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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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2
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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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3
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Cao J, Yang L, Sarrigiannis PG, Blackburn D, Zhao Y. Dementia classification using a graph neural network on imaging of effective brain connectivity. Comput Biol Med 2024; 168:107701. [PMID: 37984205 DOI: 10.1016/j.compbiomed.2023.107701] [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: 08/03/2023] [Revised: 10/16/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023]
Abstract
Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK; School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Lichao Yang
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK
| | | | - Daniel Blackburn
- Department of Neurosciences, Sheffield Teaching Hospitals, NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, UK
| | - Yifan Zhao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK.
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Pandya S, Jain S, Verma J. A comprehensive analysis towards exploring the promises of AI-related approaches in autism research. Comput Biol Med 2024; 168:107801. [PMID: 38064848 DOI: 10.1016/j.compbiomed.2023.107801] [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: 08/26/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents challenges in communication, social interaction, repetitive behaviour, and limited interests. Detecting ASD at an early stage is crucial for timely interventions and an improved quality of life. In recent times, Artificial Intelligence (AI) has been increasingly used in ASD research. The rise in ASD diagnoses is due to the growing number of ASD cases and the recognition of the importance of early detection, which leads to better symptom management. This study explores the potential of AI in identifying early indicators of autism, aligning with the United Nations Sustainable Development Goals (SDGs) of Good Health and Well-being (Goal 3) and Peace, Justice, and Strong Institutions (Goal 16). The paper aims to provide a comprehensive overview of the current state-of-the-art AI-based autism classification by reviewing recent publications from the last decade. It covers various modalities such as Eye gaze, Facial Expression, Motor skill, MRI/fMRI, and EEG, and multi-modal approaches primarily grouped into behavioural and biological markers. The paper presents a timeline spanning from the history of ASD to recent developments in the field of AI. Additionally, the paper provides a category-wise detailed analysis of the AI-based application in ASD with a diagrammatic summarization to convey a holistic summary of different modalities. It also reports on the successes and challenges of applying AI for ASD detection while providing publicly available datasets. The paper paves the way for future scope and directions, providing a complete and systematic overview for researchers in the field of ASD.
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Affiliation(s)
- Shivani Pandya
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Swati Jain
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Jaiprakash Verma
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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Sun B, Wang B, Wei Z, Feng Z, Wu ZL, Yassin W, Stone WS, Lin Y, Kong XJ. Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking. Front Neurosci 2023; 17:1236637. [PMID: 37886678 PMCID: PMC10598595 DOI: 10.3389/fnins.2023.1236637] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.
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Affiliation(s)
- Binbin Sun
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Bryan Wang
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of English and Creative Writing, Brandeis University, Waltham, MA, United States
| | - Zhen Wei
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhe Feng
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhi-Liu Wu
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Walid Yassin
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- McLean Hospital, Harvard Medical School, Belmont, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - William S. Stone
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yan Lin
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Xue-Jun Kong
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Das S, Zomorrodi R, Mirjalili M, Kirkovski M, Blumberger DM, Rajji TK, Desarkar P. Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110705. [PMID: 36574922 DOI: 10.1016/j.pnpbp.2022.110705] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/04/2022] [Accepted: 12/21/2022] [Indexed: 12/26/2022]
Abstract
There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.
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Affiliation(s)
- Sushmit Das
- Centre for Addiction and Mental Health, Toronto, Canada; Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mina Mirjalili
- Centre for Addiction and Mental Health, Toronto, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Insitute for Health and Sport, Victoria University, Melbourne, Australia
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pushpal Desarkar
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.
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9
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Singh Y, Farrelly CM, Hathaway QA, Leiner T, Jagtap J, Carlsson GE, Erickson BJ. Topological data analysis in medical imaging: current state of the art. Insights Imaging 2023; 14:58. [PMID: 37005938 PMCID: PMC10067000 DOI: 10.1186/s13244-023-01413-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/22/2023] [Indexed: 04/04/2023] Open
Abstract
Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.
