Computational models for understanding the role of STN in encoding different aspects of speech production

In this project I will develop a computational framework for the automatic detection of task-related biomarkers in PD patients during speech production. This framework will help us to predict the timing of electrical stimulation in STN with the purpose of understanding the role of STN in encoding different aspects of speech production. To achieve this goal, I, in collaboration with my supervisors at the University of Toronto and Massachusetts General Hospital, will develop novel inference methods for online identification of state transitions in speech production from neural and behavioral recordings. Specifically, I aim to test and benchmark this novel framework with previously acquired intraoperative data during speech production tasks to integrate neuromodulation technology for assessing the STN rule in speech production. I anticipate that the result of this project will provide new insights for closed-loop neuromodulation for PD patients.

Faculty Supervisor:

Milad Lankarany

Student:

Partner:

Massachusetts General Hospital

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology; Artificial Intelligence; Information and Communications Technology

University:

University of Toronto

Program: