Update DERIVATIVES.md

source/DERIVATIVES.md

@@ -13,7 +13,10 @@ The outputs of fMRIprep can be found under the folder of each dataset (e.g. `mov
   - `_boldref.nii.gz` : a BOLD single volume reference.
   - `_*-brain_mask.nii.gz` : the brain mask in fMRI space.
   - `_*-preproc_bold.nii.gz` : the preprocessed BOLD timeseries.
-  - `_*-confounds_regressors.tsv` : a tabular tsv file, containing a large set of confounds to use in analysis steps (eg. GLM). Note that regressors are likely correlated, thus it is recommended to use a subset of these regressors. Also note that preprocessed time series have not been corrected for any confounds, but simply realigned in space, and it is therefore critical to regress some of the available confounds prior to analysis. For python users, we recommend using [nilearn](https://nilearn.github.io) and the tool [load_confounds](https://github.com/SIMEXP/load_confounds) to load confounds from the fMRIprep outputs, using with the `Params24` strategy. In particular, as the NeuroMod data consistently exhibits low levels of motion, we recommend against removing time points with excessive motion (aka scrubbing).
+  - `_*-confounds_regressors.tsv` : a tabular tsv file, containing a large set of confounds to use in analysis steps (eg. GLM). 
+
+### Recommended preprocessing strategy
+The confounding regressors are correlated, thus it is recommended to use a subset of these regressors. Also note that preprocessed time series have not been corrected for any confounds, but simply realigned in space, and it is therefore critical to regress some of the available confounds prior to analysis. For python users, we recommend using [nilearn](https://nilearn.github.io) and the tool [load_confounds](https://github.com/SIMEXP/load_confounds) to load confounds from the fMRIprep outputs, using with the `Params24` strategy. As the NeuroMod data consistently exhibits low levels of motion, we recommend against removing time points with excessive motion (aka scrubbing). Because of the 2 mm spatial resolution of the fMRI scan, there is substantial impact of thermal noise, and some amount of spatial smoothing is advisable. Our preliminary analyses suggest thah `smoothing_fwhm=8` in nilearn nifti maskers to work well. 
 
 ### Pipeline description
 The following boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts *unchanged*. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license. All references in the text link to a `.bib` file with detailed reference list, ready to be incorporated in a `LaTeX` document.