FileNotFoundError: No BOLD data found in allowed spaces (fsLR)

Hello,

Summary of what happened:

I’m running into an issue with XCP-D while trying to process ABCD data downloaded via Datalad.

Setup:

  • Data: ABCD dataset, downloaded with Datalad.
  • Container: Singularity container for XCP-D.
  • Version: XCP-D v0.10.0rc3

Problem:

  1. When I specify the arguments that seem appropriate for the data (i.e., setting -mode 'abcd' and -input-type fmriprep) XCP-D throws an error that it cannot locate the MNI infant template. This doesn’t make sense, as my data is in fsLR space (it appears to look for the MNI infant template by default, whether I include -mode 'abcd' or -mode 'none')
  2. To get around this, I tried using -input-type hcp, even though the data is from ABCD. This bypassed the MNI infant template error, but it led to another issue:
    • I now get an error message saying XCP-D “cannot find fsLR files,” even though the fsLR files are definitely present. The file permissions and paths look correct, and I verified that it’s a CIFTI file in fsLR 91k density format. I’m afraid that using the wrong input type (hcp instead of fmriprep) may be contributing to this error.
    • I have also tried specifying a bids-filter-file, but this yields the same error

Is there a better workaround than using --input-type hcp? Am I missing something else in the setup? Any insights on how to get XCP-D to recognize the fsLR files would be greatly appreciated.

One of the latest commands I’ve tried (for a single subject), though I’ve tested various iterations including different combinations of the arguments, without success:

singularity run -B /data/pt_02667/data/ABCD:/home/xcp_data \
--home /home/xcp \
--cleanenv /data/u_serio_software/xcp_d-0.10.0rc3.simg \
/home/xcp_data/ABCD_fMRIprep/fmriprep \
/home/xcp_data/XCP-D_output \
participant \
--mode 'none' \
--participant-label 'NDARINV00CY2MDM' \
--bids-filter-file /home/xcp_data/bianca/bids_filter_file.json \
--datasets custom=/home/xcp_data/bianca \
--nprocs 36 \
--input-type 'hcp' \
--file-format 'cifti' \
--dummy-scans 'auto' \
--despike 'y' \
--nuisance-regressors /home/xcp_data/bianca/custom_confounds_24P_csf_wm.yaml \
--fd-thresh 0.3 \
--output-type 'censored' \
--combine-runs 'n' \
--smoothing 6 \
--motion-filter-type 'none' \
--head-radius 50 \
--lower-bpf 0.01 \
--upper-bpf 0.08 \
--bpf-order 2 \
--min-time 240 \
--atlases '4S456Parcels' \
--min-coverage 0.5 \
--create-matrices 300 \
--work-dir /data/pt_02667/data/ABCD/ \
--warp-surfaces-native2std 'y' \
--abcc-qc 'n' \
--linc-qc 'n'

Associated error:

2024-11-14 14:00:59,044 [IMPORTANT] Running XCP-D version 0.10.0rc3
241114-14:00:59,53 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/hcp.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-140047_2feb386d-1d56-4d64-983e-a1971d5bd1d2.
           * Searching for derivatives and atlases: {'custom': PosixPath('/home/xcp_data/bianca'), 'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-14 14:00:59,053 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/hcp.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-140047_2feb386d-1d56-4d64-983e-a1971d5bd1d2.
           * Searching for derivatives and atlases: {'custom': PosixPath('/home/xcp_data/bianca'), 'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 127, in init_single_subject_wf
    subj_data = collect_data(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/utils/bids.py", line 212, in collect_data
    raise FileNotFoundError(
FileNotFoundError: No BOLD data found in allowed spaces (fsLR).

Query: {'datatype': 'func', 'desc': ['preproc', None], 'suffix': 'bold', 'extension': '.dtseries.nii', 'session': ['baselineYear1Arm1'], 'task': ['rest'], 'space': 'fsLR'}

Found files:


Thanks a lot in advance,

Bianca


Hi @biancaserio, and welcome to neurostars!

Which ABCD inputs are you using? Can you provide a tree directory structure? XCP_D expects ABCD-BIDS (GitHub - DCAN-Labs/abcd-hcp-pipeline: bids application for processing functional MRI data, robust to scanner, acquisition and age variability.), which is associated with --input-type dcan (see here: Processing Pipeline Details — xcp_d 0.10.0rc4.dev5+g7694faa documentation).

Best,
Steven

Hi @Steven ,

Thank you so much for your quick reply!

I’m using the ABCD-BIDS dataset (fmriprep output) downloaded through Datalad.

I have tried running XCP-D with --input-type dcan along with --mode abcd, but encountered the same issue.

