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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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Yang Y, Luo S, Wang W, Gao X, Yao X, Wu T. From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications. Neuroimage Clin 2024; 42:103608. [PMID: 38653131 PMCID: PMC11059345 DOI: 10.1016/j.nicl.2024.103608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.
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Affiliation(s)
- Yanling Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Shichang Luo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenjie Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiumin Gao
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
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3
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Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T. The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network. Cereb Cortex 2023; 33:764-776. [PMID: 35297491 DOI: 10.1093/cercor/bhac100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.
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Affiliation(s)
- Yu Feng
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No.37, Twelfth Bridge Road,Chengdu 610075, China
| | - Hesong Wang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, No. 1023-1063, Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
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Mengi M, Malhotra D. A systematic literature review on traditional to artificial intelligence based socio-behavioral disorders diagnosis in India: Challenges and future perspectives. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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6
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Catherine Joy R, Thomas George S, Albert Rajan A, Subathra MSP. Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN. Clin EEG Neurosci 2022; 53:12-23. [PMID: 34424101 DOI: 10.1177/15500594211036788] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.
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Affiliation(s)
- R Catherine Joy
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - S Thomas George
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - A Albert Rajan
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - M S P Subathra
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
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7
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Treacher AH, Garg P, Davenport E, Godwin R, Proskovec A, Bezerra LG, Murugesan G, Wagner B, Whitlow CT, Stitzel JD, Maldjian JA, Montillo AA. MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks. Neuroimage 2021; 241:118402. [PMID: 34274419 PMCID: PMC9125748 DOI: 10.1016/j.neuroimage.2021.118402] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 06/18/2021] [Accepted: 07/15/2021] [Indexed: 11/28/2022] Open
Abstract
Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression. The contribution of this work is three-fold. First, using a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA), a highly accurate artifact classifier is constructed as an amalgam of deep 1-D and 2-D Convolutional Neural Networks (CNNs) to automate the identification and removal of ubiquitous whole brain artifacts including eye-blink, saccade, and cardiac artifacts. The specific architecture of this network is optimized through an unbiased, computer-based hyperparameter random search. Second, visualization methods are applied to the learned abstraction to reveal what features the model uses and to bolster user confidence in the model's training and potential for generalization. Finally, the model is trained and tested on both resting-state and task MEG data from 217 subjects, and achieves a new state-of-the-art in artifact detection accuracy of 98.95% including 96.74% sensitivity and 99.34% specificity on the held out test-set. This work automates MEG processing for both clinical and research use, adapts to the acquired acquisition time, and can obviate the need for EOG or ECG electrodes for artifact detection.
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Affiliation(s)
- Alex H Treacher
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States
| | - Prabhat Garg
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States; Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Elizabeth Davenport
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States; Magnetoencephalography Center of Excellence, UT Southwestern Medical Center, Dallas, TX, United States
| | - Ryan Godwin
- Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Amy Proskovec
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States; Magnetoencephalography Center of Excellence, UT Southwestern Medical Center, Dallas, TX, United States
| | | | - Gowtham Murugesan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Ben Wagner
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States; Magnetoencephalography Center of Excellence, UT Southwestern Medical Center, Dallas, TX, United States
| | | | - Joel D Stitzel
- Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Joseph A Maldjian
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States; Magnetoencephalography Center of Excellence, UT Southwestern Medical Center, Dallas, TX, United States
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States; Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States; Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States.
