So the next thing we will need to do after normalization is spacial filtering.
So in fMRI is common to spatially smooth
the acquired data prior to statistical analysis.
This can increase the signal to noise ration, but it can also
validate certain distributional assumptions and remove artifacts.
So for example, we often assume that data is normally distributed, and so
by smoothing we can increase that likelihood by averaging over lots of
the different voxels.
So what are the pros and cons of spatial filtering?
Well, one of the pros is that may overcome limitations in the normalization by
blurring and residual anatomical differences.
So the normalization procedure is not always perfect.
And so sometimes a little bit of smoothing is needed in order to sort of blur
differences between, in anatomical differences between subjects.
Also, the pro is that it can increase the signal-to-noise ratio.
So, if you have coherent region of activation, it's actually beneficial to
average over that region because you keep the same signal but
you decrease the noise, and thus you get higher signal to noise ration.
Again as we said, it may increase the validity of statistical analysis, and then
finally it's also required for Gaussian random field theory, which is often used
in multiple comparison, and which we'll talk about later in the course.
So what are some cons?
Well again we reduce the image resolution by spatially smoothing, and
so we lose information in space.