My tree directory structure is the following (truncated, for one subject):

├── CHANGELOG.md
├── code
│   ├── ABCD_fmriprep.jobs
│   ├── ABCD_postpro_fp.sh
│   ├── call_fmriprep.sh
│   ├── createjobs_fMRIprep.sh
│   ├── merge.txt
│   ├── participant_job
│   ├── pipeline
│   ├── process.sbatch
│   ├── process.submit
│   ├── README.md
│   └── run_fMRIprep.sh
├── fmriprep
│   ├── dataset_description.json
│   ├── sub-NDARINV003RTV85
│   │   ├── figures
│   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_desc-about_T1w.html -> ../.git/annex/objects/G6/vV/MD5E-s615--3b75213658c89e66fad3eb1ab928cb6e.html/MD5E-s615--3b75213658c89e66fad3eb1ab928cb6e.html
│   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_desc-summary_T1w.html -> ../.git/annex/objects/qf/5w/MD5E-s410--8be2c99c83efc29c08aa804fc14f67b5.html/MD5E-s410--8be2c99c83efc29c08aa804fc14f67b5.html
│   │   │   ├
│   │   ├── log
│   │   │   └── 20231014-154012_26f173a8-9a18-4d1f-ac77-362733c0553f
│   │   │       └── fmriprep.toml -> ../../.git/annex/objects/7J/M1/MD5E-s2392--1d846302d524ddc3bbf7ad6d3e9f388f.toml/MD5E-s2392--1d846302d524ddc3bbf7ad6d3e9f388f.toml
│   │   ├── ses-baselineYear1Arm1
│   │   │   ├── anat
│   │   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_rec-normalized_desc-aparcaseg_dseg.nii.gz -> ../../.git/annex/objects/VP/K6/MD5E-s269075--413204405a1601772e36e08262fd3caf.nii.gz/MD5E-s269075--413204405a1601772e36e08262fd3caf.nii.gz
│   │   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_rec-normalized_desc-aseg_dseg.nii.gz -> ../../.git/annex/objects/15/Wg/MD5E-s222498--a984f511d38f93ebb4946ebaec1951ba.nii.gz/MD5E-s222498--a984f511d38f93ebb4946ebaec1951ba.nii.gz
│   │   │   │   
│   │   │   ├── fmap
│   │   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_fmapid-auto00000_desc-coeff_fieldmap.nii.gz -> ../../.git/annex/objects/Zv/Xw/MD5E-s282851--949929347b61d42d9f67582d96974047.nii.gz/MD5E-s282851--949929347b61d42d9f67582d96974047.nii.gz
│   │   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_fmapid-auto00000_desc-epi_fieldmap.nii.gz -> ../../.git/annex/objects/PK/zq/MD5E-s265601--2dba585c191bb29c5ec4a54f54e72e5f.nii.gz/MD5E-s265601--2dba585c191bb29c5ec4a54f54e72e5f.nii.gz
│   │   │   │   ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_fmapid-auto00000_desc-preproc_fieldmap.json -> ../../.git/annex/objects/W8/m6/MD5E-s3302--aa65c5655515fac592e74bfb20c0c512.json/MD5E-s3302--aa65c5655515fac592e74bfb20c0c512.json
│   │   │   │   └── sub-NDARINV003RTV85_ses-baselineYear1Arm1_fmapid-auto00000_desc-preproc_fieldmap.nii.gz -> ../../.git/annex/objects/4q/vx/MD5E-s1796139--254a4e67dd31ba4579a91744006859e6.nii.gz/MD5E-s1796139--254a4e67dd31ba4579a91744006859e6.nii.gz
│   │   │   └── func
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_AROMAnoiseICs.csv -> ../../.git/annex/objects/1z/5Z/MD5E-s83--9fc80d45d96da1336ff0c30e93f0c149.csv/MD5E-s83--9fc80d45d96da1336ff0c30e93f0c149.csv
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_boldref.nii.gz -> ../../.git/annex/objects/5V/56/MD5E-s806576--3e2243d694a292b4eca020f675f7b8af.nii.gz/MD5E-s806576--3e2243d694a292b4eca020f675f7b8af.nii.gz
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-brain_mask.json -> ../../.git/annex/objects/JZ/G5/MD5E-s149--412be36f4368bd77ca1859cf916a401d.json/MD5E-s149--412be36f4368bd77ca1859cf916a401d.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-brain_mask.nii.gz -> ../../.git/annex/objects/pJ/Gz/MD5E-s4860--f3e93a21ce810ebffb711ca03de9b5c9.nii.gz/MD5E-s4860--f3e93a21ce810ebffb711ca03de9b5c9.nii.gz
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.json -> ../../.git/annex/objects/MK/XK/MD5E-s166991--9b507bac28020efa54a7b9b180c8ede4.json/MD5E-s166991--9b507bac28020efa54a7b9b180c8ede4.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv -> ../../.git/annex/objects/Xp/8K/MD5E-s1748143--1e796b762830795e015a7478f3f87229.tsv/MD5E-s1748143--1e796b762830795e015a7478f3f87229.tsv
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-MELODIC_mixing.tsv -> ../../.git/annex/objects/1k/qk/MD5E-s996172--3d1ad25ffb144e0a9a1b6819f403380c.tsv/MD5E-s996172--3d1ad25ffb144e0a9a1b6819f403380c.tsv
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-preproc_bold.json -> ../../.git/annex/objects/kW/Qf/MD5E-s129--7cf9e7cc80f35ac27c1f8af9bf72f7a9.json/MD5E-s129--7cf9e7cc80f35ac27c1f8af9bf72f7a9.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_desc-preproc_bold.nii.gz -> ../../.git/annex/objects/XV/ZQ/MD5E-s191661045--94978b06eeb17fd47025e85ab920fcdb.nii.gz/MD5E-s191661045--94978b06eeb17fd47025e85ab920fcdb.nii.gz
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_from-scanner_to-boldref_mode-image_xfm.txt -> ../../.git/annex/objects/zw/Xm/MD5E-s84792--62674076a6216538fdb147788ae60bd8.txt/MD5E-s84792--62674076a6216538fdb147788ae60bd8.txt
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_from-scanner_to-T1w_mode-image_xfm.txt -> ../../.git/annex/objects/wp/J4/MD5E-s239--71b3675aa64b0e266c9e2c0c005ed317.txt/MD5E-s239--71b3675aa64b0e266c9e2c0c005ed317.txt
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_from-T1w_to-scanner_mode-image_xfm.txt -> ../../.git/annex/objects/Z2/4z/MD5E-s241--dbafab39479723a6d5e757860c40e249.txt/MD5E-s241--dbafab39479723a6d5e757860c40e249.txt
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_hemi-L_space-fsaverage5_bold.func.gii -> ../../.git/annex/objects/wm/3z/MD5E-s18516420--9ec54b7a7b75370cde41d6cbc4f4e853.func.gii/MD5E-s18516420--9ec54b7a7b75370cde41d6cbc4f4e853.func.gii
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_hemi-L_space-fsaverage5_bold.json -> ../../.git/annex/objects/Kz/zq/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_hemi-R_space-fsaverage5_bold.func.gii -> ../../.git/annex/objects/wj/Q9/MD5E-s18519501--c86660dbb260b67f69b5a70263f6e68d.func.gii/MD5E-s18519501--c86660dbb260b67f69b5a70263f6e68d.func.gii
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_hemi-R_space-fsaverage5_bold.json -> ../../.git/annex/objects/Kz/zq/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii -> ../../.git/annex/objects/7x/qw/MD5E-s70551468--522f907ea83feeda181fb24e57ad4e02.nii/MD5E-s70551468--522f907ea83feeda181fb24e57ad4e02.nii
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.json -> ../../.git/annex/objects/92/z8/MD5E-s680--53ad38974dd5743e43f58f7247ed3e1b.json/MD5E-s680--53ad38974dd5743e43f58f7247ed3e1b.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.json -> ../../.git/annex/objects/Kz/zq/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json/MD5E-s103--67efbad07564ed9a40f79c9740a1dc9e.json
│   │   │       ├── sub-NDARINV003RTV85_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gz -> ../../.git/annex/objects/x6/0W/MD5E-s469118178--894d66006d91d029d292e391fe7b8b4a.nii.gz/MD5E-s469118178--894d66006d91d029d292e391fe7b8b4a.nii.gz
│   │   │      
│   │   └── sub-NDARINV003RTV85.html -> .git/annex/objects/fg/qv/MD5E-s127764--bae25b9d4f9ddb5bcd804dce1c85b8ac.html/MD5E-s127764--bae25b9d4f9ddb5bcd804dce1c85b8ac.html
│   ├── sub-NDARINV003RTV85.html -> ../.git/annex/objects/fg/qv/MD5E-s127764--bae25b9d4f9ddb5bcd804dce1c85b8ac.html/MD5E-s127764--bae25b9d4f9ddb5bcd804dce1c85b8ac.html

