clinica 0.9.1

Description:

clinica 0.9.1

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:


Clinical and neuroimaging data are downloaded from the ADNI website and data
are converted into BIDS with the adni-to-bids
converter.
Estimation of the cortical and white surface is then produced by the
t1-freesurfer
pipeline.
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.
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.