Clinica Documentation

What is Clinica?

Installation

Clinica can be installed on Mac OS X and Linux (CentOS or Debian/Ubuntu) machines, and possibly on Windows computers with a Linux Virtual Machine. We assume that users installing and using Clinica are comfortable with using the command line.

• Installation
• Third-party software

User documentation

Clinica environment

• Interacting with Clinica
• BIDS: the input data structure
• CAPS: the processed data structure

Pipelines (clinica run)

• Anatomical MRI (T1-weighted)
  • t1-linear - Linear processing of T1w MR images: affine registration to the MNI standard space
  • t1-volume - Processing of T1w MR images using SPM: tissue segmentation and spatial normalization
  • t1-freesurfer - Processing of T1w MR images using FreeSurfer: cortical surface, subcortical structures and volumetrics
  • t1-freesurfer-longitudinal - Longitudinal processing of T1w MR images using FreeSurfer: cortical surface, subcortical structures and volumetrics
• Diffusion MRI (DWI)
  • dwi-preprocessing-* - DWI pre-processing: correction of head motion, magnetic susceptibility, eddy current and bias field induced distortions
  • dwi-dti - DTI scalar maps (FA, MD, AD, RD) and spatial normalization: extraction of DTI-based measures (FA, MD, AD, RD)
  • dwi-connectome - Construction of structural connectome: computation of fiber orientation distributions, tractogram and connectome
• PET
  • Introduction to concepts used in the PET pipelines: partial volume correction and standardized uptake value ratio (SUVR) map computation
  • pet-volume - Volume-based processing of PET images: registration to T1w MRI, intensity normalization, partial volume correction and spatial normalization
  • pet-surface - Surface-based processing of PET images: projection of the PET signal onto the subject’s cortical surface
• Statistics
  • statistics-surface - Surface-based mass-univariate analysis with SurfStat
  • statistics-volume - Volume-based mass-univariate analysis with SPM

• Deep Learning

  • deeplearning-prepare-data - Prepare input data for deep learning with PyTorch

• Machine Learning

  • machinelearning-prepare-spatial-svm - Prepare input data for spatially regularized SVM
  • Classification based on machine learning

Dataset converters (clinica convert)

Clinica provides tools to curate several publicly available neuroimaging datasets and convert them to BIDS namely:

• adni-2-bids - ADNI: Alzheimer’s Disease Neuroimaging Initiative
• aibl-2-bids - AIBL: Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing
• nifd-2-bids - NIFD: Neuroimaging in Frontotemporal Dementia
• oasis-2-bids - OASIS: Open Access Series of Imaging Studies

Note

We provide converters for the datasets used in the Aramis Lab. Feel free to contact us if you are interested in another dataset or to contribute!

I/O tools (clinica iotools)

• Data handling tools for BIDS and CAPS compliant datasets

Visualize pipeline outputs (clinica visualize)

Clinica allows visualization of the main outputs of some pipelines. Currently only supported for the t1-freesurfer pipeline.

Clinica at conferences

Find on this page the presentations and demo materials used when we showcase Clinica.

Support

• Report an issue on GitHub
• Use the Clinica Google Group to ask for help!

License

Clinica is distributed under the terms of the MIT license given here.

Citing Clinica

For publications or communications using Clinica, please cite [Routier et al] as well as the references mentioned on the wiki page of the pipelines you used. Each page includes text to cite the software packages that are used by Clinica (for example, citing SPM when using the t1-volume pipeline).

Disclaimer

Clinica is a software for research studies. It is not intended for use in medical routine

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