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Siddiqui A, Abu Hasan R, Saad Azhar Ali S, Elamvazuthi I, Lu CK, Tang TB. Detection of Low Resilience Using Data-Driven Effective Connectivity Measures. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3657-3668. [PMID: 39302782 DOI: 10.1109/tnsre.2024.3465269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approaches may not always be reliable as conventional thresholding approaches are subjected to human biases. Using a mental resilience study, we investigate if data-driven thresholding techniques such as Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) could provide equivalent results, whilst eliminating human biases. We implemented Phase Slope Index (PSI) to compute effective brain connectivity, and applied data-driven thresholding approaches to filter the brain networks in order to identify key features of low resilience within a cohort of healthy individuals. Our dataset encompassed resting-state EEG recordings gathered from a total of 36 participants (31 females and 5 males). Relevant features were extracted to train and validate a classifier model (Support Vector Machine, SVM). The detection of low stress resilience among healthy individuals using the SVM model scores an accuracy of 80.6% with GCE-abs, and 75% with OMSTs, respectively.
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Hua A, Wang G, Bai J, Hao Z, Yang Y, Luo X, Liu J, Meng J, Wang J. Rapid reconfiguration of cortical networks after repeated exposure to visual-vestibular conflicts. Sci Rep 2024; 14:21943. [PMID: 39304732 DOI: 10.1038/s41598-024-73111-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 09/13/2024] [Indexed: 09/22/2024] Open
Abstract
Visual-vestibular conflicts can induce motion sickness and further postural instability. Visual-vestibular habituation is recommended to reduce the symptoms of motion sickness and improve postural stability with an altered multisensory reweighting progress. However, it is unclear how the human brain reweights multisensory information after repeated exposure to visual-vestibular conflicts. Therefore, we synchronized a rotating platform and a virtual scene to present visual-vestibular congruent (natural visual stimulation) and incongruent (conflicted visual stimulation) conditions and collected EEG and center of pressure (COP) data. We constructed the effective brain connectivity of region of interest (ROI) derived from source-space EEG in theta-band activity, and quantified the postural stability and the inflow and outflow of each ROI. We found repeated exposure to congruent and incongruent conditions both decreased COP path length and increased COP complexity. Besides, we found that repeated exposure to the incongruent environment decreased the inflow into visual cortex, suggesting the brain down-weighted the less reliable visual information for postural stability. In contrast, repeated exposure to the congruent environment increased the inflow into posterior parietal cortex and the outflow from visual cortex and S1, suggesting an increase in efficiency of multisensory integration. We concluded that repeated exposure to congruent and incongruent conditions both improved postural stability with different multisensory reweighting patterns as revealed by different dynamic changes of brain networks.
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Affiliation(s)
- Anke Hua
- Department of Sports Science, Zhejiang University, Hangzhou, 310058, China
- Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, 21201, USA
| | - Guozheng Wang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310058, China
- Taizhou Key Laboratory of Medical Devices and Advanced Materials, Research Institute of Zhejiang University-Taizhou,, Taizhou, 318000, China
| | - Jingyuan Bai
- Department of Sports Science, Zhejiang University, Hangzhou, 310058, China
| | - Zengming Hao
- School of Sport And Physical Education, North University of China, Taiyuan, 030051, China
| | - Yi Yang
- Department of Sports Science, Zhejiang University, Hangzhou, 310058, China
| | - Xin Luo
- Department of Sports Science, Zhejiang University, Hangzhou, 310058, China
| | - Jun Liu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310058, China
| | - Jun Meng
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Jian Wang
- Department of Sports Science, Zhejiang University, Hangzhou, 310058, China.
- Center for Psychological Science, Zhejiang University, Hangzhou, 310058, China.
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Farhadi Sedehi J, Jafarnia Dabanloo N, Maghooli K, Sheikhani A. Multimodal insights into granger causality connectivity: Integrating physiological signals and gated eye-tracking data for emotion recognition using convolutional neural network. Heliyon 2024; 10:e36411. [PMID: 39253213 PMCID: PMC11381760 DOI: 10.1016/j.heliyon.2024.e36411] [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: 05/28/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 09/11/2024] Open
Abstract
This study introduces a groundbreaking method to enhance the accuracy and reliability of emotion recognition systems by combining electrocardiogram (ECG) with electroencephalogram (EEG) data, using an eye-tracking gated strategy. Initially, we propose a technique to filter out irrelevant portions of emotional data by employing pupil diameter metrics from eye-tracking data. Subsequently, we introduce an innovative approach for estimating effective connectivity to capture the dynamic interaction between the brain and the heart during emotional states of happiness and sadness. Granger causality (GC) is estimated and utilized to optimize input for a highly effective pre-trained convolutional neural network (CNN), specifically ResNet-18. To assess this methodology, we employed EEG and ECG data from the publicly available MAHNOB-HCI database, using a 5-fold cross-validation approach. Our method achieved an impressive average accuracy and area under the curve (AUC) of 91.00 % and 0.97, respectively, for GC-EEG-ECG images processed with ResNet-18. Comparative analysis with state-of-the-art studies clearly shows that augmenting ECG with EEG and refining data with an eye-tracking strategy significantly enhances emotion recognition performance across various emotions.
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Affiliation(s)
- Javid Farhadi Sedehi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Sheikhani
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Manickam T, Ramasamy V, Doraisamy N. Comparison of data-driven thresholding methods using directed functional brain networks. Rev Neurosci 2024:revneuro-2024-0020. [PMID: 39217451 DOI: 10.1515/revneuro-2024-0020] [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: 01/27/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024]
Abstract
Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes and the linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fully connected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks.
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Affiliation(s)
- Thilaga Manickam
- Department of Mathematics, Amrita School of Physical Sciences, 77649 Amrita Vishwa Vidyapeetham , Coimbatore, Tamilnadu 641112, India
| | - Vijayalakshmi Ramasamy
- College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30458, USA
| | - Nandagopal Doraisamy
- Cognitive Neuroengineering Laboratory, School of Information Technology and Mathematical Sciences, Division of IT, Engineering and the Environments, University of South Australia, Adelaide 5000, Australia
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Cai T, Lin Y, Wang G, Luo J. Predicting radiofrequency thermocoagulation surgical outcomes in refractory focal epilepsy patients using functional coupled neural mass model. Front Neurol 2024; 15:1402004. [PMID: 39246608 PMCID: PMC11377261 DOI: 10.3389/fneur.2024.1402004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 08/12/2024] [Indexed: 09/10/2024] Open
Abstract
Objective The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Methods Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. Results The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. Conclusion The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses.
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Affiliation(s)
- Tianxin Cai
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, Sun Yat-sen University, Guangzhou, China
| | - Yaoxin Lin
- Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, China
| | - Guofu Wang
- Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, China
| | - Jie Luo
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, Sun Yat-sen University, Guangzhou, China
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Marino M, Mantini D. Human brain imaging with high-density electroencephalography: Techniques and applications. J Physiol 2024. [PMID: 39173191 DOI: 10.1113/jp286639] [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: 05/04/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.
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Affiliation(s)
- Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
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Zhao Z, Feng Y, Wang M, Wei J, Tan T, Li R, Hu H, Wang M, Chen P, Gao X, Wei Y, Wang C, Gao Z, Jiang W, Zhou X, Li M, Wang C, Pang T, Yu Y. Investigating cortical complexity and connectivity in rats with schizophrenia. Front Neuroinform 2024; 18:1392271. [PMID: 39211912 PMCID: PMC11358091 DOI: 10.3389/fninf.2024.1392271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Background The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. Methods We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Results Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. Conclusion The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yifan Feng
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Menghan Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Jiarong Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Tao Tan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ruijiao Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Heshun Hu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mengke Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Peiqi Chen
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xudong Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yinping Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Wenshuai Jiang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xuezhi Zhou
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mingcai Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chong Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ting Pang
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
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Bahrami F, Taghizadeh M, Shayegh F. Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:24. [PMID: 39234588 PMCID: PMC11373796 DOI: 10.4103/jmss.jmss_15_24] [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: 03/02/2024] [Revised: 04/22/2024] [Accepted: 05/03/2024] [Indexed: 09/06/2024]
Abstract
Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.
