In this discussion, Catie walks us through her thought process regarding pulling information out of the fMRI time series. After discussing some of the ongoing issues in fMRI such as whether or not to use global regression to remove noise, she leads us into a commonly overlooked effect in fMRI - that of changes in arousal and vigilance. In particular, this has measurable effects on the resting-state signal. She discusses the useful perspective that one person’s artifact may be another’s useful signal. It all depends on the goal of the study. At the end, we all agree that there’s quite a bit more information to be plumbed from the time series.

Guest:

Catie Chang, Ph.D. received her S.B. in Electrical Engineering and Computer Science from MIT, and received her M.S. and Ph.D. in Electrical Engineering from Stanford University. While in graduate school, she opened up the field of fMRI by publishing a seminar paper using time-frequency analysis of resting state fMRI, showing that it was quite dynamic. Since then she has been exploring the effect of basic physiological processes such as cardiac function and respiration on the fMRI signal and has recently been uncovering unique information regarding the influence that vigilance changes have on the time series signal.