There are many opinions about how to process
resting state data. But if you have physiological
recordings, then it would be good to incorporate
The main difference between examples 5c and 11
is that 5c uses bandpassing, while 11 uses tissue
based regression. Either way is reasonable and
defensible. I personally do not like band passing,
but that is up to you.
Here is some of my more formal whining about
the cost of band passing:
One aspect of band passing that may be commonly ignored is its cost in
terms of degrees of freedom (DoF). Projecting out frequencies can be
viewed in terms of a linear regression with sines and cosines at the
relevant frequencies to project out. So the question becomes, how many
Consider 3 examples, each having 1000 s of data, but at varying
TRs of 2 s (500 time points), 1 s (1000 time points) and 0.1 s
(10000 time points). Focusing only on the low-pass side, assume
the common pass band of 0.1 Hz, or one cycle every 10 s.
At TR=2s, 40% of the DoF remain, because the Nyquist frequency
is 0.25, and 0.1/0.25 = 0.4. So out of 500 time points, 200 remain
and 300 are lost. At TR=1s, only 20% of the DoF remain, as 0.1/0.5
= 0.2. So out of 1000 time points, 200 remain and 800 are lost. And
at TR=0.1s, only 2% of the DoF remain, as 0.1/5 = 0.02. So out of
10000 time points, 200 remain and 9800 are lost.
This demonstrates that when band passing below 0.1, the effective
TR becomes 5 s, leaving only 200 time points for 1000 s of data,
regardless of the scanning TR. At a very fast TR such as 0.1 s, the
cost is staggering. Even at TR=2 s, using 200 regressors to model
one aspect of noise in a 500 time point model is very expensive.
That band passing can be done in a separate step using a Fourier
Transform does not change the cost, it just makes it harder to see.