Thank you in advance for your time,

Bianca

Hi @biancaserio,

Have you used datalad get to download these files, or are they still empty symlinks?

Best,
Steven

Hi @Steven ,

I did use datalad get on the single subject that I flagged in my command (--participant-label 'NDARINV00CY2MDM' ) and additionally checked that the data was indeed there and accessible.

I also tried copying one of the relevant .dtseries.nii files to a location outside the Git-annex structure (using cp -L ) and rerunning the command (binding to that new location) in order to check if it might have been the symlink causing the issue, but I still get the same FileNotFoundError: No BOLD data found in allowed spaces (fsLR) doing this.

Best,

Bianca

Hi @biancaserio,

It looks like XCP_D is looking here based on the error message, which does not seem to match with the input_dir. What if you leave this out, or add the path to your abcd-bids fmriprep folder to that argument?

Best,
Steven

Hi @Steven ,

Thanks for that.

The reason why I included custom is that I thought this would be necessary to specify the location of the custom_confounds_24P_csf_wm.yaml and bids_filter_file.json. Apparently this is not the case:

I tried removing it and I still get the error

2024-11-14 16:55:27,034 [IMPORTANT] Running XCP-D version 0.10.0rc3
241114-16:55:27,61 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-165458_64cfba4b-50ba-4c2b-b408-a2df475a9789.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-14 16:55:27,061 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-165458_64cfba4b-50ba-4c2b-b408-a2df475a9789.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 127, in init_single_subject_wf
    subj_data = collect_data(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/utils/bids.py", line 212, in collect_data
    raise FileNotFoundError(
FileNotFoundError: No BOLD data found in allowed spaces (fsLR).

