This is a fork of openneuro ds003216 to showcase how to make VASO data more BIDS compatible

Remi Gau b7a7173456 update README 1 month ago
.datalad 5c0c750d8a [DATALAD] new dataset 1 year ago
code 5c2eccc066 add events.tsv 1 month ago
derivatives 0a135f5cee update README and pacify bids validator 1 month ago
sub-02 bca03d09d1 add AcquisitionDuration to comply with\#timing-parameters_1 1 month ago
.bids-validator-config.json 76b50fd855 update README and silence some validator warnings 1 month ago
.bidsignore 0a135f5cee update README and pacify bids validator 1 month ago
.gitattributes 94a992724a [DATALAD] exclude paths from annex'ing 1 year ago
.gitignore 597bed2e47 add volume timing metadata 1 month ago
CHANGES 097f2543ec [OpenNeuro] Recorded changes 4 months ago b7a7173456 update README 1 month ago
dataset_description.json a5370dc419 [OpenNeuro] Recorded changes 4 months ago
participants.tsv f2c29d1cc6 keep one tapping and one movie session for subject 2 1 month ago
task-movie_events.tsv 5c2eccc066 add events.tsv 1 month ago

Project description: Whole Brain Layer-fMRI Connectome: An Open Dataset

This is a fork of openneuro ds003216 to showcase how to make VASO data more BIDS compatible

Log of changes:


  • Add REQUIRED metadata of derivatives (TaskName and RepetitionTime for each .nii file)


├── code             # some matlab scripts that were used to 'consolidate the dataset'
│   ├── add_context.m
│   ├── add_volume_timing.m
│   └── create_events_tsv.m
├── dataset_description.json
├── derivatives
│   └── sub-02
│       ├── atlas
│       ├── BOLD_func
│       │   └── sub-02_ses-04_task-movie_run-01_bold.nii
│       ├── layerification
│       │   └── sub-02_layers.nii
│       ├── moco_result
│       │   ├── sub-02_ses-04_task-movie_run-01_notnulled.nii # non-null volumes
│       │   └── sub-02_ses-04_task-movie_run-01_nulled.nii    # nulled volumes
│       └── VASO_func
│           └── sub-02_ses-04_task-movie_run-01_desc-boco_vaso.nii # volumes with the bold correction applied
├── participants.tsv
├── sub-02
│   ├── ses-01
│   │   └── anat
│   │       ├── sub-02_ses-01_run-01_T1w.json
│   │       └── sub-02_ses-01_run-01_T1w.nii.gz
│   └── ses-04
│       └── func
│           ├── sub-02_ses-04_task-movie_run-01_context.tsv  # contains the volume type for each volume
│           ├── sub-02_ses-04_task-movie_run-01_vaso.json    # contains the volume timing and acquisition time for each volume
│           └── sub-02_ses-04_task-movie_run-01_vaso.nii.gz  # mixture of nulled and non-null volumes
└── task-movie_events.tsv


Here, we provide a whole-brain layer-dependent connectome dataset with cerebral blood volume and BOLD contrast. It is coming along with a quality assessment comprising metrics of skew, kurtosis, tSNR and sharpness. The purpose of this dataset is 1.) to characterize the prospects and challenges of whole brain layer-fMRI acquisition sequences in a test-retest setting 2.) and to provide a test bed for developing and benchmarking new layer-dependent analysis tools that may be able to address novel neuroscientific research questions. Note that these data have been acquired with a CBV-sensitive VASO sequence, thus the datatype is rather unconventional compared to standard fMRI datasets. TRs as well as resolution is variable across time and contrasts within one run. Please notice, this is an initial release of an ongoing study. We are happy to share the data via SIEMENS C2P.

Data Acquisition

Scanning was performed at Scannexus as part of the Maastricht Brain Imaging Centre (MBIC) embedded in theMaastricht University, Maastricht, The Netherlands. VASO and BOLD contrasts of the whole brain were obtained from one participant at a SIEMENS MAGNETOM 7T scanner at 6 days during watching the Human Connectome Project (HCP) audio movie. The link to the psychopy stimulation script is: Link to the movie: The link to the movie file is here: For information about detailed scanning parameters see: There is no data available for day 3 due to technical issues at scanning (PCI_RX receiver error). At day 6, respiration and pulse were recorded to provide the required physiological measures for further analysis. Link to physiological recording on Github:

Data processing

Defacing was performed with freeSurfer. Motion correction (MOCO), BOLD correction (BOCO), and segmentation of grey + white matter were performed. The additional folder "derivatives" contains the processed data in steps: MOCO, BOCO, and segmentation of grey + white matter. For detailed information see: (MOCO), (BOCO), and (preprocessing pipeline and script)

Presenting on ISMRM 2022

You can find our abstract draft here: The main participant of this abstract is subject 02 on this open dataset.