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Affiliation(s)
- Farzaneh Bahrami
- Faculty of Technology and Engineering, Shahrekord University, Shahrekord, Iran
| | - Maryam Taghizadeh
- Faculty of Technology and Engineering, Shahrekord University, Shahrekord, Iran
| | - Farzaneh Shayegh
- Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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Li L, Wang X, Li J, Zhao Y. An EEG-based marker of functional connectivity: detection of major depressive disorder. Cogn Neurodyn 2024; 18:1671-1687. [PMID: 39104678 PMCID: PMC11297863 DOI: 10.1007/s11571-023-10041-5] [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: 04/28/2023] [Revised: 09/15/2023] [Accepted: 11/09/2023] [Indexed: 08/07/2024] Open
Abstract
Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.
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Affiliation(s)
- Ling Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Xianshuo Wang
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Jiahui Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Yanping Zhao
- College of Communication Engineering, Jilin University, Changchun, Jilin China
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Chen YC, Lo IP, Tsai YY, Zhao CG, Hwang IS. Dual-task improvement of older adults after treadmill walking combined with blood flow restriction of low occlusion pressure: the effect on the heart-brain axis. J Neuroeng Rehabil 2024; 21:116. [PMID: 38997727 PMCID: PMC11241870 DOI: 10.1186/s12984-024-01412-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 06/23/2024] [Indexed: 07/14/2024] Open
Abstract
OBJECTIVE This study explored the impact of one session of low-pressure leg blood flow restriction (BFR) during treadmill walking on dual-task performance in older adults using the neurovisceral integration model framework. METHODS Twenty-seven older adults participated in 20-min treadmill sessions, either with BFR (100 mmHg cuff pressure on both thighs) or without it (NBFR). Dual-task performance, measured through light-pod tapping while standing on foam, and heart rate variability during treadmill walking were compared. RESULTS Following BFR treadmill walking, the reaction time (p = 0.002) and sway area (p = 0.012) of the posture dual-task were significantly reduced. Participants exhibited a lower mean heart rate (p < 0.001) and higher heart rate variability (p = 0.038) during BFR treadmill walking. Notably, BFR also led to band-specific reductions in regional brain activities (theta, alpha, and beta bands, p < 0.05). The topology of the EEG network in the theta and alpha bands became more star-like in the post-test after BFR treadmill walking (p < 0.005). CONCLUSION BFR treadmill walking improves dual-task performance in older adults via vagally-mediated network integration with superior neural economy. This approach has the potential to prevent age-related falls by promoting cognitive reserves.
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Affiliation(s)
- Yi-Ching Chen
- Department of Physical Therapy, College of Medical Science and Technology, Chung Shan Medical University, Taichung City, Taiwan
- Physical Therapy Room, Chung Shan Medical University Hospital, Taichung City, Taiwan
| | - I-Ping Lo
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, 701, Taiwan
| | - Yi-Ying Tsai
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, 701, Taiwan
| | - Chen-Guang Zhao
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, 701, Taiwan
| | - Ing-Shiou Hwang
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, 701, Taiwan.
- Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
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Wu S, Zhan P, Wang G, Yu X, Liu H, Wang W. Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis. BMC Neurosci 2024; 25:30. [PMID: 38965489 PMCID: PMC11223280 DOI: 10.1186/s12868-024-00877-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 06/18/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment. METHODS We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters. RESULTS Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD. CONCLUSIONS Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.
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Affiliation(s)
- Shijing Wu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Ping Zhan
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Guojing Wang
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Xiaohua Yu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Hongyun Liu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China.
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China.
| | - Weidong Wang
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China.
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China.
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12
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Santos Cuevas DC, Campos Ruiz RE, Collina DD, Tierra Criollo CJ. Effective brain connectivity related to non-painful thermal stimuli using EEG. Biomed Phys Eng Express 2024; 10:045044. [PMID: 38834037 DOI: 10.1088/2057-1976/ad53ce] [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: 03/15/2024] [Accepted: 06/04/2024] [Indexed: 06/06/2024]
Abstract
Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.
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Affiliation(s)
| | | | - Denny Daniel Collina
- Department of Electronics and Biomedical Engineering, Federal Center for Technological Education of Minas Gerais, Belo Horizonte, 30510-000, Brazil
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13
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Jin J, Zheng Q, Liu H, Feng K, Bai Y, Ni G. Musical experience enhances time discrimination: Evidence from cortical responses. Ann N Y Acad Sci 2024; 1536:167-176. [PMID: 38829709 DOI: 10.1111/nyas.15153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music-experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music-experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.
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Affiliation(s)
- Jiaqi Jin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Qi Zheng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hongxing Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Kunyun Feng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
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14
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Wang W, Li H, Wang Y, Liu L, Qian Q. Changes in effective connectivity during the visual-motor integration tasks: a preliminary f-NIRS study. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2024; 20:4. [PMID: 38468270 DOI: 10.1186/s12993-024-00232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 03/05/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels. METHODS A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance. RESULTS We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance. CONCLUSIONS The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.
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Affiliation(s)
- Wenchen Wang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yufeng Wang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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15
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Santamaria L, Koopman ACM, Bekinschtein T, Lewis P. Effects of Targeted Memory Reactivation on Cortical Networks. Brain Sci 2024; 14:114. [PMID: 38391689 PMCID: PMC10886727 DOI: 10.3390/brainsci14020114] [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: 12/15/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/24/2024] Open
Abstract
Sleep is a complex physiological process with an important role in memory consolidation characterised by a series of spatiotemporal changes in brain activity and connectivity. Here, we investigate how task-related responses differ between pre-sleep wake, sleep, and post-sleep wake. To this end, we trained participants on a serial reaction time task using both right and left hands using Targeted Memory Reactivation (TMR), in which auditory cues are associated with learned material and then re-presented in subsequent wake or sleep periods in order to elicit memory reactivation. The neural responses just after each cue showed increased theta band connectivity between frontal and other cortical regions, as well as between hemispheres, in slow wave sleep compared to pre- or post-sleep wake. This pattern was consistent across the cues associated with both right- and left-handed movements. We also searched for hand-specific connectivity and found that this could be identified in within-hemisphere connectivity after TMR cues during sleep and post-sleep sessions. The fact that we could identify which hand had been cued during sleep suggests that these connectivity measures could potentially be used to determine how successfully memory is reactivated by our manipulation. Collectively, these findings indicate that TMR modulates the brain cortical networks showing clear differences between wake and sleep connectivity patterns.
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Affiliation(s)
| | | | | | - Penelope Lewis
- School of Psychology, Cardiff University, Wales CF10 3AT, UK
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16
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Huang CY, Chen YA, Wu RM, Hwang IS. Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024; 14:283-296. [PMID: 38457151 PMCID: PMC10977445 DOI: 10.3233/jpd-230245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/07/2024] [Indexed: 03/09/2024]
Abstract
Background Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood. Objective We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies. Methods Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed. Results Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity. Conclusions A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.
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Affiliation(s)
- Cheng-Ya Huang
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Physical Therapy Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-An Chen
- Department of Rehabilitation, Division of Physical Therapy, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | - Ruey-Meei Wu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ing-Shiou Hwang
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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17
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Wang G, Yang Y, Dong K, Hua A, Wang J, Liu J. Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis. Neurosci Bull 2024; 40:79-89. [PMID: 37989834 PMCID: PMC10774487 DOI: 10.1007/s12264-023-01143-5] [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: 02/19/2023] [Accepted: 07/16/2023] [Indexed: 11/23/2023] Open
Abstract
Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.
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Affiliation(s)
- Guozheng Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310058, China
- Taizhou Key Laboratory of Medical Devices and Advanced Materials, Research Institute of Zhejiang University-Taizhou, Taizhou, 318000, China
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, 310058, China
| | - Yi Yang
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, 310058, China
| | - Kangli Dong
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310058, China
| | - Anke Hua
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, 310058, China
| | - Jian Wang
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, 310058, China.
- Center for Psychological Science, Zhejiang University, Hangzhou, 310058, China.
| | - Jun Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310058, China.
- Taizhou Key Laboratory of Medical Devices and Advanced Materials, Research Institute of Zhejiang University-Taizhou, Taizhou, 318000, China.
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18
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Guo W, Zhang W, Zhang J, Li Z, Zhu W. Effective connectivity analysis of verbal working memory advantage across materials for pathological smartphone users by fNIRS. Psychiatry Res Neuroimaging 2023; 336:111731. [PMID: 37875058 DOI: 10.1016/j.pscychresns.2023.111731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/31/2023] [Accepted: 10/10/2023] [Indexed: 10/26/2023]
Abstract
Previous studies have found working memory (WM) advantages of the pathological smartphone use (PSU) group, but most of which were emphasized in the network-related domain. Whether the advantages can transfer to other domains has yet to be confirmed. In particular, exploring from a brain mechanism perspective is necessary. Using the classical N-back paradigm, this study selected network-related words and neutral words as materials combined with fNIRS to probe the verbal WM characteristics of the PSU group. The results showed that β in channel 3, channel 4, and channel 5 were significantly lower in the PSU group than those in the control group The analysis of the region of interest revealed that the PSU group showed significantly lower β in the l-DLPFC and frontopolar. Granger Causality results showed that functional connectivity between frontopolar and R-DLPFC for the PSU group was significantly higher than for the control group in the network word condition. These results demonstrate that the PSU group has an advantage in WM, transferring from the network-related stimulus to the neutral stimulus. The advantages of network stimulus were related to bidirectional connectivity between frontopolar and R-DLPFC. Also, the l-DLPFC and frontopolar are associated with the cross-material consistency of WM.
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Affiliation(s)
- Wenxin Guo
- School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Hubei 430079, Wuhan, PR China
| | - Wei Zhang
- School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Hubei 430079, Wuhan, PR China.
| | - Jianli Zhang
- Chengdu Longquan No.1 High School, Chengdu, Sichuan, PR China
| | - Ziyi Li
- School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Hubei 430079, Wuhan, PR China
| | - Wanling Zhu
- School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Hubei 430079, Wuhan, PR China
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19
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Li M, Yang J, Tian W, Ju X. Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network. Front Comput Neurosci 2023; 17:1263710. [PMID: 38024448 PMCID: PMC10644253 DOI: 10.3389/fncom.2023.1263710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed.
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20
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Sun X, Doose J, Faller J, McIntosh JR, Saber GT, Huffman S, Pantazatos SP, Yuan H, Goldman RI, Brown TR, George MS, Sajda P. Increased entrainment and decreased excitability predict efficacious treatment of closed-loop phase-locked rTMS for treatment-resistant depression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.09.23296751. [PMID: 37873424 PMCID: PMC10593047 DOI: 10.1101/2023.10.09.23296751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Transcranial magnetic stimulation (TMS) is an FDA-approved therapy for major depressive disorder (MDD), specifically for patients who have treatment-resistant depression (TRD). However, TMS produces response or remission in about 50% of patients but is ineffective for the other 50%. Limits on efficacy may be due to individual patient variability, but to date, there are no good biomarkers or measures of target engagement. In addition, TMS efficacy is typically not assessed until a six-week treatment ends, precluding the evaluation of intermediate improvements during the treatment duration. Here, we report on results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation informed by functional magnetic resonance imaging (fMRI). We find that, in responders, synchronized delivery of rTMS produces two systematic changes in brain dynamics. The first change is a decrease in global cortical excitability, and the second is an increase in the phase entrainment of cortical dynamics. These two effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly tracking of these biomarkers allows for efficacy prediction and potential of dynamic adjustments through a treatment course, improving the overall response rates.
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21
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Xu Z, Zhang P, Tu M, Zhang M, Lai Y. Brain optimization with additional study time: potential brain differences between high- and low-performance college students. Front Psychol 2023; 14:1209881. [PMID: 37829066 PMCID: PMC10566635 DOI: 10.3389/fpsyg.2023.1209881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/07/2023] [Indexed: 10/14/2023] Open
Abstract
This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8-9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students' diverse academic needs.
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Affiliation(s)
- Zhiwei Xu
- School of Business, Hubei University, Wuhan, Hubei Province, China
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22
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Soleymani F, Khosrowabadi R, Pedram MM, Hatami J. Impact of negative links on the structural balance of brain functional network during emotion processing. Sci Rep 2023; 13:15983. [PMID: 37749164 PMCID: PMC10519959 DOI: 10.1038/s41598-023-43178-8] [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: 02/26/2023] [Accepted: 09/20/2023] [Indexed: 09/27/2023] Open
Abstract
Activation of specific brain areas and synchrony between them has a major role in process of emotions. Nevertheless, impact of anti-synchrony (negative links) in this process still requires to be understood. In this study, we hypothesized that quantity and topology of negative links could influence a network stability by changing of quality of its triadic associations. Therefore, a group of healthy participants were exposed to pleasant and unpleasant images while their brain responses were recorded. Subsequently, functional connectivity networks were estimated and quantity of negative links, balanced and imbalanced triads, tendency to make negative hubs, and balance energy levels of two conditions were compared. The findings indicated that perception of pleasant stimuli was associated with higher amount of negative links with a lower tendency to make a hub in theta band; while the opposite scenario was observed in beta band. It was accompanied with smaller number of imbalanced triads and more stable network in theta band, and smaller number of balanced triads and less stable network in beta band. The findings highlighted that inter regional communications require less changes to receive new information from unpleasant stimuli, although by decrement in beta band stability prepares the network for the upcoming events.
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Affiliation(s)
| | - Reza Khosrowabadi
- Institute for Cognitive Science Studies, Tehran, Iran.
- Institute for Cognitive and Brain Science, Shahid Beheshti University GC, Tehran, Iran.
| | - Mir Mohsen Pedram
- Institute for Cognitive Science Studies, Tehran, Iran
- Faculty of Engineering, Kharazmi University, Tehran, Iran
| | - Javad Hatami
- Institute for Cognitive Science Studies, Tehran, Iran
- Faculty of Psychology and Educational Sciences, University of Tehran, Tehran, Iran
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23
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Fan C, Yang B, Li X, Zan P. Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement. Front Neurosci 2023; 17:1250991. [PMID: 37700746 PMCID: PMC10493321 DOI: 10.3389/fnins.2023.1250991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023] Open
Abstract
Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.
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Affiliation(s)
- Chengcheng Fan
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
- School of Medical Instrument, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Xiaoou Li
- School of Medical Instrument, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Peng Zan
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
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24
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Akhand MAH, Maria MA, Kamal MAS, Murase K. Improved EEG-based emotion recognition through information enhancement in connectivity feature map. Sci Rep 2023; 13:13804. [PMID: 37612354 PMCID: PMC10447430 DOI: 10.1038/s41598-023-40786-2] [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] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/16/2023] [Indexed: 08/25/2023] Open
Abstract
Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs' measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.