Query: {'datatype': 'func', 'desc': ['preproc', None], 'suffix': 'bold', 'extension': '.dtseries.nii', 'session': ['baselineYear1Arm1'], 'task': ['rest'], 'space': 'fsLR'}

Found files:

You have however drawn my attention to the line Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan. I saw that it contained a folder for the subject /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func which was however empty. I tried to cp -L a dtseries.nii file in fsLR space that I would like XCP-D to pick up in this path and interestingly, I get the error No BOLD NIfTI or transforms found to allowed space (MNIInfant) again:

	 Running XCP-D version 0.10.0rc3
2024-11-14 17:06:54,109 [IMPORTANT] Running XCP-D version 0.10.0rc3
241114-17:06:54,144 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-170626_b27350d7-5128-4ee9-9bf9-612373f25887.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-14 17:06:54,144 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-170626_b27350d7-5128-4ee9-9bf9-612373f25887.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 127, in init_single_subject_wf
    subj_data = collect_data(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/utils/bids.py", line 243, in collect_data
    raise FileNotFoundError(
FileNotFoundError: No BOLD NIfTI or transforms found to allowed space (MNIInfant)

I have removed the dtseries.nii file from /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func. My understanding is that this file gets created by XCP-D when running it? Its tree structure is the following:

serio@curtis:/data/pt_02667/data/ABCD/dset_bids > tree -L 6
.
└── derivatives
    ├── dcan
    │   ├── dataset_description.json
    │   └── sub-NDARINV00CY2MDM
    │       ├── ses-baselineYear1Arm1
    │       │   ├── anat
    │       │   │   ├── sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.txt
    │       │   │   └── sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.txt
    │       │   └── func
    │       ├── sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_scans.tsv
    │       └── work
    └── hcp
        ├── dataset_description.json
        └── sub-NDARINV00CY2MDM
            ├── anat
            │   ├── sub-NDARINV00CY2MDM_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.txt
            │   └── sub-NDARINV00CY2MDM_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.txt
            ├── func
            ├── sub-NDARINV00CY2MDM_scans.tsv
            └── work

Best,

Bianca

Just for my readability, can you try to update the command so that you do not rename folders when you mount them? The combination of renamed folders and symlinks tend to lead to unnecessary headache. I’ll also loop @tsalo in here.

I have updated the command to

singularity run \
-B /data/pt_02667/data/ABCD:/data/pt_02667/data/ABCD \
--cleanenv /data/u_serio_software/xcp_d-0.10.0rc3.simg \
/data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep \
/data/pt_02667/data/ABCD/XCP-D_output \
participant \
--mode 'abcd' \
--participant-label 'NDARINV00CY2MDM' \
--bids-filter-file /data/pt_02667/data/ABCD/bianca/bids_filter_file.json \
--nprocs 36 \
--input-type 'dcan' \
--file-format 'cifti' \
--dummy-scans 'auto' \
--despike 'y' \
--nuisance-regressors /data/pt_02667/data/ABCD/bianca/custom_confounds_24P_csf_wm.yaml \
--fd-thresh 0.3 \
--output-type 'auto' \
--combine-runs 'n' \
--smoothing 6 \
--motion-filter-type 'none' \
--head-radius 50 \
--lower-bpf 0.01 \
--upper-bpf 0.08 \
--bpf-order 2 \
--min-time 240 \
--atlases '4S456Parcels' \
--min-coverage 0.5 \
--create-matrices 300 \
--work-dir /data/pt_02667/data/ABCD/ \
--warp-surfaces-native2std 'y' \
--abcc-qc 'n' \
--linc-qc 'n'

And get the error:

2024-11-14 17:33:32,510 [IMPORTANT] Running XCP-D version 0.10.0rc3
241114-17:33:32,540 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-173312_8fdabab9-5f32-4323-855d-cef0fff7ad99.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-14 17:33:32,540 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/dset_bids/derivatives/dcan.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-173312_8fdabab9-5f32-4323-855d-cef0fff7ad99.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 127, in init_single_subject_wf
    subj_data = collect_data(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/utils/bids.py", line 212, in collect_data
    raise FileNotFoundError(
FileNotFoundError: No BOLD data found in allowed spaces (fsLR).

Query: {'datatype': 'func', 'desc': ['preproc', None], 'suffix': 'bold', 'extension': '.dtseries.nii', 'session': ['baselineYear1Arm1'], 'task': ['rest'], 'space': 'fsLR'}

Found files:

AFAICT, your data were processed with fMRIPrep, not ABCD-BIDS.

You’re setting your working directory to a parent of your data directory. I’d recommend against that. Maybe change it to /data/pt_02667/data/ABCD/work or something similar.

Hi @tsalo,

Thank you for your reply.

I have changed my working directory to what you have suggested.

Indeed the data was preprocessed using fMRIprep (apologies for the confusion). This is what I posted about in my first message, saying that running the following command (with --input-type 'fmriprep'):

singularity run \
-B /data/pt_02667/data/ABCD:/data/pt_02667/data/ABCD \
--cleanenv /data/u_serio_software/xcp_d-0.10.0rc3.simg \
/data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep \
/data/pt_02667/data/ABCD/XCP-D_output \
participant \
--mode 'abcd' \
--participant-label 'NDARINV00CY2MDM' \
--bids-filter-file /data/pt_02667/data/ABCD/bianca/bids_filter_file.json \
--nprocs 36 \
--input-type 'fmriprep' \
--file-format 'cifti' \
--dummy-scans 'auto' \
--despike 'y' \
--nuisance-regressors /data/pt_02667/data/ABCD/bianca/custom_confounds_24P_csf_wm.yaml \
--fd-thresh 0.3 \
--output-type 'auto' \
--combine-runs 'n' \
--smoothing 6 \
--motion-filter-type 'none' \
--head-radius 50 \
--lower-bpf 0.01 \
--upper-bpf 0.08 \
--bpf-order 2 \
--min-time 240 \
--atlases '4S456Parcels' \
--min-coverage 0.5 \
--create-matrices 300 \
--work-dir /data/pt_02667/data/ABCD/work \
--warp-surfaces-native2std 'y' \
--abcc-qc 'n' \
--linc-qc 'n'

