting and possibly measuring dysfunctions in specific brain regions and neuronal regulation in ASD. Power spectra analysis reveals a U-shaped pattern of power abnormalities, with excess power in the low and high frequency bands. These might be the consequence of a complicated network of neurochemical changes affecting the inhibitory GABAergic interneurons and their regulation of excitatory activity in pyramidal cells. EEG coherence studies of functional connectivity found general local over-connectivity and long-range under-connectivity between different brain areas. GABAergic interneuron growth and connections are presumably impaired in the prefrontal and temporal cortices in ASD, which is important for excitatory/inhibitory balance. Recent advances in quantitative EEG data analysis and well-known epilepsy ASD co-morbidity consistently indicate a role of aberrant GABAergic transmission that has consequences on neuronal organization and connectivity especially in the frontal cortex.
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Affiliation(s)
- Maja Milovanovic
- Department for Epilepsy and Clinical Neurophysiology, Institute of Mental Health, Belgrade, Serbia.,Faculty for Special Education and Rehabilitation, University of Belgrade, Belgrade, Serbia
| | - Roberto Grujicic
- Clinical Department for Children and Adolescents, Institute of Mental Health, Belgrade, Serbia
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16
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Wang J, Wang X, Wang X, Zhang H, Zhou Y, Chen L, Li Y, Wu L. Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders. Brain Behav 2020; 10:e01796. [PMID: 32815287 PMCID: PMC7559606 DOI: 10.1002/brb3.1796] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is a complex and prevalent neurodevelopmental disorder characterized by deficits in social communication and social interaction as well as repetitive behaviors. Alterations in function connectivity are widely recognized in recent electroencephalogram (EEG) studies. However, most studies have not reached consistent conclusions, which could be due to the developmental nature and the heterogeneity of ASD. METHODS Here, EEG coherence analysis was used in a cohort of children with ASD (n = 13) and matched typically developing controls (TD, n = 15) to examine the functional connectivity characteristics in long-distance and short-distance electrode pairs. Subsequently, we explore the association between the connectivity strength of coherence and symptom severity in children with ASD. RESULTS Compared with TD group, individuals with ASD showed increased coherence in short-distance electrode pairs in the right temporal-parietal region (delta, alpha, beta bands), left temporal-parietal region (all frequency bands), occipital region (theta, alpha, beta bands), right central-parietal region (delta, alpha, beta bands), and the prefrontal region (only beta band). In the long-distance coherence analysis, the ASD group showed increased coherence in bilateral frontal region, temporal region, parietal region, and frontal-occipital region in alpha and beta bands. The strength of such connections was associated with symptom severity. DISCUSSION Our study indicates that abnormal connectivity patterns in neuroelectrophysiology may be of critical importance to acknowledge the underlying brain mechanism.
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Affiliation(s)
- Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Xiaomin Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Xuelai Wang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiying Zhang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Yong Zhou
- Heilongjiang Province Center for Disease Control and Prevention, Harbin, China
| | - Lei Chen
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Yutong Li
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
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