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Affiliation(s)
- M A H Akhand
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
| | - Mahfuza Akter Maria
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Abdus Samad Kamal
- Graduate School of Science and Technology, Gunma University, Kiryu, 376-8515, Japan
| | - Kazuyuki Murase
- Department of Information Technology, International Professional University of Technology in Osaka, 3-3-1 Umeda, Kita-ku, Osaka, 530-0001, Japan
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Li S, Meng Q, Yao W, Guo S, Liu H, Wang R, Meng J, Xu M. Aerobic Exercise Changes Low-Frequency Functional and Effective Connectivity in Cognitive Load Task. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082696 DOI: 10.1109/embc40787.2023.10340660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
As is well known, cognitive performances are highly influenced by cognitive load, so it is meaningful to find some ways to effectively reduce the cognitive load. In particular, aerobic exercise is a promising way. However, the neural evidence is still lacking in understanding how aerobic exercise minimizes cognitive load. To solve the problem, this study adopted the N-back task in both the before (BE) and after (AE) aerobic exercise periods, behavioral and EEG data were recorded from 21 participants. Functional connectivity was constructed by the weighted phase lag index (WPLI), and effective connectivity was constructed by the partially directed coherent (PDC). Consequently, by comparing the connection strength and pattern of BE and AE, it is found that in low-frequency (0~8 Hz), aerobic exercise could enhance the connection strength of WPLI networks under high cognitive load, and increase the importance of the forehead region in the communication of PDC networks under low cognitive load. These results could advance our understanding of the underlying mechanisms of how aerobic exercise modulates cognitive load.
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26
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Hernández O, Zurek E, Barbosa J, Villasana M. A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations. PLoS One 2023; 18:e0274713. [PMID: 37368883 DOI: 10.1371/journal.pone.0274713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
This study intends to determine whether similarities of the functioning of the cerebral cortex exist, modeled as a graph, during the execution of mathematical tasks and programming related tasks. The comparison is done using network parameters and during the development of computer programming tasks and the solution of first-order algebraic equations. For that purpose, electroencephalographic recordings (EEG) were made with a volunteer group of 16 students of systems engineering of Universidad del Norte in Colombia, while they were performing computer programming tasks and solving first-order algebraic equations with three levels of difficulty. Then, based on the Synchronization Likelihood method, graph models of functional cortical networks were developed, whose parameters of Small-Worldness (SWN), global(Eg) and local (El) efficiency were compared between both types of tasks. From this study, it can be highlighted, first, the novelty of studying cortical function during the solution of algebraic equations and during programming tasks; second, significant differences between both types of tasks observed only in the delta and theta bands. Likewise, the differences between simpler mathematical tasks with the other levels in both types of tasks; third, the Brodmann areas 21 and 42, associated with auditory sensory processing, can be considered as differentiating elements of programming tasks; as well as Brodmann area 8, during equation solving.
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Affiliation(s)
- Oscar Hernández
- Departamento de Química y Biología, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - Eduardo Zurek
- Departamento de Ingeniería de sistemas, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - John Barbosa
- Departamento de Ingeniería de sistemas, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - Minaya Villasana
- Departamento de Cómputo Científico y Estadística, Universidad Simón Bolivar, Caracas, Venezuela
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27
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Zhang W, Qiu L, Tang F, Li H. Affective or cognitive interpersonal emotion regulation in couples: an fNIRS hyperscanning study. Cereb Cortex 2023; 33:7960-7970. [PMID: 36944535 DOI: 10.1093/cercor/bhad091] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/23/2023] Open
Abstract
Sadness regulation is crucial for maintaining the romantic relationships of couples. Interpersonal emotion regulation, including affective engagement (AE) and cognitive engagement (CE), activates social brain networks. However, it is unclear how AE and CE regulate sadness in couples through affective bonds. We recruited 30 heterosexual couple dyads and 30 heterosexual stranger dyads and collected functional near-infrared spectroscopy hyperscanning data while each dyad watched sad or neutral videos and while the regulator regulated the target's sadness. Then, we characterized interbrain synchronization (IBS) and Granger causality (GC). The results indicated that AE and CE were more effective for couples than for strangers and that sadness evaluation of female targets was lower than that of male targets. CE-induced IBS at CH13 (BA10, right middle frontal gyrus) was lower for female targets than for male targets, while no gender difference in AE was detected. GC change at CH13 during CE was lower in the sad condition for male targets than for female targets, while no gender difference in AE was discovered. These observations suggest that AE and CE activate affective bonds but that CE was more effective for regulating sadness in female targets, revealing different neural patterns of cognitive and affective sadness regulation in couples.
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Affiliation(s)
- Wenhai Zhang
- The Big Data Centre for Neuroscience and AI, Hengyang Normal University, Hengyang 421002, China
- Mental Health Center, Yancheng Institute of Technology, Yancheng 224051, China
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, East China Normal University, Shanghai 200062, China
| | - Lanting Qiu
- The Big Data Centre for Neuroscience and AI, Hengyang Normal University, Hengyang 421002, China
| | - Fanggui Tang
- The Big Data Centre for Neuroscience and AI, Hengyang Normal University, Hengyang 421002, China
| | - Hong Li
- Key Laboratory of Brain Cognition and Educational Science, Ministry of Education; School of Psychology, South China Normal University, Guangzhou Guangdong, China
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28
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Shao C, Zhang X, Wu Y, Zhang W, Sun B. Increased Interpersonal Brain Synchronization in Romantic Couples Is Associated with Higher Honesty: An fNIRS Hyperscanning Study. Brain Sci 2023; 13:brainsci13050833. [PMID: 37239304 DOI: 10.3390/brainsci13050833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
Previous studies on the brain-brain interaction of deception have shown different patterns of interpersonal brain synchronization (IBS) between different genders. However, the brain-brain mechanisms in the cross-sex composition need to be better understood. Furthermore, there needs to be more discussion about how relationships (e.g., romantic couples vs. strangers) affect the brain-brain mechanism under interactive deception. To elaborate on these issues, we used the functional near-infrared spectroscopy (fNIRS)-based hyperscanning approach to simultaneously measure interpersonal brain synchronization (IBS) in romantic couples (heterosexual) and cross-sex stranger dyads during the sender-receiver game. The behavioral results found that the deception rate of males was lower than that of females, and romantic couples were deceived less than strangers. Significantly increased IBS was observed in the frontopolar cortex (FPC) and right temporoparietal junction (rTPJ) of the romantic couple group. Moreover, the IBS is negatively correlated with the deception rate. No significantly increased IBS was observed in cross-sex stranger dyads. The result corroborated the lower deception of males and romantic couples in cross-sex interactions. Furthermore, IBS in the PFC and rTPJ was the underlying dual-brain neural basis for supporting honesty in romantic couples.
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Affiliation(s)
- Chong Shao
- School of Psychology, Zhejiang Normal University, Jinhua 321004, China
| | - Xuecheng Zhang
- School of Psychology, Zhejiang Normal University, Jinhua 321004, China
| | - You Wu
- School of Psychology, Zhejiang Normal University, Jinhua 321004, China
| | - Wenhai Zhang
- School of Psychology, Zhejiang Normal University, Jinhua 321004, China
- Big Data Center for Educational Neuroscience and Artificial Intelligence, Hengyang Normal University, Hengyang 421001, China
| | - Binghai Sun
- School of Psychology, Zhejiang Normal University, Jinhua 321004, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
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29
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Zhao H, Zhang C, Tao R, Duan H, Xu S. Distinct inter-brain synchronization patterns underlying group decision-making under uncertainty with partners in different interpersonal relationships. Neuroimage 2023; 272:120043. [PMID: 37003448 DOI: 10.1016/j.neuroimage.2023.120043] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 02/04/2023] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Humans may behave in different manners when making decisions with friends and strangers. Whether the interpersonal relationship and the characteristics of the individuals in the group affected the group decision-making under uncertainty in the real-time interaction remains unknown. Using the turn-based Balloon Analogue Risk Task (BART), the present study examined the group decision-making propensity under uncertainty with partners in different interpersonal relationships and interpersonal orientations. Corresponding inter-brain synchronization (IBS) patterns at the prefrontal cortex (PFC) were also uncovered with the fNIRS-based hyperscanning approach. Behavioral results identified that dyads in the friend group exhibited the uncertainty-averse propensity when comparing with the stranger group. The fNIRS results reported that feedback-related IBS at the left inferior frontal gyrus (l-IFG) and medial frontopolar cortex (mFPC) during different feedbacks was modulated by interpersonal relationships. The IBS at all channels in the PFC during the positive and negative feedbacks, respectively, predicted the decision-making propensity under uncertainty in the stranger and friend groups based on the support vector machine (SVM) algorithm. The moderating role of the social value orientation (SVO) was also verified in the mediation effect of the dyad closeness on the decision-making propensity under uncertainty via the IBS at the right lateral frontopolar cortex (r-FPC). These findings demonstrated disparate behavioral responses and inter-brain synchronization patterns underlying group decision-making under uncertainty with partners in different interpersonal relationships.
