Time-series biomarkers of neurological disorders

Summary

This research will develop a new machine learning framework for finding and quantifying patterns of brain dynamics that distinguish patients with brain disorders from healthy controls.

Supervisor(s)

Dr Ben Fulcher

Research Location

School of Physics

Program Type

Masters/PHD

Synopsis

Despite the ability of modern human brain-imaging technologies (such as EEG and fMRI) to produce incredible new data of our brain in action, scientists are yet to develop robust biomarkers for diagnosing and treating brain disorders. Methods for processing these data remain relatively crude, and analytical approaches have focused almost exclusively on how different brain areas communicate by quantifying pairwise statistical relationships between the activity dynamics of different brain areas (as ‘functional connectivity’). Our research suggests a way forward that would allow us to incorporate the activity dynamics of individual brain regions into our predictive models. This would allow us to understand where and what is different about brain dynamics in different disorders, with the potential to motivate new diagnosis and treatment protocols for debilitating brain disorders like schizophrenia.

Additional Information

Excellent facilities are available to carry out all aspects of the work, including access to computing resources, large collections of brain-imaging data. There is much flexibility to adjust the specific project to the interests of the student, who should have a strong interest in data science (with a quantitative background in e.g., physics, mathematics, statistics, engineering, or computer science). Top-up funding is available for the highest quality of applicants, with additional funding available to support travel to present research results at national and international conferences (as well as collaborators in brain imaging at Monash University).



HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

Want to find out more?

Contact us to find out what’s involved in applying for a PhD. Domestic students and International students

Contact Research Expert to find out more about participating in this opportunity.

Browse for other opportunities within the School of Physics .

Keywords

Machine learning, time-series analysis, dynamical systems, complex systems, nonlinear dynamics, data science, Neuroscience, medicine, Statistics, brain disorders, Schizophrenia, autism, depression

Opportunity ID

The opportunity ID for this research opportunity is: 2385

Other opportunities with Dr Ben Fulcher