1
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Zanjani MF, Ghoshuni M. Directional information flow analysis in memory retrieval: a comparison between exaggerated and normal pictures. Med Biol Eng Comput 2024:10.1007/s11517-024-03179-9. [PMID: 39141314 DOI: 10.1007/s11517-024-03179-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
Working memory plays an important role in cognitive science and is a basic process for learning. While working memory is limited in regard to capacity and duration, different cognitive tasks are designed to overcome these difficulties. This study investigated information flow during a novel visual working memory task in which participants respond to exaggerated and normal pictures. Ten healthy men (mean age 28.5 ± 4.57 years) participated in two stages of the encoding and retrieval tasks. The electroencephalogram (EEG) signals are recorded. Moreover, the adaptive directed transfer function (ADTF) method is used as a computational tool to investigate the dynamic process of visual working memory retrieval on the extracted event-related potentials (ERPs) from the EEG signal. Network connectivity and P300 sub-components (P3a, P3b, and LPC) are also extracted during visual working memory retrieval. Then, the nonparametric Wilcoxon test and five classifiers are applied to network properties for features selection and classification between exaggerated-old and normal-old pictures. The Z-values of Ge is more distinctive rather than other network properties. In terms of the machine learning approach, the accuracy, F1-score, and specificity of the k-nearest neighbors (KNN), classifiers are 81%, 77%, and 81%, respectively. KNN classifier ranked first compared with other classifiers. Furthermore, the results of in-degree/out-degree matrices show that the information flows continuously in the right hemisphere during the retrieval of exaggerated pictures, from P3a to P3b. During the retrieval of visual working memory, the networks associated with attentional processes show greater activation for exaggerated pictures compared to normal pictures. This suggests that the exaggerated pictures may have captured more attention and thus required greater cognitive resources for retrieval.
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Affiliation(s)
- Mani Farajzadeh Zanjani
- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Majid Ghoshuni
- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
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2
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Zhou S, Cai H, Chen H, Ye L. A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:4908. [PMID: 39123955 PMCID: PMC11314704 DOI: 10.3390/s24154908] [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: 07/01/2024] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution.
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Affiliation(s)
- Sun Zhou
- Department of Automation, Xiamen University, Xiamen 361102, China;
| | - He Cai
- Department of Automation, Xiamen University, Xiamen 361102, China;
| | - Huazhen Chen
- School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China;
| | - Lishan Ye
- Institute of Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
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3
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Pei H, Jiang S, Liu M, Ye G, Qin Y, Liu Y, Duan M, Yao D, Luo C. Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia. Int J Neural Syst 2024; 34:2450031. [PMID: 38623649 DOI: 10.1142/s012906572450031x] [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] [Indexed: 04/17/2024]
Abstract
Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.
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Affiliation(s)
- Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Mei Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Guofeng Ye
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Yayun Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Mingjun Duan
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation Chinese, Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation Chinese, Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
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4
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Yang Y, Song P, Wang Y. Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis. Heliyon 2024; 10:e31746. [PMID: 38828287 PMCID: PMC11140796 DOI: 10.1016/j.heliyon.2024.e31746] [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: 12/07/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.
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Affiliation(s)
- Yingxue Yang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
| | - Penghui Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
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Yi C, Li F, Wang J, Li Y, Zhang J, Chen W, Jiang L, Yao D, Xu P, He B, Dong W. Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia. Med Biol Eng Comput 2024:10.1007/s11517-024-03133-9. [PMID: 38834855 DOI: 10.1007/s11517-024-03133-9] [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: 06/08/2023] [Accepted: 05/18/2024] [Indexed: 06/06/2024]
Abstract
Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.
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Affiliation(s)
- Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Jiuju Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jiamin Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wanjun Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China.
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, 250012, China.
| | - Baoming He
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, 610072, China.
| | - Wentian Dong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, 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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6
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Hao W, Dai X, Wei M, Li S, Peng M, Xue Q, Lin H, Wang H, Song P, Wang Y. Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS-EEG and pilot study. PHOTODERMATOLOGY, PHOTOIMMUNOLOGY & PHOTOMEDICINE 2024; 40:e12957. [PMID: 38470033 DOI: 10.1111/phpp.12957] [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: 08/06/2023] [Revised: 12/30/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) was a prevalent mental condition that may be accompanied by decreased excitability of left frontal pole (FP) and abnormal brain connections. An 820 nm tPBM can induce an increase in stimulated cortical excitability. The purpose of our study was to establish how clinical symptoms and time-varying brain network connectivity of MDD were affected by transcranial photobiomodulation (tPBM). METHODS A total of 11 patients with MDD received 820 nm tPBM targeting the left FP for 14 consecutive days. The severity of symptoms was evaluated by neuropsychological assessments at baseline, after treatment, 4-week and 8-week follow-up; 8-min transcranial magnetic stimulation combined electroencephalography (TMS-EEG) was performed for five healthy controls and five patients with MDD before and after treatment, and time-varying EEG network was analyzed using the adaptive-directed transfer function. RESULTS All of scales scores in the 11 patients decreased significantly after 14-day tPBM (p < .01) and remained at 8-week follow-up. The time-varying brain network analysis suggested that the brain regions with enhanced connection information outflow in MDD became gradually more similar to healthy controls after treatment. CONCLUSIONS This study showed that tPBM of the left FP could improve symptoms of patients with MDD and normalize the abnormal network connections.
