[color=#000000]Hi Alfonso,[/color]
[color=#000000]One last question for the time being (I hope). I am planning to use the default CONN ROI atlas, at least for the time being. I know that in the "regular" CONN processing pipeline, functional images are resampled to 2mm MNI space (91 x 109 x 91). The MNI152NLin2009cAsym space from fMRIPrep is 97 x 115 x 97. Do I need to be outputting fMRIPrep in MNI152NLin6Asym (91 x 109 x 91) for importing to CONN if the only preprocessing step is smoothing?[/color]
[color=#000000]Also, if I have both spaces, how will CONN choose which to use?[/color]
[i]Originally posted by Alfonso Nieto-Castanon:[/i][quote][color=#000000]Hi Adam,[/color]
[color=#000000]Mainly for consistency with other analyses in CONN, when importing data from fMRIPrep CONN will use the white/CSF segmentation masks computed in fMRIPrep and it will re-compute the aCompCor signals from these areas (instead of using the fMRIPrep [i]a_comp_cor[/i] parameters). Those re-computed aCompCor signals will be the ones used by default during CONN's denoising step (corresponding with the 'White' and 'CSF' elements in the 'Confounds' list in the [i]Denoising [/i]tab). Those aCompCor parametes will be slightly different than the ones estimated by fMRIPrep since both packages use slightly different definitions (CONN will threhsold and erode the white&csf masks, and compute a PCA decomposition after orthogonalization with other other known temporal effects, such as constant and linear session effects, motion parameters, task parameters if applicable, and scrubbing / outlier scans)[/color]
[color=#000000]Just for reference, f you would prefer to use fMRIPrep [i]a_comp_cor[/i] timeseries during denoising instead of CONN's estimated timeseries, those will still be available/imported to your project as a separate first-level covariate named [i]QC_a_comp_cor[/i] (so simply remove the 'White' and 'CSF' elements from the Confounds list, and enter your desired aCompCor term instead)[/color]
[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Adam Raikes:[/i][quote]Hi Alfonso,
That patch worked well.
One further question. For the aCompCor decompositions from fMRIPrep, how is CONN importing the regressors? I ask because I know they changed the setup recently for the confounds to where all aCompCor regressors are "a_comp_cor_Xxx" and the classification (WM/CSF/combined) is written into a JSON file. Before I go through denoising, I want to make sure that it's pulling the right confounds.[/quote][/quote]
[color=#000000]One last question for the time being (I hope). I am planning to use the default CONN ROI atlas, at least for the time being. I know that in the "regular" CONN processing pipeline, functional images are resampled to 2mm MNI space (91 x 109 x 91). The MNI152NLin2009cAsym space from fMRIPrep is 97 x 115 x 97. Do I need to be outputting fMRIPrep in MNI152NLin6Asym (91 x 109 x 91) for importing to CONN if the only preprocessing step is smoothing?[/color]
[color=#000000]Also, if I have both spaces, how will CONN choose which to use?[/color]
[i]Originally posted by Alfonso Nieto-Castanon:[/i][quote][color=#000000]Hi Adam,[/color]
[color=#000000]Mainly for consistency with other analyses in CONN, when importing data from fMRIPrep CONN will use the white/CSF segmentation masks computed in fMRIPrep and it will re-compute the aCompCor signals from these areas (instead of using the fMRIPrep [i]a_comp_cor[/i] parameters). Those re-computed aCompCor signals will be the ones used by default during CONN's denoising step (corresponding with the 'White' and 'CSF' elements in the 'Confounds' list in the [i]Denoising [/i]tab). Those aCompCor parametes will be slightly different than the ones estimated by fMRIPrep since both packages use slightly different definitions (CONN will threhsold and erode the white&csf masks, and compute a PCA decomposition after orthogonalization with other other known temporal effects, such as constant and linear session effects, motion parameters, task parameters if applicable, and scrubbing / outlier scans)[/color]
[color=#000000]Just for reference, f you would prefer to use fMRIPrep [i]a_comp_cor[/i] timeseries during denoising instead of CONN's estimated timeseries, those will still be available/imported to your project as a separate first-level covariate named [i]QC_a_comp_cor[/i] (so simply remove the 'White' and 'CSF' elements from the Confounds list, and enter your desired aCompCor term instead)[/color]
[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Adam Raikes:[/i][quote]Hi Alfonso,
That patch worked well.
One further question. For the aCompCor decompositions from fMRIPrep, how is CONN importing the regressors? I ask because I know they changed the setup recently for the confounds to where all aCompCor regressors are "a_comp_cor_Xxx" and the classification (WM/CSF/combined) is written into a JSON file. Before I go through denoising, I want to make sure that it's pulling the right confounds.[/quote][/quote]