Outputs
The output of this app aims to follow the guideline of the BIDS extension proposal 17 - Generic BIDS connectivity data schema.
Metadata files content is described in this BIDS extension proposal.
Note
A Brain Imaging Data Structure (BIDS) Extension Proposal (BEP) is a community-driven process to add a new modality or set of data types to the BIDS Specification.
Although BEPs are not official part of the main BIDS Specification, they still can be adopted by users for data organisation and development.
giga-connectome aims to review the changes in a timely manner and update the outputs in accordance with the development of BEP017.
To learn more about BIDS Extension see this page.
Participant level
For each participant that was passed to --participant-label
(or all participants under bids_dir if no --participant-label is passed),
the output will be save in sub-<participant_id>/[ses-<ses_id>]/func.
Data files
For each input image (that is, preprocessed BOLD time series) and each atlas the following data files will be generated
a
[matches]_seg-{atlas}{atlas_description}_meas-PearsonCorrelation_desc-denoise{denoise_strategy}_relmat.tsvfile that contains the correlation matrix between all the regions of the atlasa
[matches]_seg-{atlas}{atlas_description}_desc-denoise{denoise_strategy}_timeseries.tsvfile that contains the extracted timeseries for each region of the atlas{atlas}refers to the name of the atlas used (for example,Schaefer2018){atlas_description}refers to the sub type of atlas used (for example,100Parcels7Networks){denoise_strategy}refers to the denoise strategy passed to the command line
Metadata
A JSON file is generated in the root of the output dataset (meas-PearsonCorrelation_relmat.json)
that contains metadata applicable to all relmat.tsv files.
For each input image (that is, preprocessed BOLD time series)
a [matches]_desc-denoise{denoise_strategy}_timeseries.json
Example
├── dataset_description.json
├── logs
│ └── CITATION.md
├── meas-PearsonCorrelation_relmat.json
├── sub-1
│ ├── ses-timepoint1
│ │ └── func
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_desc-denoiseSimple_timeseries.json
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_desc-denoiseSimple_timeseries.json
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ │ ├── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ │ └── sub-1_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ └── ses-timepoint2
│ └── func
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_desc-denoiseSimple_timeseries.json
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_desc-denoiseSimple_timeseries.json
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ └── sub-1_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
└── sub-2
├── ses-timepoint1
│ └── func
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_desc-denoiseSimple_timeseries.json
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_desc-denoiseSimple_timeseries.json
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
│ ├── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
│ └── sub-2_ses-timepoint1_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
└── ses-timepoint2
└── func
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_desc-denoiseSimple_timeseries.json
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-01_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_desc-denoiseSimple_timeseries.json
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_report.html
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_desc-denoiseSimple_timeseries.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018100Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_report.html
├── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_desc-denoiseSimple_timeseries.tsv
└── sub-2_ses-timepoint2_task-probabilisticclassification_run-02_seg-Schaefer2018200Parcels7Networks_meas-PearsonCorrelation_desc-denoiseSimple_relmat.tsv
Atlases
The merged grey matter masks per subject and the atlases resampled to the individual EPI data are in the directory specified at --atlases_dir.
For each subject and each atlas the following data files will be generated
a
sub-<sub>_space-MNI152NLin2009cAsym_res-2_label-GM_mask.nii.gzGrey matter mask in the dedicated space for a given subject, created from merging all the EPI brain masks of a given subject, and converges with the grey matter mask of the given space.sub-<sub>_seg-{atlas}{atlas_description}_[dseg|probseg].nii.gzfiles where the atlas were sampled tosub-<sub>_space-MNI152NLin2009cAsym_res-2_label-GM_mask.nii.gzfor each individual.{atlas}refers to the name of the atlas used (for example,Schaefer2018){atlas_description}refers to the sub type of atlas used (for example,100Parcels7Networks)
Example
└── sub-1
└── func
├── sub-1_seg-Schaefer2018100Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018200Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018300Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018400Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018500Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018600Parcels7Networks_dseg.nii.gz
├── sub-1_seg-Schaefer2018800Parcels7Networks_dseg.nii.gz
└── sub-1_space-MNI152NLin2009cAsym_res-2_label-GM_mask.nii.gz