“Third, XCP-D outputs processed BOLD data, including denoised unsmoothed and smoothed timeseries in MNI2009 and fsLR32k spaces, parcellated time series, functional connectivity matrices, and ALFF and ReHo (smoothed and unsmoothed).”
I do have both denoised_bold.nii.gz and denoisedSmoothed_bold.nii.gz in my outputs, but I can find only one tipe of timeseries.tsv for each parcellation (e.g. sub-…-_atlas-Schaefer1017_timeseries.tsv). Are these timeserieses calculated from the smoothed or from the unsmoothed images?
The parcellated time series come from the unsmoothed images. We wouldn’t recommend parcellating the smoothed data.
On a related note, at the moment, both the denoised data and the parcellated time series contain interpolated data. Starting with the next release, these files will only contain the censored (i.e., low-motion) volumes.
Thank you for your quick answer. May I ask you why you would not recommend parcellating the smoothed data? Isn’t it common practice to smooth the bold images before extracting the regional (parcellation) timeseries?
Thanks a lot.
Smoothing improves SNR in a given voxel by blurring the signal across voxels, but when you are averaging the signal in an ROI, there’s no reason to do that, since you’re not interested in individual voxels. Smoothing before parcellating could, at worst, blur signals from voxels outside the region into the region.
It is the "Schaefer2018_100Parcels_17Networks_order.lut. You can tell by looking at the order of the names in the connectome TSV output. In addition, the citation does not include the Kong 2022 update.
Thank you so much for the quick response! I checked the connectome TSV file (i.e., sub-…_atlas-Schaefer117_measure-pearsoncorrelation_conmat.tsv), but it is a pure 100*100 digital matrix without any ROI information.
Is this the connectome file you refer to?
Where I can find the atlas XCD-P that applied to the imaging data?