MRI is ultimately about separating a known but variable signal from highly variable noise. How one does this makes all the difference and fMRI is particularly challenging since what is signal and what is noise is not always clear as they both vary in time and space. He is a huge proponent of fMRI encoding or more, generally, careful model building to probe the time series and thinks that more model free approaches and paradigm free methods are ultimately limited. The discussion gets technical as well as intense at times. The points he makes are important. While we agreed most of the time, there were some nuanced differences of opinion - mostly when it came to discussing alternative methods for probing fMRI data. Overall, it was a fun and hopefully useful discussion! What does come through is his passion for what he does, and given that we only barely got into my questions, we will likely be having follow up conversations with him!

Guest:

Jack Gallant, Ph.D. is a neuroscientist and engineer at heart who trained with David Van Essen at Wash U. He is currently a Chancellor’s Professor of Psychology and Class of 1940 Endowed Chair at UC Berkeley and is affiliated with the Department of Electrical Engineering and Computer Science. He is also affiliated with the graduate programs in Bioengineering, Biophysics, Neuroscience and Vision Science. His work spans from single unit recordings, to whole brain fMRI, embracing the whole of computational neuroscience, setting extremely high standard technical rigor, creativity, and insight.