Grant: Fellowship: Canada Impact+ Research Training Awards - Postdoctoral

"Over the past two decades, advances in life-sustaining technologies have enabled survival after severe traumatic brain injury, cardiac arrest, or major stroke—conditions once deemed fatal. However, many survivors remain minimally responsive and are often classified as “vegetative.” A landmark 2024 study revealed a “silent epidemic” of misdiagnosis: 25% of these patients exhibit Cognitive Motor Dissociation (CMD), responding to commands through covert cognitive tasks detectable only via functional MRI (fMRI) or electroencephalography (EEG). Identifying CMD is critical for accurate diagnosis, prognosis, and ethical care. In this study, we will develop a diagnostic tool for CMD using an open-source fMRI dataset on covert consciousness under anesthesia. Dr. Blain-Moraes has previously shown that EEG signal complexity and entropy predict consciousness level and recovery. Although traditionally applied to high-temporal-resolution data, recent work has extended these metrics to fMRI BOLD signals. We will apply Multiscale Entropy (MSE) and Lempel–Ziv complexity (LZC) to data from N = 26 participants performing mental imagery and motor tasks under graded propofol sedation. We hypothesize that complexity and entropy in the precuneus—a “hot spot” for self-awareness—predict consciousness level and covert engagement. Building on this work, we will develop a simultaneous fMRI–EEG paradigm that integrates the temporal resolution and established complexity metrics of EEG with the spatial precision and whole-brain coverage of fMRI to improve localization, cross-modal validation, and diagnostic robustness in detecting covert awareness. Together, this work will establish a robust, noninvasive, multimodal framework for detecting CMD in unresponsive patients and improving diagnostic accuracy."