Yields the error message related to the MNI Infant template:

	 Running XCP-D version 0.10.0rc3
2024-11-14 18:31:41,648 [IMPORTANT] Running XCP-D version 0.10.0rc3
241114-18:31:42,193 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-183108_1313f957-96dc-4f49-a366-d962976d7e3a.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-14 18:31:42,193 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241114-183108_1313f957-96dc-4f49-a366-d962976d7e3a.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 127, in init_single_subject_wf
    subj_data = collect_data(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/utils/bids.py", line 243, in collect_data
    raise FileNotFoundError(
FileNotFoundError: No BOLD NIfTI or transforms found to allowed space (MNIInfant)

However, when specifying --input-type 'hcp' or --input-type 'dcan', the error message is: FileNotFoundError: No BOLD data found in allowed spaces (fsLR)

It looks like your fMRIPrep derivatives are missing transforms to and from MNI152NLin6Asym. Here are a few things that XCP-D needs that appear to be missing:

  1. Native anatomical-space T1w or T2w NIfTI (e.g., sub-<label>/anat/sub-<label>_desc-preproc_T1w.nii.gz).
  2. Native anatomical-space brain mask (e.g., sub-<label>/anat/sub-<label>_desc-brain_mask.nii.gz).
    • EDIT: Sorry about that- this needs to be in MNI152NLin6Asym, not native anatomical space.
  3. Transforms between native anatomical space and MNI152NLin6Asym space:
    • sub-<label>/anat/sub-<label>_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5
    • sub-<label>/anat/sub-<label>_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.h5
  4. Preprocessed BOLD data in MNI152NLin6Asym space.
  5. BOLD-reference image in MNI152NLin6Asym space.

Thanks for this.

It seems like I do have the required anat files:

"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-aparcaseg_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-aseg_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-brain_mask.json"@
**"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-brain_mask.nii.gz"@**
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.json"@
**"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.nii.gz"@**
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-fsnative_to-T1w_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5"@
**"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.h5"@**
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-fsnative_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5"@
**"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5"@**
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_curv.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_inflated.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_midthickness.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_pial.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_smoothwm.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_sulc.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_thickness.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_curv.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_inflated.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_midthickness.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_pial.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_smoothwm.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_sulc.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_thickness.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-CSF_probseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-GM_probseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-WM_probseg.nii.gz"@

However in my func file, I seem to be missing Preprocessed BOLD data in MNI152NLin6Asym space and BOLD-reference image in MNI152NLin6Asym space (although I have AROMA output in this space).

"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_AROMAnoiseICs.csv"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_boldref.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-brain_mask.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-brain_mask.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-MELODIC_mixing.tsv"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-preproc_bold.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-preproc_bold.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_from-scanner_to-boldref_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_from-scanner_to-T1w_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_from-T1w_to-scanner_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_hemi-L_space-fsaverage5_bold.func.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_hemi-L_space-fsaverage5_bold.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_hemi-R_space-fsaverage5_bold.func.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_hemi-R_space-fsaverage5_bold.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gz"@

Is there a way around this or would I need to rerun fmriprep to include the missing BOLD files you have mentioned?

The MNI152NLin6Asym BOLD files are just used for figures. You could probably symlink the following:

  • sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gzsub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
  • sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.jsonsub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-preproc_bold.json (this one might not actually be necessary- not sure)

You’ll need a 3D image for the boldref though. You could just grab the first volume of sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gz and name it sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_boldref.nii.gz

Thank you very much for your suggestions. I have created the symlink named as MNI152NLin6Asym BOLD as well as the boldref 3D image.

I have run the XCP-D command with --input-type 'dcan' and still get FileNotFoundError: No BOLD data found in allowed spaces (fsLR).

But I then tried with --input-type 'fmriprep' (which is what would be correct/would make sense to begin with) and this seems to work. Now get the new error FileNotFoundError: No anat_brainmask found with query: {'datatype': 'anat', 'desc': 'brain', 'extension': '.nii.gz', 'suffix': 'mask', 'space': 'MNI152NLin6Asym'}.

As a reminder, here are the contents of my anat file:

"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-aparcaseg_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-aseg_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-brain_mask.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-brain_mask.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.json"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_dseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-fsnative_to-T1w_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.h5"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-fsnative_mode-image_xfm.txt"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_curv.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_inflated.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_midthickness.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_pial.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_smoothwm.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_sulc.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_thickness.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_curv.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_inflated.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_midthickness.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_pial.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_smoothwm.surf.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_sulc.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_thickness.shape.gii"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-CSF_probseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-GM_probseg.nii.gz"@
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_label-WM_probseg.nii.gz"@

As pointed out by the error, I am indeed missing a mask in MNI152NLin6Asym space (mentioned as minimal inputs for CIFTI processing in the XCP-D docs):

  • sub-x_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
  • sub-x_space-MNI152NLin6Asym_desc-brain_mask.json

But I have the native anatomical space T1 NIfTI and mask, as well ask the transforms between native anatomical space and MNI152NLin6Asym space, which you’ve mentioned in your previous message.

