Measuring and modeling large-scale electrophysiological brain dynamics: From neural mechanisms to cognitive processes

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The overarching goal of this research program is to advance the mechanistic characterization of the relationship between electrophysiological brain network dynamics and higher-order cognitive processes in humans. In particular, our research will continue to probe the role of oscillatory brain dynamics in a variety of higher-order cognitive processes. Building on progress achieved in the last cycle, we will expand our research by deploying predictive models and by focusing on rhythmic processes involved in decision-making and consciousness. This multidisciplinary research program will span three complementary research axes: I. Cognitive neuroscience research axis: The proposed research will investigate the functional role of local and long-range oscillatory synchronization in cognition and behavior. We will target a range of cognitive processes with, in the short- and mid-term, a focus on decision-making and consciousness (including sleep, anesthesia and conscious versus unconscious stimulus processing). II. Systems neuroscience research axis: We will seek to advance our understanding of the spatial, temporal and spectral dynamics of rhythmic and arrhythmic brain networks by bridging the gap between multiple levels of observation. To this end, we will analyze brain data using a wide array of imaging methods. More specifically, we will compare measurements from Magnetoencephalography (MEG) and scalp-EEG with data from intracranial EEG, microelectrode recordings or functional magnetic resonance imaging (fMRI). III. Neuro-AI axis: An important part of this proposal will be dedicated to the implementation of computational models and data-driven tools to establish mechanistic links between behavior/cognition on one hand, and electrophysiological brain measurements on the other. This proposed body of research includes (i) large-scale datamining (e.g. supervised and unsupervised data classification), (ii) computational predictive modeling (incl. AI-inspired models based on deep learning) and (iii) methodological developments for MEG/EEG analyses. The multidisciplinary expertise I have established in my lab over the last cycle of NSERC Discovery support, allows us now to take our research an important step further, expanding our understanding of complex brain network dynamics and their link to higher-order cognition using cutting-edge techniques, including AI-inspired methods. By moving towards brain encoding models, this research will seek to establish more generalizable and better mechanistic characterizations of how oscillatory brain patterns shape cognition. In addition, the highly interdisciplinary nature of this program (combining neuroscience, machine learning and neuroimaging) and our very strong commitment to EDI principles will create a strong, diverse and inclusive training environment for HQP.