No volumetric outputs when running XCP-D on HCP-YA data

Software Support
1

Summary of what happened:

Hi,

I am running XCP-D on HCP-YA data, and am unable to get volumetric outputs (in the MNI152NLin6Asym space) when I do so - there are only outputs in the fsLR space. We would like to get volumetric outputs for our particular analyses. Any help would be appreciated! Thank you.

Command used (and if a helper script was used, a link to the helper script or the command generated):

singularity run \

--cleanenv \

-B /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/:/<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/ \

-B /<project_dir>/HCP_Data_Releases/HCP_1200/:<project_dir>/HCP_Data_Releases/HCP_1200/ \

/<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/xcpd-0.7.1rc6.simg \

/<project_dir>/HCP_Data_Releases/HCP_1200/ \

/<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/Protocols/XCP/ \

participant \

--participant_label ${sub} \

--input-type hcp \

--bids-filter-file /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/bids_filter.json \

-p gsr_only \

-w <my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/work \

--motion-filter-type lp \

--band-stop-min 6 \

--fd-thresh 0 \

--head-radius auto \

--dummy-scans auto \

--min-coverage 0.5 \

--smoothing 0 \

--fs-license-file /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/license.txt

BIDS_filter.json (previously, I tried to add “space”:”MNI152NLin6Asym” to this file but I would get the error: FileNotFoundError: No BOLD data found in allowed spaces (MNI152NLin6Asym):

{

    "bold": {

        "task":"rest",

        "run" : "01",

        "direction" : "LR"

    }

}

Note that in the log file I also get this line: "With input_type hcp, cifti processing (--cifti) will be enabled automatically."

Additionally, here are directory trees in case that would be helpful:

Tree for one subject in the BIDS derivative folder in my working directory (pathway: /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/work/dset_bids/derivatives/hcp) (removed extra tasks to make it shorter)

├── anat

│   ├── sub-100206_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.txt

│   ├── sub-100206_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.txt

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_pial.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_smoothwm.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_pial.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_smoothwm.surf.gii

│   ├── sub-100206_space-fsLR_den-91k_curv.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_desc-corrected_thickness.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_desc-smoothed_myelinw.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_myelinw.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_sulc.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_thickness.dscalar.nii

│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-aparcaseg_dseg.nii.gz

│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-brain_mask.nii.gz

│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-preproc_T1w.nii.gz

│   └── sub-100206_space-MNI152NLin6Asym_res-2_desc-ribbon_T1w.nii.gz

├── func

│   ├── sub-100206_task-rest_dir-LR_run-1_desc-confounds_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_desc-confounds_timeseries.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_bold.dtseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_bold.dtseries.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_boldref.nii.gz

│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz


├── sub-100206_scans.tsv

tree for one subject in the output directory (pathway: /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/Protocols/XCP/sub-100206) (shortened to show only a few parcellatons)

├── anat

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_desc-hcp_inflated.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_desc-hcp_midthickness.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_desc-hcp_vinflated.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_pial.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-L_smoothwm.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_desc-hcp_inflated.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_desc-hcp_midthickness.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_desc-hcp_vinflated.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_pial.surf.gii

│   ├── sub-100206_space-fsLR_den-32k_hemi-R_smoothwm.surf.gii

│   ├── sub-100206_space-fsLR_den-91k_curv.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_desc-corrected_thickness.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_desc-smoothed_myelinw.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_myelinw.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_sulc.dscalar.nii

│   ├── sub-100206_space-fsLR_den-91k_thickness.dscalar.nii

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-curv_morph.tsv

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-myelin_morph.tsv

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-myelinSmoothed_morph.tsv

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-sulc_morph.tsv

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-thicknessCorrected_morph.tsv

│   ├── sub-100206_space-fsLR_seg-4S1056Parcels_stat-mean_desc-thickness_morph.tsv

│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-aparcaseg_dseg.nii.gz

│   └── sub-100206_space-MNI152NLin6Asym_res-2_desc-preproc_T1w.nii.gz

├── func

│   ├── sub-100206_task-rest_dir-LR_run-1_desc-dcan_qc.hdf5

│   ├── sub-100206_task-rest_dir-LR_run-1_desc-filtered_motion.json

│   ├── sub-100206_task-rest_dir-LR_run-1_desc-filtered_motion.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_design.json

│   ├── sub-100206_task-rest_dir-LR_run-1_design.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_desc-denoised_bold.dtseries.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_desc-denoised_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_desc-linc_qc.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_stat-alff_boldmap.dscalar.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_stat-alff_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_stat-reho_boldmap.dscalar.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_stat-reho_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-coverage_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-coverage_boldmap.pscalar.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-mean_timeseries.ptseries.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-pearsoncorrelation_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_den-91k_stat-pearsoncorrelation_boldmap.pconn.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-alff_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-alff_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-coverage_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-coverage_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-mean_timeseries.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-pearsoncorrelation_relmat.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-pearsoncorrelation_relmat.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-reho_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-4S1056Parcels_stat-reho_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-coverage_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-coverage_boldmap.pscalar.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-mean_timeseries.ptseries.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-pearsoncorrelation_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_den-91k_stat-pearsoncorrelation_boldmap.pconn.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-alff_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-alff_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-coverage_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-coverage_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-mean_timeseries.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-pearsoncorrelation_relmat.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-pearsoncorrelation_relmat.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-reho_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-HCP_stat-reho_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-coverage_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-coverage_boldmap.pscalar.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-mean_timeseries.ptseries.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-pearsoncorrelation_boldmap.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_den-91k_stat-pearsoncorrelation_boldmap.pconn.nii