Would there be a way around the MNI152NLin6Asym mask, or a way to obtain it without needing to rerun fmriprep? The type of workaround you have suggested for the missing BOLD files in MNI152NLin6Asym space (ie creating symlinks to existing similar files) does not seem to be appropriate in this case/with the currently available files in my directory…

Thank you very much in advance.

Hi @biancaserio,

You can apply your T1w-to-MNI transform to your mask to get the MNI mask.

E.g.,

antsApplyTransform \
-d 3 \
-i $SUB_MASK # e.g, sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-brain_mask.nii.gz \
-r $MNIREF # e.g., an MNI152NLin6Asym file from templateflow 
-n NearestNeighbor # Since this is a binary mask file
-t $T1w_TO_MNI152NLin6Asym_XFM # e.g, sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5
-o $OUTPUT # e.g., sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_space-MNI152NLin6Asym_desc-brain_mask.nii.gz

Best,
Steven

Thank you @Steven, this is really helpful.

It seems that the preprocessing now starts, however halting after this output:

2024-11-16 22:32:23,273 [ WARNING] WARNING: The sentry_sdk.metrics module is deprecated and will be removed in the next major release. Sentry will reject all metrics sent after October 7, 2024. Learn more: https://sentry.zendesk.com/hc/en-us/articles/26369339769883-Upcoming-API-Changes-to-Metrics
2024-11-16 22:32:23,280 [ WARNING] WARNING: `sentry_sdk.Hub` is deprecated and will be removed in a future major release. Please consult our 1.x to 2.x migration guide for details on how to migrate `Hub` usage to the new API: https://docs.sentry.io/platforms/python/migration/1.x-to-2.x
2024-11-16 22:32:23,580 [ WARNING] A valid FreeSurfer license file is recommended. Set the FS_LICENSE environment variable or use the '--fs-license-file' flag.
2024-11-16 22:32:26,633 [ WARNING] WARNING: sentry_sdk.configure_scope is deprecated and will be removed in the next major version. Please consult our migration guide to learn how to migrate to the new API: https://docs.sentry.io/platforms/python/migration/1.x-to-2.x#scope-configuring
2024-11-16 22:32:32,344 [ WARNING] WARNING: The sentry_sdk.metrics module is deprecated and will be removed in the next major release. Sentry will reject all metrics sent after October 7, 2024. Learn more: https://sentry.zendesk.com/hc/en-us/articles/26369339769883-Upcoming-API-Changes-to-Metrics
2024-11-16 22:32:32,347 [ WARNING] WARNING: `sentry_sdk.Hub` is deprecated and will be removed in a future major release. Please consult our 1.x to 2.x migration guide for details on how to migrate `Hub` usage to the new API: https://docs.sentry.io/platforms/python/migration/1.x-to-2.x
241116-22:32:33,402 nipype.workflow IMPORTANT:
	 Running XCP-D version 0.10.0rc3
2024-11-16 22:32:33,402 [IMPORTANT] Running XCP-D version 0.10.0rc3
241116-22:32:33,643 nipype.workflow IMPORTANT:
	 Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241116-223221_60c7288e-05a3-4811-9192-a69f188b7e58.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
2024-11-16 22:32:33,643 [IMPORTANT] Building XCP-D's workflow:
           * Preprocessing derivatives path: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep.
           * Participant list: ['NDARINV00CY2MDM'].
           * Run identifier: 20241116-223221_60c7288e-05a3-4811-9192-a69f188b7e58.
           * Searching for derivatives and atlases: {'xcpdatlases': PosixPath('/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases'), 'xcpd4s': PosixPath('/AtlasPack')}.
241116-22:32:34,251 nipype.utils IMPORTANT:
	 Collected data:
anat_brainmask: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
anat_to_template_xfm: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5
bold:
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-02_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-03_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-04_space-fsLR_den-91k_bold.dtseries.nii
t1w: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.nii.gz
t2w: null
template_to_anat_xfm: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.h5

2024-11-16 22:32:34,251 [IMPORTANT] Collected data:
anat_brainmask: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
anat_to_template_xfm: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-T1w_to-MNI152NLin6Asym_mode-image_xfm.h5
bold:
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-02_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-03_space-fsLR_den-91k_bold.dtseries.nii
- /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-04_space-fsLR_den-91k_bold.dtseries.nii
t1w: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_desc-preproc_T1w.nii.gz
t2w: null
template_to_anat_xfm: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_from-MNI152NLin6Asym_to-T1w_mode-image_xfm.h5

241116-22:32:34,325 nipype.utils INFO:
	 No standard-space surfaces found.
2024-11-16 22:32:34,325 [    INFO] No standard-space surfaces found.
241116-22:32:34,548 nipype.utils IMPORTANT:
	 Collected mesh files:
lh_pial_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_pial.surf.gii
lh_subject_sphere: null
lh_wm_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_smoothwm.surf.gii
rh_pial_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_pial.surf.gii
rh_subject_sphere: null
rh_wm_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_smoothwm.surf.gii