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Affiliation(s)
- Wensi Hao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
- Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaona Dai
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
- Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Min Wei
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
| | - Siran Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mao Peng
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qing Xue
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huicong Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
| | - Penghui Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
- Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Center for sleep and consciousness disorders, Beijing Institute for Brain Disorders, Beijing, China
- Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
- Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Center for sleep and consciousness disorders, Beijing Institute for Brain Disorders, Beijing, China
- Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Hebei Hospital of Xuanwu Hospital, Capital Medical University, Shijiazhuang, China
- Neuromedical Technology Innovation Center of Hebei Province, Shijiazhuang, China
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Miao Y, Suzuki H, Sugano H, Ueda T, Iimura Y, Matsui R, Tanaka T. Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy. IEEE Trans Biomed Eng 2024; 71:531-541. [PMID: 37624716 DOI: 10.1109/tbme.2023.3308616] [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: 08/27/2023]
Abstract
Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.
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8
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Liu H, Bai Y, Xu Z, Liu J, Ni G, Ming D. The scalp time-varying network of auditory spatial attention in "cocktail-party" situations. Hear Res 2024; 442:108946. [PMID: 38150794 DOI: 10.1016/j.heares.2023.108946] [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: 07/09/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 12/29/2023]
Abstract
Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.
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Affiliation(s)
- Hongxing Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China
| | - Zihao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Jihan Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China
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9
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Chen C, Liang Y, Xu S, Yi C, Li Y, Chen B, Yang L, Liu Q, Yao D, Li F, Xu P. The dynamic causality brain network reflects whether the working memory is solidified. Cereb Cortex 2024; 34:bhad467. [PMID: 38061696 DOI: 10.1093/cercor/bhad467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 01/19/2024] Open
Abstract
Working memory, which is foundational to higher cognitive function, is the "sketchpad of volitional control." Successful working memory is the inevitable outcome of the individual's active control and manipulation of thoughts and turning them into internal goals during which the causal brain processes information in real time. However, little is known about the dynamic causality among distributed brain regions behind thought control that underpins successful working memory. In our present study, given that correct responses and incorrect ones did not differ in either contralateral delay activity or alpha suppression, further rooting on the high-temporal-resolution EEG time-varying directed network analysis, we revealed that successful working memory depended on both much stronger top-down connections from the frontal to the temporal lobe and bottom-up linkages from the occipital to the temporal lobe, during the early maintenance period, as well as top-down flows from the frontal lobe to the central areas as the delay behavior approached. Additionally, the correlation between behavioral performance and casual interactions increased over time, especially as memory-guided delayed behavior approached. Notably, when using the network metrics as features, time-resolved multiple linear regression of overall behavioral accuracy was exactly achieved as delayed behavior approached. These results indicate that accurate memory depends on dynamic switching of causal network connections and shifting to more task-related patterns during which the appropriate intervention may help enhance memory.
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Affiliation(s)
- Chunli Chen
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yi Liang
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Shiyun Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chanlin Yi
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuqin Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Baodan Chen
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lei Yang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qiang Liu
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu 610000, China
| | - Dezhong Yao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fali Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Peng Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
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10
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Peng G, Nourani M, Dave H, Harvey J. SEEG-based epileptic seizure network modeling and analysis for pre-surgery evaluation. Comput Biol Med 2023; 167:107692. [PMID: 37976827 DOI: 10.1016/j.compbiomed.2023.107692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/27/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
Stereo-electroencephalography is a minimally invasive technique for patients with refractory epilepsy pursuing surgery to reduce or control seizures. Electrodes are implanted based on pre-surgery evaluations and can collect deep brain activities for surgery decisions. This paper presents a methodology to analyze stereo-electroencephalography and assist clinicians by recommending the optimal surgical option and target areas for focal epilepsy patients. A seizure network (graph) model is proposed to characterize the spatial distribution and temporal changes of ictal events. The network nodes and edges correspond to specific epileptogenic regions and propagation/impact pathways (weighted by directed transfer function), respectively. We then employ a K-means clustering strategy to group nodes into a few clusters, from which the target surgical areas can be identified. Ten patients with different types of focal seizures were thoroughly analyzed. Promising consistency between results of our method's recommendations, clinical decisions and surgery outcomes were observed.
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Affiliation(s)
- Genchang Peng
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Mehrdad Nourani
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Hina Dave
- Department of Neurology and Neurotherapeutics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Jay Harvey
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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11
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Xi Y, Lan Z, Chen Y, Zhang Q, Wu Z, Li G. Patients with epilepsy without cognitive impairment show altered brain networks in multiple frequency bands in an audiovisual integration task. Neurophysiol Clin 2023; 53:102888. [PMID: 37660635 DOI: 10.1016/j.neucli.2023.102888] [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: 02/20/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Comorbid cognitive and behavioral deficits are often observed in patients with epilepsy. It is not clear whether the brain networks of patients with epilepsy without cognitive decline differs from that of healthy controls in different frequency bands in the task-state. The purpose of our study was to explore whether epilepsy affects the structure of brain networks associated with cognitive processing, even when patients with epilepsy do not have cognitive impairment. METHODS We designed an audiovisual discrimination task and recorded electroencephalogram (EEG) data from healthy controls and patients with epilepsy. We established constructed time-varying brain networks across the delta, theta, alpha, and beta bands on the task-state EEG data during audiovisual integration processing. RESULTS The results showed changes in the structure of the brain networks in the theta, alpha, and beta bands in patients with epilepsy who had no cognitive deficit. No significant difference in the connectivity strength, clustering coefficient, characteristic path length, or global efficiency was noted between patients and healthy controls. Moreover, the structure of brain networks in patients showed no correlation with the behavioral performance. CONCLUSION The repeated abnormal firing of neurons in the brain of patients with epilepsy may inhibit it from optimizing networks into more efficient structures. Epilepsy might affect decision-making ability by damaging the neural activity in the beta band and preventing its correlation with decision-making behaviors.