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Affiliation(s)
- Hanxuan Zhao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, 550, Dalian West Street, Shanghai 200083, China; College of International Business, Shanghai International Studies University, Shanghai, China
| | - Can Zhang
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, 550, Dalian West Street, Shanghai 200083, China; College of International Business, Shanghai International Studies University, Shanghai, China
| | - Ruiwen Tao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, 550, Dalian West Street, Shanghai 200083, China; College of International Business, Shanghai International Studies University, Shanghai, China
| | - Haijun Duan
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, 199 South Chang' an Road, Xi'an 710062, China.
| | - Sihua Xu
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, 550, Dalian West Street, Shanghai 200083, China; College of International Business, Shanghai International Studies University, Shanghai, China; School of Education, Huaibei Normal University, Huaibei, China; Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior, Huaibei Normal University, Huaibei, China.
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30
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Ray KL, Griffin NR, Shumake J, Alario A, Allen JJB, Beevers CG, Schnyer DM. Altered electroencephalography resting state network coherence in remitted MDD. Brain Res 2023; 1806:148282. [PMID: 36792002 DOI: 10.1016/j.brainres.2023.148282] [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: 10/10/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023]
Abstract
Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.
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Affiliation(s)
| | | | | | - Alexandra Alario
- University of Texas, Austin, United States; University of Iowa, United States
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31
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Bu L, Qu J, Zhao L, Zhang Y, Wang Y. A neuroergonomic approach to assessing motor performance in stroke patients using fNIRS and behavioral data. APPLIED ERGONOMICS 2023; 109:103979. [PMID: 36689868 DOI: 10.1016/j.apergo.2023.103979] [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: 09/29/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 06/17/2023]
Abstract
Stroke is characterized by high morbidity and disability, and proposing effective methods for assessing and designing rehabilitation products is an attractive topic in current research. In this study, a hand function rehabilitation aid was developed for stroke patients. Ten stroke patients and 20 healthy older people as a control group were recruited to perform a 600 s task after a 600 s resting by gripping a stick while clicking on a flashing light in an electronic insert in sequence according to a pattern. The functional near-infrared spectroscopy (fNIRS) and behavioral data were collected during their rehabilitation training. Brain function was analyzed using three indicators, namely brain area activation, functional connectivity and effective connectivity, while behavioral performance was analyzed using ten indicators, such as velocity and acceleration, and correlations were made between both. Followed by proposing a quantitative assessment method based on the fusion of multiple data sources. The results showed that the developed rehabilitation tool could effectively stimulate the patient's brain and help recover their cognitive and behavioral capacities. The scientific validity of the proposed assessment approach was further confirmed by contrasting the data results of the stroke group with those of the healthy elderly group. This study has integrated brain function and behavioral data, providing a practical quantitative evaluation method of product ergonomics and data-driven product design concepts for stroke patients.
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Affiliation(s)
- Lingguo Bu
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China; School of Software, Shandong University, Jinan, 250101, China.
| | - Jing Qu
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China; School of Software, Shandong University, Jinan, 250101, China
| | - Lei Zhao
- School of Mechanical and Electronic Engineering, Shandong Jianzhu University, Jinan, 250101, China
| | - Yanjie Zhang
- Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, 999077, Hong Kong SAR, China
| | - Yonghui Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, 250012, China.
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32
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Chalas N, Omigie D, Poeppel D, van Wassenhove V. Hierarchically nested networks optimize the analysis of audiovisual speech. iScience 2023; 26:106257. [PMID: 36909667 PMCID: PMC9993032 DOI: 10.1016/j.isci.2023.106257] [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: 10/01/2022] [Revised: 12/22/2022] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
In conversational settings, seeing the speaker's face elicits internal predictions about the upcoming acoustic utterance. Understanding how the listener's cortical dynamics tune to the temporal statistics of audiovisual (AV) speech is thus essential. Using magnetoencephalography, we explored how large-scale frequency-specific dynamics of human brain activity adapt to AV speech delays. First, we show that the amplitude of phase-locked responses parametrically decreases with natural AV speech synchrony, a pattern that is consistent with predictive coding. Second, we show that the temporal statistics of AV speech affect large-scale oscillatory networks at multiple spatial and temporal resolutions. We demonstrate a spatial nestedness of oscillatory networks during the processing of AV speech: these oscillatory hierarchies are such that high-frequency activity (beta, gamma) is contingent on the phase response of low-frequency (delta, theta) networks. Our findings suggest that the endogenous temporal multiplexing of speech processing confers adaptability within the temporal regimes that are essential for speech comprehension.
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Affiliation(s)
- Nikos Chalas
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, P.C., 48149 Münster, Germany
- CEA, DRF/Joliot, NeuroSpin, INSERM, Cognitive Neuroimaging Unit; CNRS; Université Paris-Saclay, 91191 Gif/Yvette, France
- School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, P.C., 54124 Thessaloniki, Greece
- Corresponding author
| | - Diana Omigie
- Department of Psychology, Goldsmiths University London, London, UK
| | - David Poeppel
- Department of Psychology, New York University, New York, NY 10003, USA
- Ernst Struengmann Institute for Neuroscience, 60528 Frankfurt am Main, Frankfurt, Germany
| | - Virginie van Wassenhove
- CEA, DRF/Joliot, NeuroSpin, INSERM, Cognitive Neuroimaging Unit; CNRS; Université Paris-Saclay, 91191 Gif/Yvette, France
- Corresponding author
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33
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Salvari V, Korth D, Paraskevopoulos E, Wollbrink A, Ivansic D, Guntinas-Lichius O, Klingner C, Pantev C, Dobel C. Tinnitus-frequency specific activity and connectivity: A MEG study. Neuroimage Clin 2023; 38:103379. [PMID: 36933347 PMCID: PMC10031544 DOI: 10.1016/j.nicl.2023.103379] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/15/2023]
Abstract
Tinnitus pathophysiology has been associated with an atypical cortical network that involves functional changes in auditory and non-auditory areas. Numerous resting-state studies have replicated a tinnitus brain network to be significantly different from healthy-controls. Yet it is still unknown whether the cortical reorganization is attributed to the tinnitus frequency specifically or if it is frequency-irrelevant. Employing magnetoencephalography (MEG), the current study aimed to identify frequency-specific activity patterns by using an individual tinnitus tone (TT) and a 500 Hz-control tone (CT) as auditory stimuli, across 54 tinnitus patients. MEG data were analyzed in a data-driven approach employing a whole-head model in source space and in sources' functional connectivity. Compared to the CT, the event related source space analysis revealed a statistically significant response to TT involving fronto-parietal regions. The CT mainly involved typical auditory activation-related regions. A comparison of the cortical responses to a healthy control group that underwent the same paradigm rejected the alternative interpretation that the frequency-specific activation differences were due to the higher frequency of the TT. Overall, the results suggest frequency-specificity of tinnitus-related cortical patterns. In line with previous studies, we demonstrated a tinnitus-frequency specific network comprising left fronto-temporal, fronto-parietal and tempo-parietal junctions.