2024-11-16 22:32:34,548 [IMPORTANT] Collected mesh files:
lh_pial_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_pial.surf.gii
lh_subject_sphere: null
lh_wm_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-L_smoothwm.surf.gii
rh_pial_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_pial.surf.gii
rh_subject_sphere: null
rh_wm_surf: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/anat/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_rec-normalized_hemi-R_smoothwm.surf.gii

241116-22:32:34,644 nipype.utils IMPORTANT:
	 Collected morphometry files:
cortical_thickness: null
cortical_thickness_corr: null
myelin: null
myelin_smoothed: null
sulcal_curv: null
sulcal_depth: null

2024-11-16 22:32:34,644 [IMPORTANT] Collected morphometry files:
cortical_thickness: null
cortical_thickness_corr: null
myelin: null
myelin_smoothed: null
sulcal_curv: null
sulcal_depth: null

241116-22:32:34,644 nipype.utils WARNING:
	 GeneratedBy key DNE: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/dataset_description.json. Using partial info.
2024-11-16 22:32:34,644 [ WARNING] GeneratedBy key DNE: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/dataset_description.json. Using partial info.
241116-22:32:34,777 nipype.workflow INFO:
	 Loading atlas: 4S456Parcels
2024-11-16 22:32:34,777 [    INFO] Loading atlas: 4S456Parcels
241116-22:32:36,992 nipype.utils IMPORTANT:
	 Collected run data for sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii:
boldref: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_boldref.nii.gz
motion_file: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv
motion_json: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.json
nifti_file: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz

2024-11-16 22:32:36,992 [IMPORTANT] Collected run data for sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii:
boldref: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_boldref.nii.gz
motion_file: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv
motion_json: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.json
nifti_file: /data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep/sub-NDARINV00CY2MDM/ses-baselineYear1Arm1/func/sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz

Not required: xcpdatlases
Not required: xcpd4s
241116-22:32:37,52 nipype.utils INFO:
	 Found 5 dummy scans in sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv
2024-11-16 22:32:37,052 [    INFO] Found 5 dummy scans in sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_desc-confounds_timeseries.tsv
241116-22:32:37,68 nipype.utils WARNING:
	 251.20000000000002 seconds in sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii survive high-motion outlier scrubbing. Only retaining exact-time values greater than this ([]).
2024-11-16 22:32:37,068 [ WARNING] 251.20000000000002 seconds in sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_bold.dtseries.nii survive high-motion outlier scrubbing. Only retaining exact-time values greater than this ([]).
Process Process-2:
Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/local/miniconda/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/cli/workflow.py", line 100, in build_workflow
    retval["workflow"] = init_xcpd_wf()
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 81, in init_xcpd_wf
    single_subject_wf = init_single_subject_wf(subject_id)
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/base.py", line 469, in init_single_subject_wf
    postprocess_bold_wf = init_postprocess_bold_wf(
  File "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/workflows/bold/cifti.py", line 301, in init_postprocess_cifti_wf
    workflow.connect([
  File "/usr/local/miniconda/lib/python3.10/site-packages/nipype/pipeline/engine/workflows.py", line 239, in connect
    raise Exception("\n".join(["Some connections were not found"] + infostr))
Exception: Some connections were not found
Module qc_report_wf has no input called inputnode.name_source

Module qc_report_wf has no input called inputnode.preprocessed_bold

Module qc_report_wf has no input called inputnode.dummy_scans

Module qc_report_wf has no input called inputnode.motion_file

Module qc_report_wf has no input called inputnode.temporal_mask

Module qc_report_wf has no input called inputnode.denoised_interpolated_bold

Module qc_report_wf has no input called inputnode.censored_denoised_bold

I found an issue on the Xcp_d: Some connections were not found problem, although in my case this does not seem to relate to surface files but rather to outputs of the XCP-D post-processing pipeline itself? I would be very grateful to get your thoughts on this - thank you in advance.

Note: I am using the following command/parameters:

singularity run \
-B /data/pt_02667/data/ABCD:/data/pt_02667/data/ABCD \
--cleanenv /data/u_serio_software/xcp_d-0.10.0rc3.simg \
/data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep \
/data/pt_02667/data/ABCD/XCP-D_output \
participant \
--mode 'abcd' \
--participant-label 'NDARINV00CY2MDM' \
--bids-filter-file /data/pt_02667/data/ABCD/bianca/bids_filter_file.json \
--nprocs 36 \
--input-type 'fmriprep' \
--file-format 'cifti' \
--dummy-scans 'auto' \
--despike 'y' \
--nuisance-regressors /data/pt_02667/data/ABCD/bianca/custom_confounds_24P_csf_wm.yaml \
--fd-thresh 0.3 \
--output-type 'auto' \
--combine-runs 'n' \
--smoothing 6 \
--motion-filter-type 'none' \
--head-radius 50 \
--lower-bpf 0.01 \
--upper-bpf 0.08 \
--bpf-order 2 \
--min-time 240 \
--atlases '4S456Parcels' \
--min-coverage 0.5 \
--create-matrices 300 \
--work-dir /data/pt_02667/data/ABCD/work \
--warp-surfaces-native2std 'n' \
--abcc-qc 'n' \
--linc-qc 'n'

Yes, that’s a bug that another user detected recently. The problem comes from using --file-format cifti + --linc-qc n + --abcc-qc n. I have it fixed in a recent PR, but I don’t think I’ve made a new release yet. In the meantime, the easiest workaround would be to switch to --linc-qc y.