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Affiliation(s)
- Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China.
| | - Zhu Lan
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Ying Chen
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Zhenyu Wu
- Department of Orthopedics of Affiliated Hospital of Beihua University, Beihua University, Jilin 132012, P.R. China
| | - Guangjian Li
- Department of Neurology of First Affiliated Hospital of Jilin University, Jilin University, Changchun 130022, P.R. China
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12
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Jiang Y, Qiao R, Shi Y, Tang Y, Hou Z, Tian Y. The effects of attention in auditory-visual integration revealed by time-varying networks. Front Neurosci 2023; 17:1235480. [PMID: 37600005 PMCID: PMC10434229 DOI: 10.3389/fnins.2023.1235480] [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: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.
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Affiliation(s)
- Yuhao Jiang
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
| | - Rui Qiao
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yupan Shi
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yi Tang
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Zhengjun Hou
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yin Tian
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
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13
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Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering (Basel) 2023; 10:bioengineering10030372. [PMID: 36978763 PMCID: PMC10044923 DOI: 10.3390/bioengineering10030372] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
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Affiliation(s)
- Giovanni Chiarion
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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14
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Yi C, Qiu Y, Chen W, Chen C, Wang Y, Li P, Yang L, Zhang X, Jiang L, Yao D, Li F, Xu P. Constructing Time-varying Directed EEG network by Multivariate Nonparametric Dynamical Granger Causality. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1412-1421. [PMID: 35576427 DOI: 10.1109/tnsre.2022.3175483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.
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15
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Reconfiguration of Cortical Brain Network from Searching to Spotting for Dynamic Visual Targets. J Neurosci Methods 2022; 375:109577. [PMID: 35339507 DOI: 10.1016/j.jneumeth.2022.109577] [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: 07/11/2021] [Revised: 12/28/2021] [Accepted: 03/20/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Detecting dynamic targets from complex visual scenes is an important problem in real world. However, the cognitive mechanism accounting for dynamic visual target detection remains unclear. NEW METHOD Herein, we aim to explore the cognitive process of dynamic visual target detection from searching to spotting and provide more concrete evidence for cognitive studies related to target detection. Cortical source responses with high spatiotemporal resolution were reconstructed from scalp EEG signals. Then, time-varying cortical networks were built using adaptive directed transfer function to explore the cognitive processes while detecting the dynamic visual target. RESULTS The experimental results demonstrated that the dynamic visual target detection enhanced the activation in both the visual and attention networks. Specially, the information flow from the middle occipital gyrus (MOG) mainly contributed to the position function, whereas the activation of the prefrontal cortex (PFC) reflected spatial attention maintenance. CONCLUSION The left "frontal-central-parietal" network played as a leading information source in dynamic target detection tasks. These findings provide new insights into cognitive processes of dynamic visual target detection. DATA AVAILABILITY STATEMENT The datasets in this study are available on request to the corresponding author.
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16
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Li M, Zhang N. A dynamic directed transfer function for brain functional network-based feature extraction. Brain Inform 2022; 9:7. [PMID: 35304652 PMCID: PMC8933605 DOI: 10.1186/s40708-022-00154-8] [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: 09/17/2021] [Accepted: 02/19/2022] [Indexed: 05/31/2023] Open
Abstract
Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with \documentclass[12pt]{minimal}
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\begin{document}$$\beta_{2}$$\end{document}β2(21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, \documentclass[12pt]{minimal}
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\begin{document}$${ }\beta_{2}$$\end{document}β2) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.
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Affiliation(s)
- Mingai Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China.,Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
| | - Na Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
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17
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Li P, Li C, Bore JC, Si Y, Li F, Cao Z, Zhang Y, Wang G, Zhang Z, Yao D, Xu P. L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery. J Neural Eng 2022; 19. [PMID: 35234668 DOI: 10.1088/1741-2552/ac59a4] [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: 09/15/2021] [Accepted: 03/01/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE EEG-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. APPROACH In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers (ADMM), which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. MAIN RESULTS A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. SIGNIFICANCE The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.
