Study level

PhD

Faculty/Lead unit

Topic status

We're looking for students to study this topic.

Supervisors

Associate Professor Beatrix Feigl
Position
Associate Professor
Division / Faculty
Faculty of Health
Professor Graham Kerr
Position
Professor
Division / Faculty
Faculty of Health
Professor Andrew J. Zele
Position
Principal Research Fellow
Division / Faculty
Faculty of Health

Overview

Up to 98 % of patients with Parkinson’s Disease (PD) have non-motor symptoms (Poewe et al. Nature Rev Dis 2017, 3: 17013) and of those, circadian and sleep disorders are the most common (for review, Gros & Videnovic. 2020, Clin Geriatr Med 36: 119). These symptoms become increasingly prevalent during the course of PD and are key determinants affecting quality of life, advancement of overall disability and placement in nursing homes (Shapira et al. Nat Rev Neurosci 2017,18:435). Circadian and sleep disorders are also frequently under-recognised by patients, their care givers and health professionals and remain untreated as effective treatments are lacking. Our research identified melanopsin cells in the eye of PD patients as the retinal source for dysfunctional light transmission to the brain for setting the circadian clock (Joyce, Feigl, Kerr, Roeder Zele Sci Rep. 2018;8(1):7796). This discovery provides the foundation for development of non-pharmaceutical approaches that improve PD symptoms through the application of new lighting technologies.

This research aims to understand circadian disruption in people with PD and the effects of a cutting-edge lighting technology engineered at QUT used to limit motor and non-motor symptoms. The project includes the measurement of circadian and visual dysfunction quantified using sophisticated techniques for recording small electrical signals from the surface of the brain (electroencephalogram, EEG) and the eye (electroretinogram, ERG; visual evoked potentials, VEP). Artificial intelligence (AI) will be developed to analyse the big data sets (EEG, ERG, VEP) recorded from the Parkinson’s patients.

The outcomes will positively impact the quality of life of many patients with PD by limiting their circadian, sleep and motor dysfunction.

Approaches/Skills and techniques

  • Human electrophysiological recordings of circadian rhythms, sleep and vision using the electroencephalogram (EEG), electroretinogram (ERG) and visual evoked potentials (VEP)
  • Artificial Intelligence (AI) for analyses big data sets (EEG, ERG, VEP) from the Parkinson’s patients
  • Lighting design and engineering
  • Eye (ophthalmic) measurement and imaging
  • Neurological assessment

Outcomes

The experimental outcomes will lead to improved non-motor and motor symptoms in Parkinson’s disease and introduce new measures for the early detection of non-motor symptoms, which often precede motor symptoms.

Required skills and experience

Background in electrical engineering and/or mathematics, with an interest in big data, small signal analysis of biological data and the application of artificial intelligence to human clinical data sets for detection and monitoring of disease progression.

Keywords

Contact

Contact the Supervisor for more information.