The purpose of this grant is to develop a neural prosthesis to help paralyzed patients. The prosthesis will be developed in non-human primates as a precursor to applying a similar approach in humans. The rationale of the prosthesis is to record from an area of the cerebral cortex that plans reach movements. If these plans can be read-out in real-time, then patients who are paralyzed from spinal cord section, ALS, or other peripheral neuropathies could still think about making movements, and these thoughts could be used to operate external devices. In the experiments, the activity from the parietal reach region (PRR) will be recorded using arrays of electrodes, an area that is responsible for the initial planning of reach movements. Decode algorithms will be developed that will allow these plans to be read out in real time. The output device will be a robot limb whose controller is designed to be instructed by high level signals and to compute many of the lower level aspects of the movement trajectory that are normally computed at levels of the brain closer to the motor output. This hybrid control system represents a new area of robotics research. Additionally, local field potentials will be used to convey similar information to the robotic controller and this should provide a breakthrough for long-term recordings.
The specific aims will proceed from the simplest experiment of demonstrating that a monkey can control an animated limb on a computer screen to more complex experiments where the experimenters will determine how PRR codes information about reaches in more natural situations. These later experiments will include neurophysiological studies of the coding of sequential movements, curved trajectories, combined hand-eye movements, and visua-motor plasticity. The concurrent engineering studies will develop alogorithms that can reconstruct the intended movements from neural record and develop supervisory control architectures that can move the robotic limb appropriately, all in real-time.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
1R01EY013337-01
Application #
6258213
Study Section
Special Emphasis Panel (ZRG1-IFCN-5 (01))
Program Officer
Oberdorfer, Michael
Project Start
2001-02-01
Project End
2006-01-31
Budget Start
2001-02-01
Budget End
2002-01-31
Support Year
1
Fiscal Year
2001
Total Cost
$436,588
Indirect Cost
Name
California Institute of Technology
Department
Type
Schools of Arts and Sciences
DUNS #
078731668
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Graf, Arnulf B A; Andersen, Richard A (2015) Predicting oculomotor behaviour from correlated populations of posterior parietal neurons. Nat Commun 6:6024
Klaes, Christian; Kellis, Spencer; Aflalo, Tyson et al. (2015) Hand Shape Representations in the Human Posterior Parietal Cortex. J Neurosci 35:15466-76
Aflalo, Tyson; Kellis, Spencer; Klaes, Christian et al. (2015) Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348:906-10
Andersen, Richard A; Andersen, Kristen N; Hwang, Eun Jung et al. (2014) Optic ataxia: from Balint's syndrome to the parietal reach region. Neuron 81:967-983
Andersen, Richard A; Kellis, Spencer; Klaes, Christian et al. (2014) Toward more versatile and intuitive cortical brain-machine interfaces. Curr Biol 24:R885-R897
Hwang, Eun Jung; Hauschild, Markus; Wilke, Melanie et al. (2014) Spatial and temporal eye-hand coordination relies on the parietal reach region. J Neurosci 34:12884-92
Graf, Arnulf Ba; Andersen, Richard A (2014) Inferring eye position from populations of lateral intraparietal neurons. Elife 3:e02813
Graf, Arnulf B A; Andersen, Richard A (2014) Brain-machine interface for eye movements. Proc Natl Acad Sci U S A 111:17630-5
Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer et al. (2014) Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task. J Neural Eng 11:066014
Hwang, Eun Jung; Andersen, Richard A (2013) The utility of multichannel local field potentials for brain-machine interfaces. J Neural Eng 10:046005

Showing the most recent 10 out of 20 publications