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Outputs of Connectome Mapper 3

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

BIDS derivatives entities

.. tabularcolumns:: |l|p{5cm}|

Entity Description
sub-<label> Distinguish different subjects
ses-<label> Distinguish different acquisition sessions
label-<label> Describe the type of brain tissue segmented (for _probseg/dseg)
atlas-<label> Distinguish data derived from different types of parcellation atlases
res-<label> Distinguish data derived from the different scales of Lausanne2008 and Lausanne2018 parcellation atlases
space-DWI Distinguish anatomical MRI derivatives in the target diffusion MRI space
model-<label> Distinguish different diffusion signal models (DTI, CSD, SHORE, MAPMRI)

See Original BIDS Entities Appendix for more description.

Note

Connectome Mapper 3 introduced a new BIDS entity atlas-<atlas_label> (where <atlas_label>: Desikan/ L2008/ L2018), that is used in combination with the res-<atlas_scale> (where <atlas_scale>: scale1 / scale2 / scale3 / scale4 / scale5) entity to distinguish data derived from different parcellation atlases and different scales.

Main Connectome Mapper Derivatives

Main outputs produced by Connectome Mapper 3 are written to 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>_atlas-<atlas_label>[_res-<scale_label>]_dseg.nii.gz

        where:

        • <atlas_label>: Desikan / L2008 / L2018 is the parcellation scheme used
        • <scale_label>: scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale if applicable

      with two .tsv side-car files that follow the BIDS derivatives, one describing the parcel label/index mapping (_dseg.tsv), one reporting volumetry of the different parcels (_stats.tsv), and two files used internally by CMP3, one describing the parcel labels in .graphml format (dseg.graphml), one providing the color lookup table of the parcel labels in Freesurfer format which can used directly in freeview (_FreeSurferColorLUT.txt):

      • anat/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_dseg.tsv
      • anat/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_stats.tsv
      • anat/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_dseg.graphml
      • anat/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_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_atlas-<atlas_label>[_res-<scale_label>]_dseg.nii.gz

        where:

        • <atlas_label>: Desikan / L2008 / L2018 is the parcellation scheme used
        • <scale_label>: scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale if applicable
    • 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)
      • <label> is the type of tractography algorithm employed (DET for deterministic, PROB for probabilistic)
  • The structural connectivity (SC) graphs:

    • dwi/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_conndata-network_connectivity.<fmt>

      where:

      • <atlas_label>: Desikan / L2008 / L2018 is the parcellation scheme used
      • <scale_label>: scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale if applicable
      • <fmt>: mat / gpickle / tsv / graphml is the format used to store the graph

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-<atlas_label>[_res-<scale_label>]_timeseries.npy

    • func/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_timeseries.mat

      where:

      • <atlas_label>: Desikan / L2008 / L2018 is the parcellation scheme used
      • <scale_label>: scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale if applicable
  • The functional connectivity (FC) graphs:

    • func/sub-<subject_label>_atlas-<atlas_label>[_res-<scale_label>]_conndata-network_connectivity.<fmt>

      where:

      • <atlas_label>: Desikan / L2008 / L2018 is the parcellation scheme used
      • <scale_label>: scale1, scale2, scale3, scale4, scale5 corresponds to the parcellation scale if applicable
      • <fmt>: mat / gpickle / tsv / graphml is the format used to store the graph

FreeSurfer Derivatives

A FreeSurfer subjects directory is created in <bids_dataset/derivatives>/freesurfer-7.2.0.

freesurfer-7.2.0/
    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 :abbr:`BIDS (brain imaging data structure)` v1.4.0.