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Barik K, Watanabe K, Bhattacharya J, Saha G. Functional connectivity based machine learning approach for autism detection in young children using MEG signals. J Neural Eng 2023; 20. [PMID: 36812588 DOI: 10.1088/1741-2552/acbe1f] [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: 09/03/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023]
Abstract
Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
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Affiliation(s)
- Kasturi Barik
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Joydeep Bhattacharya
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Goutam Saha
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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11
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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12
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Cao J, Zhao Y, Shan X, Wei H, Guo Y, Chen L, Erkoyuncu JA, Sarrigiannis PG. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum Brain Mapp 2022; 43:860-879. [PMID: 34668603 PMCID: PMC8720201 DOI: 10.1002/hbm.25683] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Xiaocai Shan
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
- Institute of Geology and Geophysics, Chinese Academy of SciencesBeijingChina
| | - Hua‐liang Wei
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | - Yuzhu Guo
- School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
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13
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Yu Y, Li J. Feature Fusion-Based Capsule Network for Cross-Subject Mental Workload Classification. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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14
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Classification of ASD based on fMRI data with deep learning. Cogn Neurodyn 2021; 15:961-974. [PMID: 34790264 DOI: 10.1007/s11571-021-09683-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/30/2021] [Accepted: 05/12/2021] [Indexed: 12/31/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects the social abilities of patients. Studies have shown that a small number of abnormal functional connections (FCs) exist in the cerebral hemisphere of ASD patients. The identification of these abnormal FCs provides a biological ground for the diagnosis of ASD. In this paper, we propose a combined deep feature selection (DFS) and graph convolutional network method to classify ASD. Firstly, in the DFS process, a sparse one-to-one layer is added between the input and the first hidden layer of a multilayer perceptron, thus each functional connection (FC) feature can be weighted and a subset of FC features can be selected accordingly. Then based on the selected FCs and the phenotypic information of subjects, a graph convolutional network is constructed to classify ASD and typically developed controls. Finally, we test our proposed method on the ABIDE database and compare it with some other methods in the literature. Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features. With DFS, the performance of GCN classifier can be improved dramatically. The proposed method achieves state-of-the-art performance with an accuracy of 79.5% and an area under the receiver operating characteristic curve (AUC) of 0.85 on the preprocessed ABIDE dataset; it is superior to the other methods. Further studies on the top-ranked thirty FCs obtained by DFS show that these FCs are widespread over the cerebral hemisphere, and the ASD group appears a significantly higher number of weak connections compared to the typically developed group.
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15
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Huberty S, Carter Leno V, van Noordt SJR, Bedford R, Pickles A, Desjardins JA, Webb SJ, Elsabbagh M. Association between spectral electroencephalography power and autism risk and diagnosis in early development. Autism Res 2021; 14:1390-1403. [PMID: 33955195 PMCID: PMC8360065 DOI: 10.1002/aur.2518] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 04/02/2021] [Accepted: 04/09/2021] [Indexed: 02/06/2023]
Abstract
Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant-sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi-site dataset with 432 participants, including 222 at high-risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3-36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed. LAY SUMMARY: Infants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.
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Affiliation(s)
- Scott Huberty
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
| | - Virginia Carter Leno
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England, UK
| | - Stefon J R van Noordt
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
| | - Rachael Bedford
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England, UK.,University of Bath, Bath, England, UK
| | - Andrew Pickles
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England, UK
| | | | - Sara Jane Webb
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, Washington, USA
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- Centre for Brain and Cognitive Development, England, UK
| | - Mayada Elsabbagh
- Montreal Neurological Institute-Hospital, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
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16
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Alotaibi N, Maharatna K. Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis. Neural Comput 2021; 33:1914-1941. [PMID: 34411269 DOI: 10.1162/neco_a_01394] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/04/2021] [Indexed: 11/04/2022]
Abstract
Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.
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Affiliation(s)
- Noura Alotaibi
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Koushik Maharatna
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
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17
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Baygin M, Dogan S, Tuncer T, Datta Barua P, Faust O, Arunkumar N, Abdulhay EW, Emma Palmer E, Rajendra Acharya U. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Comput Biol Med 2021; 134:104548. [PMID: 34119923 DOI: 10.1016/j.compbiomed.2021.104548] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model. MATERIALS AND METHOD We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection. RESULTS A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model. CONCLUSIONS The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, United Kingdom.
| | - N Arunkumar
- Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur, 613401, India.
| | - Enas W Abdulhay
- Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan.
| | - Elizabeth Emma Palmer
- Department of Medical Genetics, Sydney Children's Hospital, High Street, Randwick, NSW, Australia.
