Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods.
- Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
- S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
- Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
Brain network toolbox
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
To come soon
- Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
- Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF