Abstract:

Neuroimaging has significantly expanded our understanding of brain changes in neuropsychiatric disorders as well as in aging and neurodegenerative diseases. However, it wasn’t until the advent of machine learning tools that imaging signatures that can be detected in individuals, rather than groups, were constructed. More importantly, imaging signatures derived via machine learning models have shown promise in prognostication, as well as in guiding personalized treatments. This talk will present work on deriving imaging signatures of diagnostic and predictive value. It will then focus on weakly-supervised machine learning methods for analysis of the heterogeneity of brain imaging phenotypes, arriving at a dimensional representation reflecting the heterogeneity of brain aging and of various brain diseases. Finally, international consortia pooling and harmonizing large numbers of brain MRIs from many studies are presented as means for creating sufficiently large datasets for robust machine learning training and heterogeneity analysis, but also pose new challenges, including that or harmonization and domain adaptation across studies.

Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/92905415705