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Huang L, Du F, Huang W, Ren H, Qiu W, Zhang J, Wang Y. Three-stage Dynamic Brain-cognitive Model of Understanding Action Intention Displayed by Human Body Movements. Brain Topogr 2024:10.1007/s10548-024-01061-3. [PMID: 38874853 DOI: 10.1007/s10548-024-01061-3] [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: 05/01/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024]
Abstract
The ability to comprehend the intention conveyed through human body movements is crucial for effective interpersonal interactions. If people can't understand the intention behind other individuals' isolated or interactive actions, their actions will become meaningless. Psychologists have investigated the cognitive processes and neural representations involved in understanding action intention, yet a cohesive theoretical explanation remains elusive. Hence, we mainly review existing literature related to neural correlates of action intention, and primarily propose a putative Three-stage Dynamic Brain-cognitive Model of understanding action intention, which involves body perception, action identification and intention understanding. Specifically, at the first stage, body parts/shapes are processed by those brain regions such as extrastriate and fusiform body areas; During the second stage, differentiating observed actions relies on configuring relationships between body parts, facilitated by the activation of the Mirror Neuron System; The last stage involves identifying various intention categories, utilizing the Mentalizing System for recruitment, and different activation patterns concerning the nature of the intentions participants dealing with. Finally, we delves into the clinical practice, like intervention training based on a theoretical model for individuals with autism spectrum disorders who encounter difficulties in interpersonal communication.
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Affiliation(s)
- Liang Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China.
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
| | - Fangyuan Du
- Fuzhou University of International Studies and Trade, Fuzhou, China
| | - Wenxin Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Hanlin Ren
- Third People's Hospital of Zhongshan, Zhongshan, China
| | - Wenzhen Qiu
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Jiayi Zhang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Yiwen Wang
- The School of Economics and Management, Fuzhou University, Fuzhou, China.
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Liang Z, Chang Y, Liu X, Cao S, Chen Y, Wang T, Xu J, Li D, Zhang J. Changes in information integration and brain networks during propofol-, dexmedetomidine-, and ketamine-induced unresponsiveness. Br J Anaesth 2024; 132:528-540. [PMID: 38105166 DOI: 10.1016/j.bja.2023.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Information integration and network science are important theories for quantifying consciousness. However, whether these theories propose drug- or conscious state-related changes in EEG during anaesthesia-induced unresponsiveness remains unknown. METHODS A total of 72 participants were randomised to receive i.v. infusion of propofol, dexmedetomidine, or ketamine at a constant infusion rate until loss of responsiveness. High-density EEG was recorded during the consciousness transition from the eye-closed baseline to the unresponsiveness state and then to the recovery of the responsiveness state. Permutation cross mutual information (PCMI) and PCMI-based brain networks in broadband (0.1-45 Hz) and sub-band frequencies were used to analyse drug- and state-related EEG signature changes. RESULTS PCMI and brain networks exhibited state-related changes in certain brain regions and frequency bands. The within-area PCMI of the frontal, parietal, and occipital regions, and the between-area PCMI of the parietal-occipital region (median [inter-quartile ranges]), baseline vs unresponsive were as follows: 0.54 (0.46-0.58) vs 0.46 (0.40-0.50), 0.58 (0.52-0.60) vs 0.48 (0.44-0.53), 0.54 (0.49-0.59) vs 0.47 (0.42-0.52) decreased during anaesthesia for three drugs (P<0.05). Alpha PCMI in the frontal region, and gamma PCMI in the posterior area significantly decreased in the unresponsive state (P<0.05). The frontal, parietal, and occipital nodal clustering coefficients and parietal nodal efficiency decreased in the unresponsive state (P<0.05). The increased normalised path length in delta, theta, and gamma bands indicated impaired global integration (P<0.05). CONCLUSIONS The three anaesthetics caused changes in information integration patterns and network functions. Thus, it is possible to build a quantifying framework for anaesthesia-induced conscious state changes on the EEG scale using PCMI and network science.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China; Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, P.R. China
| | - Yu Chang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China; Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, P.R. China
| | - Xiaoge Liu
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Shumei Cao
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Yali Chen
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Tingting Wang
- Department of Anaesthesiology, Huashan Hospital, Fudan University, Shanghai, P.R. China
| | - Jianghui Xu
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Duan Li
- Center for Consciousness Science, Department of Anaesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jun Zhang
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China.
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Xu B, Deng L, Zhang D, Xue M, Li H, Zeng H, Song A. Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding. Front Neurosci 2021; 15:797990. [PMID: 34916905 PMCID: PMC8669594 DOI: 10.3389/fnins.2021.797990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.
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Affiliation(s)
- Baoguo Xu
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Leying Deng
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Dalin Zhang
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Muhui Xue
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Huijun Li
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Hong Zeng
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Aiguo Song
- The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
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Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1462369. [PMID: 34858491 PMCID: PMC8632405 DOI: 10.1155/2021/1462369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/24/2021] [Accepted: 11/02/2021] [Indexed: 11/17/2022]
Abstract
Objective Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.
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