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Outputs of Connectome Mapper 3

Processed, or derivative, data are outputed to <bids_dataset/derivatives>/.

Main Connectome Mapper Derivatives

Main outputs produced by Connectome Mapper 3 are written to <bids_dataset/derivatives>/cmp/sub-<subject_label>/. In this folder, a configuration file generated for each modality pipeline (i.e. anatomical/diffusion/fMRI) and used for processing each participant is saved as sub-<subject_label>_anatomical/diffusion/fMRI_config.ini. It summarizes pipeline workflow options and parameters used for processing. An execution log of the full workflow is saved as sub-<subject_label>_log.txt`

Anatomical derivatives

  • Anatomical derivatives in the individual T1w space are placed in each subject's anat/ subfolder, including:

    • The original T1w image:

      • anat/sub-<subject_label>_desc-head_T1w.nii.gz
    • The masked T1w image with its corresponding brain mask:

      • anat/sub-<subject_label>_desc-brain_T1w.nii.gz
      • anat/sub-<subject_label>_desc-brain_mask.nii.gz
    • The segmentations of the white matter (WM), gray matter (GM), and Cortical Spinal Fluid (CSF) tissues:

      • anat/sub-<subject_label>_label-WM_dseg.nii.gz
      • anat/sub-<subject_label>_label-GM_dseg.nii.gz
      • anat/sub-<subject_label>_label-CSF_dseg.nii.gz
    • The five different brain parcellations:

    • anat/sub-<subject_label>_label-L2018_desc-<scale_label>_atlas.nii.gz

      where <scale_label> : scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale.

      with the description of parcel labels and the updated FreeSurfer color lookup table:

      • anat/sub-<subject_label>_label-L2018_desc-<scale_label>_atlas.graphml
      • anat/sub-<subject_label>_label-L2018_desc-<scale_label>_atlas_FreeSurferColorLUT.txt
  • Anatomical derivatives in the``DWI`` space produced by the diffusion pipeline are placed in each subject's anat/ subfolder, including:

    • The unmasked T1w image:

      • anat/sub-<subject_label>_space-DWI_desc-head_T1w.nii.gz
    • The masked T1w image with its corresponding brain mask:

      • anat/sub-<subject_label>_space-DWI_desc-brain_T1w.nii.gz
      • anat/sub-<subject_label>_space-DWI_desc-brain_mask.nii.gz
    • The segmentation of WM tissue used for tractography seeding:

      • anat/sub-<subject_label>_space-DWI_label-WM_dseg.nii.gz
    • The five different brain parcellation are saved as:

      • anat/sub-<subject_label>_space-DWI_label-L2018_desc-<scale_label>_atlas.nii.gz

      where <scale_label> : scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale.

    • The 5TT image used for Anatomically Constrained Tractorgaphy (ACT):

      • anat/sub-<subject_label>_space-DWI_label-5TT_probseg.nii.gz
    • The patial volume maps for white matter (WM), gray matter (GM), and Cortical Spinal Fluid (CSF) used for Particale Filtering Tractography (PFT), generated from 5TT image:

      • anat/sub-<subject_label>_space-DWI_label-WM_probseg.nii.gz
      • anat/sub-<subject_label_space-DWI>_label-GM_probseg.nii.gz
      • anat/sub-<subject_label>_space-DWI_label-CSF_probseg.nii.gz
    • The GM/WM interface used for ACT and PFT seeding:

      • anat/sub-<subject_label>_space-DWI_label-GMWMI_probseg.nii.gz

Diffusion derivatives

Diffusion derivatives in the individual DWI space are placed in each subject's dwi/ subfolder, including:

  • The final preprocessed DWI image used to fit the diffusion model for tensor or fiber orientation distribution estimation:

    • dwi/sub-<subject_label>_desc-preproc_dwi.nii.gz
  • The brain mask used to mask the DWI image:

    • dwi/sub-<subject_label>_desc-brain_mask_resampled.nii.gz
  • The diffusion tensor (DTI) fit (if used for tractography):

    • dwi/sub-<subject_label>]_desc-WLS_model-DTI_diffmodel.nii.gz

    with derived Fractional Anisotropic (FA) and Mean Diffusivity (MD) maps:

    • dwi/sub-<subject_label>]_model-DTI_FA.nii.gz
    • dwi/sub-<subject_label>]_model-DTI_MD.nii.gz
  • The Fiber Orientation DIstribution (FOD) image from Constrained Spherical Deconvolution (CSD) fit (if performed):

    • dwi/sub-<subject_label>]_model-CSD_diffmodel.nii.gz
  • The MAP-MRI fit for DSI and multi-shell DWI data (if performed):

    • dwi/sub-<subject_label>]_model-MAPMRI_diffmodel.nii.gz

    with derived Generalized Fractional Anisotropic (GFA), Mean Squared Displacement (MSD), Return-to-Origin Probability (RTOP) and Return-to-Plane Probability (RTPP) maps:

    • dwi/sub-<subject_label>]_model-MAPMRI_GFA.nii.gz
    • dwi/sub-<subject_label>]_model-MAPMRI_MSD.nii.gz
    • dwi/sub-<subject_label>]_model-MAPMRI_RTOP.nii.gz
    • dwi/sub-<subject_label>]_model-MAPMRI_RTPP.nii.gz
  • The SHORE fit for DSI data:

    • dwi/sub-<subject_label>]_model-SHORE_diffmodel.nii.gz

    with derived Generalized Fractional Anisotropic (GFA), Mean Squared Displacement (MSD), Return-to-Origin Probability (RTOP) maps:

    • dwi/sub-<subject_label>]_model-SHORE_GFA.nii.gz
    • dwi/sub-<subject_label>]_model-SHORE_MSD.nii.gz
    • dwi/sub-<subject_label>]_model-SHORE_RTOP.nii.gz
  • The tractogram:

    • dwi/sub-<subject_label>_model-<model_label>_desc-<label>_tractogram.trk

    where:

    • <model_label> is the diffusion model used to drive tractography (DTI, CSD, SHORE)
    • <model_label> is the type of tractography algorithm employed (DET for deterministic, PROB for probabilistic)

Functional derivatives

Functional derivatives in the 'meanBOLD' (individual) space are placed in each subject's func/ subfolder including:

  • The original BOLD image:

    • func/sub-<subject_label>_task-rest_desc-cmp_bold.nii.gz
  • The mean BOLD image:

    • func/sub-<subject_label>_meanBOLD.nii.gz
  • The fully preprocessed band-pass filtered used to compute ROI time-series:

    • func/sub-<subject_label>_desc-bandpass_task-rest_bold.nii.gz
  • For scrubbing (if enabled):

    • The change of variance (DVARS):

      • func/sub-<subject_label>_desc-scrubbing_DVARS.npy
    • The frame displacement (FD):

      • func/sub-<subject_label>_desc-scrubbing_FD.npy
  • Motion-related time-series:

    • func/sub-<subject_label>_motion.tsv
  • The ROI time-series for each parcellation scale:

    • func/sub-<subject_label>_atlas-L2018_desc-<scale_label>_timeseries.npy
    • func/sub-<subject_label>_atlas-L2018_desc-<scale_label>_timeseries.mat

    where <scale_label> : scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale

FreeSurfer Derivatives

A FreeSurfer subjects directory is created in <bids_dataset/derivatives>/freesurfer.

freesurfer/
    fsaverage/
        mri/
        surf/
        ...
    sub-<subject_label>/
        mri/
        surf/
        ...
    ...

The fsaverage subject distributed with the running version of FreeSurfer is copied into this directory.

Nipype Workflow Derivatives

The execution of each Nipype workflow (pipeline) dedicated to the processing of one modality (i.e. anatomical/diffusion/fMRI) involves the creation of a number of intermediate outputs which are written to <bids_dataset/derivatives>/nipype/sub-<subject_label>/<anatomical/diffusion/fMRI>_pipeline respectively:

images/nipype_wf_derivatives.png

To enhance transparency on how data is processed, outputs include a pipeline execution graph saved as <anatomical/diffusion/fMRI>_pipeline/graph.svg which summarizes all processing nodes involves in the given processing pipeline:

images/nipype_wf_graph.png

Execution details (data provenance) of each interface (node) of a given pipeline are reported in <anatomical/diffusion/fMRI>_pipeline/<stage_name>/<interface_name>/_report/report.rst

images/nipype_node_report.png

Note

Connectome Mapper 3 outputs are currently being updated to conform to the :abbr:`BIDS (brain imaging data structure)` Derivatives specification (see BIDS Derivatives Extension).

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