Multivariate brain activity patterns underlying consciousness

Summary

Clinical assessment of consciousness is one of the most significant issues in brain injury and general anaesthesia, yet it remains challenging for medical practitioners. Terrifyingly, up to 40% of brain-damaged patients who are assessed as unconscious are actually conscious. We desperately need an accurate consciousness measure based on clinically-measurable brain data, which would improve clinical care for patients under general anaesthesia and those who have suffered brain damage. In this project, we will develop new quantitative metrics for consciousness level from objective brain-imaging data.

Supervisor(s)

Dr Ben Fulcher

Research Location

School of Physics

Program Type

Masters/PHD

Synopsis

This work is in collaboration with an interdisciplinary Australian and international network of researchers working in consciousness theory and measuring state-of-the-art clinical datasets. Projects can be flexibly tailored to the students interests among the following tasks. (1) Develop a highly comparative multivariate time-series statistics that leverages the multivariate time-series analysis literature and the machine learning literature to find new objective measures of brain communication that are informative of consciousness level. This will include pairwise dependence measures (to deduce relationships between brain areas), and measures of distributed network structure (to quantify the structure of whole-brain communication); (2) Develop new quantitative measures of consciousness guided by the multivariate time-series analysis literature and inspired by a leading theory of consciousness: Integrated Information Theory (IIT); (3) assess different strategies against clinical neuroimaging datasets measured from collaborators at Melbourne University and around the world.

Additional Information

Excellent facilities are available to carry out all aspects of the work, including access to computing resources and large collections of labeled neuroimaging data. The student should have a quantitative background (e.g., physics, mathematics, statistics, engineering, or computer science) and a strong interest in applying sophisticated quantitative techniques to important clinical problems. The student should enjoy working in such an interdisciplinary collaborative environment. 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 and to visit collaborators (including in Melbourne).Additional Supervisor: Professor Nao Tsuchiya, Monash University.

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Keywords

consciousness, Brain imaging, neuroimaging, time-series analysis, multivariate, data science, Machine learning

Opportunity ID

The opportunity ID for this research opportunity is: 2856

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