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Affiliation(s)
- Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, NO.2,Chongwen Road,Nan'an District, Chongqing, China, Chongqing, 400065, CHINA
| | - Cunbo Li
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Joyce Chelangat Bore
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Yajing Si
- Department of Psychology, Xinxiang Medical University, No. 601, Jinsui Avenue, Hongqi District, Xinxiang, Henan, 453003, CHINA
| | - Fali Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 610054, CHINA
| | - Zehong Cao
- University of South Australia, Adelaide, SA 5095, Australia, Adelaide, South Australia, 5001, AUSTRALIA
| | - Yangsong Zhang
- Southwest University of Science and Technology, 59 Qinglong Road, Mianyang,Sichuan, P.R.China, Mianyang, 621010, CHINA
| | - Gang Wang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 610054, CHINA
| | - Zhijun Zhang
- South China University of Technology, 777 Xingye Avenue East, Panyu District, Guangzhou, Guangzhou, 510640, CHINA
| | - Dezhong Yao
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
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18
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Widge AS, Ellard KK, Paulk AC, Basu I, Yousefi A, Zorowitz S, Gilmour A, Afzal A, Deckersbach T, Cash SS, Kramer MA, Eden UT, Dougherty DD, Eskandar EN. Treating Refractory Mental Illness With Closed-Loop Brain Stimulation: Progress Towards a Patient-Specific Transdiagnostic Approach. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2022; 20:137-151. [PMID: 35746936 PMCID: PMC9063604 DOI: 10.1176/appi.focus.20102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Accepted: 07/25/2016] [Indexed: 01/03/2023]
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19
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Peng M, Xu Z, Huang H. How Does Information Overload Affect Consumers' Online Decision Process? An Event-Related Potentials Study. Front Neurosci 2021; 15:695852. [PMID: 34744601 PMCID: PMC8567038 DOI: 10.3389/fnins.2021.695852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/17/2021] [Indexed: 11/13/2022] Open
Abstract
One of the advantages of e-retailers is their capability to provide a large amount of information to consumers. However, when the amount of information exceeds consumers' information processing capacities, it will lead to worse decision quality and experience, causing the information overload effect. In this study, the event-related potentials (ERPs) were applied to examine the hidden neural mechanism of the impact of information overload on consumers' decision processes. Behavioral data showed that people would spend more time making decisions when faced with information overload. Neurophysiologically, consumers would invest less attentional resources in the high amount of information (HAI) condition than those in the low amount of information (LAI) condition and lead to less positive P2 amplitudes. The HAI condition would increase decision difficulty than would the LAI condition and result in smaller P3 amplitudes. In addition, an increased late positive component (LPC) was observed for the HAI condition in contrast to the LAI condition, indicating that consumers were more inclined to have decision process regret when consumers were overloaded. We further investigated the dynamic information processing when consumers got over information overload by mining the brain's time-varying networks. The results revealed that during the decision process and the neural response stage, the central area controlled other brain regions' activities for the HAI condition, suggesting that people may still consider and compare other important information after the decision process when faced with information overload. In general, this study may provide neural evidence of how information overload affects consumers' decision processes and ultimately damages decision quality.
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Affiliation(s)
- Minjing Peng
- School of Economics and Management, Wuyi University, Jiangmen, China
| | - Zhicheng Xu
- School of Economics and Management, Wuyi University, Jiangmen, China
| | - Haiyang Huang
- School of Economics and Management, Wuyi University, Jiangmen, China
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20
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Li F, Jiang L, Zhang Y, Huang D, Wei X, Jiang Y, Yao D, Xu P, Li H. The time-varying networks of the wrist extension in post-stroke hemiplegic patients. Cogn Neurodyn 2021; 16:757-766. [PMID: 35847531 PMCID: PMC9279526 DOI: 10.1007/s11571-021-09738-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/05/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09738-2.
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21
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Cometa A, D'Orio P, Revay M, Micera S, Artoni F. Stimulus evoked causality estimation in stereo-EEG. J Neural Eng 2021; 18. [PMID: 34534968 DOI: 10.1088/1741-2552/ac27fb] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Objective.Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain networks interactions, especially when these connections are stimulus-evoked. However, choosing the best approach to evaluate the flow of information is not trivial, due to the lack of validated methods explicitly designed for SEEG.Approach.We propose a novel non-parametric statistical test for event-related causality (ERC) assessment on SEEG recordings. Here, we refer to the ERC as the causality evoked by a particular part of the stimulus (a response window (RW)). We also present a data surrogation method to evaluate the performance of a causality estimation algorithm. We finally validated our pipeline using surrogate SEEG data derived from an experimentally collected dataset, and compared the most used and successful measures to estimate effective connectivity, belonging to the Geweke-Granger causality framework.Main results.Here we show that our workflow correctly identified all the directed connections in the RW of the surrogate data and prove the robustness of the procedure against synthetic noise with amplitude exceeding physiological-plausible values. Among the causality measures tested, partial directed coherence performed best.Significance.This is the first non-parametric statistical test for ERC estimation explicitly designed for SEEG datasets. The pipeline, in principle, can also be applied to the analysis of any type of time-varying estimator, if there exists a clearly defined RW.
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Affiliation(s)
- Andrea Cometa
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, Pontedera, 56025, Italy
| | - Piergiorgio D'Orio
- 'Claudio Munari' Center for Epilepsy Surgery, ASST GOM Niguarda Hospital, Piazza dell'Ospedale Maggiore, 3, 20162 Milano, Italy.,Institute of Neuroscience, CNR, via Volturno 39E, Parma 43125, Italy
| | - Martina Revay
- 'Claudio Munari' Center for Epilepsy Surgery, ASST GOM Niguarda Hospital, Piazza dell'Ospedale Maggiore, 3, 20162 Milano, Italy.,Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Via Giovanni Battista Grassi 74, Milan 20157, Italy
| | - Silvestro Micera
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, Pontedera, 56025, Italy.,Ecole Polytechnique Federale de Lausanne, Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and School of Engineering, Chemin des Mines, 9, Geneva, GE CH 1202, Switzerland
| | - Fiorenzo Artoni
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, Pontedera, 56025, Italy.,Ecole Polytechnique Federale de Lausanne, Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and School of Engineering, Chemin des Mines, 9, Geneva, GE CH 1202, Switzerland
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Song X, Zeng Y, Tong L, Shu J, Li H, Yan B. Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex. Brain Res 2021; 1765:147502. [PMID: 33901488 DOI: 10.1016/j.brainres.2021.147502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 11/30/2022]
Abstract
In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method. The time-varying networks among important brain regions in different cognitive phases, including information integration, decision-making, and execution, were identified to investigate the neural mechanism of dynamic distractor processing. The experimental results indicated that dynamic distractors could induce a P3-like component. In addition, there was obvious asymmetry between the two hemispheres during video target detection. Specifically, the brain responses induced by dynamic distractors were weak and more concentrated in the left hemisphere during the information integration phase; left superior frontal gyrus activity related to preparation for the presence of distractors was critical, while the attention network and primary visual network, especially in the left visual pathway, were more active for dynamic targets during the decision-making phase. These findings provide guidance for designing an effective EEG-based model for dynamic video target detection.
