Implementing and Interpreting Contemporary MRI Preprocessing Pipelines for Neuroscience

Taught by Dr. Adam Pines (Stanford University)

March 13 & 20, 11:00am - 12:30pm 

Alderson Auditorium, Kansas Union, fourth floor

Magnetic Resonance Imaging is a revolutionary, versatile, and complex technology for non-invasive brain imaging. However, the sensitivity of MRI to acute physiological events also renders MRI extremely vulnerable to the confounding influence of non-neural variables. In worst-case scenarios, these image artifacts can drive years to decades of spurious findings across entire subfields of neuroscience. Conversely, in best-case scenarios, scientists can simultaneously image spontaneous and task-evoked brain activity across tens of thousands of locations in the brain simultaneously, yielding unprecedented insight into brain function and structure in vivo. The neuroscience community is continuously and rigorously adapting to the challenges of MRI. Attend this virtual seminar to learn why and how to implement contemporary MRI preprocessing pipelines for functional MRI and diffusion MRI. Attendees will also learn basic neuroinformatics, visualization, and quality assurance techniques, as well as remaining challenges in the pursuit of meaningful neural signal from MRI.

These hybrid workshops can be attended in-person at the University of Kansas or online. Those registering for online attendance will receive a zoom link prior to the workshop. Workshop attendance is free, although registration is required.

This event is made possible by the Brain, Behavior, and Quantitative Science (BBQ) PhD Program and the Kansas Data Science Consortium (KDSC).

Workshop Topics

The two workshop dates will cover differing techniques and uses of MRI preprocessing. Although attending both workshops is not required, the duo will provide a more robust understanding.

March 13,11:00am (CST): Brain Imaging Data Structure (BIDS), fMRI preprocessing (fMRIprep), and the XCP engine

March 20, 11:00am (CST): BIDS, dMRI preprocessing (QSIprep), and data visualization