Grant: Discovery Grants Program - Individual

The long-term goal of my research program is to build knowledge about human brain networks via analysis of large repositories of neurophysiological data. We are detecting and characterising short high-power bursts of electrophysiological activity, known as transient bursts, in data from hundreds of participants. Historically, most studies have focused on data from tens of participants. Our big data approach provides substantially better statistical power to tease out how brain signals differ with age, sex, and cognitive ability. We interpret these findings in the context of discovering the specific brain network changes that underlie the trends observed in MEG data. To date, our work on transient bursts in big data has focused on the Cambridge Centre for Ageing and Neuroscience (CamCAN) dataset, and we have studied sensorimotor bursts and paroxysmal slow waves. In the next five years, we will discover new trends (e.g., age, sex, cognitive ability) and develop new analysis methods for transient bursts occurring in neurophysiology LOADs. We will explore trends in other transient bursts in the CamCAN dataset, establish generalizability of these trends by comparing to independent datasets, and develop new deep learning algorithms to discover transient bursts in big data. In keeping with the tenets of equity via open science, all data, code, and findings will be shared broadly through open access platforms to ensure the broadest impact. Students working in this area will develop skills in deep learning, imaging physics, code development, data management and analysis, and open science. They will make high impact contributions in an emerging interdisciplinary field and be well positioned for health-related data analytics work in academia, health care, government, and industry.