Literature DB >> 26719088

Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.

Natalie Mrachacz-Kersting1, Ning Jiang2, Andrew James Thomas Stevenson3, Imran Khan Niazi3, Vladimir Kostic4, Aleksandra Pavlovic4, Sasa Radovanovic4, Milica Djuric-Jovicic5, Federica Agosta6, Kim Dremstrup3, Dario Farina2.   

Abstract

Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.
Copyright © 2016 the American Physiological Society.

Entities:  

Keywords:  Hebb; associativity; brain-computer interface; neuroplasticity; stroke

Mesh:

Year:  2015        PMID: 26719088      PMCID: PMC4808132          DOI: 10.1152/jn.00918.2015

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  71 in total

1.  Analysis of recovery processes after stroke by means of transcranial magnetic stimulation.

Authors:  Hank T Hendricks; Jaco W Pasman; Johannes L Merx; Jacques van Limbeek; Machiel J Zwarts
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Authors:  Michael J Grey; Nazarena Mazzaro; Jens Bo Nielsen; Thomas Sinkjaer
Journal:  Can J Physiol Pharmacol       Date:  2004 Aug-Sep       Impact factor: 2.273

3.  Movement related cortical potentials in severe chronic stroke.

Authors:  O Yilmaz; W Cho; C Braun; N Birbaumer; A Ramos-Murguialday
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

4.  Spatial filter selection for EEG-based communication.

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5.  The optimal interstimulus interval and repeatability of paired associative stimulation when the soleus muscle is targeted.

Authors:  Susanne Kumpulainen; Natalie Mrachacz-Kersting; Jussi Peltonen; Michael Voigt; Janne Avela
Journal:  Exp Brain Res       Date:  2012-07-27       Impact factor: 1.972

6.  Development of a rating scale for primary depressive illness.

Authors:  M Hamilton
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7.  Functional potential in chronic stroke patients depends on corticospinal tract integrity.

Authors:  Cathy M Stinear; P Alan Barber; Peter R Smale; James P Coxon; Melanie K Fleming; Winston D Byblow
Journal:  Brain       Date:  2007-01       Impact factor: 13.501

8.  Detection of movement intention from single-trial movement-related cortical potentials.

Authors:  Imran Khan Niazi; Ning Jiang; Olivier Tiberghien; Jørgen Feldbæk Nielsen; Kim Dremstrup; Dario Farina
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9.  Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.

Authors:  Doris Broetz; Christoph Braun; Cornelia Weber; Surjo R Soekadar; Andrea Caria; Niels Birbaumer
Journal:  Neurorehabil Neural Repair       Date:  2010-06-02       Impact factor: 3.919

10.  A modified National Institutes of Health Stroke Scale for use in stroke clinical trials: preliminary reliability and validity.

Authors:  P D Lyden; M Lu; S R Levine; T G Brott; J Broderick
Journal:  Stroke       Date:  2001-06       Impact factor: 7.914

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  39 in total

Review 1.  Effective assessments of electroencephalography during stroke recovery: contemporary approaches and considerations.

Authors:  Kartik K Iyer
Journal:  J Neurophysiol       Date:  2017-06-21       Impact factor: 2.714

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

Authors:  N N Johnson; J Carey; B J Edelman; A Doud; A Grande; K Lakshminarayan; B He
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3.  Therapeutic Applications of BCI Technologies.

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Journal:  Brain Comput Interfaces (Abingdon)       Date:  2017-04-10

Review 4.  Pathological changes of brain oscillations following ischemic stroke.

Authors:  Yoshimichi Sato; Oliver Schmitt; Zachary Ip; Gratianne Rabiller; Shunsuke Omodaka; Teiji Tominaga; Azadeh Yazdan-Shahmorad; Jialing Liu
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5.  Rewiring cortico-muscular control in the healthy and post-stroke human brain with proprioceptive beta-band neurofeedback.

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Journal:  J Neurosci       Date:  2022-08-08       Impact factor: 6.709

6.  EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training.

Authors:  Gege Zhan; Shugeng Chen; Yanyun Ji; Ying Xu; Zuoting Song; Junkongshuai Wang; Lan Niu; Jianxiong Bin; Xiaoyang Kang; Jie Jia
Journal:  Front Hum Neurosci       Date:  2022-06-27       Impact factor: 3.473

7.  Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm.

Authors:  Emma Colamarino; Floriana Pichiorri; Jlenia Toppi; Donatella Mattia; Febo Cincotti
Journal:  Brain Topogr       Date:  2022-01-19       Impact factor: 3.020

8.  Cortical excitability correlates with the event-related desynchronization during brain-computer interface control.

Authors:  Ian Daly; Caroline Blanchard; Nicholas P Holmes
Journal:  J Neural Eng       Date:  2018-04       Impact factor: 5.379

9.  tDCS and Robotics on Upper Limb Stroke Rehabilitation: Effect Modification by Stroke Duration and Type of Stroke.

Authors:  Sofia Straudi; Felipe Fregni; Carlotta Martinuzzi; Claudia Pavarelli; Stefano Salvioli; Nino Basaglia
Journal:  Biomed Res Int       Date:  2016-03-31       Impact factor: 3.411

10.  Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation.

Authors:  Ren Xu; Ning Jiang; Natalie Mrachacz-Kersting; Kim Dremstrup; Dario Farina
Journal:  Front Neurosci       Date:  2016-01-21       Impact factor: 4.677

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