Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Psychology

Supervisor

Owen, Adrian

2nd Supervisor

Debicki, Derek

Abstract

Assessing the residual cognition of behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There is a need for tools that can predict patient outcomes to better inform decisions being made in the intensive care unit. In this study, we examine brain complexity through various measures that quantify the intricate patterns and dynamics of electrical signals recorded through high-density electroencephalography (EEG). We compare responses to a naturalistic auditory stimulation task with those from the scrambled versions of the same stimuli to determine if these differences can predict the survival outcomes of patients with acute brain injury. I assess 52 acutely brain-injured patients and 18 healthy controls. Results are presented in three parts. Part One showed that while complexity measures could distinguish between conditions in healthy controls, they were less successful in predicting patient outcomes. However, Part Two found a significant difference between patients with favourable and unfavourable outcomes when complexity was examined independent of Task differences. In Part Three, I found that various complexity measures can be used to predict outcomes at an individual level with machine learning. These findings suggest that EEG complexity measures have the potential as prognostic tools for individual patients, with the quality of complex brain activity being more informative for prognosis than task-based differences in acute brain-injury patients.

Summary for Lay Audience

My study explored whether brain activity patterns could help predict recovery in patients with severe brain injuries. I looked at brain activity (using EEG) of patients in intensive care with serious brain injuries and compared it to healthy individuals. Participants listened to a movie audio clip and a scrambled version of it while we recorded their brain activity. I used complex mathematical analysis (called "complexity measures") to analyze the brain activity patterns, with the goal of seeing if these patterns could tell us which patients might recover better.

I found that for injured patients, I could not reliably use this audio task to predict their recovery. Despite this, I did find that patients who survived had different overall brain activity patterns compared to those who did not recover.

While my specific audio task was not the best for predicting recovery, my study shows promise in using the overall complexity of brain signals to understand the chances of recovery in severe brain injury. This research is a step towards developing better tools to help doctors make informed decisions about patient care and recovery potential. More research is needed to refine these methods and make them useful in real-world medical settings. This study contributes to our understanding of brain activity in injured patients and opens doors for future improvements in predicting recovery.

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