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Gopalakrishnan R, Cunningham DA, Hogue O, Schroedel M, Campbell BA, Baker KB, Machado AG. Electrophysiological Correlates of Dentate Nucleus Deep Brain Stimulation for Poststroke Motor Recovery. J Neurosci 2024; 44:e2149232024. [PMID: 38724284 PMCID: PMC11223455 DOI: 10.1523/jneurosci.2149-23.2024] [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: 11/16/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 07/05/2024] Open
Abstract
While ipsilesional cortical electroencephalography has been associated with poststroke recovery mechanisms and outcomes, the role of the cerebellum and its interaction with the ipsilesional cortex is still largely unknown. We have previously shown that poststroke motor control relies on increased corticocerebellar coherence (CCC) in the low beta band to maintain motor task accuracy and to compensate for decreased excitability of the ipsilesional cortex. We now extend our work to investigate corticocerebellar network changes associated with chronic stimulation of the dentato-thalamo-cortical pathway aimed at promoting poststroke motor rehabilitation. We investigated the excitability of the ipsilesional cortex, the dentate (DN), and their interaction as a function of treatment outcome measures. Relative to baseline, 10 human participants (two women) at the end of 4-8 months of DN deep brain stimulation (DBS) showed (1) significantly improved motor control indexed by computerized motor tasks; (2) significant increase in ipsilesional premotor cortex event-related desynchronization that correlated with improvements in motor function; and (3) significant decrease in CCC, including causal interactions between the DN and ipsilesional cortex, which also correlated with motor function improvements. Furthermore, we show that the functional state of the DN in the poststroke state and its connectivity with the ipsilesional cortex were predictive of motor outcomes associated with DN-DBS. The findings suggest that as participants recovered, the ipsilesional cortex became more involved in motor control, with less demand on the cerebellum to support task planning and execution. Our data provide unique mechanistic insights into the functional state of corticocerebellar-cortical network after stroke and its modulation by DN-DBS.
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Affiliation(s)
- Raghavan Gopalakrishnan
- Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Cleveland FES Center, Cleveland, Ohio 44106
| | - David A Cunningham
- Cleveland FES Center, Cleveland, Ohio 44106
- Physical Medicine and Rehabilitation, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106
- Center for Rehabilitation Research, MetroHealth Systems, Cleveland, Ohio 44109
| | - Olivia Hogue
- Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195
| | - Madeleine Schroedel
- Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
| | - Brett A Campbell
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195
| | - Kenneth B Baker
- Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195
| | - Andre G Machado
- Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195
- Department of Neurosurgery, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195
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Li M, Zou F, Zheng T, Zou W, Li H, Lin Y, Peng L, Zheng S. Electroacupuncture alters brain network functional connectivity in subacute stroke: A randomised crossover trial. Medicine (Baltimore) 2024; 103:e37686. [PMID: 38579054 PMCID: PMC10994512 DOI: 10.1097/md.0000000000037686] [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: 11/30/2023] [Accepted: 03/01/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Electroacupuncture (EA) is a promising rehabilitation treatment for upper-limb motor recovery in stroke patients. However, the neurophysiological mechanisms underlying its clinical efficacy remain unclear. This study aimed to explore the immediate modulatory effects of EA on brain network functional connectivity and topological properties. METHODS The randomized, single-blinded, self-controlled two-period crossover trial was conducted among 52 patients with subacute subcortical stroke. These patients were randomly allocated to receive either EA as the initial intervention or sham electroacupuncture (SEA) as the initial intervention. After a washout period of 24 hours, participants underwent the alternate intervention (SEA or EA). Resting state electroencephalography signals were recorded synchronously throughout both phases of the intervention. The functional connectivity (FC) of the parietofrontal network and small-world (SW) property indices of the whole-brain network were compared across the entire course of the two interventions. RESULTS The results demonstrated that EA significantly altered ipsilesional parietofrontal network connectivity in the alpha and beta bands (alpha: F = 5.05, P = .011; beta: F = 3.295, P = .047), whereas no significant changes were observed in the SEA group. When comparing between groups, EA significantly downregulated ipsilesional parietofrontal network connectivity in both the alpha and beta bands during stimulation (alpha: t = -1.998, P = .049; beta: t = -2.342, P = .022). Significant differences were also observed in the main effects of time and the group × time interaction for the SW index (time: F = 5.516, P = .026; group × time: F = 6.892, P = .01). In terms of between-group comparisons, the EA group exhibited a significantly higher SW index than the SEA group at the post-stimulation stage (t = 2.379, P = .018). CONCLUSION These findings suggest that EA downregulates ipsilesional parietofrontal network connectivity and enhances SW properties, providing a potential neurophysiological mechanism for facilitating motor performance in stroke patients.
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Affiliation(s)
- Mingfen Li
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, China
| | - Fei Zou
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Tingting Zheng
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Weigeng Zou
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Haifeng Li
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yifang Lin
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Peng
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, China
| | - Su Zheng
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
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Zhan G, Chen S, Ji Y, Xu Y, Song Z, Wang J, Niu L, Bin J, Kang X, Jia J. EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training. Front Hum Neurosci 2022; 16:909610. [PMID: 35832876 PMCID: PMC9271662 DOI: 10.3389/fnhum.2022.909610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/25/2022] [Indexed: 12/05/2022] Open
Abstract
Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI–FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl–Meyer assessment scale (FMA) score was significantly improved in the BCI–FES group (p < 0.05), and there was no significant difference in the FES group. (ii) Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. (iii) The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention (p < 0.05), and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group (p < 0.05). These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training.
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Affiliation(s)
- Gege Zhan
- Laboratory for Neural Interface and Brain Computer Interface, State Key Laboratory of Medical Neurobiology, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yanyun Ji
- Shanghai Jinshan Zhongren Geriatric Nursing Hospital, Shanghai, China
| | - Ying Xu
- Shanghai Jinshan Zhongren Geriatric Nursing Hospital, Shanghai, China
| | - Zuoting Song
- Laboratory for Neural Interface and Brain Computer Interface, State Key Laboratory of Medical Neurobiology, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, State Key Laboratory of Medical Neurobiology, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Lan Niu
- Ji Hua Laboratory, Foshan, China
| | | | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, State Key Laboratory of Medical Neurobiology, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
- Ji Hua Laboratory, Foshan, China
- Yiwu Research Institute of Fudan University, Yiwu, China
- Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, China
- *Correspondence: Xiaoyang Kang
| | - Jie Jia
- Department of Rehabilitation Medicine, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
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