Literature DB >> 28320670

BCI Use and Its Relation to Adaptation in Cortical Networks.

Kaitlyn Casimo, Kurt E Weaver, Jeremiah Wander, Jeffrey G Ojemann.   

Abstract

Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.

Entities:  

Mesh:

Year:  2017        PMID: 28320670      PMCID: PMC5685806          DOI: 10.1109/TNSRE.2017.2681963

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


  57 in total

1.  Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing.

Authors:  Giuseppina Rota; Giacomo Handjaras; Ranganatha Sitaram; Niels Birbaumer; Grzegorz Dogil
Journal:  Brain Lang       Date:  2010-10-02       Impact factor: 2.381

2.  Coadaptive brain-machine interface via reinforcement learning.

Authors:  Jack DiGiovanna; Babak Mahmoudi; Jose Fortes; Jose C Principe; Justin C Sanchez
Journal:  IEEE Trans Biomed Eng       Date:  2009-01       Impact factor: 4.538

3.  Distributed cortical adaptation during learning of a brain-computer interface task.

Authors:  Jeremiah D Wander; Timothy Blakely; Kai J Miller; Kurt E Weaver; Lise A Johnson; Jared D Olson; Eberhard E Fetz; Rajesh P N Rao; Jeffrey G Ojemann
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-10       Impact factor: 11.205

Review 4.  Connectivity measures applied to human brain electrophysiological data.

Authors:  R E Greenblatt; M E Pflieger; A E Ossadtchi
Journal:  J Neurosci Methods       Date:  2012-03-16       Impact factor: 2.390

Review 5.  Engineering the next generation of clinical deep brain stimulation technology.

Authors:  Cameron C McIntyre; Ashutosh Chaturvedi; Reuben R Shamir; Scott F Lempka
Journal:  Brain Stimul       Date:  2014-07-30       Impact factor: 8.955

6.  Single-trial connectivity estimation for classification of motor imagery data.

Authors:  Martin Billinger; Clemens Brunner; Gernot R Müller-Putz
Journal:  J Neural Eng       Date:  2013-06-11       Impact factor: 5.379

7.  The resting human brain and motor learning.

Authors:  Neil B Albert; Edwin M Robertson; R Chris Miall
Journal:  Curr Biol       Date:  2009-05-07       Impact factor: 10.834

Review 8.  A quantitative meta-analysis and review of motor learning in the human brain.

Authors:  Robert M Hardwick; Claudia Rottschy; R Chris Miall; Simon B Eickhoff
Journal:  Neuroimage       Date:  2012-11-27       Impact factor: 6.556

9.  Neural constraints on learning.

Authors:  Patrick T Sadtler; Kristin M Quick; Matthew D Golub; Steven M Chase; Stephen I Ryu; Elizabeth C Tyler-Kabara; Byron M Yu; Aaron P Batista
Journal:  Nature       Date:  2014-08-28       Impact factor: 49.962

Review 10.  Restoring sensorimotor function through intracortical interfaces: progress and looming challenges.

Authors:  Sliman J Bensmaia; Lee E Miller
Journal:  Nat Rev Neurosci       Date:  2014-05       Impact factor: 34.870

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