Advanced data science methods to improve spinal electrical stimulation for paralysis
We aim to augment recovery in spinal cord (SC) injured patients. Electrical stimulation of the SC can facilitate recovery, but the mechanisms are not yet understood. One knowledge gap lies in the exact pathways that are recruited by stimulation. To close this gap, we have tested the effects of SC stimulation in people undergoing clinically indicated surgery. By testing the distribution and size of muscle responses to SC stimulation, we can infer which circuits are activated. We are also examining how SC injury changes those responses. We propose to use Bayesian methods to understand the interaction between muscle responses to stimulation and the MRI indicated pattern of damage. The project will involve construction of models linking multiple data modalities that predict muscle activity, followed by the modification of these models to account for patterns of damage. Construction of such models would enable a deeper understanding of SC stimulation leading to more effective stimulation paradigms.
The successful applicant would be able to work with this exciting clinical data, and be taught the relevant anatomy and physiology by Prof. Carmel and data analysis methods by Dr. McIntosh.
Selected candidate(s) will receive a stipend directly from the faculty advisor. Amount is subject to available funding.
- Professor: Jason Carmel
- Department/School: Orthopaedic Surgery
- Location: 14-1412, William Black Medical Research Building, 650 West 168th Street, Rm 14-1412
- The Movement Recovery Laboratory investigates the nervous system circuits that enable movement in health and limit movement after injury to the central nervous system.
- Earliest starting date: 3/1/2020
- Number of hours per week of research expected during Spring 2020: ~10
- Number of hours per week of research expected during Summer 2020: ~40
- Skill sets: Candidates should be comfortable programming (ideally python or Matlab), and be familiar with basic statistics and machine learning. Experience with Bayesian methods would be helpful but is not essential.
- Student eligibility:
freshman, sophomore, junior, senior, master’s
- International students on F1 or J1 visa: eligible