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de Sampaio Barros MF, Stefano Filho CA, de Menezes LT, Araújo-Moreira FM, Trevelin LC, Pimentel Maia R, Radel R, Castellano G. Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking. PeerJ 2024; 12:e17743. [PMID: 39076780 PMCID: PMC11285370 DOI: 10.7717/peerj.17743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/24/2024] [Indexed: 07/31/2024] Open
Abstract
It has been indicated that extreme sport activities result in a highly rewarding experience, despite also providing fear, stress and anxiety. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate-HR, and breathing rate-BR) and neural (electroencephalographic-EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m-low-walk- and 45 m-high-walk-from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network-VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network-DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a 'task-constantly-opened-to-subtle-information experience', rather than a 'task-focused experience'.
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Affiliation(s)
- Marcelo Felipe de Sampaio Barros
- Programa de Pós-graduação em Biotecnologia, Universidade Federal de São Carlos (UFSCar), São Carlos, São Paulo, Brazil
- Laboratoire LAMHESS, Université de Nice Sophia Antipolis, Nice, Côte d’Azur, France
| | - Carlos Alberto Stefano Filho
- Neurophysics Group, Gleb Wataghin Institute of Physics, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Lucas Toffoli de Menezes
- Neurophysics Group, Gleb Wataghin Institute of Physics, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Fernando Manuel Araújo-Moreira
- Programa de Pós-graduação em Biotecnologia, Universidade Federal de São Carlos (UFSCar), São Carlos, São Paulo, Brazil
- Programa de pós-graduação em Engenharia Nuclear, Instituto Militar de Engenharia/IME, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Luis Carlos Trevelin
- Programa de Pós-graduação em Biotecnologia, Universidade Federal de São Carlos (UFSCar), São Carlos, São Paulo, Brazil
- Departamento de Computação, Universidade Federal de São Carlos (UFSCar), São Carlos, São Paulo, Brazil
| | - Rafael Pimentel Maia
- Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
| | - Rémi Radel
- Laboratoire LAMHESS, Université de Nice Sophia Antipolis, Nice, Côte d’Azur, France
| | - Gabriela Castellano
- Neurophysics Group, Gleb Wataghin Institute of Physics, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
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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: 3] [Impact Index Per Article: 3.0] [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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Siviero I, Bonfanti D, Menegaz G, Savazzi S, Mazzi C, Storti SF. Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8833. [PMID: 37960532 PMCID: PMC10650175 DOI: 10.3390/s23218833] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.
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Affiliation(s)
- Ilaria Siviero
- Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Davide Bonfanti
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Silvia Savazzi
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Chiara Mazzi
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Silvia Francesca Storti
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
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Liang W, Jin J, Daly I, Sun H, Wang X, Cichocki A. Novel channel selection model based on graph convolutional network for motor imagery. Cogn Neurodyn 2023; 17:1283-1296. [PMID: 37786654 PMCID: PMC10542066 DOI: 10.1007/s11571-022-09892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/03/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, 518063 China
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia 143026
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
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5
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Tang J, Xi X, Wang T, Wang J, Li L, Lü Z. Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient. J Neural Eng 2023; 20:056017. [PMID: 37683652 DOI: 10.1088/1741-2552/acf7f7] [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: 06/12/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.
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Affiliation(s)
- Jianpeng Tang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Xugang Xi
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Ting Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Junhong Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Lihua Li
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, People's Republic of China
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6
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Si Y, Li P, Wang X, Yao G, Liu C, Liu Y, Zhang J, Zhang H, Luo Y. Cueing effect of attention among nurses with different anxiety levels: an EEG study. Med Biol Eng Comput 2023; 61:2269-2279. [PMID: 36988789 DOI: 10.1007/s11517-023-02829-8] [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: 10/24/2022] [Accepted: 03/08/2023] [Indexed: 03/30/2023]
Abstract
The attention to cueing among nurses with anxiety affects their nursing quality seriously. Nevertheless, the neural mechanism of attention under anxiety among nurses has not been revealed. In this study, we utilized the event-related potential (ERP) and functional brain networks to investigate the neural mechanism of the cueing attention differences between anxiety and non-anxiety nurse groups (AG-20 nurses; NAG-20 nurses) in the spatial cueing task. The results revealed that in the invalid cues (144 trials), longer reaction times, larger P2 amplitudes, and more linkages between the right frontal and parietal areas were found in AG compared to NAG. In the valid cues (288 trials), there were no significant behavioral and neural differences between the two groups. The AG in the invalid cues showed slower response times, larger P2 and N5 amplitudes, and denser linkages originating from the occipital cortex than those in the valid cues. The convolutional neural network was trained for discriminating between the anxiety nurses and the normal ones, with the average accuracy being 0.76. The findings provided a potential physiological biomarker to predict the anxiety group who need to give more psychological attention. Nurse leaders maybe get more information for offering solutions to retain mental health among nurses.
