Dr. Liu is the Director of the UCSD Center for Functional MRI and a Professor of Radiology, Psychiatry, and Bioengineering. Areas of research include: (1) Investigation of resting-state brain connectivity with multi-modal imaging approaches (fMRI, MEG, and EEG); (2) Characterization and modeling of the hemodynamic response to neural activity, including the effects of drugs such as caffeine; (3) Development and optimization of arterial spin labeling MRI methods for the non-invasive measurement of cerebral blood flow; (4) Design and analysis of experiments for functional MRI (fMRI), with an emphasis on statistical optimization, nonlinear signal processing, and physiological noise reduction; and (5) Development of quantitative fMRI methods for the study of Alzheimer's disease and associated disorders

Presentation Title: The Global Signal, Vigilance Fluctuations, and Nuisance Regression in Resting State fMRI

Abstract:

Resting-state fMRI (rsfMRI) is now a widely used method to assess the functional connectivity (FC) of the brain. However, the mechanisms underlying rsfMRI are still poorly understood. In this talk I will address several related aspects of the rsfMRI signal. The first is the global signal, which represents the whole brain average signal and has been widely used as a regressor for removing the effects of global variations in resting-state activity. I will discuss the controversy surrounding global signal regression and describe new approaches for minimizing global signal effects. A related topic concerns the origins of global activity in the brain. There is growing evidence that a considerable portion of this global activity arises from fluctuations in vigilance and arousal. I will discuss the recent findings in this area and discuss the implications for the analysis and interpretation of rsfMRI studies. Finally, I will describe recent empirical and theoretical work demonstrating the limitations of regression based methods that are widely used to minimize the effects of nuisance components in rsfMRI studies.