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Affiliation(s)
- Xiyu Song
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.
| | - Ying Zeng
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 610000, China.
| | - Li Tong
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.
| | - Jun Shu
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.
| | - Huimin Li
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; Software Technology School of Zhengzhou University, Zhengzhou 450001, China.
| | - Bin Yan
- The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.
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23
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Leal A. Spatial and temporal dynamics of epileptic activity at sleep onset in the Encephalopathy with Status Epilepticus during slow sleep (ESES) after unilateral thalamic lesions. Clin Neurophysiol 2021; 132:114-125. [DOI: 10.1016/j.clinph.2020.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/19/2020] [Accepted: 11/06/2020] [Indexed: 02/02/2023]
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Hao J, Cui Y, Niu B, Yu L, Lin Y, Xia Y, Yao D, Guo D. Roles of Very Fast Ripple (500-1000[Formula: see text]Hz) in the Hippocampal Network During Status Epilepticus. Int J Neural Syst 2020; 31:2150002. [PMID: 33357153 DOI: 10.1142/s0129065721500027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Very fast ripples (VFRs, 500-1000[Formula: see text]Hz) are considered more specific than high-frequency oscillations (80-500[Formula: see text]Hz) as biomarkers of epileptogenic zones. Although VFRs are frequent abnormal phenomena in epileptic seizures, their functional roles remain unclear. Here, we detected the VFRs in the hippocampal network and tracked their roles during status epilepticus (SE) in rats with pilocarpine-induced temporal lobe epilepsy (TLE). All regions in the hippocampal network exhibited VFRs in the baseline, preictal, ictal and postictal states, with the ictal state containing the most VFRs. Moreover, strong phase-locking couplings existed between VFRs and slow oscillations (1-12[Formula: see text]Hz) in the ictal and postictal states for all regions. Further investigation indicated that during VFRs, the build-up of slow oscillations in the ictal state began from the temporal lobe and then spread through the whole hippocampal network via two different pathways, which might be associated with the underlying propagation of epileptiform discharges in the hippocampal network. Overall, we provide a functional description of the emergence of VFRs in the hippocampal network during SE, and we also establish that VFRs may be the physiological representation of the pathological alterations in hippocampal network activity during SE in TLE.
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Affiliation(s)
- Jianmin Hao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Yan Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Bochao Niu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Liang Yu
- Department of Neurology, Sichuan Academy of Medical, Sciences and Sichuan Provincial People's Hospital, Chengdu Sichuan, P. R. China
| | - Yuhang Lin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Yang Xia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China.,School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, P. R. China
| | - Daqing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
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Xu N, Doerschuk PC, Keilholz SD, Spreng RN. Spatiotemporal functional interactivity among large-scale brain networks. Neuroimage 2020; 227:117628. [PMID: 33316394 DOI: 10.1016/j.neuroimage.2020.117628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/21/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023] Open
Abstract
The macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N = 200) we applied prediction correlation techniques to four resting-state fMRI scans (each scan has TRs = 1200). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual, and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in the hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.
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Affiliation(s)
- Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
| | - Peter C Doerschuk
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States; Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States.
| | - Shella D Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada; Douglas Hospital Research Centre, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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26
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Characterizing the Time-Varying Brain Networks of Audiovisual Integration across Frequency Bands. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09783-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Lu Z, Li Q, Gao N, Yang J. Time-varying networks of ERPs in P300-speller paradigms based on spatially and semantically congruent audiovisual bimodality. J Neural Eng 2020; 17:046015. [DOI: 10.1088/1741-2552/aba07f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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28
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Ashourvan A, Pequito S, Khambhati AN, Mikhail F, Baldassano SN, Davis KA, Lucas TH, Vettel JM, Litt B, Pappas GJ, Bassett DS. Model-based design for seizure control by stimulation. J Neural Eng 2020; 17:026009. [PMID: 32103826 PMCID: PMC8341467 DOI: 10.1088/1741-2552/ab7a4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.
