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Interacting with Clinica

Preparing your data

Clinica pipelines require to have your data organized in the BIDS format. BIDS is currently becoming the standard for data organization in the brain imaging community and we strongly recommend to use it.

If your dataset does not follow this standard, you will need to convert it :

  • If your data are in DICOM format, you can use one of the converters referenced on the BIDS website.
  • Otherwise, Clinica includes converters for public datasets :
Clinica available converters

Clinica and cross-sectional BIDS datasets

If you run Clinica with a dataset containing no timepoints e.g.:

BIDS
└── sub-CLNC0001
    ├── anat
    │   └── sub-CLNC0001_T1w.nii.gz
    └── pet
        ├── sub-CLNC0001_trc-18FFDG_pet.json
        └── sub-CLNC0001_trc-18FFDG_pet.nii.gz
Clinica will propose you to create a new BIDS dataset with a fake timepoint. This will result in a new dataset resembling to: 
BIDS
└── sub-CLNC0001
    └── ses-M000
        ├── anat
        │   └── sub-CLNC0001_ses-M000_T1w.nii.gz
        └── pet
            ├── sub-CLNC0001_ses-M000_trc-18FFDG_pet.json
            └── sub-CLNC0001_ses-M000_trc-18FFDG_pet.nii.gz
You should accept the creation of this dataset, else Clinica will fail.

Tip

If you need to create BIDS compliant datasets or need tutorials on BIDS, you can look at this BIDS Starter Kit.

Clinica command-line interface

Clinica's main usage is through command-line.

See the list of available commands on your terminal

  • Do not hesitate to use -h or --help after any component of the command line, ex : clinica -h, clinica run -h ... to see what options are available.
  • Clinica supports autocompletion for zsh shell users : to see the list of commands, simply type clinica followed by Tab.

In general, a Clinica command-line has the following syntax:

clinica category_of_command command argument options

where the arguments are usually your input/output folders, and where the options look like --flag_1 option_1 --flag_2 option_2.

Components order

Please note that the ordering of options on the command-line is not important, whereas arguments must be given in the exact order specified in the documentation (or in the command line helper).

Categories of command line

The command-line clinica has been divided into four main categories :

clinica run

This category allows the user to run the different image processing and analysis pipelines using the following syntax:

clinica run modality-pipeline bids_directory caps_directory options

"modality" is a prefix that corresponds to the data modality (e.g. T1, DWI, fMRI, PET) or to the category of processing (machine learning, statistics...). If you execute clinica run --help, you can see the list of modality-pipeline available :

Clinica available pipelines

Clinica run logs

Clinica run logs are written in the current working directory by default. A different directory may be specified by setting the CLINICA_LOGGING_DIR environment variable.

clinica convert

These tools allow you to convert unorganized datasets from publicly available neuroimaging studies into a BIDS hierarchy.

Clinica currently includes some converters for public datasets :

Clinica available converters

clinica iotools

iotools is a set of tools that allows the user to handle BIDS and CAPS datasets. It allows generating lists of subjects or merging all tabular data into a single TSV file for analysis with external statistical software packages.

Clinica available IOTools

clinica generate (for developers)

This category allows developers to generate the skeleton for a new pipeline. The syntax is:

clinica generate template "Modality My Pipeline" -d output_folder

The main arguments

BIDS_DIRECTORY and/or CAPS_DIRECTORY

Running a pipeline involves most of the time these two parameters:

  • BIDS_DIRECTORY, which is the input folder containing the dataset in a BIDS hierarchy;
  • CAPS_DIRECTORY, which is the output folder containing the expected results in a CAPS hierarchy. It can be also the input folder containing the dataset in a CAPS hierarchy.

GROUP_LABEL

You will see the GROUP_LABEL argument when working on any group-wise analysis (e.g. template creation from a list of subjects, statistical analysis). This is simply a label name that will define the group of subjects used for this analysis. It will be written in your output CAPS folder, for possible future reuses. For example, an AD group ID label could be used when creating a template for a group of Alzheimer’s disease patients. Any time you would like to use this AD template you will need to provide the group ID used to identify the pipeline output obtained from this group. You might also use CNvsAD, for instance, as group ID for a statistical group comparison between patients with Alzheimer's disease (AD) and cognitively normal (CN) subjects.

