The MINDS lab conducts state-of-the-art research to help us better measure and understand human functional connectivity and is at the cutting edge of early life neuroimaging. The lab’s research aims at developing novel statistical and machine learning methods for functional connectivity to meet challenges arising with the ‘big’ neuroscience data. The lab is one of a few in the country that use functional magnetic resonance imaging to examine fetal brain development, which complements our functional connectivity studies in neonates and infants. Together these approaches can help to understand human cognition.
Dustin Scheinost received his Bachelor's degree in Biomedical Engineering from Washington University in 2007 and her PhD in Biomedical Engineering from Yale University in 2013. He launched the Welcome to Multi-modal Imaging, Neuroinformatics, & Data Science Laboratory at Yale in 2016. Outside of the lab, he enjoys cycling, mountain biking, and spending time with his wife, Marisa, and son, Hunter.
Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics(2021)
Molecular Psychiatry (2022)