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Affiliation(s)
- Yajing Si
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
- Xinxiang Municipal Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinge Wang
- Department of Nursing, Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Guiying Yao
- School of Nursing, Xinxiang Medical University, Xinxiang, Henan, China
| | - Congcong Liu
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yize Liu
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jiajia Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China.
- Xinxiang Municipal Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China.
| | - Yanyan Luo
- School of Nursing, Xinxiang Medical University, Xinxiang, Henan, China.
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7
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Siviero I, Menegaz G, Storti SF. Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance. SENSORS (BASEL, SWITZERLAND) 2023; 23:7520. [PMID: 37687976 PMCID: PMC10490741 DOI: 10.3390/s23177520] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023]
Abstract
(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
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Affiliation(s)
- Ilaria Siviero
- Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Silvia Francesca Storti
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
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8
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Zhuang W, Jia H, Liu Y, Cong J, Chen K, Yao D, Kang X, Xu P, Zhang T. Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity. Autism Res 2023; 16:1512-1526. [PMID: 37365978 DOI: 10.1002/aur.2974] [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: 11/08/2022] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD.
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Affiliation(s)
- Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Hai Jia
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Yunhong Liu
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Kai Chen
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, Chengdu, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
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9
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Zhang X, Lu B, Chen C, Yang L, Chen W, Yao D, Hou J, Qiu J, Li F, Xu P. The correlation between upper body grip strength and resting-state EEG network. Med Biol Eng Comput 2023:10.1007/s11517-023-02865-4. [PMID: 37338738 DOI: 10.1007/s11517-023-02865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.
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Affiliation(s)
- Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bin Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lei Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wanjun Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, 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 Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Jingming Hou
- Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Jing Qiu
- Robotics Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, 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 Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 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 Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China.
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10
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Liao W, Li J, Zhang X, Li C. Motor imagery brain-computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study. Front Hum Neurosci 2023; 17:1117670. [PMID: 36999132 PMCID: PMC10043218 DOI: 10.3389/fnhum.2023.1117670] [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: 12/06/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI) combined with physiotherapy and physiotherapy alone in ischemic stroke before and after rehabilitation training. We wanted to explore whether the rehabilitation effect of MI-BCI is affected by the severity of the patient's condition and whether MI-BCI was effective for all patients. Forty hospitalized patients with ischemic stroke with motor deficits participated in this study. The patients were divided into MI and control groups. Functional assessments were performed before and after rehabilitation training. The Fugl-Meyer Assessment (FMA) was used as the primary outcome measure, and its shoulder and elbow scores and wrist scores served as secondary outcome measures. The motor assessment scale (MAS) was used to assess motor function recovery. We used non-contrast CT (NCCT) to investigate the influence of different types of middle cerebral artery high-density signs on the prognosis of ischemic stroke. Brain topographic maps can directly reflect the neural activity of the brain, so we used them to detect changes in brain function and brain topological power response after stroke. Compared the MI group and control group after rehabilitation training, better functional outcome was observed after MI-BCI rehabilitation, including a significantly higher probability of achieving a relevant increase in the Total FMA scores (MI = 16.70 ± 12.79, control = 5.34 ± 10.48), FMA shoulder and elbow scores (MI = 12.56 ± 6.37, control = 2.45 ± 7.91), FMA wrist scores (MI = 11.01 ± 3.48, control = 3.36 ± 5.79), the MAS scores (MI = 3.62 ± 2.48, control = 1.85 ± 2.89), the NCCT (MI = 21.94 ± 2.37, control = 17.86 ± 3.55). The findings demonstrate that MI-BCI rehabilitation training could more effectively improve motor function after upper limb motor dysfunction after stroke compared with routine rehabilitation training, which verifies the feasibility of active induction of neural rehabilitation. The severity of the patient's condition may affect the rehabilitation effect of the MI-BCI system.
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Affiliation(s)
- Wenzhe Liao
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Jiahao Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Xuesong Zhang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Chen Li
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China
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11
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Guo L, Liu D, Wu Y, Xu G. Comparison of spiking neural networks with different topologies based on anti-disturbance ability under external noise. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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12
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Guo L, Zhang S, Wu Y, Xu G. Complex spiking neural networks with synaptic time-delay based on anti-interference function. Cogn Neurodyn 2022; 16:1485-1503. [PMID: 36408076 PMCID: PMC9666611 DOI: 10.1007/s11571-022-09803-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 02/13/2022] [Accepted: 03/21/2022] [Indexed: 01/16/2023] Open
Abstract
The research on a brain-like model with bio-interpretability is conductive to promoting its information processing ability in the field of artificial intelligence. Biological results show that the synaptic time-delay can improve the information processing abilities of the nervous system, which are an important factor related to the formation of brain cognitive functions. However, the synaptic plasticity with time-delay of a brain-like model still lacks bio-interpretability. In this study, combining excitatory and inhibitory synapses, we construct the complex spiking neural networks (CSNNs) with synaptic time-delay that more conforms biological characteristics, in which the topology has scale-free property and small-world property, and the nodes are represented by an Izhikevich neuron model. Then, the information processing abilities of CSNNs with different types of synaptic time-delay are comparatively evaluated based on the anti-interference function, and the mechanism of this function is discussed. Using two indicators of the anti-interference function and three kinds of noise, our simulation results consistently verify that: (i) From the perspective of anti-interference function, an CSNN with synaptic random time-delay outperforms an CSNN with synaptic fixed time-delay, which in turn outperforms an CSNN with synaptic none time-delay. The results imply that brain-like networks with more bio-interpretable synaptic time-delay have stronger information processing abilities. (ii) The synaptic plasticity is the intrinsic factor of the anti-interference function of CSNNs with different types of synaptic time-delay. (iii) The synaptic random time-delay makes an CSNN present better topological characteristics, which can improve the information processing ability of a brain-like network. It implies that synaptic time-delay is a factor that affects the anti-interference function at the level of performance.
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Affiliation(s)
- Lei Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130 China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, 300130 China
| | - Sijia Zhang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130 China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, 300130 China
| | - Youxi Wu
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300130 China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130 China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, 300130 China
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13
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Zhang J, Zhang X, Chen G, Zhao Q. Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition. Brain Sci 2022; 12:1649. [PMID: 36552109 PMCID: PMC9776073 DOI: 10.3390/brainsci12121649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/08/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal-valence classifications, respectively.
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Affiliation(s)
| | - Xueying Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
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14
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Tao Q, Jiang L, Li F, Qiu Y, Yi C, Si Y, Li C, Zhang T, Yao D, Xu P. Dynamic networks of P300-related process. Cogn Neurodyn 2022; 16:975-985. [PMID: 36237399 PMCID: PMC9508298 DOI: 10.1007/s11571-021-09753-3] [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: 02/22/2021] [Revised: 10/19/2021] [Accepted: 10/29/2021] [Indexed: 11/03/2022] Open
Abstract
P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.
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Affiliation(s)
- Qin Tao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Fali Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yuan Qiu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Chanlin Yi
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Hena, 453000 China
| | - Cunbo Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Tao Zhang
- School of Science, Xihua University, Chengdu, 610039 China
| | - Dezhong Yao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Peng Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
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15
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Li Z, Yi C, Chen C, Liu C, Zhang S, Li S, Gao D, Cheng L, Zhang X, Sun J, He Y, Xu P. Predicting individual muscle fatigue tolerance by resting-state EEG brain network. J Neural Eng 2022; 19. [PMID: 35901723 DOI: 10.1088/1741-2552/ac8502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 07/28/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored. APPROACH Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance. MAIN RESULTS Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network. SIGNIFICANCE Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.
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Affiliation(s)
- Zhiwei Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Chanlin Yi
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chunli Chen
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chen Liu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shu Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shunchang Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Dongrui Gao
- Chengdu University of Information Technology, No.24 Block 1, Xuefu Road, Chengdu, Sichuan, 610225, CHINA
| | - Liang Cheng
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Xiabing Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Junzhi Sun
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Ying He
- Small Ball Department of Physical Education and Sport Sciences, Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
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16
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Liang T, Hong L, Xiao J, Wei L, Liu X, Wang H, Dong B, Liu X. Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks. J Neural Eng 2022; 19. [PMID: 35767971 DOI: 10.1088/1741-2552/ac7d0c] [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: 02/24/2022] [Accepted: 06/29/2022] [Indexed: 11/12/2022]
Abstract
Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.
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Affiliation(s)
- Tie Liang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Lei Hong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Jinzhuang Xiao
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Lixin Wei
- Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Xiaoguang Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Bin Dong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Development Planning Office, Affiliated Hospital of Hebei University, Baoding 071002, People's Republic of China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
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17
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Li F, Jiang L, Zhang Y, Huang D, Wei X, Jiang Y, Yao D, Xu P, Li H. The time-varying networks of the wrist extension in post-stroke hemiplegic patients. Cogn Neurodyn 2021; 16:757-766. [PMID: 35847531 PMCID: PMC9279526 DOI: 10.1007/s11571-021-09738-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/05/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09738-2.
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