Principles of intensive human neuroimaging: a viewpoint from visual and computational neuroscience

Abstract: 

We discuss our vision of "intensive neuroimaging" in which cognitive phenomena are extensively sampled in order to support computational modeling and detailed investigation of brain function. Importantly, intensive neuroimaging does not simply mean collecting more data --- it seeks to also maximize data quality and design experiments that enable rich hypothesis spaces. As case examples, I will discuss the large-scale 7T fMRI Natural Scenes Dataset (NSD), consisting of whole-brain BOLD responses while each of 8 participants viewed tens of thousands of natural scenes over the course of 30-40 scan sessions, as well as our ongoing Visual Cognition Dataset (VCD) which seeks to broadly sample how different tasks influence visually evoked responses in the human brain.

Speaker Bio

Dr. Kendrick Kay is an Associate Professor at the Center for Magnetic Resonance Research at the University of Minnesota. Research in his Computational Visual Neuroscience lab (http://cvnlab.net) focuses on understanding the computational principles by which the brain processes visual information, and lies at the intersection of cognitive neuroscience, functional magnetic resonance imaging methods, and computational modeling. He is known for advancing high-resolution fMRI methods and for developing quantitative models that describe neural computations in the visual cortex. Dr. Kay's lab has created large-scale datasets and tools that have become widely used across the human neuroscience community.