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.
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18
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Sheela P, Puthankattil SD. A noise-robust sparse approach to the time-frequency representation of visual evoked potentials. Comput Biol Med 2021; 135:104561. [PMID: 34153788 DOI: 10.1016/j.compbiomed.2021.104561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 06/06/2021] [Accepted: 06/06/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Visual evoked potential (VEP) offers a promising research strategy in the effort to characterise brain disorders. Pertinent signal processing techniques enable the development of potential applications of VEP. A joint time-frequency (TF) representation provides more comprehensive information about the underlying complex structures of these signals than individual time or frequency analysis. However, this representation comes at the expense of low TF resolution, increased data volume, poor energy concentration and increased computational time. Owing to the high non-stationarity and low signal-to-noise ratio of VEP, a TF representation that retains only the pertinent components is indispensable. METHOD The objective of this study is to investigate and demonstrate the ability of various TF approaches to provide an energy-concentrated and sparse TF representation of VEP. The performance of each method has been assessed for its energy concentration and reconstruction ability on both simulated and real VEPs. Renyi entropy, computation time and correlation coefficient are chosen as the performance measures for the assessment. RESULTS In comparison with the other state-of-the-art approaches, Synchroextracting transform (SET) exhibits the lowest Renyi entropy and the highest correlation coefficient, thereby ensuring a compact TF representation for the better characterisation of VEP signals. These results are also statistically verified through the Friedman test (p<0.001). CONCLUSION SET assures a powerful TF framework with improved energy concentration at a faster pace while remaining invertible and preserving vital information.
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Affiliation(s)
- Priyalakshmi Sheela
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India.
| | - Subha D Puthankattil
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India.
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19
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He Q, Wang Q, Wu Y, Yi L, Wei K. Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task. Psych J 2021; 10:550-565. [PMID: 33847077 DOI: 10.1002/pchj.447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 12/04/2020] [Accepted: 03/09/2021] [Indexed: 11/09/2022]
Abstract
Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants' gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less-social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD.
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Affiliation(s)
- Qiao He
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yaxue Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Li Yi
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Kunlin Wei
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
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20
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Linear and nonlinear analysis of intrinsic mode function after facial stimuli presentation in children with autism spectrum disorder. Comput Biol Med 2021; 133:104376. [PMID: 33866255 DOI: 10.1016/j.compbiomed.2021.104376] [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: 02/20/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 11/23/2022]
Abstract
In this work, a method for classifying Autism Spectrum Disorders (ASD) from typically developing (TD) children is presented using the linear and nonlinear Event-Related Potential (ERP) analysis of the Electro-encephalogram (EEG) signals. The signals were acquired during the presentation of three types of face expression stimuli -happy, fearful and neutral faces. EEGs are first decomposed using the Multivariate Empirical Mode Decomposition (MEMD) method to extract its Intrinsic Mode Functions (IMFs), which provide information about the underlying activities of ERP components. The nonlinear sample entropy (SampEn) features, as well as the standard linear measurements utilizing maximum (Max.), minimum (Min), and standard deviation (Std.), are then extracted from each set of IMFs. These features are then evaluated by the statistical analysis tests and used to construct the input vectors for the Discriminant analysis (DA), Support vector machine (SVM), and k-Nearest Neighbors (kNN) classifiers. Experimental results show that the proposed features can differentiate the ASD and TD children using the happy stimulus dataset with high classification performance for all classifiers that reached 100% accuracy. This result suggests a general deficit in recognizing the positive expression in ASD children. Additionally, we found that the SampEn measurements computed from the alpha and theta bands and the linear features extracted from the delta band can be considered biomarkers for disturbances in Emotional Facial Expression (EFE) processing in ASD children.
