BME Seminar: Ilknur Telkes
Monday, April 15, 2024 - 12:00 p.m.
Ilknur Telkes, PhD
Assistant Professor
Department of Neurosurgery
University of Arizona
"Decoding Pain Relief: Electrophysiological Markers of Chronic Pain in Spinal Cord Stimulation"
Keating 103
Zoom link | Password: BearDown
Hosts: Dr. Mario Romero-Ortega and Dr. Shang Song
(Instructor permission required for enrolled students to attend via Zoom)
Persons with a disability may request a reasonable accommodation by contacting the Disability Resource Center at 621-3268 (V/TTY).
Objectives:
Describe neural signatures in chronic pain patients with and without spinal cord stimulation (SCS)
Evaluate machine learning models in prediction of SCS outcomes
Explore the effective utilization of electrophysiological signals for the advancement of novel SCS technologies
Abstract: Chronic pain has been estimated to affect more than 50 million adult Americans and remains one of the most common reasons that patients seek healthcare. Spinal cord stimulation (SCS) is an FDA-approved neuromodulation treatment to relieve chronic refractory pain. While SCS can be used effectively in many patients with refractory chronic pain conditions, a significant portion of the patients receive suboptimal pain suppression. Predicting responders also remains a challenge due to a lack of objective pain biomarkers. The utility of machine learning (ML) models for clinical prediction has become increasingly prevalent in neurosurgical literature recently. Previous work has indicated that electroencephalogram (EEG) patterns may be correlated with patient-reported outcome measures. Given that SCS treatment of chronic pain still carries challenges and there remains a lack of clear understanding of which patient respond to treatment well, developing predictive models using objective measures would augment the patient selection and pain management approaches. Thus, we aimed to characterize the neural signatures of SCS-induced pain relief and explore the functional utility of peri-operative EEG features to predict which pain patients will be responders to SCS. Our findings suggest that combination of subjective self-reports, pre-operatively and intra-operatively obtained EEGs under SCS ON/OFF conditions, and well-designed ML algorithms might be potentially used to distinguish responders and non-responders resulting in refined patient selection and improved patient outcomes.
Bio: Dr. Ilknur Telkes has recently joined the University of Arizona as an assistant professor in the Department of Neurosurgery, and she is also an affiliate faculty member in the Department of Biomedical Engineering.
Dr. Telkes obtained her PhD degree in biomedical engineering from the University of Houston in 2017 and completed her postdoctoral fellowship at Albany Medical College, New York, in 2022. Prior to joining the UA, Dr. Telkes served as an assistant professor at the Charles E. Schmidt College of Medicine at Florida Atlantic University (FAU), where she also held an affiliate faculty position in the Department of Electrical Engineering and Computer Science at FAU.
Dr. Telkes is an engineer-scientist with over 10 years of experience in electrophysiological data recordings, applying neural engineering and biomedical signal processing techniques to discover neuromarkers and develop algorithms for enhancing clinical neuromodulation therapies. She was awarded the prestigious NIH K99/R00 grant to investigate neural signatures of spinal cord stimulation (SCS) in patients with chronic pain using EEG signals. Dr. Telkes's research focuses on understanding the neurophysiological mechanisms of chronic pain, identifying quantified neural signatures of pain relief, and developing computational tools for clinical applications such as brain/spinal mapping, target localization, and data visualization. Additionally, she works on developing new technologies to enhance treatment outcomes for patients with neuromodulation implants, including
deep brain stimulation and spinal cord stimulation.
Additionally, Dr. Telkes served as the principal investigator on an FAU COECS/I-SENSE SEED grant, leading her team in exploring the functional utility of multimodal sensing signals in adults with Alzheimer’s disease and chronic pain. Furthermore, she acted as a co-investigator on a HEAL Initiative study, collaborating with an interdisciplinary team to investigate the application of a high-resolution SCS paddle and spinal motor mapping in patients undergoing SCS therapy.
In recognition of her contributions to the field, Dr. Telkes was honored with the 2023 Neurosurgery Pain Paper of the Year Award, the 2023 North American Neuromodulation Society (NANS) Krishna Kumar Young Investigator Award, and the 2021 Congress of Neurological Surgeons (CNS) Ronald R. Tasker Young Investigator Award. Dr. Telkes remains actively engaged in professional organizations, including her service on multiple committees for NANS and the NYC Neuromodulation Conference, as well as her editorial board roles for various journals.