To help you get started, we've collected the most common ways that clinica is being used within popular public projects.

aramis-lab / clinica / clinica / utils / ux.py View on Github

def print_crash_files_and_exit(log_file, working_directory):
    """Print command(s) to type in order to extract details after a Nipype RuntimeError and exit with an exception."""
    from colorama import Fore
    from .filemanip import extract_crash_files_from_log_file
    from .exceptions import ClinicaException
    from .stream import cprint

    cprint('%s\nError details can be found by opening the crash file(s) with the following command(s):%s' %
           (Fore.YELLOW, Fore.RESET))

    crash_files = extract_crash_files_from_log_file(log_file)
    for file in crash_files:
        cprint('%s- nipypecli crash %s%s' %
               (Fore.YELLOW, file,  Fore.RESET))

    cprint('%s\n'
           'If your pipeline crashed due to lack of space of network issues, '
           're-run the pipeline with the working directory (-wd %s).\n'
           'Known issues are displayed here: http://www.clinica.run/doc/InteractingWithClinica/#known-issues\n'
           'Otherwise, you can delete it.%s' %
           (Fore.YELLOW, working_directory, Fore.RESET))
    # Force the display of "Documentation can be found..."
    raise ClinicaException('')

View more ways to use clinica

Clinica

Software platform for clinical neuroimaging studies

Homepage | Documentation | Paper | Forum | See also: AD-ML, AD-DL, ClinicaDL

About The Project

Clinica is a software platform for clinical research studies involving patients with neurological and psychiatric diseases and the acquisition of multimodal data (neuroimaging, clinical and cognitive evaluations, genetics...), most often with longitudinal follow-up.

Clinica is command-line driven and written in Python. It uses the Nipype system for pipelining and combines widely-used software packages for neuroimaging data analysis (ANTs, FreeSurfer, FSL, MRtrix, PETPVC, SPM), machine learning (Scikit-learn) and the BIDS standard for data organization.

Clinica provides tools to convert publicly available neuroimaging datasets into BIDS, namely:

• ADNI: Alzheimer’s Disease Neuroimaging Initiative
• AIBL: Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing
• HABS: Harvard Aging Brain Study
• NIFD: Neuroimaging in Frontotemporal Dementia
• OASIS: Open Access Series of Imaging Studies
• OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer’s Disease

Clinica can process any BIDS-compliant dataset with a set of complex processing pipelines involving different software packages for the analysis of neuroimaging data (T1-weighted MRI, diffusion MRI and PET data). It also provides integration between feature extraction and statistics, machine learning or deep learning.

Clinica is also showcased as a framework for the reproducible classification of Alzheimer's disease using machine learning and deep learning.

Getting Started

> Full instructions for installation and additional information can be found in the user documentation.

Using pipx (recommended)

Clinica can be easily installed and updated using pipx.

pipx install clinica

Using pip

pip install clinica

Using Conda

Clinica relies on multiple third-party tools to perform processing.

An environment file is provided in this repository to facilitate their installation in a Conda environment:

git clone https://github.com/aramis-lab/clinica && cd clinica
conda env create
conda activate clinica

After activation, use pip to install Clinica.

Additional dependencies (required)

Depending on the pipeline that you want to use, you need to install pipeline-specific interfaces. Some of which uses a different runtime or use incompatible licensing terms, which prevent their distribution alongside Clinica. Not all the dependencies are necessary to run Clinica. Please refer to this page to determine which third-party libraries you need to install.

Example

Diagram illustrating the Clinica pipelines involved when performing a group comparison of FDG PET data projected on the cortical surface between patients with Alzheimer's disease and healthy controls from the ADNI database:

1. Clinical and neuroimaging data are downloaded from the ADNI website and data are converted into BIDS with the adni-to-bids converter.
2. Estimation of the cortical and white surface is then produced by the t1-freesurfer pipeline.
3. FDG PET data can be projected on the subject’s cortical surface and normalized to the FsAverage template from FreeSurfer using the pet-surface pipeline.
4. TSV file with demographic information of the population studied is given to the statistics-surface pipeline to generate the results of the group comparison.

> For more examples and details, please refer to the > Documentation.

Support

• Check for past answers in the old Clinica Google Group
• Start a discussion on Github
• Report an issue on GitHub

Contributing

We encourage you to contribute to Clinica! Please check out the Contributing to Clinica guide for guidelines about how to proceed. Do not hesitate to ask questions if something is not clear for you, report an issue, etc.

License

This software is distributed under the MIT License. See license file for more information.

Citing us

• Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T., Lu, P., Marcoux, A., Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.: Clinica: An Open Source Software Platform for Reproducible Clinical Neuroscience Studies Frontiers in Neuroinformatics, 2021 doi:10.3389/fninf.2021.689675

Related Repositories

• AD-DL: Classification of Alzheimer's disease status with convolutional neural networks.
• AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning.
• ClinicaDL: Framework for the reproducible processing of neuroimaging data with deep learning methods.