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21
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Abou-Abbas L, van Noordt S, Desjardins JA, Cichonski M, Elsabbagh M. Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder. Brain Sci 2021; 11:brainsci11040409. [PMID: 33804986 PMCID: PMC8063929 DOI: 10.3390/brainsci11040409] [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: 02/16/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/01/2022] Open
Abstract
Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.
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Affiliation(s)
- Lina Abou-Abbas
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
- Correspondence:
| | - Stefon van Noordt
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
| | - James A. Desjardins
- Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada; (J.A.D.); (M.C.)
| | - Mike Cichonski
- Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada; (J.A.D.); (M.C.)
| | - Mayada Elsabbagh
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
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22
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The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_50] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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23
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Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder. Comput Struct Biotechnol J 2020; 19:545-554. [PMID: 33510860 PMCID: PMC7809157 DOI: 10.1016/j.csbj.2020.12.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/10/2020] [Accepted: 12/13/2020] [Indexed: 02/07/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition for which early identification and intervention is crucial for optimum prognosis. Our previous work showed gut Immunoglobulin A (IgA) to be significantly elevated in the gut lumen of children with ASD compared to typically developing (TD) children. Gut microbiota variations have been reported in ASD, yet not much is known about virulence factor-related gut microbiota (VFGM) genes. Upon determining the VFGM genes distinguishing ASD from TD, this study is the first to utilize VFGM genes and IgA levels for a machine learning-based classification of ASD. Sequence comparisons were performed of metagenome datasets from children with ASD (n = 43) and TD children (n = 31) against genes in the virulence factor database. VFGM gene composition was associated with ASD phenotype. VFGM gene diversity was higher in children with ASD and positively correlated with IgA content. As Group B streptococcus (GBS) genes account for the highest proportion of 24 different VFGMs between ASD and TD and positively correlate with gut IgA, GBS genes were used in combination with IgA and VFGMs diversity to distinguish ASD from TD. Given that VFGM diversity, increases in IgA, and ASD-enriched VFGM genes were independent of sex and gastrointestinal symptoms, a classification method utilizing them will not pertain only to a specific subgroup of ASD. By introducing the classification value of VFGM genes and considering that VFs can be isolated in pregnant women and newborns, these findings provide a novel machine learning-based early risk identification method for ASD.
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Gutierrez Nuno RA, Chung CHR, Maharatna K. Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction. J Neural Eng 2020; 18. [PMID: 33326940 DOI: 10.1088/1741-2552/abd462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/16/2020] [Indexed: 11/11/2022]
Abstract
In this work, we proposed a novel architecture for real-time quantitative characterization of functional brain connectivity networks derived from Electroencephalogram (EEG). It consists of two main parts - calculation of Phase Lag Index (PLI) to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131µs, utilizes 71% logic resources, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS Application Specific Integrated Circuit (ASIC) technology.
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Affiliation(s)
- Rafael Angel Gutierrez Nuno
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Hang Raphael Chung
- University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Koushik Maharatna
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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25
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Abdulhay E, Alafeef M, Hadoush H, Arunkumar N. Resting state EEG-based diagnosis of Autism via elliptic area of continuous wavelet transform complex plot. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Autism is a developmental disorder that influences social communication skills. It is currently diagnosed only by behavioral assessment. The assessment is susceptible to the experience of the examiner as well as to the descriptive scaling standard. This paper presents a computer aided approach to discrimination between neuro-typical and autistic children. A new method- based on the computing of the elliptic area of the Continuous Wavelet Transform complex plot of resting state EEG- is presented. First, the complex values of CWT, as a function of both time and frequency, are calculated for every EEG channel. Second, the CWT complex plot is obtained by plotting the real parts of the resulted CWT values versus the related imaginary components. Third, the 95% confidence value of the elliptic area of the complex plot is computed for every channel for both autistic and healthy subjects; and the obtained values are considered as the first set of features. Fourth, three additional features are computed for every channel: the average CWT, the maximum EEG amplitude, and the maximum real part of CWT. The classification of those features is realized through artificial neural network (ANN). The obtained accuracy, sensitivity and specificity values are: 95.9%, 96.7%, and 95.1% respectively.
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Affiliation(s)
- Enas Abdulhay
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
| | - Maha Alafeef
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Hikmat Hadoush
- Department of Rehabilitation Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - N. Arunkumar
- Department of Biomedical Engineering, Rathinam Technical Campus, Coimbatore, India
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Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
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Nobukawa S, Yamanishi T, Kasakawa S, Nishimura H, Kikuchi M, Takahashi T. Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease. Front Psychiatry 2020; 11:255. [PMID: 32317994 PMCID: PMC7154080 DOI: 10.3389/fpsyt.2020.00255] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/16/2020] [Indexed: 12/22/2022] Open
Abstract
Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management Information Science, Fukui University of Technology, Fukui, Japan
| | - Shinya Kasakawa
- AI & IoT Center, Department of Management Information Science, Fukui University of Technology, Fukui, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Psychiatry & Behavioral Science, Kanazawa University, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
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Qiu N, Tang C, Zhai M, Huang W, Weng J, Li C, Xiao X, Fu J, Zhang L, Xiao T, Fang H, Ke X. Application of the Still-Face Paradigm in Early Screening for High-Risk Autism Spectrum Disorder in Infants and Toddlers. Front Pediatr 2020; 8:290. [PMID: 32582594 PMCID: PMC7290044 DOI: 10.3389/fped.2020.00290] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 05/07/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Although autism spectrum disorder (ASD) can currently be diagnosed at the age of 2 years, age at ASD diagnosis is still 40 months or even later. In order to early screening for ASD with more objective method, behavioral videos were used in a number of studies in recent years. Method: The still-face paradigm (SFP) was adopted to measure the frequency and duration of non-social smiling, protest behavior, eye contact, social smiling, and active social engagement in high-risk ASD group (HR) and typical development group (TD) (HR: n = 45; TD: n = 43). The HR group was follow-up until they were 2 years old to confirm final diagnosis. Machine learning methods were used to establish models for early screening of ASD. Results: During the face-to-face interaction (FF) episode of the SFP, there were statistically significant differences in the duration and frequency of eye contact, social smiling, and active social engagement between the two groups. During the still-face (SF) episode, there were statistically significant differences in the duration and frequency of eye contact and active social engagement between the two groups. The 45 children in the HR group were reclassified into two groups after follow-up: five children in the N-ASD group who were not meet the criterion of ASD and 40 children in the ASD group. The results showed that the accuracy of Support Vector Machine (SVM) classification was 83.35% for the SF episode. Conclusion: The use of the social behavior indicator of the SFP for a child with HR before 2 years old can effectively predict the clinical diagnosis of the child at the age of 2 years. The screening model constructed using SVM based on the SF episode of the SFP was the best. This also proves that the SFP has certain value in high-risk autism spectrum disorder screening. In addition, because of its convenient, it can provide a self-screening mode for use at home. Trial registration: Chinese Clinical Trial Registry, ChiCTR-OPC-17011995.
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Affiliation(s)
- Nana Qiu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Chuangao Tang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, China
| | - Mengyao Zhai
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Wanqing Huang
- College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Jiao Weng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Chunyan Li
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Xiang Xiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Junli Fu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | | | - Ting Xiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Hui Fang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Xiaoyan Ke
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
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Trends and Features of Human Brain Research Using Artificial Intelligence Techniques: A Bibliometric Approach. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-981-15-1398-5_5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Ansel A, Posen Y, Ellis R, Deutsch L, Zisman PD, Gesundheit B. Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis. Rambam Maimonides Med J 2019; 10:RMMJ.10375. [PMID: 31675302 PMCID: PMC6824829 DOI: 10.5041/rmmj.10375] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE To compare the reported accuracy and sensitivity of the various modalities used to diagnose autism spectrum disorders (ASD) in efforts to help focus further biomarker research on the most promising methods for early diagnosis. METHODS The Medline scientific literature database was searched to identify publications assessing potential clinical ASD biomarkers. Reports were categorized by the modality used to assess the putative markers, including protein, genetic, metabolic, or objective imaging methods. The reported sensitivity, specificity, area under the curve, and overall agreement were summarized and analyzed to determine weighted averages for each diagnostic modality. Heterogeneity was measured using the I2 test. RESULTS Of the 71 papers included in this analysis, each belonging to one of five modalities, protein-based followed by metabolite-based markers provided the highest diagnostic accuracy, each with a pooled overall agreement of 83.3% and respective weighted area under the curve (AUC) of 89.5% and 88.3%. Sensitivity provided by protein markers was highest (85.5%), while metabolic (85.9%) and protein markers (84.7%) had the highest specificity. Other modalities showed degrees of sensitivity, specificity, and overall agreements in the range of 73%-80%. CONCLUSIONS Each modality provided for diagnostic accuracy and specificity similar or slightly higher than those reported for the gold-standard Autism Diagnostic Observation Schedule (ADOS) instrument. Further studies are required to identify the most predictive markers within each modality and to evaluate biological pathways or clustering with possible etiological relevance. Analyses will also be necessary to determine the potential of these novel biomarkers in diagnosing pediatric patients, thereby enabling early intervention.
