Commands & Actions

Before we implement a CuBIDS workflow, let’s define the terminology and take a look at some of the commands available in the software.

More definitions

Key Group

A Key Group is a unique set of BIDS key-value pairs, excluding identifiers such as subject and session. For example the files:

bids-root/sub-1/ses-1/func/sub-1_ses-1_acq-mb_dir-PA_task-rest_bold.nii.gz
bids-root/sub-1/ses-2/func/sub-1_ses-2_acq-mb_dir_PA_task-rest_bold.nii.gz
bids-root/sub-2/ses-1/func/sub-2_ses-1_acq-mb_dir-PA_task-rest_bold.nii.gz

Would all share the same Key Group. If these scans were all acquired as a part of the same study on the same scanner with exactly the same acquisition parameters, this naming convention would suffice.

However, in large multi-scanner, multi-site, or longitudinal studies where acquisition parameters change over time, it’s possible that the same Key Group could comprise of scans that differ in important ways.

CuBIDS examines all acquisitions within a Key Group to see if there are any images that differ in a set of important acquisition parameters. The subsets of consistent acquisition parameter sets within a Key Group are called a Parameter Group.

Parameter Group

Even though two images may belong to the same Key Group and are valid BIDS, they may have images with different acquisition parameters. There is nothing fundamentally wrong with this — the bids-validator will often simply flag these differences, with a Warning, but not necessarily suggest changes. That being said, there can be detrimental consequences downstream if the different parameters cause the same preprocessing pipelines to configure differently to images of the same Key Group.

Acquisition Group

Acquisition Groups are sets of subjects who’s images belong to all the same Key and Parameter Groups. The Acquistion Groups that subjects belong to are listed in _AcqGrouping.csv, while the Key Groups and Parameter Groups that define each Acquisition Group are noted in _AcqGroupingInfo.txt.

Acquisition Group

We define an “Acquisition Group” as a collection of sessions across participants that contain the exact same set of Key and Parameter Groups. Since Key Groups are based on the BIDS filenames—and therefore both MRI image type and acquisition specific—each BIDS session directory contains images that belong to a set of Parameter Groups. CuBIDS assigns each session––or set of Parameter Groups––to an Acquisition Group such that all sessions in an Acquisition Group possesses an identical set of scan acquisitions and metadata parameters across all image modalities present in the dataset. We find Acquisition Groups to be a particularly useful categorization of BIDS data, as they identify homogeneous sets of sessions (not individual scans) in a large dataset. They are also useful for expediting the testing of pipelines; if a BIDS App runs successfully on a single subject from each Acquisition Group, one can be confident that it will handle all combinations of scanning parameters in the entire dataset.

The _summary.tsv File

This file contains all the detected Key Groups and Parameter Groups. It provides an opportunity to evaluate your data and decide how to handle heterogeneity.

Below is an example _summary.tsv of the run-1 DWI Key Group in the PNC [1]. This reflects the original data that has been converted to BIDS using a heuristic. It is similar to what you will see when you first use this functionality:

Pre Apply Groupings

Notes	ManualCheck	MergeInto	RenameKeyGroup	KeyParamGroup	KeyGroup	ParamGroup	Counts	Dim1Size	Dim2Size	Dim3Size	EchoTime	EffectiveEchoSpacing	FlipAngle	HasFieldmap	KeyGroupCount	Modality	NSliceTimes	NumVolumes	Obliquity	ParallelReductionFactorInPlane	PartialFourier	PhaseEncodingDirection	RepetitionTime	TotalReadoutTime	UsedAsFieldmap	VoxelSizeDim1	VoxelSizeDim2	VoxelSizeDim3
			acquisition-VARIANTNoFmap_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__2	datatype-dwi_run-1_suffix-dwi	2	25	128	128	70	0.082	0.000267	90	FALSE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__3	datatype-dwi_run-1_suffix-dwi	3	6	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	9.0	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__4	datatype-dwi_run-1_suffix-dwi	4	3	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	9.8	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTDim3SizeVoxelSizeDim3_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__5	datatype-dwi_run-1_suffix-dwi	5	2	128	128	46	0.082	0.000267	90	TRUE	1426	dwi	46	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	3.0
			acquisition-VARIANTEchoTimeEffectiveEchoSpacingRepetitionTimeTotalReadoutTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__6	datatype-dwi_run-1_suffix-dwi	6	1	128	128	70	0.102	0.0008	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	12.3	0.102	FALSE	1.875	1.875	2.0
			acquisition-VARIANTObliquity_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__7	datatype-dwi_run-1_suffix-dwi	7	1	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	TRUE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0

