Principal Investigator: Aysegul Gunduz
Sponsor: National Science Foundation
Start Date: March 15, 2016
End Date: February 28, 2021
The human brain consists of numerous networks distributed over space and connected over time to orchestrate meaningful interaction with the external world. Neurological disorders disrupt this interaction, as well as our control over our bodies. Deep brain stimulation (DBS) has emerged in the nineties as a neurosurgical intervention for the treatment of movement disorders. The principle behind DBS is to implant electrodes into deep brain structures and to inject electrical pulses to suppress pathological brain activity. The clinical personnel that perform programming of stimulation settings however, base their decisions on the observable patient responses rather than a scientific understanding of the underlying pathology, or the physiological response to stimulation. The PI’s proposed effort includes studying the neural signatures of movement disorders, and the aftereffects of stimulation to provide insight into treatment options that can be tailored to the current clinical condition of the patient. Responsive DBS is expected to provide improved symptom suppression, reduce adverse effects of continuous stimulation, and prolong battery life of DBS implants. This project will also provide students in the PI’s lab with an environment that promotes learning in the design of neural engineering systems, data collection in clinical settings, and analysis of large-scale datasets. All of these skills are prolific to the development of translational medicine applications for those suffering from disabilities, and to the education of the next generation of biomedical engineers.
The overall research goal of this project is to study the electrophysiological underpinnings of neurological disorders using next generation DBS devices capable of recording brain signals in humans, in order to responsively deliver stimulation to the current pathological state of the brain. To this end, the PI is investigating the neurophysiology of Tourette syndrome, which affects an estimated 3 to 9 school-age children in 1000, and to develop responsive DBS systems for its improved and targeted treatment in humans. Online classifieres will be built to detect involuntary tics that characterize Tourette syndrome from neural activity in the centromedian nucleus of the thalamus and the motor cortex. The input-output relationship between DBS parameters and neural activity will be studied to build inverse adaptive controllers that will yield optimal stimulation parameters. The knowledge gained from this project and the established platform can be extended to other movement disorders. The overall objective of the educational plan is to increase interest and engagement in STEM fields and to proliferate the study of engineering through a series of focused educational activities at the K-12, undergraduate, and graduate education levels.