Thank you very much @tsalo , I was now able to have XCP-D run without errors through to completion of this single subject.

I have some outstanding questions about the output, if that’s ok:

  1. I was expecting output for 4 runs, but I only see output for run1:
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_design.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_design.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_motion.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_motion.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_outliers.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_outliers.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_desc-denoised_bold.dtseries.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_desc-denoised_bold.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_desc-denoisedSmoothed_bold.dtseries.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_desc-denoisedSmoothed_bold.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_desc-linc_qc.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-alff_boldmap.dscalar.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-alff_boldmap.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-alff_desc-smooth_boldmap.dscalar.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-alff_desc-smooth_boldmap.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-reho_boldmap.dscalar.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_den-91k_stat-reho_boldmap.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_den-91k_stat-coverage_boldmap.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_den-91k_stat-coverage_boldmap.pscalar.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_den-91k_stat-mean_timeseries.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_den-91k_stat-mean_timeseries.ptseries.nii"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-alff_bold.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-alff_bold.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-coverage_bold.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-coverage_bold.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-mean_timeseries.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-mean_timeseries.tsv"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-reho_bold.json"
"sub-NDARINV00CY2MDM_ses-baselineYear1Arm1_task-rest_run-01_space-fsLR_seg-4S456Parcels_stat-reho_bold.tsv"

This is despite the fact that the xcp_d.toml file in the /log folder includes: layout = "BIDS Layout: ...ta/ABCD/ABCD_fMRIprep/fmriprep | Subjects: 1 | Sessions: 1 | Runs: 4" , therefore recognizing the 4 runs. For your information, in my bids_filter_file.json, I did not specify a specific run. Could you please indicate where I could look for an explanation for the missing runs?

  1. I was expecting correlation matrices in the output and do not see any. I had specified --create-matrices 300 in my command, however the xcp_d.toml file includes the following: output_correlations = false , yet recognises dcan_correlation_lengths = [ 300.0,]. Should I have requested the creation of correlation matrices in a different way?

Full contents of the xcp_d.toml file:

[environment]
cpu_count = 32
exec_env = "posix"
free_mem = 549.5
overcommit_policy = "heuristic"
overcommit_limit = "50%"
nipype_version = "1.8.6"
templateflow_version = "24.2.2"
version = "0.10.0rc3"

[execution]
fmri_dir = "/data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep"
aggr_ses_reports = 4
bids_database_dir = "/data/pt_02667/data/ABCD/work/20241117-164533_0654463c-ec21-4c4c-b9cb-e521e46c0c3e/bids_db"
bids_description_hash = "de1306035c3231fcdf2277f3288006d6dfa7b6814e448d1b75582a725173abfe"
boilerplate_only = false
confounds_config = "/data/pt_02667/data/ABCD/bianca/custom_confounds_24P_csf_wm.yaml"
debug = []
layout = "BIDS Layout: ...ta/ABCD/ABCD_fMRIprep/fmriprep | Subjects: 1 | Sessions: 1 | Runs: 4"
log_dir = "/data/pt_02667/data/ABCD/XCP-D_output/logs"
log_level = 25
low_mem = false
md_only_boilerplate = false
notrack = false
reports_only = false
output_dir = "/data/pt_02667/data/ABCD/XCP-D_output"
atlases = [ "4S456Parcels",]
run_uuid = "20241117-164533_0654463c-ec21-4c4c-b9cb-e521e46c0c3e"
participant_label = [ "NDARINV00CY2MDM",]
templateflow_home = "/data/hu_serio/.cache/templateflow"
work_dir = "/data/pt_02667/data/ABCD/work"
write_graph = false

[workflow]
mode = "abcd"
file_format = "cifti"
dummy_scans = "auto"
input_type = "fmriprep"
despike = true
smoothing = 6.0
output_interpolated = true
output_correlations = false
combine_runs = false
motion_filter_order = 4
head_radius = 50.0
fd_thresh = 0.3
min_time = 240.0
bandpass_filter = true
high_pass = 0.01
low_pass = 0.08
bpf_order = 2
min_coverage = 0.5
dcan_correlation_lengths = [ 300.0,]
process_surfaces = false
abcc_qc = false
linc_qc = true

[nipype]
crashfile_format = "txt"
get_linked_libs = false
nprocs = 36
omp_nthreads = 1
plugin = "MultiProc"
resource_monitor = false
stop_on_first_crash = false

[seeds]
master = 63555

[execution.datasets]
xcpdatlases = "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases"
xcpd4s = "/AtlasPack"

[execution.dataset_links]
preprocessed = "/data/pt_02667/data/ABCD/ABCD_fMRIprep/fmriprep"
templateflow = "/data/hu_serio/.cache/templateflow"
atlas = "/data/pt_02667/data/ABCD/XCP-D_output/atlases"
xcpdatlases = "/usr/local/miniconda/lib/python3.10/site-packages/xcp_d/data/atlases"
xcpd4s = "/AtlasPack"

[nipype.plugin_args]
maxtasksperchild = 1
raise_insufficient = false

[execution.bids_filters.bold]
session = [ "baselineYear1Arm1",]
task = [ "rest",]
space = [ "fsLR",]

Thank you very much in advance.

Can you share the stdout and stderr files from your run?