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Neuroanatomical correlates of brain-computer interface performance.

Kazumi Kasahara1, Charles Sayo DaSalla1, Manabu Honda1

  • 1Department of Functional Brain Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan; Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan.

Neuroimage
|February 10, 2015
PubMed
Summary

Brain-computer interface (BCI) success varies by individual brain structure. Higher gray matter volume in motor areas correlates with better BCI performance, suggesting neuroanatomy influences usability.

Keywords:
BCI performanceBrain–computer interfaceEvent-related desynchronizationMotor cortexMotor imageryVoxel-based morphometry

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCIs) aim to restore motor function.
  • BCI performance shows significant inter-individual variability.
  • The neuroanatomical basis for this variability is not well understood.

Purpose of the Study:

  • To investigate the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure.
  • To identify neuroanatomical correlates of BCI success rate.

Main Methods:

  • Participants performed motor imagery tasks to control a cursor via an SMR-based BCI.
  • Structural magnetic resonance imaging (MRI) was used to acquire T1-weighted 3D images.
  • Voxel-based morphometry analyzed the correlation between BCI success rate and gray matter volume.

Main Results:

  • Most participants achieved above-chance BCI control, but with substantial variability.
  • BCI performance positively correlated with gray matter volume in the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex.
  • These areas are involved in non-primary sensorimotor processing.

Conclusions:

  • SMR-based BCI performance is associated with the structural development of non-primary motor and somatosensory areas.
  • Understanding neuroanatomical correlates can inform personalized BCI customization.
  • This research may lead to improved BCI efficacy for individuals with motor impairments.