What is Percent Signal Change? And Why are People Afraid of It?

A few years ago when I was writing up my first publication, I was gently reprimanded by a postdoc for creating a figure showing my results in percent signal change. After boxing my ears and cuffing me across the pate, he exhorted me to never do that again; his tone was much the same as parents telling their wayward daughters, recently dishonored, to never again darken their door.

Years later, I am beginning to understand the reason for his outburst; much of the time what we speak of as percent signal change, really isn't. All of the major neuroimaging analysis packages scale the data to compare signal across sessions and subjects, but expressing it in terms of percent signal change can be at best misleading, at worst fatal.

Why, then, was I compelled to change my figure to parameter estimates? Because what we usually report are the beta weights themselves, which are not synonymous with percent signal change. When we estimate a beta weight, we are looking at the amount of scaling to best match a canonical BOLD response to the raw data; a better approximation of true percent signal change would be the fitted response, and not the beta weight itself.

Even then, percent signal change is not always appropriate: recall the term "global scaling." This means comparing signal at each voxel against a baseline average of signal taken from the entire brain; this does not take into consideration intrinsic signal differences between, say, white and grey matter, or other tissue classes that one may encounter in the wilderness of those few cubic centimeters within your skull.

You can calculate more accurate percent signal change; see Gläscher (2009), or the MarsBar documentation.

Not everybody should analyze FMRI data; but if they cannot contain, it is better for one to be like me, and report parameter estimates, than to report spurious percent signal change, and burn.