Literature DB >> 30946671

Brain-Machine Interface-Driven Post-Stroke Upper-Limb Functional Recovery Correlates With Beta-Band Mediated Cortical Networks.

Dheeraj Rathee, Anirban Chowdhury, Yogesh Kumar Meena, Ashish Dutta, Suzanne McDonough, Girijesh Prasad.   

Abstract

Brain-machine interface (BMI)-driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This paper examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA)-based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting up to six weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p=0.00028) and 88.0% was attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15-26 Hz) RS-MEG FCs and UL functional recovery provided the positively correlated sub-networks in both the contralesional and ipsilesional motor cortices. The frontoparietal FC exhibited hemispheric lateralization wherein the majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.

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Year:  2019        PMID: 30946671     DOI: 10.1109/TNSRE.2019.2908125

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  6 in total

1.  Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation.

Authors:  Nikunj A Bhagat; Nuray Yozbatiran; Jennifer L Sullivan; Ruta Paranjape; Colin Losey; Zachary Hernandez; Zafer Keser; Robert Grossman; Gerard E Francisco; Marcia K O'Malley; Jose L Contreras-Vidal
Journal:  Neuroimage Clin       Date:  2020-11-19       Impact factor: 4.881

2.  Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.

Authors:  Paul Dominick E Baniqued; Emily C Stanyer; Muhammad Awais; Ali Alazmani; Andrew E Jackson; Mark A Mon-Williams; Faisal Mushtaq; Raymond J Holt
Journal:  J Neuroeng Rehabil       Date:  2021-01-23       Impact factor: 4.262

3.  Optimization of Surface Electromyography-Based Neurofeedback Rehabilitation Intervention System.

Authors:  Wenlin Sun; Yujun Qi; Yang Sun; Tiantian Zhao; Xiaoyong Su; Yang Liu
Journal:  J Healthc Eng       Date:  2021-03-17       Impact factor: 2.682

4.  A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.

Authors:  Dheeraj Rathee; Haider Raza; Sujit Roy; Girijesh Prasad
Journal:  Sci Data       Date:  2021-04-29       Impact factor: 6.444

Review 5.  The role of brain oscillations in post-stroke motor recovery: An overview.

Authors:  Giulia Leonardi; Rosella Ciurleo; Francesca Cucinotta; Bartolo Fonti; Daniele Borzelli; Lara Costa; Adriana Tisano; Simona Portaro; Angelo Alito
Journal:  Front Syst Neurosci       Date:  2022-07-29

6.  Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis.

Authors:  Zhongfei Bai; Kenneth N K Fong; Jack Jiaqi Zhang; Josephine Chan; K H Ting
Journal:  J Neuroeng Rehabil       Date:  2020-04-25       Impact factor: 4.262

  6 in total

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