Output files
Every processing stage writes its important outputs into datasink/ folders,
created inside the output directory given at execution. This page describes
every file produced.
Note
The output directory also contains the Nipype working directory and the
serialised workflow state (.pkl). Those are internal, but they are what the
next stage reads to locate the previous stage’s files — do not move or delete
them between stages.
Preprocessing outputs
Preprocessing writes five datasinks: anat_datasink/, bold_datasink/,
unbiased_template_datasink/, transforms_datasink/ and motion_datasink/.
The quality control images are written separately to preprocess_QC_report/,
described in Preprocessing QC outputs.
anat_datasink/
Inhomogeneity-corrected anatomical scans.
anat_preproc/: anatomical scans after inhomogeneity correction
bold_datasink/
All outputs related to the functional scans. Files are resampled either onto the native space or the commonspace of the EPI. Native space outputs are resampled over the anatomical scan from the corresponding MRI session; commonspace outputs are resampled over the reference atlas. The original EPI voxel resolution is unchanged during resampling unless specified otherwise in the RABIES command.
Native space
native_bold/: preprocessed EPI timeseries resampled to native spacenative_brain_mask/: brain mask in native spacenative_WM_mask/: WM mask in native spacenative_CSF_mask/: CSF mask in native spacenative_labels/: atlas labels in native spacenative_bold_ref/: a volumetric 3D EPI average generated from the 4Dnative_bold/
Commonspace
commonspace_bold/: preprocessed EPI timeseries resampled to commonspacecommonspace_mask/: brain mask in commonspacecommonspace_WM_mask/: WM mask in commonspacecommonspace_CSF_mask/: CSF mask in commonspacecommonspace_vascular_mask/: vascular mask in commonspacecommonspace_labels/: atlas labels in commonspacecommonspace_resampled_template/: the commonspace anatomical template, resampled to the EPI’s dimensions
Inputs and intermediates
input_bold/: the raw EPI scans provided as inputs in the BIDS data folderinitial_bold_ref/: the initial volumetric 3D EPI average generated from the 4Dinput_bold/raw_brain_mask/: brain mask resampled onto the 4Dinput_bold/inho_cor_bold/: the volumetric 3D EPI (initial_bold_ref/) after inhomogeneity correction, later used for registration of the EPIinho_cor_bold_warped2anat/:inho_cor_boldafter co-registration to the associated anatomical image (anat_preproc/)std_map_preprocess/: the temporal standard deviation at each voxel ofcommonspace_bold/tSNR_map_preprocess/: the temporal signal-to-noise ratio (tSNR) ofcommonspace_bold/
unbiased_template_datasink/
Outputs from the generation of the unbiased template using
optimized_antsMultivariateTemplateConstruction.
The unbiased template is the average of all anatomical scans (or functional
scans with --bold_only) after their alignment.
unbiased_template/: the unbiased template generated from the input dataset scanswarped_unbiased_template/: the unbiased template, registered to the reference atlas in commonspace
transforms_datasink/
All transform files for resampling between spaces.
The bold_to_anat registration transforms the raw EPI to overlap with the
anatomical image, correcting susceptibility distortions, which defines native
space. The native_to_unbiased registration overlaps every scan onto the
generated unbiased template. The unbiased_to_atlas registration aligns the
unbiased template with the reference atlas, which defines commonspace.
bold_to_anat_affine/: affine transforms from the EPI co-registration to the anatomical imagebold_to_anat_warp/: non-linear transforms from the EPI co-registration to the anatomical imagebold_to_anat_inverse_warp/: inverse ofbold_to_anat_warp/native_to_unbiased_affine/: affine transforms for the alignment between native space and the unbiased templatenative_to_unbiased_warp/: non-linear transforms for the same alignmentnative_to_unbiased_inverse_warp/: inverse ofnative_to_unbiased_warp/unbiased_to_atlas_affine/: affine transforms for the alignment between the unbiased template and the atlas in commonspaceunbiased_to_atlas_warp/: non-linear transforms for the same alignmentunbiased_to_atlas_inverse_warp/: inverse ofunbiased_to_atlas_warp/
motion_datasink/
Files derived from motion estimation.
