indicating the image where we had an artifact.
And that sort of uses one degree of freedom to kind of mop up the variation
due to that spike, and that's a way that we often analyze data in practice.
So physiological noise such as respiration and
heart rate, again as we talked about earlier,
give rise to periodic noise, which is often aliased into the task frequencies.
And it can potentially be modeled if the temporal resolution of the study is high
enough, but if the TR is too low, there's always going to be problems with aliasing.
And so, again, according to the Nyquist criterion, the sampling rating must be at
least twice as big as the frequency of the curve that we seek to model.
So, for these reasons, this type of noise is often difficult to remove,
and is often left in the data, giving rise to temporal autocorrelations.
However, there are ways to sort of monitor physiological artifacts and
thereafter remove them from, include them in your model.
So, there's two main ways of modelling this,
and this includes RETROICOR and RVHRCOR.
And so they do it in slightly different ways,
taking into consideration factors such as neuronal activation,
respiration cycle, the cardiac cycle, respiration volume, and heart rate.
Here's a slide showing differences in activation maps
when you use no RETROICOR and when you use RETROICOR.
Here we see that there's more activation when using RETROICOR in areas
that we expect to be active during this particular
task >> Head movement
presents one of the biggest challenges in the analysis and correction of artifacts.
What you're seeing here is the head movement parameter
estimates from the realignment from one person.
And as you can see, everybody moves their head, some people more than others,
sometimes more than others.
And often people will exclude participants who move their head more than a certain
amount, like more than one millimeter, for example, within a run.
But this can also present its own challenges.