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Affiliation(s)
| | - Yehudit Posen
- Cell-El Therapeutics Ltd, Jerusalem, Israel
- PSW Ltd, Rehovot, Israel
| | - Ronald Ellis
- Cell-El Therapeutics Ltd, Jerusalem, Israel
- Biotech & Biopharma Consulting, Jerusalem, Israel
| | - Lisa Deutsch
- Biostats Statistical Consulting Ltd, Modiin, Israel
| | | | - Benjamin Gesundheit
- Cell-El Therapeutics Ltd, Jerusalem, Israel
- To whom correspondence should be addressed. E-mail:
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Eill A, Jahedi A, Gao Y, Kohli JS, Fong CH, Solders S, Carper RA, Valafar F, Bailey BA, Müller RA. Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism. Brain Connect 2019; 9:604-612. [PMID: 31328535 DOI: 10.1089/brain.2019.0689] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (N = 93) included 47 TD and 46 ASD participants, matched on age, motion, and nonverbal IQ. Four main analyses consistently indicated that fcMRI variables were significantly more informative than anatomical variables from aMRI and DWI. This was found (1) when the top 100 variables from CRF (run separately in each modality) were combined for multimodal CRF; (2) when only 19 top variables reaching >67% accuracy in each modality were combined in multimodal CRF; and (3) when the large number of initial variables (before dimension reduction) potentially biasing comparisons in favor of fcMRI was reduced using a less granular region of interest scheme. Consistent superiority of fcMRI was even found (4) when 100 variables per modality were randomly selected, removing any such potential bias. Greater informative value of functional than anatomical modalities may relate to the nature of fcMRI data, reflecting more closely behavioral condition, which is also the basis of diagnosis, whereas brain anatomy may be more reflective of neurodevelopmental history.
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Affiliation(s)
- Aina Eill
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Afrooz Jahedi
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Computational Science Research Center, San Diego State University, San Diego, California
| | - Yangfeifei Gao
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Jiwandeep S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Christopher H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Seraphina Solders
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Ruth A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Faramarz Valafar
- Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Barbara A Bailey
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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Tanu, Kakkar D. Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2019. [DOI: 10.4018/ijehmc.2019070105] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initiate the diagnosis process. Clinicians and psychologists propose therapies depending on the examination done by these methodologies. The literature has accounted dozens of diagnostic methods and alternative and complementary therapies but still lack in highlighting the proper biomarker for early detection and intervention. The emerging multi-modal neuro-imaging techniques have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches. The purpose of this review article is: (i) to provide an overview of the emerging ASD diagnosis methods and different markers and; (ii) to present the idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity. This system possesses the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.
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Affiliation(s)
- Tanu
- Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
| | - Deepti Kakkar
- Dr B R Ambedkar National institute of Technology, Jalandhar, India
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Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes. Trends Cogn Sci 2019; 23:584-601. [PMID: 31153774 PMCID: PMC6821457 DOI: 10.1016/j.tics.2019.03.009] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 12/12/2022]
Abstract
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
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Affiliation(s)
- Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mollie Marr
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alice M Graham
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joel T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center Oregon Health & Science University, Portland, OR 97239, USA.
