Hello,
I plan to use Block Regression for denoising resting-state fMRI, specifically regressing out motion confounders within each sliding window, as recommended and performed in the following references:
https://doi.org/10.1016/j.neuroimage.2018.09.024
I’m seeking advice on how to handle filtering and detrending in this context. On one hand, filtering before windowing and regression may risk reintroducing filtered noise. On the other hand, filtering a small sample of signal within each window might lead to inaccurate results.
I would appreciate any insights you can provide.