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Affiliation(s)
- Vasiliki Salvari
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, P.C. D-48149, Münster, Germany
| | - Daniela Korth
- Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University of Jena, P.C. D-07747 Jena, Germany
| | - Evangelos Paraskevopoulos
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece; Department of Psychology, University of Cyprus, P.C. CY 1678, Nicosia, Cyprus
| | - Andreas Wollbrink
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, P.C. D-48149, Münster, Germany
| | - Daniela Ivansic
- Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University of Jena, P.C. D-07747 Jena, Germany
| | - Orlando Guntinas-Lichius
- Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University of Jena, P.C. D-07747 Jena, Germany
| | - Carsten Klingner
- Department of Neurology, Jena University Hospital, Friedrich-Schiller-University of Jena, D-07747 Jena Germany
| | - Christo Pantev
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, P.C. D-48149, Münster, Germany
| | - Christian Dobel
- Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University of Jena, P.C. D-07747 Jena, Germany
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Hudson D, Wiltshire TJ, Atzmueller M. multiSyncPy: A Python package for assessing multivariate coordination dynamics. Behav Res Methods 2023; 55:932-962. [PMID: 35513768 PMCID: PMC10027834 DOI: 10.3758/s13428-022-01855-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase 'Rho' metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy , and also available at the Python package index.
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Affiliation(s)
- Dan Hudson
- Semantic Information Systems Group, Institute of Computer Science, Osnabrück University, P.O. Box 4469, 49069, Osnabrueck, Germany.
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands.
| | - Travis J Wiltshire
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Martin Atzmueller
- Semantic Information Systems Group, Institute of Computer Science, Osnabrück University, P.O. Box 4469, 49069, Osnabrueck, Germany
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35
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Duan K, Xie S, Zhang X, Xie X, Cui Y, Liu R, Xu J. Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT). Brain Sci 2023; 13:brainsci13020247. [PMID: 36831790 PMCID: PMC9954291 DOI: 10.3390/brainsci13020247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1-30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.
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36
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Pandria N, Athanasiou A, Styliadis C, Terzopoulos N, Mitsopoulos K, Paraskevopoulos E, Karagianni M, Pataka A, Kourtidou-Papadeli C, Makedou K, Iliadis S, Lymperaki E, Nimatoudis I, Argyropoulou-Pataka P, Bamidis PD. Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity? Front Behav Neurosci 2023; 17:1096122. [PMID: 36778131 PMCID: PMC9911884 DOI: 10.3389/fnbeh.2023.1096122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/02/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design. Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs). Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions. Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02991781.
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Affiliation(s)
- Niki Pandria
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Alkinoos Athanasiou
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Charis Styliadis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Nikos Terzopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Konstantinos Mitsopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece,Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Maria Karagianni
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Athanasia Pataka
- Pulmonary Department-Oncology Unit, “G. Papanikolaou” General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Kali Makedou
- Laboratory of Biochemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stavros Iliadis
- Laboratory of Biochemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Evgenia Lymperaki
- Department of Biomedical Sciences, International Hellenic University, Thessaloniki, Greece
| | - Ioannis Nimatoudis
- Third Department of Psychiatry, AHEPA University General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Panagiotis D. Bamidis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece,*Correspondence: Panagiotis D. Bamidis
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Chen YC, Chang GC, Huang WM, Hwang IS. Quick balance skill improvement after short-term training with error amplification feedback for older adults. NPJ SCIENCE OF LEARNING 2023; 8:3. [PMID: 36635300 PMCID: PMC9837031 DOI: 10.1038/s41539-022-00151-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
This study investigated behavioral and cortical mechanisms for short-term postural training with error amplification (EA) feedback in the elderly. Thirty-six elderly subjects (65.7 ± 2.2 years) were grouped (control and EA, n = 18) for training in stabilometer balance under visual guidance. During the training session (8 training rounds of 60 s in Day 2), the EA group received visual feedback that magnified errors to twice the real size, whereas the control group received visual feedback that displayed real errors. Scalp EEG and kinematic data of the stabilometer plate and ankle joint were recorded in the pre-test (Day 1) and post-test (Day 3). The EA group (-46.5 ± 4.7%) exhibited greater post-training error reduction than that of the control group (-27.1 ± 4.0%)(p = 0.020), together with a greater decline in kinematic coupling between the stabilometer plate and ankle joint (EA: -26.6 ± 4.8%, control: 2.3 ± 8.6%, p = 0.023). In contrast to the control group, the EA group manifested greater reductions in mean phase-lag index (PLI) connectivity in the theta (4-7 Hz)(p = 0.011) and alpha (8-12 Hz) (p = 0.027) bands. Only the EA group showed post-training declines in the mean PLI in the theta and alpha bands. Minimal spanning tree analysis revealed that EA-based training led to increases in the diameter (p = 0.002) and average eccentricity (p = 0.004) of the theta band for enhanced performance monitoring and reduction in the leaf fraction (p = 0.030) of the alpha band for postural response with enhanced automaticity. In conclusion, short-term EA training optimizes balance skill, favoring multi-segment coordination for the elderly, which is linked to more sophisticated error monitoring with less attentive control over the stabilometer stance.
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Affiliation(s)
- Yi-Ching Chen
- Department of Physical Therapy, College of Medical Science and Technology, Chung Shan Medical University, Taichung City, Taiwan
- Physical Therapy Room, Chung Shan Medical University Hospital, Taichung City, Taiwan
| | - Gwo-Ching Chang
- Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan
| | - Wei-Min Huang
- Department of Management Information System, National Chung Cheng University, Chiayi, Taiwan
| | - Ing-Shiou Hwang
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
- Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
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Tobe M, Nobukawa S, Mizukami K, Kawaguchi M, Higashima M, Tanaka Y, Yamanishi T, Takahashi T. Hub structure in functional network of EEG signals supporting high cognitive functions in older individuals. Front Aging Neurosci 2023; 15:1130428. [PMID: 37139091 PMCID: PMC10149684 DOI: 10.3389/fnagi.2023.1130428] [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: 12/23/2022] [Accepted: 03/15/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Maintaining high cognitive functions is desirable for "wellbeing" in old age and is particularly relevant to a super-aging society. According to their individual cognitive functions, optimal intervention for older individuals facilitates the maintenance of cognitive functions. Cognitive function is a result of whole-brain interactions. These interactions are reflected in several measures in graph theory analysis for the topological characteristics of functional connectivity. Betweenness centrality (BC), which can identify the "hub" node, i.e., the most important node affecting whole-brain network activity, may be appropriate for capturing whole-brain interactions. During the past decade, BC has been applied to capture changes in brain networks related to cognitive deficits arising from pathological conditions. In this study, we hypothesized that the hub structure of functional networks would reflect cognitive function, even in healthy elderly individuals. Method To test this hypothesis, based on the BC value of the functional connectivity obtained using the phase lag index from the electroencephalogram under the eyes closed resting state, we examined the relationship between the BC value and cognitive function measured using the Five Cognitive Functions test total score. Results We found a significant positive correlation of BC with cognitive functioning and a significant enhancement in the BC value of individuals with high cognitive functioning, particularly in the frontal theta network. Discussion The hub structure may reflect the sophisticated integration and transmission of information in whole-brain networks to support high-level cognitive function. Our findings may contribute to the development of biomarkers for assessing cognitive function, enabling optimal interventions for maintaining cognitive function in older individuals.
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Affiliation(s)
- Mayuna Tobe
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Research Center for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- *Correspondence: Sou Nobukawa
| | - Kimiko Mizukami
- Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Megumi Kawaguchi
- Department of Nursing, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
| | | | | | | | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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Huang CY, Chen YA, Wu RM, Hwang IS. Dual-task walking improvement with enhanced kinesthetic awareness in Parkinson’s disease with mild gait impairment: EEG connectivity and clinical implication. Front Aging Neurosci 2022; 14:1041378. [DOI: 10.3389/fnagi.2022.1041378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/10/2022] [Indexed: 12/02/2022] Open
Abstract
Due to basal ganglia dysfunction, short step length is a common gait impairment in Parkinson’s disease (PD), especially in a dual-task walking. Here, we use electroencephalography (EEG) functional connectivity to investigate neural mechanisms of a stride awareness strategy that could improve dual-task walking in PD. Eighteen individuals with PD who had mild gait impairment walked at self-paced speed while keeping two interlocking rings from touching each other. During the dual-task walking trial, the participants received or did not receive awareness instruction to take big steps. Gait parameters, ring-touching time, and EEG connectivity in the alpha and beta bands were analyzed. With stride awareness, individuals with PD exhibited greater gait velocity and step length, along with a significantly lower mean EEG connectivity strength in the beta band. The awareness-related changes in the EEG connectivity strength of the beta band positively correlated with the awareness-related changes in gait velocity, cadence, and step length, but negatively correlated with the awareness-related change in step-length variability. The smaller reduction in beta connectivity strength was associated with greater improvement in locomotion control with stride awareness. This study is the first to reveal that a stride awareness strategy modulates the beta band oscillatory network and is related to walking efficacy in individuals with PD in a dual-task condition.
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Pitetzis D, Frantzidis C, Psoma E, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. EEG Network Analysis in Epilepsy with Generalized Tonic-Clonic Seizures Alone. Brain Sci 2022; 12:1574. [PMID: 36421898 PMCID: PMC9688338 DOI: 10.3390/brainsci12111574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 08/13/2024] Open
Abstract
Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic-clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.
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Affiliation(s)
- Dimitrios Pitetzis
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece
| | - Christos Frantzidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Elizabeth Psoma
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Georgia Deretzi
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece
| | - Anna Kalogera-Fountzila
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Martha Spilioti
- 1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
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Cheng J, Ren Y, Gu Q, He Y, Wang Z. QEEG Biomarkers for ECT Treatment Response in Schizophrenia. Clin EEG Neurosci 2022; 53:499-505. [PMID: 34792399 DOI: 10.1177/15500594211058260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background: Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. Methods: Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. Results: In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (p < .05) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (p < .05). Conclusions: QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.
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Affiliation(s)
- Jiayue Cheng
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yanyan Ren
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Qiumeng Gu
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yongguang He
- Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
| | - Zhen Wang
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Alsuradi H, Park W, Eid M. Assessment of EEG-based functional connectivity in response to haptic delay. Front Neurosci 2022; 16:961101. [PMID: 36330339 PMCID: PMC9623064 DOI: 10.3389/fnins.2022.961101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/03/2022] [Indexed: 11/18/2022] Open
Abstract
Haptic technologies enable users to physically interact with remote or virtual environments by applying force, vibration, or motion via haptic interfaces. However, the delivery of timely haptic feedback remains a challenge due to the stringent computation and communication requirements associated with haptic data transfer. Haptic delay disrupts the realism of the user experience and interferes with the quality of interaction. Research efforts have been devoted to studying the neural correlates of delayed sensory stimulation to better understand and thus mitigate the impact of delay. However, little is known about the functional neural networks that process haptic delay. This paper investigates the underlying neural networks associated with processing haptic delay in passive and active haptic interactions. Nineteen participants completed a visuo-haptic task using a computer screen and a haptic device while electroencephalography (EEG) data were being recorded. A combined approach based on phase locking value (PLV) functional connectivity and graph theory was used. To assay the effects of haptic delay on functional connectivity, we evaluate a global connectivity property through the small-worldness index and a local connectivity property through the nodal strength index. Results suggest that the brain exhibits significantly different network characteristics when a haptic delay is introduced. Haptic delay caused an increased manifestation of the small-worldness index in the delta and theta bands as well as an increased nodal strength index in the middle central region. Inter-regional connectivity analysis showed that the middle central region was significantly connected to the parietal and occipital regions as a result of haptic delay. These results are expected to indicate the detection of conflicting visuo-haptic information at the middle central region and their respective resolution and integration at the parietal and occipital regions.
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Affiliation(s)
- Haneen Alsuradi
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Tandon School of Engineering, New York University, New York City, NY, United States
- *Correspondence: Haneen Alsuradi
| | - Wanjoo Park
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Mohamad Eid
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Mohamad Eid
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Zhou J, Zhao T, Xie Y, Xiao F, Sun L. Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Tsai YY, Chen YC, Zhao CG, Hwang IS. Adaptations of postural sway dynamics and cortical response to unstable stance with stroboscopic vision in older adults. Front Physiol 2022; 13:919184. [PMID: 36105297 PMCID: PMC9465385 DOI: 10.3389/fphys.2022.919184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/22/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Stroboscopic vision (SV), intermittent visual blocking, has recently been incorporated into postural training in rehabilitation. This study investigated interactions of postural fluctuation dynamics and cortical processing for the elderly during stabilometer stance with SV. Methods: Thirty-five healthy elderly maintained an upright stance on a stabilometer. Along with postural fluctuation dynamics, EEG relative power and EEG-EEG connectivity were used to contrast neuromechanical controls of stabilometer stance with SV and full-vision. Results: Compared with the full-vision, SV led to greater postural fluctuations with lower sample entropy and mean frequency (MF). SV also reduced regional power in the mid-frontal theta cluster, which was correlated to SV-dependent changes in the size of postural fluctuations. SV also enhanced the alpha band supra-threshold connectivity in the visual dorsal and frontal–occipital loops of the right hemisphere, and the supra-threshold connectivity from Fp2 positively related to variations in the MF of postural fluctuations. Conclusion: SV adds challenge to postural regulation on the stabilometer, with the increasing regularity of postural movements and fewer corrective attempts to achieve the postural goal. The elderly shift over-reliance on visual inputs for posture control with more non-visual awareness, considering deactivation of the dorsal visual stream and visual error processing.
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Affiliation(s)
- Yi-Ying Tsai
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
| | - Yi-Ching Chen
- Department of Physical Therapy, College of Medical Science and Technology, Chung Shan Medical University, Taichung City, Taiwan
- Physical Therapy Room, Chung Shan Medical University Hospital, Taichung City, Taiwan
| | - Chen-Guang Zhao
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
| | - Ing-Shiou Hwang
- Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
- Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
- *Correspondence: Ing-Shiou Hwang,
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Shafiei G, Baillet S, Misic B. Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex. PLoS Biol 2022; 20:e3001735. [PMID: 35914002 PMCID: PMC9371256 DOI: 10.1371/journal.pbio.3001735] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 08/11/2022] [Accepted: 06/30/2022] [Indexed: 11/21/2022] Open
Abstract
Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these 2 types of neural activity remains unknown. Here, we map electromagnetic networks (measured using magnetoencephalography (MEG)) to haemodynamic networks (measured using functional magnetic resonance imaging (fMRI)). We find that the relationship between the 2 modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the 2 modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15 to 29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multimodal connectivity patterns.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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Sun X, Zheng X, Li T, Li Y, Cui L. Multimodal Emotion Classification Method and Analysis of Brain Functional Connectivity Networks. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2022-2031. [PMID: 35857726 DOI: 10.1109/tnsre.2022.3192533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Since multimodal emotion classification in different human states has rarely been studied, this paper explores the emotional mechanisms of the brain functional connectivity networks after emotional stimulation. We devise a multimodal emotion classification method fusing a brain functional connectivity network based on electroencephalography (EEG) and eye gaze (ECFCEG) to study emotional mechanisms. First, the nonlinear phase lag index (PLI) and phase-locked value (PLV) are calculated to construct the multiband brain functional connectivity networks, which are then converted into binary brain networks, and the seven features of the binary brain networks are extracted. At the same time, the features of the eye gaze signals are extracted. Then, a fusion algorithm called kernel canonical correlation analysis, based on feature level and randomization (FRKCCA), is executed for feature-level fusion (FLF) of brain functional connectivity networks and eye gaze. Finally, support vector machines (SVMs) are utilized to classify positive and negative emotions in multiple frequency bands with single modal features and multimodal features. The experimental results demonstrate that multimodal complementary representation properties can effectively improve the accuracy of emotion classification, achieving a classification accuracy of 91.32±1.81%. The classification accuracy of pupil diameter in the valence dimension is higher than that of additional features. In addition, the average emotion classification effect of the valence dimension is preferable to that of arousal. Our findings demonstrate that the brain functional connectivity networks of the right brain exhibit a deficiency. In particular, the information processing ability of the right temporal (RT) and right posterior (RP) regions is weak in the low frequency after emotional stimulation; Conversely, phase synchronization of the brain functional connectivity networks based on PLI is stronger than that of PLV.
