A new paper in Neuroimage
suggests that methods for removing head motion and physiological noise from fMRI data might be inadvertently excluding real signal as well. The authors, Molly G. Bright and Kevin Murphy of Cardiff, studied the technique called nuisance regression. It's a popular approach for removing fMRI noise. Noise reduction is important because factors such as head movement, the heart beat, and breathing, can contaminate the fMRI signal and lead to biased results. Nuisance regression works by estimating parameters, e.g. head position at each point in time, and statistically controlling for these using regression. In theory, nuisance regression should leave the 'real' fMRI signal, the brain activity, untouched, and just get rid of the unwanted noise. However, Bright and Murphy show that this isn't the case. Here's a comparison showing the remarkably similar spatial structure of the "cleaned" fMRI data (after noise is removed), in green, and of ...