The _files.tsv file

This file contains one row per imaging file in the BIDS directory. You won’t need to edit this file directly, but it keeps track of every file’s assignment to Key and Parameter Groups.

Modifying Key and Parameter Group Assignments

Sometimes we see that there are important differences in acquisition parameters within a Key Group. If these differences impact how a pipeline will process the data, it makes sense to assign the scans in that Parameter Group to a different Key Group (i.e. assign them a different BIDS name). This can be accomplished by editing the empty columns in the _summary.csv file produced by cubids-group.

Once the columns have been edited you can apply the changes to BIDS data using

$ cubids-apply /bids/dir keyparam_edited new_keyparam_prefix

The changes in keyparam_edited_summary.csv will be applied to the BIDS data in /bids/dir and the new Key and Parameter groups will be saved to csv files starting with new_keyparam_prefix. Note: fieldmaps keygroups with variant parameters will be identified but not renamed.

The _AcqGrouping.tsv file

The _AcqGrouping.tsv file organizes the dataset by session and tags each one with its Acquisition Group number.

The _AcqGroupInfo.txt file

The _AcqGroupInfo.txt file lists all Key Groups that belong to a given Acquisition Group along with the number of sessions each group possesses.

Visualizing and summarizing metadata heterogenaity

Use cubids-group to generate your dataset’s Key Groups and Parameter Groups:

$ cubids-group FULL/PATH/TO/BIDS/DIR FULL/PATH/TO/v0

This will output four files, including the summary and files tsvs described above, prefixed by the second argument v0.

Applying changes

The cubids-apply program provides an easy way for users to manipulate their datasets. Specifically, cubids-apply can rename files according to the users’ specification in a tracked and organized way. Here, the summary.tsv functions as an interface modifications; users can mark Parameter Groups they want to rename (or delete) in a dedicated column of the summary.tsv and pass that edited tsv as an argument to cubids-apply.

Detecting Variant Groups

Additionally, cubids-apply can automatically rename files in Variant Groups based on their scanning parameters that vary from those in their Key Groups’ Dominant Parameter Groups. Renaming is automatically suggested when the summary.tsv is generated from a cubids-group run, with the suggested new name listed in the tsv’s “Rename Key Group” column. CuBIDS populates this column for all Variant Groups—e.g., every Parameter Group except the Dominant one. Specifically, CuBIDS will suggest renaming all non-dominant Parameter Group to include VARIANT* in their acquisition field where * is the reason the Parameter Group varies from the Dominant Group. For example, when CuBIDS encounters a Parameter Group with a repetition time that varies from the one present in the Dominant Group, it will automatically suggest renaming all scans in that Variant Group to include acquisition-VARIANTRepetitionTime in their filenames. When the user runs cubids-apply, filenames will get renamed according to the auto-generated names in the “Rename Key Group” column in the summary.tsv

Deleting a mistake

To remove files in a Parameter Group from your BIDS data, you simply set the MergeInto value to 0. We see in our data that there is a strange scan that has a RepetitionTime of 12.3 seconds and is also variant with respect to EffectiveEchoSpacing and EchoTime. We elect to remove this scan from our dataset because we do not want these parameters to affect our analyses. To remove these files from your BIDS data, add a 0 to MergeInto and save the new tsv as v0_edited_summary.tsv

Pre Apply Groupings with Deletion Requested

Notes	ManualCheck	MergeInto	RenameKeyGroup	KeyParamGroup	KeyGroup	ParamGroup	Counts	Dim1Size	Dim2Size	Dim3Size	EchoTime	EffectiveEchoSpacing	FlipAngle	HasFieldmap	KeyGroupCount	Modality	NSliceTimes	NumVolumes	Obliquity	ParallelReductionFactorInPlane	PartialFourier	PhaseEncodingDirection	RepetitionTime	TotalReadoutTime	UsedAsFieldmap	VoxelSizeDim1	VoxelSizeDim2	VoxelSizeDim3
			acquisition-VARIANTNoFmap_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__2	datatype-dwi_run-1_suffix-dwi	2	25	128	128	70	0.082	0.000267	90	FALSE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__3	datatype-dwi_run-1_suffix-dwi	3	6	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	9.0	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__4	datatype-dwi_run-1_suffix-dwi	4	3	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	9.8	0.034	FALSE	1.875	1.875	2.0
			acquisition-VARIANTDim3SizeVoxelSizeDim3_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__5	datatype-dwi_run-1_suffix-dwi	5	2	128	128	46	0.082	0.000267	90	TRUE	1426	dwi	46	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	3.0
		0	acquisition-VARIANTEchoTimeEffectiveEchoSpacingRepetitionTimeTotalReadoutTime_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__6	datatype-dwi_run-1_suffix-dwi	6	1	128	128	70	0.102	0.0008	90	TRUE	1426	dwi	70	35.0	FALSE	3.0	0.75	j-	12.3	0.102	FALSE	1.875	1.875	2.0
			acquisition-VARIANTObliquity_datatype-dwi_run-1_suffix-dwi	datatype-dwi_run-1_suffix-dwi__7	datatype-dwi_run-1_suffix-dwi	7	1	128	128	70	0.082	0.000267	90	TRUE	1426	dwi	70	35.0	TRUE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0

In this example, users can apply the changes to BIDS data using the following command:

$ cubids-apply FULL/PATH/TO/BIDS/DIR FULL/PATH/TO/v0_edited_summary.tsv FULL/PATH/TO/v0_files.tsv FULL/PATH/TO/v1

The changes in v0_edited_summary.tsv will be applied to the BIDS data and the new Key and Parameter Groups will be saved to tsv files starting with v1.

Applying these changes we would see:

Post Apply Groupings

Notes	ManualCheck	MergeInto	RenameKeyGroup	KeyParamGroup	KeyGroup	ParamGroup	Counts	Dim1Size	Dim2Size	Dim3Size	EchoTime	EffectiveEchoSpacing	FlipAngle	HasFieldmap	KeyGroupCount	Modality	NSliceTimes	NumVolumes	Obliquity	ParallelReductionFactorInPlane	PartialFourier	PhaseEncodingDirection	RepetitionTime	TotalReadoutTime	UsedAsFieldmap	VoxelSizeDim1	VoxelSizeDim2	VoxelSizeDim3
				datatype-dwi_run-1_suffix-dwi__1	datatype-dwi_run-1_suffix-dwi	1	1388	128	128	70	0.082	0.000267	90	TRUE	1388	dwi	70	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0
				acquisition-VARIANTNoFmap_datatype-dwi_run-1_suffix-dwi__1	acquisition-VARIANTNoFmap_datatype-dwi_run-1_suffix-dwi	1	25	128	128	70	0.082	0.000267	90	FALSE	25	dwi	70	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0
				acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi__1	acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	1	6	128	128	70	0.082	0.000267	90	TRUE	9	dwi	70	35.0	FALSE	3.0	0.75	j-	9.0	0.034	FALSE	1.875	1.875	2.0
				acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi__2	acquisition-VARIANTRepetitionTime_datatype-dwi_run-1_suffix-dwi	2	3	128	128	70	0.082	0.000267	90	TRUE	9	dwi	70	35.0	FALSE	3.0	0.75	j-	9.8	0.034	FALSE	1.875	1.875	2.0
				acquisition-VARIANTDim3SizeVoxelSizeDim3_datatype-dwi_run-1_suffix-dwi__1	acquisition-VARIANTDim3SizeVoxelSizeDim3_datatype-dwi_run-1_suffix-dwi	1	2	128	128	46	0.082	0.000267	90	TRUE	2	dwi	46	35.0	FALSE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	3.0
				acquisition-VARIANTEchoTimeEffectiveEchoSpacingRepetitionTimeTotalReadoutTime_datatype-dwi_run-1_suffix-dwi__1	acquisition-VARIANTEchoTimeEffectiveEchoSpacingRepetitionTimeTotalReadoutTime_datatype-dwi_run-1_suffix-dwi	1	1	128	128	70	0.102	0.0008	90	TRUE	1	dwi	70	35.0	FALSE	3.0	0.75	j-	12.3	0.102	FALSE	1.875	1.875	2.0
				acquisition-VARIANTObliquity_datatype-dwi_run-1_suffix-dwi__1	acquisition-VARIANTObliquity_datatype-dwi_run-1_suffix-dwi	1	1	128	128	70	0.082	0.000267	90	TRUE	1	dwi	70	35.0	TRUE	3.0	0.75	j-	8.1	0.034	FALSE	1.875	1.875	2.0