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Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America
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Wu M, Li F, Wu Y, Zhang T, Gao J, Xu P, Luo B. Impaired Frontoparietal Connectivity in Traumatic Individuals with Disorders of Consciousness: A Dynamic Brain Network Analysis. Aging Dis 2020; 11:301-314. [PMID: 32257543 PMCID: PMC7069467 DOI: 10.14336/ad.2019.0606] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/06/2019] [Indexed: 12/25/2022] Open
Abstract
Recent advances in neuroimaging have demonstrated that patients with disorders of consciousness (DOC) may retain residual consciousness through activation of a complex functional brain network. However, an understanding of the hierarchy of residual consciousness and dynamic network connectivity in DOC patients is lacking. This study aimed to investigate residual consciousness and the dynamics of neural processing in DOC patients. We included 42 patients with DOC, categorized by aetiology. Event-related potentials combined with time-varying electroencephalography networks were used to probe affective consciousness in DOC and examine the related network mechanisms. The results showed an obvious frontal P3a component among patients in minimally conscious state (MCS), while a prominent N1 was observed in unresponsive wakefulness syndrome (UWS). No late positive potential (LPP) was detected in these patients. Next, we divided the results by aetiology. Patients with nontraumatic injury presented an obvious frontal P3a response compared to those with traumatic injury. With respect to the dynamic network mechanism, patients with UWS, both with and without trauma, exhibited impaired frontoparietal network connectivity during the middle to late emotion processing period (P3a and LPP). Surprisingly, unconscious post-traumatic patients had an evident deficit in top-down connectivity. This, it appears that early automatic sensory identification is preserved in UWS and that exogenous attention was preserved even in MCS. However, high-level cognitive abilities were severely attenuated in unconscious patients. We also speculate that reduced frontoparietal connectivity may be useful as a biomarker to distinguish patients in an MCS from those with UWS given the same aetiology.
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Affiliation(s)
- Min Wu
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Fali Li
- 2The Clinical Hospital of Chengdu Brain Science Institute, Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuehao Wu
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Tieying Zhang
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jian Gao
- 3Department of Rehabilitation, Hangzhou Hospital of Zhejiang Armed Police Corps, Hangzhou, China
| | - Peng Xu
- 2The Clinical Hospital of Chengdu Brain Science Institute, Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Benyan Luo
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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30
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Pagnotta MF, Plomp G, Pascucci D. A regularized and smoothed General Linear Kalman Filter for more accurate estimation of time-varying directed connectivity .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:611-615. [PMID: 31945972 DOI: 10.1109/embc.2019.8857915] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Adaptive algorithms based on the Kalman filter are valuable tools to model the dynamic and directed Granger causal interactions between neurophysiological signals simultaneously recorded from multiple cortical regions. Among these algorithms, the General Linear Kalman Filter (GLKF) has proven to be the most accurate and reliable. Here we propose a regularized and smoothed GLKF (spsm-GLKF) with ℓ1 norm penalties based on lasso or group lasso and a fixedinterval smoother. We show that the group lasso penalty promotes sparse solutions by shrinking spurious connections to zero, while the smoothing increases the robustness of the estimates. Overall, our results demonstrate that spsm-GLKF outperforms the original GLKF, and represents a more accurate tool for the characterization of dynamical and sparse functional brain networks.
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31
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Rajaei H, Adjouadi M, Cabrerizo M, Janwattanapong P, Pinzon A, Gonzales-Arias S, Barreto A, Andrian J, Rishe N, Yaylali I. Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps. IEEE Trans Biomed Eng 2020; 67:632-643. [DOI: 10.1109/tbme.2019.2919263] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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32
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Li F, Tao Q, Peng W, Zhang T, Si Y, Zhang Y, Yi C, Biswal B, Yao D, Xu P. Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting- to task-state: Evidence from a simultaneous event-related EEG-fMRI study. Neuroimage 2020; 205:116285. [DOI: 10.1016/j.neuroimage.2019.116285] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/12/2019] [Accepted: 10/14/2019] [Indexed: 11/15/2022] Open
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Jiang H, Cai Z, Worrell GA, He B. Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation. Ann Neurol 2019; 86:683-694. [PMID: 31566799 PMCID: PMC6856814 DOI: 10.1002/ana.25583] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 08/06/2019] [Accepted: 08/18/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear. METHODS We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively. RESULTS In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA. INTERPRETATION Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.
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Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
| | - Zhengxiang Cai
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
| | | | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
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34
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Delgado-Restituto M, Romaine JB, Rodriguez-Vazquez A. Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:957-970. [PMID: 31369385 DOI: 10.1109/tbcas.2019.2931799] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μm CMOS process, only occupies 0.05 mm 2 and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.
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Qi Y, Lin K, Wang Y, Ren F, Lian Q, Wang S, Jiang H, Zhu J, Wang Y, Wu Z, Pan G. Epileptic Focus Localization via Brain Network Analysis on Riemannian Manifolds. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1942-1951. [PMID: 31484126 DOI: 10.1109/tnsre.2019.2939010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Brain network connectivity analysis plays an important role in computer-aided automatic localization of seizure onset zone (SOZ) from Intracranial Electroencephalography (iEEG). However, how to accurately compute brain network dynamics is still not well addressed. This work aims to develop an effective measure to find out the dynamics for SOZ localization. METHODS Given multiple-channel iEEG signals, the ictal process involves continuous changes of information propagation. In each time slot, the connectivity relationship between channels can be represented as a matrix. Since the matrices from different time slots do not lie on vector spaces, the similarity between them cannot be computed directly. In this paper, we regard the matrices as points on a Riemannian manifold, so that the similarity can be measured by the geodesic distance on the manifold. It addresses the information-losing problem in existing methods using a vector to approximate a matrix. With the Riemannian method, the brain network dynamics are figured out by clustering methods. A temporal segmentation process is applied to refine the segments for SOZ localization. RESULTS Our method is evaluated on six epilepsy patients, and the SOZ localization performance is evaluated by the area under the curve (AUC) score. Overall, our method obtains an average AUC score of 0.875, which outperforms the existing approaches. CONCLUSION Our method preserves more information in measuring the relationship between brain connectivity descriptors, thus is more robust for SOZ localization. SIGNIFICANCE Our method has great potentials for clinical epilepsy treatments.