motion_params_csv/: the 24 motion parameters, usable as nuisance regressors at the confound correction stageFD_csv/: a CSV with timecourses for either the mean or maximal framewise displacement estimationsFD_voxelwise/: a NIfTI image containing framewise displacement evaluated at each voxelpos_voxelwise/: a NIfTI image tracking the displacement of each voxel across time, derived from the head motion realignment parameters
Confound correction outputs
confound_correction_datasink/
cleaned_timeseries/: cleaned timeseries after the application of confound correctionframe_censoring_mask/: CSV files recording, as a boolean vector, which timepoints were censored, if frame censoring was appliedaroma_out/: outputs from running ICA-AROMA if--ica_aromais applied, including the MELODIC ICA outputs and the component classification resultsplot_CR_overfit/: figures illustrating the variance explained by random regressors during confound correction, and the variance explained by the real regressors after subtracting the variance from random regressors
Analysis outputs
commonspace_analysis_datasink/ and nativespace_analysis_datasink/
Which of these appears depends on the space the cleaned timeseries were produced in:
Datasink |
Present when |
|---|---|
|
confound correction produced commonspace timeseries, or |
|
confound correction produced nativespace timeseries |
Both contain the same set of files, listed below. group_ICA_dir/ is always
written to commonspace_analysis_datasink/, since group ICA requires
commonspace alignment.
group_ICA_dir/: complete output from MELODIC ICA, including themelodic_IC.nii.gzNIfTI giving all spatial components and areport/folder with an HTML visualisationmatrix_data_file/: a.pklfile containing a 2D NumPy array representing the whole-brain correlation matrix. With--ROI_type parcellated, the row and column indices are matched in increasing order of the atlas ROI label numbermatrix_fig/: a.pngdisplaying the correlation matrixseed_correlation_maps/: NIfTI files for seed-based connectivity, one voxelwise correlation map per seed provided in--seed_listdual_regression_nii/: the spatial maps from dual regression, corresponding to the linear coefficients from the second regression. The 3D spatial maps are concatenated into a 4D NIfTI, with component order consistent with the priors provided in--prior_mapsdual_regression_timecourse_csv/: a CSV storing the outputs from the first linear regression during dual regression — one timecourse per prior component from--prior_mapsNPR_prior_filename/: spatial components fitted during NPRNPR_prior_timecourse_csv/: timecourses associated with each component inNPR_prior_filename/NPR_extra_filename/: the extra spatial components fitted during NPR which were not part of the priorsNPR_extra_timecourse_csv/: timecourses associated with each component inNPR_extra_filename/
data_diagnosis_datasink/
Produced when --data_diagnosis is selected. See
How to assess data quality for how to use
these.
figure_temporal_diagnosis/: scan-level temporal features from the spatiotemporal diagnosisfigure_spatial_diagnosis/: scan-level spatial features from the spatiotemporal diagnosistemporal_info_csv/: CSV containing the data plotted infigure_temporal_diagnosis/spatial_VE_nii/: NIfTI with the confound regression percentage variance explained (\(R^2\)) at each voxelCR_prediction_std_nii/: NIfTI with the confound regression variance explained at each voxelrandom_CR_std_nii/: NIfTI with the variance explained from random regressors at each voxelcorrected_CR_std_nii/: NIfTI with the confound regression variance explained at each voxel after removing the variance explained by random regressorstemporal_std_nii/: the standard deviation at each voxel after confound correctionGS_cov_nii/: the covariance of each voxel with the global signal
analysis_QC/ holds the group-level features of data quality:
sample_distributions/: the distribution plots{analysis}_sample_distribution.png: the distribution plot for a given network analysis{analysis}_outlier_detection.csv: a CSV associating the measures displayed in the distribution plot with the corresponding scan IDs
parametric_stats/: the group statistical report for analysis quality control, using parametric measuresDR{component #}_QC_maps.png: statistical maps relevant to analysis quality control.DRrefers to dual regression analysis, and{component #}relates the file to one of the BOLD components specified in--prior_bold_idxDR{component #}_QC_stats.csv: a follow-up to_QC_maps.pngallowing quantitative categorisation of data quality outcomes as in [DGregoireDGC24]seed_FC{seed #}_QC_maps.png: the same statistical maps, for seed-based connectivity analysisseed_FC{seed #}_QC_stats.csv: the same measures, for seed-based connectivity analysis
non_parametric_stats/: asparametric_stats/, but using non-parametric measures
See also
Metric definitions gives the precise computation behind every quantity named on this page.