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Katarzyna Bilska

RESEARCHER PROFILE

PhD Student

02 9399 1864


Katarzyna (Kasia) is a PhD candidate at Neuroscience Research Australia (NeuRA), University of New South Wales (UNSW). Kasia completed a Master’s degree in Psychology (specializing in Neuropsychology and Neuroscience), and practiced within diverse fields such as Psychotherapy, Social and Experimental Psychology, Neuropsychology and Neuroscience as well as Neurorehabilitation. For her PhD, Kasia’s project aims to validate a novel cortical biomarker for pain using two commonly available techniques – electroencephalogram (EEG) and transcranial magnetic stimulation (TMS). Kasia is passionate about developing ways to identify individuals at high risk of transitioning from acute to chronic pain. Translating research into clinical practice is the ultimate goal for Kasia.

Projects Katarzyna Bilska is currently involved with

CURRENT PROJECTS

PREDICT: A novel cortical biomarker signature for predicting pain sensitivity

Temporomandibular disorder (TMD) is the second most common musculoskeletal pain condition and is associated with pain and tenderness of the jaw. Although a number of biological factors have shown an association with chronic TMD in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. Because of the difficulty in treating chronic pain, development of brain signal predictive biomarkers is of growing interest.

The PREDICT project will aim to develop a predictive biomarker signature of pain severity and duration using two commonly available techniques – electroencephalogram (EEG) and transcranial magnetic stimulation (TMS) – and perform initial clinical validation in first onset TMD. The biomarker could have utility in identifying patients at high risk of transitioning from acute to chronic pain and has additional potential for clinical application in the treatment and prevention of chronic pain.

This project will be carried out in collaboration with a team at the University of Maryland, Baltimore lead by A/Prof David Seminowicz (see more information here).

PREDICT Publications

Seminowicz DA, Bilska K, Chowdhury NS, Skippen P, Millard SK, Chiang A, Chen S, Furman AJ, & Schabrun SM. (2020). A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study. Pain Reports, 5(4), e833. doi: 10.1097/PR9.0000000000000833

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PREDICT: A novel cortical biomarker signature for predicting pain sensitivity

MODULATE: Altering the brains sensitivity to pain

Pain is the single most common reason for seeking medical attention. Under normal circumstances, pain acts to signal injury and is a protective response that prevents further damage and promotes tissue healing. People differ not only in their ability to detect and tolerate pain, but also in their ability to recover from an injury, with some people experiencing pain that outlasts the duration of tissue healing. Interventions to treat or cure chronic pain have had limited success.

Recent research has identified a novel cortical biomarker that could identify individuals at risk of developing chronic pain, which could be used to identify individuals at high risk of transitioning from acute to chronic pain (PREDICT project). However, whether a causal relationship exists between this cortical biomarker and pain is unknown.

The pain biomarker is based on rhythmic patterns of electrical activity in the brain and is measured using electroencephalography (EEG). Previous research suggests that the speed of this rhythmic activity can be altered through the administration of nicotine. MODULATE will attempt to alter the speed of the brain’s rhythmic activity, using nicotine gum, and observe the impact on pain. The project will help determine whether a causal relationship exists between the biomarker and pain.

 

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MODULATE: Altering the brains sensitivity to pain

RESEARCH TEAM

PUBLICATIONS

A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study.

Seminowicz DA, Bilska K, Chowdhury NS, Skippen P, Millard SK, Chiang AKI, Chen S, Furman AJ, Schabrun SM