(02) 9399 1834
Graduated from PhD in Theoretical Physics / Complex Systems at University of Sydney, aimed to automate the characterisation of the human alpha rhythm to assist model development in EEG generation. Interested in time-series and spectral analysis algorithms and the associated deployment. Curious about the adversarial aspects of machine learning techniques, pathological cases in particular.
Current work in NeuRA focus on quantifying breathing during sleep and ways to automate the process.
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.
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).
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
Approximately 1/3 of all obstructive sleep apnoea (OSA) patients have poor upper airway muscle activity during sleep which contributes to the repetitive narrowing or closure of the airway during sleep. This leads to abrupt arousals and disruption of sleep throughout the night which can lead to various health problems including diabetes, cardiovascular diseases, obesity, high blood pressure, impaired cognitive function, decreased quality of life and patients are more likely to be involved in motor vehicular accidents.
Recent studies have found that combination of these noradrenergic and antimuscarinic agents help to improve upper airway muscle activity during sleep. Therefore, this clinical study will focus on determining the effects of these agents on the severity of sleep apnoea in OSA patients in hopes to improve treatment outcomes for OSA patients in the future. The study also aims to determine the effects of these combination of agents on cognitive alertness and other sleep parameters which are impaired in patients with OSA.
Obstructive sleep apnoea (OSA) is characterised by the recurrent collapse or narrowing of the upper airway during sleep. OSA is also associated with adverse cardiovascular, metabolic, neurocognitive, quality of life and safety consequences. The first line treatment continuous positive airway pressure (CPAP) which is highly efficacious but poorly tolerated. Oral mandibular advancement splint (MAS) therapy is the leading alternative to CPAP to treat obstructive sleep apnoea, although it is difficult to predict treatment success.
Therefore, this study aims to determine the efficacy of targeted therapeutic approaches to treat OSA whilst using novel techniques to advance knowledge of upper airway function and the mechanisms of a MAS device. We aim to develop accurate tools to predict treatment outcome with a MAS device, develop novel approaches to monitor and diagnose OSA. This study is a part of the government Cooperative Research Centre Program linking researchers and industry.
DR PETER BURKE Postdoctoral fellow
RICHARD LIM Honours student
DR AHMAD BAMAGOOS PhD student
AMAL OSMAN PhD student
Sleep Lab Manager
: 9399 1886
To investigate age trends, sex differences, and splitting of alpha peaks of the EEG spectrum in the healthy population. Observed increases in alpha frequency in children and decreases in the elderly were consistent with those from earlier studies. A large fraction of participants (≈ 44%) showed multiple distinct alpha rhythm thus investigations which only examine the alpha frequency with the highest peak power can produce misleading results. The strong dependence of alpha frequency on age and anterior-posterior position indicates use of a fixed alpha frequency band is insufficient to capture the full characteristics of the alpha rhythm.