Literature DB >> 24999037

Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback.

Shaun Boe1, Alicia Gionfriddo2, Sarah Kraeutner3, Antoine Tremblay4, Graham Little5, Timothy Bardouille6.   

Abstract

Motor imagery (MI) may be effective as an adjunct to physical practice for motor skill acquisition. For example, MI is emerging as an effective treatment in stroke neurorehabilitation. As in physical practice, the repetitive activation of neural pathways during MI can drive short- and long-term brain changes that underlie functional recovery. However, the lack of feedback about MI performance may be a factor limiting its effectiveness. The provision of feedback about MI-related brain activity may overcome this limitation by providing the opportunity for individuals to monitor their own performance of this endogenous process. We completed a controlled study to isolate neurofeedback as the factor driving changes in MI-related brain activity across repeated sessions. Eighteen healthy participants took part in 3 sessions comprised of both actual and imagined performance of a button press task. During MI, participants in the neurofeedback group received source level feedback based on activity from the left and right sensorimotor cortex obtained using magnetoencephalography. Participants in the control group received no neurofeedback. MI-related brain activity increased in the sensorimotor cortex contralateral to the imagined movement across sessions in the neurofeedback group, but not in controls. Task performance improved across sessions but did not differ between groups. Our results indicate that the provision of neurofeedback during MI allows healthy individuals to modulate regional brain activity. This finding has the potential to improve the effectiveness of MI as a tool in neurorehabilitation.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cortical oscillations; Functional neuroimaging; Imagery; Magnetoencephalography; Neurofeedback

Mesh:

Year:  2014        PMID: 24999037     DOI: 10.1016/j.neuroimage.2014.06.066

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  20 in total

1.  Resting-state functional connectivity and motor imagery brain activation.

Authors:  Catarina Saiote; Andrea Tacchino; Giampaolo Brichetto; Luca Roccatagliata; Giulia Bommarito; Christian Cordano; Mario Battaglia; Giovanni Luigi Mancardi; Matilde Inglese
Journal:  Hum Brain Mapp       Date:  2016-11       Impact factor: 5.038

Review 2.  [Neurofeedback-based motor imagery training for rehabilitation after stroke].

Authors:  C Dettmers; N Braun; I Büsching; T Hassa; S Debener; J Liepert
Journal:  Nervenarzt       Date:  2016-10       Impact factor: 1.214

3.  Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.

Authors:  Bin He; Bryan Baxter; Bradley J Edelman; Christopher C Cline; Wendy Ye
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2015-05-20       Impact factor: 10.961

4.  Endogenously generated gamma-band oscillations in early visual cortex: A neurofeedback study.

Authors:  Nina Merkel; Michael Wibral; Gareth Bland; Wolf Singer
Journal:  Hum Brain Mapp       Date:  2018-04-26       Impact factor: 5.038

5.  The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Authors:  S R McWhinney; A Tremblay; S G Boe; T Bardouille
Journal:  Med Biol Eng Comput       Date:  2017-07-08       Impact factor: 2.602

6.  Rewiring cortico-muscular control in the healthy and post-stroke human brain with proprioceptive beta-band neurofeedback.

Authors:  Fatemeh Khademi; Georgios Naros; Ali Nicksirat; Dominic Kraus; Alireza Gharabaghi
Journal:  J Neurosci       Date:  2022-08-08       Impact factor: 6.709

7.  MEG-based neurofeedback for hand rehabilitation.

Authors:  Stephen T Foldes; Douglas J Weber; Jennifer L Collinger
Journal:  J Neuroeng Rehabil       Date:  2015-09-22       Impact factor: 4.262

8.  Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke.

Authors:  Georgios Naros; Alireza Gharabaghi
Journal:  Front Hum Neurosci       Date:  2015-07-03       Impact factor: 3.169

Review 9.  Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

Authors:  Shivayogi V Hiremath; Weidong Chen; Wei Wang; Stephen Foldes; Ying Yang; Elizabeth C Tyler-Kabara; Jennifer L Collinger; Michael L Boninger
Journal:  Front Integr Neurosci       Date:  2015-06-10

10.  Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation.

Authors:  Daniel Brauchle; Mathias Vukelić; Robert Bauer; Alireza Gharabaghi
Journal:  Front Hum Neurosci       Date:  2015-10-16       Impact factor: 3.169

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