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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. See here for more details.

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

-tsv / --subjects_sessions_tsv

The -tsv 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 path/to/my/bids/dataset path/where/results/will/be/stored -tsv my_list_of_subjects.tsv

where your TSV file looks as follows:

participant_id  session_id
sub-CLNC0001    ses-M000
sub-CLNC0001    ses-M018
sub-CLNC0002    ses-M000
sub-CLNC0002    ses-M018
sub-CLNC0003    ses-M000

Writing the TSV

Note that to make the display clearer, the rows contain successive tabs, which should not happen in an actual TSV file.

-wd / --working_directory

In every pipeline, a working directory can be specified. This directory gathers all the inputs and outputs of the different steps of the pipeline. It is then very useful for the debugging process. It is specially useful in the case where your pipeline execution crashes and you relaunch it with the exact same parameters, allowing you to continue from the last successfully executed node.

Working directory

If you do not specify any working directory, a temporary one will be created, then deleted at the end if everything went well.

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

The --n_procs 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.

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.

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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