Creating Biomedical Technologies to Improve Health

2017 BESIP Project

Spinal Circuits and Plasticity Unit
NINDS
Mentor Name: 
Ariel Levine, M.D., Ph.D.
Mentor Email: 
Mentor Telephone: 
(301) 402-6935
Computational Bioscience and Engineering Laboratory, Office of Intramural Research
CIT
Mentor Name: 
Thomas Pohida Project #5
Randall Pursley
Mentor Telephone: 
(301) 435-2904

Laboratory and Project Description

Paw Withdrawal Spinal Learning
 
A powerful experimental paradigm of spinal learning is the paw withdrawal paradigm, also called Horridge spinal learning. This example of classic operant conditioning is performed following a complete lesion of the thoracic spinal cord that separates the spinal cord from the brain. The animal is then placed in a harness with its leg hanging freely, and if the foot hangs below a defined threshold a small shock is given. In a single experimental session, the animal learns to maintain its foot in a flexed position to avoid the shock. Together, the Spinal Circuits and Plasticity Unit (NINDS) and the Signal Processing and Instrumentation Section (CIT), have adapted a version of this assay that uses high spatial resolution, high-speed imaging (200 fps) of the leg position to track foot movement and to cue the shock if the leg is below threshold. This is the first time that the animal’s behavioral response can be analyzed with such precision. Because the foot position change is mediated by contraction of a single muscle (the tibialis anterior), and the contraction of muscle fibers is a high-fidelity proxy for activation of motor neurons, this provides an indirect high-speed measure of tibialis anterior motor neuron activity during behavior.
 
In this project, we propose to use the high spatial and temporal precision of the foot position during the paw withdrawal paradigm to determine the activation parameters of the motor neurons. Computational analysis will be performed to analyze many features of the foot position over time, including the responsiveness to different cues, response latency, response duration, “shape” of the response, and changes that occur following manipulation of defined neural circuit elements.
 
The BESIP student working on this project should have some experience using MATLAB.  Working closely with the interdisciplinary team, the student will gain valuable experience in a laboratory environment and developing signal and image processing algorithms.  These algorithms will be used to optimize, quantify, and identify the many features that can be extracted from the data sets.
 
Levine Lab: The overall goal of the research in the Spinal Circuits and Plasticity Section is to uncover the molecules, cells, and circuits of the spinal cord that mediate behavior and how they change to support learning and plasticity. In particular, we are interested in defining how the spinal cord learns following spinal cord injury with the long-term goal of harnessing this knowledge to improve spinal learning, rehabilitation, and recovery in patients with spinal cord injury.
 
Pohida Lab: Provides electrical, electronic, electro-optical, mechanical, computer, and software engineering expertise to NIH projects that require in-house technology development. Collaborations involve advanced signal transduction and data acquisition; real-time signal and image processing; control and monitoring systems (e.g., robotics and process automation); and rapid prototype development. Collaborations result in the design of first-of-a-kind biomedical/clinical research systems, instrumentation, and methodologies.