Common options for pipelines

--8<-- [start:all] - -tsv / --subjects_sessions_tsv

This flag allows you to specify in a TSV file the participants belonging to your subset. For instance, running the FreeSurfer pipeline on T1w MRI can be done using :

clinica run t1-freesurfer BIDS_PATH OUTPUT_PATH -tsv my_subjects.tsv

participant_id  session_id
sub-CLNC0001    ses-M000
sub-CLNC0001    ses-M018
sub-CLNC0002    ses-M000

Creating the TSV

To make the display clearer the rows here contain successive tabs but that should not happen in an actual TSV.

  • -wd / --working_directory

By default when running a pipeline, a temporary working directory is created. This directory stores all the intermediary inputs and outputs of the different steps of the pipeline. If everything goes well, the output directory is eventually created and the working directory is deleted.

With this option, a working directory of your choice can be specified. It is very useful for the debugging process or if your pipeline crashes. Then, you can relaunch it with the exact same parameters which will allow you to continue from the last successfully executed node. For the pipelines that generate many files, such as dwi-preprocessing (especially if you run it on multiple subjects), a specific drive/partition with enough space can be used to store the working directory.

  • -np / --n_procs

This flag allows you to exploit several cores of your machine to run pipelines in parallel, which is very useful when dealing with numerous subjects and multiple sessions. Thanks to Nipype, even for a single subject, a pipeline can be run in parallel by exploiting the cores available to process simultaneously independent sub-parts. We recommend using your_number_of_cpu - 1 for costly pipelines such as pet-surface-longitudinal.

If you do not specify -np / --n_procs flag, Clinica will detect the number of threads to run in parallel and propose the adequate number of threads to the user.

  • -cn / --caps-name

Use this option if you want to specify the name of the CAPS dataset that will be used inside the dataset_description.json file, at the root of the CAPS folder (see CAPS Specifications for more details). This works if this CAPS dataset does not exist yet, otherwise the existing name will be kept. --8<-- [end:all]

--8<-- [start:freesurfer] - -ap/--atlas_path : In case you wish to use another atlas, specify its folder path with this option. Your atlas will need to be in FreeSurfer gcs format (e.g hemisphere.atlasname_6p0.gcs). The results will be stored in the same folder as the original results with additional files in labels, stats and regional measures. - -overwrite/--overwrite_outputs : Force the overwrite of output files in the CAPS folder with this option. --8<-- [end:freesurfer]

--8<-- [start:linear] - -ui/--uncropped_image : By default, output images are cropped to a fixed matrix size of 169×208×179, 1 mm isotropic voxels. This allows reducing the computing power required when training deep learning models afterwards. Use this option if you do not want to get cropped images. - --random_seed : By default, results are not deterministic. Use this option if you want to obtain a deterministic output. The value you set corresponds to the random seed used by ANTs. This option requires ANTs version 2.3.0 onwards and is also compatible with ANTsPy. --8<-- [end:linear]

--8<-- [start:antspy] - --use-antspy : By default, the pipeline is running with ANTs. Use this flag option if you want to use ANTsPy instead. --8<-- [end:antspy]

--8<-- [start:pet_recon] - -rec/--reconstruction_method: Select only images based on a specific reconstruction method. --8<-- [end:pet_recon]

Optional parameters common to all converters

  • -- subjects_list / - sl : path to a text file containing a list of specific subjects to extract. The expected format is one subject per line :
  001_S_0001
  002_S_0002
  • -- n_procs / - np : Number of cores used to run in parallel. (default: (Number of available CPU minus one))

⚠️ Known issue with Matlab and SPM12

Matlab and SPM12 (whose implementation is based on Matlab) can sometimes randomly crash, causing a rather unreadable error in the console. Those events are unpredictable. In case it occurs to you, please do the following:

  • Check that you have a valid Matlab license.
  • Before relaunching the command line, be sure to remove the content of the working directory (if you specified one).

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