Hello,

I’m trying to perform an ROI-to-ROI analysis with CONN batch, but I can’t find a clear answer on exactly which dataset the software uses for the analysis.

For reproducibility, our lab recently switched from the default CONN preprocessing pipeline to a custom one. As a result, I now provide CONN with fully preprocessed images directly.

From what I understand, the ROI analysis should use the images specified in the secondary dataset, in my case, the unsmoothed scans.

To better understand CONN’s processing, I also tried to compute the correlation matrices outside of CONN, but my results didn’t match. Could you clarify:
- Which exact images are used for the ROI-to-ROI step (extraction of voxel's activation corresponding to voxels in the ROI mask)?
- What transformations are applied internally before correlations are computed?

Here’s what I currently believe happens during ROI-to-ROI analysis:
1. Extract voxel values from ROIs (using masks) on the unsmoothed images.
2. Compute the mean signal within each ROI.
3. Calculate the correlation between ROI time series.
4. Apply Fisher’s z-transform (arctanh).

Is there anything I’m missing? I’ve attached the script I used for reference.

Additionally, I noticed that the denoising results obtained using our pipeline differ from those produced when denoising in CONN (even when using the same parameters). Do you know why this might happen?

Is it possible to provide only the essential files, such as functional images, CSF, white matter masks, outlier files, and motion parameters (and not anatomical which will not be used as the preprocessing is aleady done), directly to the batch without triggering the segmentation step? (for testing on old projects I want to download the essential files and not the entire dataset)

Thank you in advance.

Best regards,
Sarah

The denoising used in our denoising pipeline:

```
wm_mask = fullfile(anat_dir, dir(fullfile(anat_dir,'wc2sub-*.nii')).name);
csf_mask = fullfile(anat_dir, dir(fullfile(anat_dir,'wc3sub-*.nii')).name);

conn_module('preprocessing',...
             'functionals',   {smoothed_imgs}, ...
             'covariates',    struct(...
                 'names',     {{'realignment','scrubbing'}},...
                 'files',     {{motion_file, outlier_file}}),...
             'masks',         struct(...
                 'White',     {{wm_mask}},...
                 'CSF',       {{csf_mask}}), ...
             'steps',         {'functional_regression', 'functional_bandpass'}, ...
             'reg_names',     {'realignment','scrubbing','White Matter','CSF'}, ...
             'reg_dimensions',[inf, inf, 5, 5], ...
             'reg_deriv',     [1, 0, 0, 0], ...
             'bp_filter',     [0.008 0.09] )
```