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He B, Astolfi L, Valdés-Sosa PA, Marinazzo D, Palva SO, Bénar CG, Michel CM, Koenig T. Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans Biomed Eng 2019; 66:10.1109/TBME.2019.2913928. [PMID: 31071012 PMCID: PMC6834897 DOI: 10.1109/tbme.2019.2913928] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome Sapienza, and with IRCCS Fondazione Santa Lucia, Rome, Italy
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37
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Bou Assi E, Rihana S, Nguyen DK, Sawan M. Effective connectivity analysis of iEEG and accurate localization of the epileptogenic focus at the onset of operculo-insular seizures. Epilepsy Res 2019; 152:42-51. [DOI: 10.1016/j.eplepsyres.2019.02.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 12/23/2018] [Accepted: 02/21/2019] [Indexed: 11/16/2022]
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38
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Kostoglou K, Robertson AD, MacIntosh BJ, Mitsis GD. A Novel Framework for Estimating Time-Varying Multivariate Autoregressive Models and Application to Cardiovascular Responses to Acute Exercise. IEEE Trans Biomed Eng 2019; 66:3257-3266. [PMID: 30843796 DOI: 10.1109/tbme.2019.2903012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE We present a novel modeling framework for identifying time-varying (TV) couplings between time-series of biomedical relevance. METHODS The proposed methodology is based on multivariate autoregressive (MVAR) models, which have been extensively used to study couplings between biosignals. Contrary to the standard estimation methods that assume time-invariant relationships, we propose a modified recursive Kalman filter (KF) to track changes in the model parameters. We perform model order selection and hyperparameter optimization simultaneously using Genetic Algorithms, greatly improving accuracy and computation time. In addition, we address the effect of residual heteroscedasticity, possibly associated with event-related changes or phase transitions during a given experimental protocol, on the TV-MVAR coupling measures by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to fit the TV-MVAR residuals. RESULTS Using simulated data, we show that the proposed framework yields more accurate parameter estimates compared to the conventional KF, particularly when the true system parameters exhibit different rate of variations over time. Furthermore, by accounting for heteroskedasticity, we obtain more accurate estimates of the strength and directionality of the underlying couplings. We also use our approach to investigate TV hemodynamic interactions during exercise in young and old healthy adults, as well as individuals with chronic stroke. We extract TV coupling patterns that reflect well known exercise-induced effects on the underlying regulatory mechanisms with excellent time resolution. CONCLUSION AND SIGNIFICANCE The proposed modeling framework can be used to efficiently quantify TV couplings between biosignals. It is fully automated and does not require prior knowledge of the system TV characteristics.
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Kouti M, Ansari-Asl K, Namjoo E. Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest. NETWORK (BRISTOL, ENGLAND) 2019; 30:1-30. [PMID: 31240983 DOI: 10.1080/0954898x.2019.1634290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 04/30/2019] [Accepted: 06/17/2019] [Indexed: 06/09/2023]
Abstract
We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.
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Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
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Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. J Neurol 2019; 266:844-859. [PMID: 30684208 DOI: 10.1007/s00415-019-09204-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 12/20/2018] [Accepted: 01/18/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Abnormal and dynamic epileptogenic networks cause difficulties for clinical epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ. METHODS We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical epileptologists and the results of epilepsy resection surgeries. RESULTS Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20-50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network. CONCLUSION The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.
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Li F, Peng W, Jiang Y, Song L, Liao Y, Yi C, Zhang L, Si Y, Zhang T, Wang F, Zhang R, Tian Y, Zhang Y, Yao D, Xu P. The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG. Int J Neural Syst 2019; 29:1850016. [PMID: 29793372 DOI: 10.1142/s0129065718500168] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Wenjing Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yuanling Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Limeng Song
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Luyan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yajing Si
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Fei Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Yin Tian
- College of Bio-information, ChongQing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yangsong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
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Si Y, Wu X, Li F, Zhang L, Duan K, Li P, Song L, Jiang Y, Zhang T, Zhang Y, Chen J, Gao S, Biswal B, Yao D, Xu P. Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG and TMS. Cereb Cortex 2018; 29:4119-4129. [PMID: 30535319 DOI: 10.1093/cercor/bhy294] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/14/2018] [Accepted: 11/02/2018] [Indexed: 01/31/2023] Open
Abstract
Abstract
This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.