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Dimitriadis SI, López ME, Maestu F, Pereda E. Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. Front Neurosci 2019; 13:542. [PMID: 31244592 PMCID: PMC6579926 DOI: 10.3389/fnins.2019.00542] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/09/2019] [Indexed: 11/13/2022] Open
Abstract
The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer's disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). The NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.
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Affiliation(s)
- Stavros I. Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
- School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - María Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense de Madrid – Universidad Politécnica de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Maestu
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense de Madrid – Universidad Politécnica de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense de Madrid – Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and Institute of Biomedical Technology, Universidad de La Laguna, Tenerife, Spain
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El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks. ARTIFICIAL LIFE AND ROBOTICS 2019. [DOI: 10.1007/s10015-019-00540-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Hadoush H, Alafeef M, Abdulhay E. Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG. Brain Topogr 2019; 32:914-921. [PMID: 31006838 DOI: 10.1007/s10548-019-00711-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 04/13/2019] [Indexed: 11/27/2022]
Abstract
Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.
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Affiliation(s)
- Hikmat Hadoush
- Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan.
| | - Maha Alafeef
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
| | - Enas Abdulhay
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
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Hadoush H, Alafeef M, Abdulhay E. Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot. Behav Brain Res 2019; 362:240-248. [DOI: 10.1016/j.bbr.2019.01.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 12/20/2018] [Accepted: 01/10/2019] [Indexed: 11/26/2022]
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Wang F, Ke H. Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network. Front Hum Neurosci 2018; 12:396. [PMID: 30333740 PMCID: PMC6176510 DOI: 10.3389/fnhum.2018.00396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 09/13/2018] [Indexed: 11/29/2022] Open
Abstract
A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. This calculation, which considers the differences between the sequence pairs to build a reasonable partition, can improve the classification performance. On this basis, however, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under circumstances of insufficient a priori knowledge of domain dependency, such as denoising, feature extraction on a special patient, etc. To address these problems by (1) effectively calculating the MI matrix (synchronization pattern) and (2) accurately classifying the seizure or non-seizure state, this study first accurately measures the synchronization between channel pairs in terms of affinity propagation clustering partition MI (APCPMI). The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs into a correlation matrix. Finally, a cross-layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify seizure or non-seizure states automatically. Experiments are performed using the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states are identified with an accuracy, sensitivity, and specificity of 0.9793 ± 0.002, 0.9942 ± 0.0005, and 0.9676 ± 0.003, respectively; the resulting performance is superior to those achieved by most existing methods over the same dataset. Furthermore, the approach alleviates the necessity for strictly preprocessing (denoising, removing interferences and artifacts) the EEG data using prior knowledge, which is usually required by existing approaches.
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Affiliation(s)
- Fengqin Wang
- Huangshi Key Laboratory of Photoelectric Technology and Materials, College of Physics and Electronics Science, Hubei Normal University, Huangshi, China
| | - Hengjin Ke
- Computer School, Wuhan University, Wuhan, China
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40
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Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3738-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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41
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Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2017.2680408] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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42
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Santamaria L, James C. Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems. Healthc Technol Lett 2018; 5:88-93. [PMID: 29922477 PMCID: PMC5998754 DOI: 10.1049/htl.2017.0049] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 12/26/2017] [Accepted: 02/05/2018] [Indexed: 11/20/2022] Open
Abstract
Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain-computer interface (BCI) systems.
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Affiliation(s)
- Lorena Santamaria
- Institute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, UK
| | - Christopher James
- Warwick Engineering in Biomedicine, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
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Feczko E, Balba NM, Miranda-Dominguez O, Cordova M, Karalunas SL, Irwin L, Demeter DV, Hill AP, Langhorst BH, Grieser Painter J, Van Santen J, Fombonne EJ, Nigg JT, Fair DA. Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm. Neuroimage 2017; 172:674-688. [PMID: 29274502 DOI: 10.1016/j.neuroimage.2017.12.044] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/21/2022] Open
Abstract
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.
