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.
- 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
- Introduction to concepts used in the PET pipelines: partial volume correction and standardized uptake value ratio (SUVR) map computation
pet-linear- Linear processing of PET images: affine registration to the MNI standard space and intensity normalization
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
pet-surface-longitudinal- Surface-based longitudinal processing of PET images: projection of the PET signal onto the subject’s cortical surface
statistics-surface- Surface-based mass-univariate analysis with SurfStat
statistics-volume- Volume-based mass-univariate analysis with SPM
Clinica & deep learning
Since the release of Clinica v0.5.2, preparation of input data for deep learning with PyTorch (aka
deeplearning-prepare-data pipeline) has moved to its sibling project:
the ClinicaDL framework for the reproducible processing of neuroimaging data with deep learning methods.
Dataset converters (
Clinica provides tools to curate several publicly available neuroimaging datasets and convert them to BIDS namely:
adni-to-bids- ADNI: Alzheimer’s Disease Neuroimaging Initiative
aibl-to-bids- AIBL: Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing
habs-to-bids- HABS: Harvard Aging Brain Study
nifd-to-bids- NIFD: Neuroimaging in Frontotemporal Dementia
oasis-to-bids- OASIS: Open Access Series of Imaging Studies
oasis3-to-bids- OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer’s Disease
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 (
Visualize pipeline outputs (
Clinica allows visualization of the main outputs of some pipelines.
Currently only supported for the
Clinica at conferences¶
Find on this page the presentations and demo materials used when we showcase Clinica.
- Check for past answers in the old Clinica Google Group
- Start a discussion on Github
- Report an issue on GitHub
Clinica is distributed under the terms of the MIT license given here.
For publications or communications using Clinica, please cite
[Routier et al., 2021]
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
Clinica is a software for research studies. It is not intended for use in medical routine.