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Affiliation(s)
- Yajing Si
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xi Wu
- Business School, Sichuan Normal University, Chengdu 610101, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Luyan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Keyi Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Peiyang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Limeng Song
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuanling Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Center for Mental Health Development and Research, Xihua University, Chengdu 610039, China
| | - Yangsong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
| | - Jing Chen
- Research Center of Psychological Development and Application, Sichuan Normal University, Chengdu 610101, China
| | - Shan Gao
- School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
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Wang X, Yao L, Zhao Y, Xing L, Qian Z, Li W, Yang Y. Effects of disparity on visual discomfort caused by short-term stereoscopic viewing based on electroencephalograph analysis. Biomed Eng Online 2018; 17:166. [PMID: 30390658 PMCID: PMC6215628 DOI: 10.1186/s12938-018-0595-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 10/25/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Discomfort evoked by stereoscopic depth has been widely concerned. Previous studies have proposed a comfortable disparity range and considered that disparities exceed this range would cause visual discomfort. Brain activity recordings including Electroencephalograph (EEG) monitoring enable better understanding of perceptual and cognitive processes related to stereo depth-induced visual comfort. METHODS EEG data was collected using a stereo-visual evoked potential (VEP) test system by providing visual stimulus to subjects aged from 21 to 25 with normal stereoscopic vision. For each type of visual stimulus, data were processed using directed transfer function (DTF) and adaptive directed transfer function (ADTF) in combination with subjective feedbacks (comfort or discomfort). The topographies of information flow were constructed to compare responses stimulated by different stereoscopic depth, and to determine the difference in comfort and discomfort situations upon stimulation with same stereoscopic depth. RESULTS Based on EEG analysis results, we found that the occipital P270 was moderately related to the disparity. Moreover, the ADTF of P270 showed that the information flows at frontal lobe and central-parietal lobe changed when stimulation with different stereoscopic depth applied. As to the stereo images with same stereoscopic depth, the DTF outflows at the temporal and temporal-parietal lobes in δ band, central and central-parietal lobes in α and θ bands, and the comparison of inflows in these three bands could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state impacted by disparity. The subjective feedbacks indicated that the comfort judgments remained as a result of cumulative effect. CONCLUSIONS This study proposed a short-term stereo-VEP experiment that shorted the duration of each stimulus in the experimental scheme to minimize the interference from other factors except the disparity. The occipital P270 had a mid-relevance to the disparity and its ADTF showed the affected areas when viewers are receiving stimulations with different disparities. DTF could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state induced by disparity. This study proposed a preferable experiment to observe the single effect of disparity and provided an intuitive and easy-to-read result in a more convenient manner.
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Affiliation(s)
- Xiao Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Liuye Yao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yuemei Zhao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Lidong Xing
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Zhiyu Qian
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China.
| | - Weitao Li
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yamin Yang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
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Haddad AE, Najafizadeh L. Source-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG. IEEE Trans Biomed Eng 2018; 66:1429-1446. [PMID: 30295610 DOI: 10.1109/tbme.2018.2874167] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
GOAL Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented. METHODS The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test. RESULTS Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli. CONCLUSION The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings. SIGNIFICANCE The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.
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Williams NJ, Daly I, Nasuto SJ. Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Front Comput Neurosci 2018; 12:76. [PMID: 30297993 PMCID: PMC6160873 DOI: 10.3389/fncom.2018.00076] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 08/20/2018] [Indexed: 12/27/2022] Open
Abstract
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.
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Affiliation(s)
- Nitin J Williams
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Slawomir J Nasuto
- Biomedical Sciences and Biomedical Engineering Division, School of Biological Sciences, University of Reading, Reading, United Kingdom
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Exploring the Neuroplastic Effects of Biofeedback Training on Smokers. Behav Neurol 2018; 2018:4876287. [PMID: 30151058 PMCID: PMC6087614 DOI: 10.1155/2018/4876287] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 05/29/2018] [Accepted: 06/10/2018] [Indexed: 01/17/2023] Open
Abstract
Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.
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Pagnotta MF, Plomp G. Time-varying MVAR algorithms for directed connectivity analysis: Critical comparison in simulations and benchmark EEG data. PLoS One 2018; 13:e0198846. [PMID: 29889883 PMCID: PMC5995381 DOI: 10.1371/journal.pone.0198846] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 05/25/2018] [Indexed: 01/01/2023] Open
Abstract
Human brain function depends on directed interactions between multiple areas that evolve in the subsecond range. Time-varying multivariate autoregressive (tvMVAR) modeling has been proposed as a way to help quantify directed functional connectivity strengths with high temporal resolution. While several tvMVAR approaches are currently available, there is a lack of unbiased systematic comparative analyses of their performance and of their sensitivity to parameter choices. Here, we critically compare four recursive tvMVAR algorithms and assess their performance while systematically varying adaptation coefficients, model order, and signal sampling rate. We also compared two ways of exploiting repeated observations: single-trial modeling followed by averaging, and multi-trial modeling where one tvMVAR model is fitted across all trials. Results from numerical simulations and from benchmark EEG recordings showed that: i) across a broad range of model orders all algorithms correctly reproduced patterns of interactions; ii) signal downsampling degraded connectivity estimation accuracy for most algorithms, although in some cases downsampling was shown to reduce variability in the estimates by lowering the number of parameters in the model; iii) single-trial modeling followed by averaging showed optimal performance with larger adaptation coefficients than previously suggested, and showed slower adaptation speeds than multi-trial modeling. Overall, our findings identify strengths and weaknesses of existing tvMVAR approaches and provide practical recommendations for their application to modeling dynamic directed interactions from electrophysiological signals.
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Affiliation(s)
- Mattia F. Pagnotta
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
- * E-mail:
| | - Gijs Plomp
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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Wang Y, Lin K, Qi Y, Lian Q, Feng S, Wu Z, Pan G. Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network. IEEE Trans Biomed Eng 2018; 65:1953-1963. [PMID: 29993397 DOI: 10.1109/tbme.2018.2842769] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly. METHODS We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays. RESULTS Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags. CONCLUSION The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation. SIGNIFICANCE The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.
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Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury. Neural Plast 2018; 2018:9354207. [PMID: 29853852 PMCID: PMC5954936 DOI: 10.1155/2018/9354207] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/06/2018] [Accepted: 03/21/2018] [Indexed: 12/18/2022] Open
Abstract
Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.
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