Friday, October 4, 2013
Olin Engineering Center, Room 202
Peter Bandetinni, Ph.D.
Chief, Section on Functional Imaging Methods Director,
Functional MRI Facility National Institute of Mental Health
There's more there: uncovering meaningful patterns and dynamics from fMRI data
Over the years, the utility of fMRI has increased as methods to identify meaningful signal and to separate it from the noise have improved. This process continues as we better understand noise sources and adjust our models to identify previously overlooked yet relevant signal. In this lecture, I will outline some of our recent work that focuses on this process for both resting state and activation-based fMRI. First, I demonstrate the implementation and use of multi-echo denoising and automated independent component sorting, leading to high fidelity resting state clustering. Secondly, I discuss our results of over 10 hours of averaging a single subject performing a simple task. Third, I show our efforts to capture the dynamic characteristics of resting state fluctuations, and preliminary results in obtaining cognitive "states" from this. Lastly, I describe some novel decoding paradigms for pushing temporal resolution and extracting subtle cognitive processes that do not lend themselves to univariate mapping approaches.