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Mao L, Che X, Wang J, Jiang X, Zhao Y, Zou L, Wei S, Pan S, Guo D, Zhu X, Hu D, Yang X, Chen Z, Wang D. Sub-acute stroke demonstrates altered beta oscillation and connectivity pattern in working memory. J Neuroeng Rehabil 2024; 21:212. [PMID: 39633420 PMCID: PMC11619298 DOI: 10.1186/s12984-024-01516-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 11/27/2024] [Indexed: 12/07/2024] Open
Abstract
INTRODUCTION Working memory (WM) is suggested to play a pivotal role in relearning and neural restoration during stroke rehabilitation. Using EEG, this study investigated the oscillatory mechanisms of WM in subacute stroke. METHODS This study included 48 first subacute stroke patients (26 good-recovery, 22 poor-recovery, based on prognosis after a 4-week period) and 24 matched health controls. We examined the oscillatory characteristics and functional connectivity of the 0-back WM paradigm and assessed their associations with prognosis. RESULTS Patients of poor recovery are characterised by a loss of significant beta rebound, beta-band connectivity, as well as impaired working memory speed and performances. Meanwhile, patients with good recovery have preserved these capacities to some extent. Our data further identified beta rebound to be closely associated with working memory speed and performances. CONCLUSIONS We provided novel findings that beta rebound and network connectivity as mechanistic evidence of impaired working memory in subacute stroke. These oscillatory features could potentially serve as a biomarker for brain stimulation technologies in stroke recovery.
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Affiliation(s)
- Lin Mao
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Xianwei Che
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310003, China
| | - Juehan Wang
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Xiaorui Jiang
- Department of Rehabilitation Medicine, The First People's Hospital of Yuhang District, Hangzhou, 311100, China
| | - Yifan Zhao
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Liliang Zou
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Shuang Wei
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Shuyi Pan
- Department of Hyperbaric Oxygen, The Sixth Medical Center of PLA General Hospital, Beijing, 100142, China
| | - Dazhi Guo
- Department of Hyperbaric Oxygen, The Sixth Medical Center of PLA General Hospital, Beijing, 100142, China
| | - Xueqiong Zhu
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China
| | - Dongxia Hu
- Departments of Physical Medicine and Rehabilitation, The Second Affiliated Hospital, Nanchang University School of Medicine, Nanchang, 330038, China
| | - Xiaofeng Yang
- Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Zuobing Chen
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China.
| | - Daming Wang
- Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China.
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Yao R, Song M, Shi L, Pei Y, Li H, Tan S, Wang B. Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions. Brain Sci 2024; 14:985. [PMID: 39451999 PMCID: PMC11505886 DOI: 10.3390/brainsci14100985] [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: 09/05/2024] [Revised: 09/23/2024] [Accepted: 09/26/2024] [Indexed: 10/26/2024] Open
Abstract
Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.
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Affiliation(s)
- Rong Yao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
| | - Meirong Song
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
| | - Langhua Shi
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
| | - Yan Pei
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
| | - Haifang Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China;
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (R.Y.); (M.S.); (L.S.); (Y.P.); (H.L.)
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Wu L, Chen Y, Liu X, Fang P, Feng T, Sun K, Ren L, Liang W, Lu H, Lin X, Li Y, Wang L, Li C, Zhang T, Ni C, Wu S. The influence of job burnout on the attention ability of army soldiers and officers: Evidence from ERP. Front Neurosci 2022; 16:992537. [PMID: 36419460 PMCID: PMC9676458 DOI: 10.3389/fnins.2022.992537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/18/2022] [Indexed: 10/19/2023] Open
Abstract
Job burnout is one of the most widespread mental problems in today's society and seriously affects the mental health and combat effectiveness of soldiers and officers. Herein, the effect of burnout on individual attention is studied from the perspective of neuroelectrophysiology. A total of 1,155 army soldiers and officers were included in this investigation and completed the Job Burnout Scale for Military Personnel. A total of 42 soldiers and officers were randomly selected from those with and without burnout to participate in an event-related potential (ERP) study using a visual oddball task. The characteristics of visual P3a and P3b at Fz, FCz, Cz, CPz, and Pz were recorded and analyzed by repeated-measures analysis of variance (ANOVA). P < 0.05 was the criterion for a significant difference. The total average score on the Job Burnout Scale for Military Personnel among the participants was 0.74 ± 0.46, and the detection rate of job burnout was 29.85%. In the Oddball task, the average number of target stimuli counted in the burnout group was lower than that in the control group, but no significant difference was found. For P3a, the Fz, FCz, Cz, CPz, and Pz amplitudes in the burnout group were significantly lower than those in the control group. The average amplitude of P3a evoked in the central parietal area was larger than that in the prefrontal area. For P3b, the amplitudes of the five electrodes in the burnout group were significantly lower than those in the control group. The average amplitude of P3b evoked in the parietal region was larger than those in the prefrontal and central parietal regions. A certain degree of job burnout is evident in army soldiers and officers. The voluntary attention and involuntary attention of individuals with burnout are both affected to some extent, as reflected by the lower amplitudes of P3a and P3b. The results suggest that P3a and P3b can be used as indicators to monitor cognitive neural function in soldiers and officers with burnout and can also be used as references for evaluating the effects of cognitive training and screening methods. In this study, ERP was used to research the attention ability of soldiers and officers with job burnout, and related issues were discussed from the aspects of the burnout results, behavioral results, ERP results, compensation effect of cognitive resources, application in the military field, limitations, and prospects.
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Affiliation(s)
- Lin Wu
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Yanfeng Chen
- Nursing School, Air Force Medical University, Xi’an, China
| | - Xufeng Liu
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Peng Fang
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Tingwei Feng
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Kewei Sun
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Lei Ren
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Wei Liang
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Huijie Lu
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Xinxin Lin
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Yijun Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Lingling Wang
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Tian Zhang
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Chunping Ni
- Nursing School, Air Force Medical University, Xi’an, China
| | - Shengjun Wu
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
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