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Affiliation(s)
- E Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland OR, 97239, USA.
| | - N M Balba
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - O Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - M Cordova
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - S L Karalunas
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - L Irwin
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - D V Demeter
- The University of Texas at Austin, Department of Psychology, Austin, TX 78713, USA
| | - A P Hill
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University, Portland, OR 97239, USA
| | - B H Langhorst
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - J Grieser Painter
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - J Van Santen
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University, Portland, OR 97239, USA
| | - E J Fombonne
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - J T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - D A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA
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Development of Brain Network in Children with Autism from Early Childhood to Late Childhood. Neuroscience 2017; 367:134-146. [PMID: 29069617 DOI: 10.1016/j.neuroscience.2017.10.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/09/2017] [Accepted: 10/12/2017] [Indexed: 01/07/2023]
Abstract
Extensive studies have indicated brain function connectivity abnormalities in autism spectrum disorder (ASD). However, there is a lack of longitudinal or cross-sectional research focused on tracking age-related developmental trends of autistic children at an early stage of brain development or based on a relatively large sample. The present study examined brain network changes in a total of 186 children both with and without ASD from 3 to 11 years, an early and key development period when significant changes are expected. The study aimed to investigate possible abnormal connectivity patterns and topological properties of children with ASD from early childhood to late childhood by using resting-state electroencephalographic (EEG) data. The main findings of the study were as follows: (1) From the connectivity analysis, several inter-regional synchronizations with reduction were identified in the younger and older ASD groups, and several intra-regional synchronization increases were observed in the older ASD group. (2) From the graph analysis, a reduced clustering coefficient and enhanced mean shortest path length in specific frequencies was observed in children with ASD. (3) Results suggested an age-related decrease of the mean shortest path length in the delta and theta bands in TD children, whereas atypical age-related alteration was observed in the ASD group. In addition, graph measures were correlated with ASD symptom severity in the alpha band. These results demonstrate that abnormal neural communication is already present at the early stages of brain development in autistic children and this may be involved in the behavioral deficits associated with ASD.
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Processing of Facial Expressions in Autism: a Systematic Review of EEG/ERP Evidence. REVIEW JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2017. [DOI: 10.1007/s40489-017-0112-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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O’Reilly C, Lewis JD, Elsabbagh M. Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies. PLoS One 2017; 12:e0175870. [PMID: 28467487 PMCID: PMC5414938 DOI: 10.1371/journal.pone.0175870] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 03/31/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings. OBJECTIVES 1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization). METHODS Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies. RESULTS Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies. CONCLUSIONS The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.
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Affiliation(s)
- Christian O’Reilly
- Douglas Mental Health University Institute, 6875 Boulevard Lasalle, Verdun, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montreal, QC, Canada
| | - John D. Lewis
- McGill Center for Integrative Neuroscience, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, Canada
| | - Mayada Elsabbagh
- Douglas Mental Health University Institute, 6875 Boulevard Lasalle, Verdun, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montreal, QC, Canada
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Global Synchronization of Multichannel EEG Based on Rényi Entropy in Children with Autism Spectrum Disorder. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7030257] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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48
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Mahyari AG, Zoltowski DM, Bernat EM, Aviyente S. A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG. IEEE Trans Biomed Eng 2017; 64:225-237. [DOI: 10.1109/tbme.2016.2553960] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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49
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Jamal W, Das S, Maharatna K, Apicella F, Chronaki G, Sicca F, Cohen D, Muratori F. On the existence of synchrostates in multichannel EEG signals during face-perception tasks. Biomed Phys Eng Express 2015. [DOI: 10.1088/2057-1976/1/1/015002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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50
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Dimitriadis SI, Laskaris NA, Micheloyannis S. Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes. Cogn Neurodyn 2015; 9:371-87. [PMID: 26157511 DOI: 10.1007/s11571-015-9330-8] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 12/07/2014] [Accepted: 01/07/2015] [Indexed: 11/26/2022] Open
Abstract
We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.
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Affiliation(s)
- S I Dimitriadis
- Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece ; NeuroInformatics Group, AUTH, Thessaloniki, Greece
| | - N A Laskaris
- Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece ; NeuroInformatics Group, AUTH, Thessaloniki, Greece
| | - S Micheloyannis
- Medical Division (Laboratory L.Widen), University of Crete, 71409 Iraklion, Crete, Greece
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