A Case Study in Reproducible Data Science: Measuring and Modeling Human Brain Connectivity

Data Science Lecture Series: A Case Study in Reproducible Data Science: Measuring and Modeling Human Brain Connectivity | February 13, 2015 | 1:00–2:30 p.m. | 190 Doe Library

Speaker: Ariel Rokem, Postdoctoral Scholar, Stanford University
Sponsors: Berkeley Institute for Data Science and Data, Society, and Inference Seminar

Mapping the connections of the human brain is a major goal of neuroscience and will have profound impact on our understanding of the role of these connections in human behavior and on our ability to diagnose a variety of neural diseases that affect these connections. MRI measurements are currently used to estimate these connections at millimeter resolution. However, the size of the data created by these measurements and the complexity of the models that are used to trace the trajectories of neural connections through the brain pose interesting and challenging data-science problems. In this talk, I will present recent efforts to evaluate and validate models of human brain connectivity estimated from MRI measurements. I will put forth methods that we have developed for in-vivo validation: the validation of connectivity models conducted in living human subjects through statistical methods. Finally, I will discuss efforts to make these methods reproducible through the development and dissemination of open source software tools for the analysis of MRI data.

About the Speaker

Ariel Rokem received a PhD in Neuroscience at Berkeley in 2010. He is currently a postdoc at Stanford University. His work in recent years has focused on quantitative MRI methods to study brain connectivity, structure, and physiology. In March 2015, he will join the eScience Institute at the University of Washington as a data scientist.