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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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Yi C, Liu C, Zhang J, Zhang X, Jiang L, Si Y, He G, Ao M, Zhao Y, Yao D, Li F, Ma X, Xu P, He B. The long-term effect of modulated acoustic stimulation on alteration in EEG brain network of chronic tinnitus patients: An exploratory study. Brain Res Bull 2023; 205:110812. [PMID: 37951276 DOI: 10.1016/j.brainresbull.2023.110812] [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: 07/10/2023] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 11/13/2023]
Abstract
Acoustic stimulation is one of the most influential techniques for distressing tinnitus, while how it functions to reverse neural changes associated with tinnitus remains undisclosed. In this study, our objective is to investigate alterations in brain networks to shed light on the enigma of acoustic intervention for tinnitus. We designed a 75-day long-term acoustic intervention experiment, during which chronic tinnitus patients received daily modulated acoustic stimulation with each session lasting 15 days. Every 15 days, professional tinnitus assessments were conducted, collecting both electroencephalogram (EEG) and tinnitus handicap inventory (THI) data from the patients. Thereafter, we investigated the changes in EEG network organizations during continuous acoustic stimulation and their progressive evolution throughout long-term therapy, alongside exploring the associations between the evolving changes of the network alterations and THI. Our current study findings reveal reorganization in alpha/beta long-range frontal-parietal-occipital connections as well as local frontal and parietal-occipital regions induced by acoustic stimulation. Furthermore, we observed a decrease in modulation effects as therapy sessions progressed. These alterations in brain networks reflect the reversal of tinnitus-related neural activities, particularly distress and perception; thus contributing to tinnitus rehabilitation through long-term modulation effects. This study provides unique insights into how long-term acoustic intervention affects the network organizations of tinnitus patients and deepens our understanding of the pathophysiological mechanisms underlying tinnitus rehabilitation.
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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
| | - Chen Liu
- 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
| | - Xiabing 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
| | - 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
| | - 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 Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Gang He
- Otolaryngology Department of Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - Min Ao
- Otolaryngology Department of Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - Yong Zhao
- Betterlife Medical Chengdu Co., Ltd, Chengdu 610000, 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; School of Psychology, Xinxiang Medical University, Xinxiang 453003, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, 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, 2019RU035 Chengdu, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, 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; 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.
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Penalver-Andres JA, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals. Brain Topogr 2022:10.1007/s10548-022-00934-9. [PMID: 36566448 DOI: 10.1007/s10548-022-00934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/05/2022] [Indexed: 12/26/2022]
Abstract
Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.
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Affiliation(s)
- Joaquin A Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Karin A Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - René M Müri
- Perception and Eye Movement Laboratory, Department of Biomedical Research (DBMR) and Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands
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