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Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 2022; 146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
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Liu D, Li S, Ren L, Li X, Wang Z. The superior colliculus/lateral posterior thalamic nuclei in mice rapidly transmit fear visual information through the theta frequency band. Neuroscience 2022; 496:230-240. [PMID: 35724770 DOI: 10.1016/j.neuroscience.2022.06.021] [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: 12/15/2021] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 10/18/2022]
Abstract
Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.
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Affiliation(s)
- Denghui Liu
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Shouhao Li
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Liqing Ren
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Xiaoyuan Li
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology.
| | - Zhenlong Wang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology; School of Life Sciences, Zhengzhou University, 450001, Zhengzhou, China.
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Martínez-Maldonado A, Rubio G, Sion A, Jurado-Barba R. Brain oscillatory functioning after long-term alcohol abstinence. Int J Psychophysiol 2022; 177:240-248. [PMID: 35662565 DOI: 10.1016/j.ijpsycho.2022.05.015] [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: 12/02/2021] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 10/18/2022]
Abstract
The heterogeneity of the population with alcohol use disorder (AUD) sometimes makes the knowledge generated in areas such as neuroscience appear to be contradictory. One aspect that may help elucidate this apparent contradiction is controlling for certain variables that are not usually controlled, such as the abstinence time in people with AUD. This research aims to study the neuroelectrical oscillations in people with AUD with longer and shorter abstinence time in comparison with healthy individuals. We recruited twenty-nine individuals with AUD with abstinence time longer than fifteen days and shorter than six months (STA), twenty-six individuals with AUD with abstinence time longer than six months and shorter than thirteen months (LTA), and sixteen healthy individuals (HC). All participants underwent electroencephalographic recording in resting-state with eyes closed. The oscillatory activity obtained was analyzed to obtain the spectral power and phase synchronization level. Regarding the obtained spectral power results, these revealed that the STA group showed higher theta band power and lower alpha band power than the LTA and HC groups. The obtained results at the phase synchronization level also show two main results. On the one hand, the STA group showed lower alpha band phase synchronization than the LTA and HC groups. On the other hand, the HC group showed higher beta band phase synchronization than the STA and LTA groups. In conclusion, the obtained results reflect that abstinence maintenance for six or more months appears to produce an important oscillatory brain functioning normalization in people with AUD.
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Affiliation(s)
- Andrés Martínez-Maldonado
- Biomedical Research Institute Hospital 12 de Octubre, Av. Cordoba, no number, 28041 Madrid, Spain; Psychology Department, Faculty of Education & Health, Camilo José Cela University, Urb. Villafranca del Castillo, Rd. Castillo de Alarcón, 49, 28692 Villanueva de la Cañada, Madrid, Spain.
| | - Gabriel Rubio
- Biomedical Research Institute Hospital 12 de Octubre, Av. Cordoba, no number, 28041 Madrid, Spain; Faculty of Medicine, The Complutense University of Madrid, Rd. Ramón y Cajal, no number, 28040 Madrid, Spain; Addictive Diseases Network, Carlos III Health Institute, Rd. Sinesio Delgado, 4, 28029 Madrid, Spain
| | - Ana Sion
- Addictive Diseases Network, Carlos III Health Institute, Rd. Sinesio Delgado, 4, 28029 Madrid, Spain; Psychobiology and Behavioral Sciences Department, Faculty of Psychology, The Complutense University of Madrid, The Somosaguas Campus, Pozuelo de Alarcón, no number, 28223 Madrid, Spain
| | - Rosa Jurado-Barba
- Biomedical Research Institute Hospital 12 de Octubre, Av. Cordoba, no number, 28041 Madrid, Spain; Psychology Department, Faculty of Education & Health, Camilo José Cela University, Urb. Villafranca del Castillo, Rd. Castillo de Alarcón, 49, 28692 Villanueva de la Cañada, Madrid, Spain
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50
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Zhou H, Han R, Chen L, Zhang Z, Zhang X, Wang J, Liu Z, Huang D. Effect of Implantable Electrical Nerve Stimulation on Cortical Dynamics in Patients With Herpes Zoster–Related Pain: A Prospective Pilot Study. Front Bioeng Biotechnol 2022; 10:862353. [PMID: 35651542 PMCID: PMC9149165 DOI: 10.3389/fbioe.2022.862353] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/08/2022] [Indexed: 02/05/2023] Open
Abstract
Implantable electrical nerve stimulation (ENS) can be used to treat neuropathic pain caused by herpes zoster. However, little is known about the cortical mechanism underlying neuromodulation therapy. Here, we recorded a 16-channel resting-state electroencephalogram after the application of spinal cord stimulation (n = 5) or peripheral nerve stimulation (n = 3). The neuromodulatory effect was compared between specific conditions (active ENS versus rest). To capture the cortical responses of ENS, spectral power and coherence analysis were performed. ENS therapy achieved satisfactory relief from pain with a mean visual analog scale score reduction of 5.9 ± 1.1. The spectral analysis indicated that theta and alpha oscillations increased significantly during active neuromodulation compared with the resting state. Furthermore, ENS administration significantly increased frontal-frontal coherence in the alpha band. Our findings demonstrate that, despite methodological differences, both spinal cord and peripheral nerve stimulation can induce cortical alpha oscillation changes in patients with zoster-related pain. The dynamic change may, in part, mediate the analgesic effect of ENS on herpes zoster–related pain.
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Affiliation(s)
- Haocheng Zhou
- Department of Pain, The Third Xiangya Hospital and Institute of Pain Medicine, Central South University, Changsha, China
- Hunan Key Laboratory of Brain Homeostasis, Central South University, Changsha, China
| | - Rui Han
- Department of Pain, The Third Xiangya Hospital and Institute of Pain Medicine, Central South University, Changsha, China
| | - Li Chen
- Department of Pain, The Third Xiangya Hospital and Institute of Pain Medicine, Central South University, Changsha, China
| | - Zhen Zhang
- Department of Pain, The Third Xiangya Hospital and Institute of Pain Medicine, Central South University, Changsha, China
| | - Xiaobo Zhang
- Department of Orthopedics, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jianlong Wang
- Department of Orthopedics, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zuoliang Liu
- Department of Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Dong Huang
- Department of Pain, The Third Xiangya Hospital and Institute of Pain Medicine, Central South University, Changsha, China
- Hunan Key Laboratory of Brain Homeostasis, Central South University, Changsha, China
- *Correspondence: Dong Huang,
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