This is the first in a series of posts in which I will attempt to provide you with the means to diagnose the manifold artifacts that plague fMRI. These artifacts can be inherent, e.g. distortion and dropout, or the consequences of a hardware issue, e.g. RF interference or gradient spiking, or may arise from your subject, e.g. cardiac pulsatility and head movement. However the artifacts arise, the aim is simple: by providing you the means to recognize what is going wrong in your experiment you may be able to discern the root cause, then remedy the problem and salvage your data.
It's a lot like plane spotting, only less fun.
The psychologists amongst you would be able to lecture me for days on category learning. Well, that's what this is. Except that before we can differentiate between one artifact and another we must first understand how EPI is designed to work in an ideal situation. (You don't need to know how planes fly to categorize them, apparently.) And to comprehend artifacts it's important for you to have a reasonable appreciation of the underlying physics, especially the concept of k-space. Here's the loose plan:
