Literature DB >> 28914232

Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke.

N N Johnson1, J Carey, B J Edelman, A Doud, A Grande, K Lakshminarayan, B He.   

Abstract

<span class="abstract_title">OBJECTIVE: Combining repetitive transcranial magnetic stimulation (r<span class="Gene">TMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS  +  BCI, compared to sham rTMS  +  BCI, on motor recovery after stroke in subjects with lasting motor paresis. APPROACH: Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone. Behavioral and electrophysiological differences were evaluated at baseline, after three weeks, and after six weeks of treatment. MAIN
RESULTS: Motor improvements were observed in both real rTMS  +  BCI and sham groups, but only the former showed significant alterations in inter-hemispheric inhibition in the desired direction and increased relative ipsilesional cortical activation from fMRI. In addition, significant improvements in BCI performance over time and adequate control of the virtual reality BCI paradigm were observed only in the former group. SIGNIFICANCE: When combined, the results highlight the feasibility and efficacy of combined rTMS  +  BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS  +  BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS  +  BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.

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Year:  2018        PMID: 28914232      PMCID: PMC5821060          DOI: 10.1088/1741-2552/aa8ce3

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  40 in total

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Journal:  J Neurophysiol       Date:  2015-12-30       Impact factor: 2.714

2.  Serial treatments of primed low-frequency rTMS in stroke: characteristics of responders vs. nonresponders.

Authors:  James R Carey; Huiqiong Deng; Bernadette T Gillick; Jessica M Cassidy; David C Anderson; Lei Zhang; William Thomas
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4.  Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.

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6.  Safety of 6-Hz primed low-frequency rTMS in stroke.

Authors:  James R Carey; Chad D Evans; David C Anderson; Ela Bhatt; Ashima Nagpal; Teresa J Kimberley; Alvaro Pascual-Leone
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

7.  Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance.

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9.  Long-term effect of repetitive transcranial magnetic stimulation on motor function recovery after acute ischemic stroke.

Authors:  E M Khedr; A E Etraby; M Hemeda; A M Nasef; A A E Razek
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Review 1.  Progress in Brain Computer Interface: Challenges and Opportunities.

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2.  BCI-FES With Multimodal Feedback for Motor Recovery Poststroke.

Authors:  Alexander B Remsik; Peter L E van Kan; Shawna Gloe; Klevest Gjini; Leroy Williams; Veena Nair; Kristin Caldera; Justin C Williams; Vivek Prabhakaran
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3.  Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

Authors:  Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05       Impact factor: 3.802

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5.  Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

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6.  Repetitive TMS does not improve cognition in patients with TBI: A randomized double-blind trial.

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7.  Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients.

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8.  Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI.

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9.  Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training.

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10.  Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study.

Authors:  Julia M Juliano; Ryan P Spicer; Athanasios Vourvopoulos; Stephanie Lefebvre; Kay Jann; Tyler Ard; Emiliano Santarnecchi; David M Krum; Sook-Lei Liew
Journal:  Sensors (Basel)       Date:  2020-02-22       Impact factor: 3.576

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