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-alff_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-alff_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-coverage_bold.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-coverage_bold.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-mean_timeseries.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-mean_timeseries.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-pearsoncorrelation_relmat.json

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-pearsoncorrelation_relmat.tsv

│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-reho_bold.json

│   └── sub-100206_task-rest_dir-LR_run-1_space-fsLR_seg-Tian_stat-reho_bold.tsv

└── log

    └── 20250222-135100_4f1ffa97-7c9b-44d9-929d-1eaad3c3d8d7

        └── xcp_d.toml

Version:

XCP-D version: 0.7.1rc6

Environment (Docker, Singularity / Apptainer, custom installation):

Singularity

Data formatted according to a validatable standard? Please provide the output of the validator:

N/a

Relevant log outputs (up to 20 lines):

N/a

Screenshots / relevant information:

2

Hi @Alisha_Kodibagkar,

Did you try —file-format nifti?

Best,
Steven

3

Hi Steven,

Thanks for your reply! After upgrading the version of XCP-D to 0.10.6 I was able to add --file-format nifti
However, after doing so I am running into this error:

raise FileNotFoundError(f'No {k} file found for {bids_file.path}')
FileNotFoundError: No boldmask file found for /<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/work/dset_bids/derivatives/hcp/sub-100307/func/sub-100307_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz

Within XCP-D, convert_hcp2bids is first used to organize the data from HCP-YA into BIDS, but there is no functional brain mask in the resulting folder.

Here is the BIDS folder tree for one subject (/<my_home_dir>/Preprocessing/XCP-D/HCPYA_fMRI_2025/work/dset_bids/derivatives/hcp/sub-100206) (removed extra tasks to make it shorter):

.
├── anat
│   ├── sub-100206_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.txt
│   ├── sub-100206_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.txt
│   ├── sub-100206_space-fsLR_den-32k_hemi-L_pial.surf.gii
│   ├── sub-100206_space-fsLR_den-32k_hemi-L_smoothwm.surf.gii
│   ├── sub-100206_space-fsLR_den-32k_hemi-R_pial.surf.gii
│   ├── sub-100206_space-fsLR_den-32k_hemi-R_smoothwm.surf.gii
│   ├── sub-100206_space-fsLR_den-91k_curv.dscalar.nii
│   ├── sub-100206_space-fsLR_den-91k_desc-corrected_thickness.dscalar.nii
│   ├── sub-100206_space-fsLR_den-91k_desc-smoothed_myelinw.dscalar.nii
│   ├── sub-100206_space-fsLR_den-91k_myelinw.dscalar.nii
│   ├── sub-100206_space-fsLR_den-91k_sulc.dscalar.nii
│   ├── sub-100206_space-fsLR_den-91k_thickness.dscalar.nii
│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-brain_mask.nii.gz
│   ├── sub-100206_space-MNI152NLin6Asym_res-2_desc-preproc_T1w.nii.gz
│   └── sub-100206_space-MNI152NLin6Asym_res-2_desc-ribbon_T1w.nii.gz
├── func
│   ├── sub-100206_task-rest_dir-LR_run-1_desc-confounds_timeseries.json
│   ├── sub-100206_task-rest_dir-LR_run-1_desc-confounds_timeseries.tsv
│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_bold.dtseries.json
│   ├── sub-100206_task-rest_dir-LR_run-1_space-fsLR_den-91k_bold.dtseries.nii
│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_boldref.nii.gz
│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.json
│   ├── sub-100206_task-rest_dir-LR_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz
│   ├── sub-100206_task-rest_dir-LR_run-2_desc-confounds_timeseries.json
│   ├── sub-100206_task-rest_dir-LR_run-2_desc-confounds_timeseries.tsv
│   ├── sub-100206_task-rest_dir-LR_run-2_space-fsLR_den-91k_bold.dtseries.json
│   ├── sub-100206_task-rest_dir-LR_run-2_space-fsLR_den-91k_bold.dtseries.nii
│   ├── sub-100206_task-rest_dir-LR_run-2_space-MNI152NLin6Asym_res-2_boldref.nii.gz
│   ├── sub-100206_task-rest_dir-LR_run-2_space-MNI152NLin6Asym_res-2_desc-preproc_bold.json
│   ├── sub-100206_task-rest_dir-LR_run-2_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz
├── sub-100206_scans.tsv
4

Thanks @Alisha_Kodibagkar. This might be a bug in ingressing volumetric HCP data. @tsalo?

5

Hi @Alisha_Kodibagkar,

I’ve opened a PR to address this at Add nifti brain mask to hcpya ingression by smeisler · Pull Request #1411 · PennLINC/xcp_d · GitHub, hopefully we can fix this soon and update you.

If you want to try this, you can download this file xcp_d/xcp_d/ingression/hcpya.py at fix/hcp_boldmask · PennLINC/xcp_d · GitHub and in your singularity command, mount it to overwrite the version that is in the container (-B /path/to/your/local/hcpya.py:/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/ingression/hcpya.py).

Best,
Steven

2 Likes
6

Hi @Steven,

I used the method you mentioned to mount the version of hcpya.py with the changes from your first 3 commits to this PR (Add nifti brain mask to hcpya ingression by smeisler · Pull Request #1411 · PennLINC/xcp_d · GitHub) adding the NIFTI brain mask to hcpya ingression, and those fixes worked - the brain masks are now in the resulting folder and I was able to get volumetric outputs. Thanks for your help.

2 Likes
7

Great to hear, @Alisha_Kodibagkar ! Thank you for bringing our attention to the bug!