Command line tools

With that brief introduction done, we can introduce the full gamut of CuBIDS command line tools:

cubids_add_nifti_info()

Add information from nifti files to the dataset’s sidecars.

cubids_apply()

Apply the tsv changes.

cubids_copy_exemplars()

Create and save a directory with one subject from each acquisition group.

cubids_datalad_save()

Perform datalad save.

cubids_group()

Find key and param groups.

cubids_print_metadata_fields()

Print unique metadata fields.

cubids_purge()

Purge scan associations.

cubids_remove_metadata_fields()

Delete fields from metadata.

cubids_undo()

Revert the most recent commit.

cubids_validate()

Run the bids validator.

Customizable configuration

CuBIDS also features an optional, customizable, MRI image type-specific configuration file. This file can be passed as an argument to cubids-group and cubids-apply using the –-config flag and allows users to customize grouping settings based on MRI image type and parameter. Each Key Group is associated with one (and only one) MRI image type, as BIDS filenames include MRI image type-specific values as their suffixes. This easy-to-modify configuration file provides several benefits to curation. First, it allows users to add and remove metadata parameters from the set that determines groupings. This can be very useful if a user deems a specific metadata parameter irrelevant and wishes to collapse variation based on that parameter into a single Parameter Group. Second, the configuration file allows users to apply tolerances for parameters with numerical values. This functionality allows users to avoid very small differences in scanning parameters (i.e., a TR of 3.0s vs 3.0001s) being split into different Parameter Groups. Third, the configuration file allows users to determine which scanning parameters are listed in the acquisition field when auto-renaming is applied to Variant Groups.

Exemplar testing

In addition to facilitating curation of large, heterogeneous BIDS datasets, CuBIDS also prepares datasets for testing BIDS Apps. This portion of the CuBIDS workflow relies on the concept of the Acquisition Group: a set of sessions that have identical scan types and metadata across all imaging modalities present in the session set. Specifically, cubids-copy-exemplars copies one subject from each Acquisition Group into a separate directory, which we call an Exemplar Dataset. Since the Exemplar Dataset contains one randomly selected subject from each unique Acquisition Group in the dataset, it will be a valid BIDS dataset that spans the entire metadata parameter space of the full study. If users run cubids-copy-exemplars with the –-use-datalad flag, the program will ensure that the Exemplar Dataset is tracked and saved in DataLad. If the user chooses to forgo this flag, the Exemplar Dataset will be a standard directory located on the filesystem. Once the Exemplar Dataset has been created, a user can test it with a BIDS App (e.g., fMRIPrep or QSIPrep) to ensure that each unique set of scanning parameters will pass through the pipelines successfully. Because BIDS Apps auto-configure workflows based on the metadata encountered, they will process all scans in each Acquisition Group in the same way. By first verifying that BIDS Apps perform as intended on the small sub-sample of participants present in the Exemplar Dataset (that spans the full variation of the metadata), users can confidently move forward processing the data of the complete BIDS dataset.

In the next section, we’ll introduce DataLad and walk through a real example.

Footnotes

[1]

PNC: The Philadelphia Developmental Cohort.