Grant: Doctoral Research Award: Canada Graduate Scholarships

Neuroimaging has the potential to significantly improve the way we diagnose and treat psychiatric disorders. The utility of neuroimaging is currently hindered by the quality of the imaging data, but parallel efforts are underway to address this issue. One such effort is the idea of showing people movies while they lay in the brain scanner, called 'naturalistic neuroimaging'. This method helps keep participants still and awake, both of which are key to getting better data. Additionally, showing movies can 'drive' specific brain activity of interest, akin to how a cardiac stress-test reveals important information that wouldn't show up at rest. To use movies for neuroimaging, researchers must first select a movie to show in the scanner. Currently, this selection is based largely on intuition - choosing movies that are engaging, contextually relevant, and unlikely to cause movement, such as laughing or sudden reactions. Although some previous research suggests that certain movies may be better than others for specific purposes, the differences between movie types remain largely unexplored. This project aims to create recommendations, based on data, for selecting movies for neuroimaging. By optimising movie selection for neuroimaging, we hope to unlock the full potential of movies to address the critical issues of data quality facing the field of neuroimaging. These improvements could function synergistically with parallel efforts to achieve the data quality necessary to improve the utility of neuroimaging for psychiatric applications.