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Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105738. [PMID: 32927404 DOI: 10.1016/j.cmpb.2020.105738] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic behavioral disorder in children. Children with ADHD face many difficulties in maintaining their concentration and controlling their behaviors. Early diagnosis of this disorder is one of the most important challenges in its control and treatment. No definitive expert method has been found to detect this disorder early. Our goal in this study is to develop an assistive tool for physicians to recognize ADHD children from healthy children using electroencephalography (EEG) based on a continuous mental task. METHODS We used EEG signals recorded from 31 ADHD children and 30 healthy children. In this study, we developed a deep learning model using a convolutional neural network that have had significant performance in image processing fields. For this purpose, we first preprocessed EEG signals to eliminate noise and artifacts. Then we segmented preprocessed samples into more samples. We extracted the theta, alpha, beta, and gamma frequency bands from each segmented sample and formed a color RGB image with three channels. Eventually, we imported the resulting images into a 13-layer convolutional neural network for feature extraction and classification. RESULTS The proposed model was evaluated by 5-fold cross validation for train, evaluation, and test data and achieved an average accuracy of 99.06%, 97.81%, 97.47% for segmented samples. The average accuracy for subject-based test samples was 98.48%. Also, the performance of the model was evaluated using the confusion matrix with precision, recall, and f1-score metrics. The results of these metrics also confirmed the outstanding performance of the model. CONCLUSIONS The accuracy, precision, recall, and f1-score of our model were better than all previous works for diagnosing ADHD in children. Based on these prominent and reliable results, this technique can be used as an assistive tool for the physicians in the early diagnosis of ADHD in children.
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Affiliation(s)
- Majid Moghaddari
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mina Zolfy Lighvan
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Sebelan Danishvar
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran; Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
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Godfrey M, Singh KD. Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales. Neuroimage 2020; 226:117551. [PMID: 33186722 PMCID: PMC7836237 DOI: 10.1016/j.neuroimage.2020.117551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 10/08/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
MRVE measures the dynamic variability of MEG signals at a range of temporal scales. MRVE correlation and AEC detected robust resting state functional connectivity. The most robust patterns were found for fS=75Hz MRVE correlation and beta band AEC. Connectivity strength negatively correlates with local MRVE at fine time scales. Eye movement affects resting state connectivity measurements across frequencies.
Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.
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Affiliation(s)
- Megan Godfrey
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
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Muthuraman M, Moliadze V, Boecher L, Siemann J, Freitag CM, Groppa S, Siniatchkin M. Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders. Sci Rep 2019; 9:20028. [PMID: 31882672 PMCID: PMC6934806 DOI: 10.1038/s41598-019-56398-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 11/22/2019] [Indexed: 12/23/2022] Open
Abstract
Functional and effective connectivity measures for tracking brain region interactions that have been investigated using both electroencephalography (EEG) and magnetoencephalography (MEG) bringing up new insights into clinical research. However, the differences between these connectivity methods, especially at the source level, have not yet been systematically studied. The dynamic characterization of coherent sources and temporal partial directed coherence, as measures of functional and effective connectivity, were applied to multimodal resting EEG and MEG data obtained from 11 young patients (mean age 13.2 ± 1.5 years) with attention-deficit/hyperactivity disorder (ADHD) and age-matched healthy subjects. Additionally, machine-learning algorithms were applied to the extracted connectivity features to identify biomarkers differentiating the two groups. An altered thalamo-cortical connectivity profile was attested in patients with ADHD who showed solely information outflow from cortical regions in comparison to healthy controls who exhibited bidirectional interregional connectivity in alpha, beta, and gamma frequency bands. We achieved an accuracy of 98% by combining features from all five studied frequency bands. Our findings suggest that both types of connectivity as extracted from EEG or MEG are sensitive methods to investigate neuronal network features in neuropsychiatric disorders. The connectivity features investigated here can be further tested as biomarkers of ADHD.
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Affiliation(s)
- Muthuraman Muthuraman
- Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Vera Moliadze
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Lena Boecher
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Julia Siemann
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Ev. Hospital Bielefeld, Bielefeld, Germany
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Sergiu Groppa
- Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michael Siniatchkin
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Ev. Hospital Bielefeld, Bielefeld, Germany
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Li Z, Li Y, Zhang K, Guo J. A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE. ENTROPY 2019. [PMCID: PMC7514560 DOI: 10.3390/e21121215] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Ship-radiated noise signal has a lot of nonlinear, non-Gaussian, and nonstationary information characteristics, which can reflect the important signs of ship performance. This paper proposes a novel feature extraction technique for ship-radiated noise based on improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE). The proposed feature extraction technique is named IITD-MDE. First, IITD is applied to decompose the ship-radiated noise signal into a series of intrinsic scale components (ISCs). Then, we select the ISC with the main information through the correlation analysis, and calculate the MDE value as feature vectors. Finally, the feature vectors are input into the support vector machine (SVM) for ship classification. The experimental results indicate that the recognition rate of the proposed technique reaches 86% accuracy. Therefore, compared with the other feature extraction methods, the proposed method provides a new solution for classifying different types of ships effectively.
