By Peter Bandettini
Episode produced by Rachael Stickland and Anastasia Brovkin
AFNI is a major processing package used by brain mapping groups all over the world. It is nearly as old as fMRI itself and has been steadily growing in functionality as the field has evolved. Here we discuss how it all started as well as a few of the challenges of fMRI processing that have arisen over the years. Importantly, we explore the philosophy underlying a key tenet of AFNI development: the ability for researchers to drill down and look directly at the data. This emphasis on flexibly and efficiently visualizing the data at all processing steps not only guards against problematic data and hidden artifacts but is also a catalyst for new analysis ideas. We discuss a bit of the future of analysis and the bottleneck for clinical implementations.
About the guests:
Bob Cox, Ph.D. is the creator of AFNI and still leads a team, the Scientific and Statistical Core, at the NIH which helps users and continues to develop AFNI. Bob received his Ph.D in Applied Mathematics from Caltech, and after several industry positions and a short stint at Indiana University and Purdue University, he moved to the Medical College of Wisconsin where he began to create AFNI. He then moved to the NIH in 2001 where his work accelerated as he grew a team of programmers to further advance AFNI.
Gang Chen, Ph.D. joined the AFNI team at the NIH in 2003. He is a staff scientist and the chief statistician for all things fMRI related. He received his PhD from the University of Arizona, Tucson and has been recently pushing our understanding of variability in datasets with large numbers of participants.
Paul Taylor, Ph.D. joined the AFNI team in 2015. He received his D. Phil in Astrophysics from Oxford University, and completed postdocs at the University of Cape Town and then with Bharat Biswal in New Jersey. He has been leading the effort to incorporate diffusion imaging and tractography into AFNI.