Interacting with Clinica¶
Preparing your data¶
The easiest way to use Clinica is to have your data organized using the BIDS standard. 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 to it:
- If your data is in DICOM format, you can use one of the converters from the BIDS website.
- Otherwise, Clinica includes converters for public datasets such as ADNI, AIBL and OASIS. See here for more details.
Clinica command-line interface¶
Clinica's main usage is through command-line. Clinica supports autocompletion: 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
--flag_1 option_1 --flag_2 option_2. The table below summarizes the different sections available in clinica:
|Type of command||Description||Examples of command|
||Tools for preparing your dataset||
||Allows you to run different pipelines||
||Used to convert dataset||
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).
The main arguments¶
Running a pipeline involves most 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.
A fairly common option is the ability to use a TSV file using the flag
-tsv. With this file, you can process your data only on a subset of your subjects (see below for more information).
For instance, running the FreeSurfer pipeline on T1 MRI can be done using :
clinica run t1-freesurfer-cross-sectional path/to/my/bids/dataset path/where/results/will/be/stored -tsv my_list_of_subjects.tsv
participant_id session_id sub-CLNC0001 ses-M00 sub-CLNC0001 ses-M18 sub-CLNC0001 ses-M36 sub-CLNC0002 ses-M00 sub-CLNC0002 ses-M18 sub-CLNC0002 ses-M36 sub-CLNC0003 ses-M00
There is one flag that you will meet when working on any group-wise analysis (e.g. template creation from a list of subjects, statistical analysis): the
group_id parameter. 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 in case you create 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_idused to identify the pipeline output obtained from this group. You might also use ‘CNvsAD’, for instance, as
group_id for a statistical group comparison.
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 to continue from the last successfully executed node. 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
t1-freesurfer (especially if you run it on multiple subjects), a specific drive/partition with enough space can be used to store the working directory
Categories of command line¶
clinica has been divided into four main categories.
This category allows the user to run the different pipelines. Most of the time, it looks like this:
clinica run modality-pipeline bids_directory caps_directory subjects_sessions_tsv
clinica run --help, you can see the list of
modality-pipelineavailable: they correspond to the different pipelines displayed on the main page of the documentation.
iotools is a set of tools that allows the user to handle his dataset before using them in a pipeline.
Conversion of publicly available neuroimaging datasets to BIDS¶
Data handling tools¶
These tools provide easy interaction mechanisms with BIDS and CAPS compliant datasets, including generating subjects list or merging all tabular data into a single tsv for analysis with external statistical software. See here for more details.