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Affiliation(s)
- Zhaoxi Li
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an 710072, China;
| | - Yaan Li
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an 710072, China;
- Correspondence: ; Tel.: +86-29-8849-5817
| | - Kai Zhang
- Department of Computer and Information of Science and Engineering, University of Florida, Gainesville, FL 32611, USA;
| | - Jianli Guo
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China;
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Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY 2018; 20:e20010035. [PMID: 33265122 PMCID: PMC7512207 DOI: 10.3390/e20010035] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/24/2022]
Abstract
The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
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Gu Y, Miao S, Han J, Zeng K, Ouyang G, Yang J, Li X. Complexity analysis of fNIRS signals in ADHD children during working memory task. Sci Rep 2017; 7:829. [PMID: 28400568 PMCID: PMC5429780 DOI: 10.1038/s41598-017-00965-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 03/21/2017] [Indexed: 12/26/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. Neuroimaging studies have revealed abnormalities of neural activities in some brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Recently, some investigators have demonstrated that nonlinear complexity analysis of neural activity may provide a new index to indicate ADHD. In the present study, we used the permutation entropy (PE) to measure the complexity of functional near-infrared spectroscopy (fNIRS) signals in children with and without ADHD during a working memory task, it was aimed to investigate the relationship between the PE values and the cortical activations, and the different PE values between the children with and without ADHD. We found that PE values exhibited significantly negative correlation with the cortical activations (r = -0.515, p = 0.003), and the PE values of right dorsolateral prefrontal cortex in ADHD children were significantly larger than those in normal controls (p = 0.027). In addition, the PE values of right dorsolateral prefrontal cortex were positively correlated to the ADHD index (r = 0.448, p = 0.012). These results suggest that complexity analysis of fNIRS signals could be a promising tool in diagnosing children with ADHD.
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Affiliation(s)
- Yue Gu
- Institute of Electrical Engineering, Yanshan University, No. 438, Hebei Street, HaiGang District, Qinhuangdao, 066004, China
| | - Shuo Miao
- Children's Hospital Attached to The Capital Institute of Paediatrics, No. 2, Yabao Street, ChaoYang District, Beijing, 100020, China
| | - Junxia Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Ke Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Jian Yang
- Children's Hospital Attached to The Capital Institute of Paediatrics, No. 2, Yabao Street, ChaoYang District, Beijing, 100020, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China.
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MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:8450241. [PMID: 27118965 PMCID: PMC4828555 DOI: 10.1155/2016/8450241] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 03/07/2016] [Indexed: 11/30/2022]
Abstract
The aim of the present study was to explore the interchannel relationships of resting-state brain activity in patients with attention-deficit/hyperactivity disorder (ADHD), one of the most common mental disorders that develop in children. Magnetoencephalographic (MEG) signals were recorded using a 148-channel whole-head magnetometer in 13 patients with ADHD (range: 8–12 years) and 14 control subjects (range: 8–13 years). Three complementary measures (coherence, phase-locking value, and Euclidean distance) were calculated in the conventional MEG frequency bands: delta, theta, alpha, beta, and gamma. Our results showed that the interactions among MEG channels are higher for ADHD patients than for control subjects in all frequency bands. Statistically significant differences were observed for short-distance values within right-anterior and central regions, especially at delta, beta, and gamma-frequency bands (p < 0.05; Mann-Whitney U test with false discovery rate correction). These frequency bands also showed statistically significant differences in long-distance interactions, mainly among anterior and central regions, as well as among anterior, central, and other areas. These differences might reflect alterations during brain development in children with ADHD. Our results support the role of frontal abnormalities in ADHD pathophysiology, which may reflect a delay in cortical maturation in the frontal cortex.
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