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RSNA 2004 > Radiology and the Cyborg: A Novel Functional Imaging ...
 
Scientific Papers
  CODE: SSA12-09
  SESSION: Neuroradiology/Head and Neck (Functional MR Imaging)
  Radiology and the Cyborg: A Novel Functional Imaging Paradigm as a Testbed for Brain-Machine Interfaces

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PARTICIPANTS
Presenter
Kristine Mosier DMD, PhD
Abstract Co-Author
Yang Wang MD
Robert Scheidt PhD
Santiago Acosta MS
Ferdinando Mussa-Ivaldi PhD
- Author stated no financial disclosure

- Disclosure information unavailable
  DATE: Sunday, November 28 2004
  START TIME: 12:05 PM
  END TIME: 12:15 PM
  LOCATION: N226

 PURPOSE
 
Brain-machine interfaces (BMI) are systems that link the output of firing neurons in the cerebral cortex with a non-biological device. The purpose of these systems is to provide a means to “thought-control” artificial limbs or other apparatus for paralyzed patients. Most systems developed have been tested in primates and only recently has approval been obtained for human application. In learning to control the artificial device, the brain must learn to map new motor commands into the artificial system. However, determining the cortical changes that occur in this re-mapping in humans with implanted systems is not feasible using current fMRI methods. We have developed a simple fMRI paradigm to simulate the learning challenges in BMI that can be applied to healthy subjects as well as other patient populations (e.g. stroke, Parkinson’s, etc).
  
 METHOD AND MATERIALS
 
Seventeen healthy adult subjects were examined in a behavioral study and three subjects were imaged on a Siemens 3T Trio or GE 1.5T Signa MR system using standard BOLD techniques. The subjects wore a MR-compatible cyberglove (DataGlove, 5DT, San Jose, CA) on the dominant hand. The 16 output signals from the glove were transformed into a two-dimensional (x,y) position of a cursor that was displayed to the subject on a computer screen. A total of 50 targets were presented to which the subject was required to align the cursor.
  
 RESULTS
 
Activation was observed in the primary motor, sensory, premotor, and parietal cortices. Impulse response functions for different cortical areas showed either phasic or tonic patterns of activity that differed with the phase of learning (early vs late).
  
 CONCLUSIONS
 
The results of this study demonstrate subjects can learn to remap motor commands into a novel coordinate system, and that differential modulation in premotor and parietal cortices is associated with learning of this task. These results provide valuable information on training strategies for BMIs, but additionally provide insight into motor recovery following stroke or following deep brain stimulation for Parkinson’s.
  
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