Data curation and processing
Data curation and ITedit
Researchers involved:
- PIs: Christophe Phillips
- Postdocs: Nikita Beliy, Christian Degueldre
The data being complex and highly valuable, we are also interested in their safe storage, complete description and clear organization. We are thus concerned by data curation and standardisation, which ensure their easier processing and sharing. This lead to a few contributions to the “Brain Imaging Data Structure” (BIDS) initiative for qMRI, PET and EEG data.
We also have some experience with data sharing across (international) research networks, while respecting GDPR rules. This typically involves setting up specific servers, for example XNAT.
Statistics, modelling & inference methods edit
Researchers involved:
- PIs: Christophe Phillips, Mohamed Bahri
- Postdocs: Nikita Beliy, Gregory Hammad
The Development in neuroimaging data acquisition and modeling lab is overall supporting the analysis of all the neuroimaging data (all flavours of MRI, PET, EEG, actigraphy,…) used other by the researchers. We have developed some expertise in data processing like
- image spatial (pre-)processing, such as realignment & coregistration, segmentation including lesioned brain, normalization in standard space, etc.;
- “statistical parametric mapping” for (massively) univariate classic inference on population(s) of subjects data;
- multivariate & multimodal SPM (mSPM) analysis. This allows the combination of multiple modalities in a single “general linear model (GLM) and inference;
- multivariate & multimodal pattern recognition for predictive models. The “machine learning” and inference techniques were gathered in the open source “Pattern Recognition for Neuroimagin Toolbox” (PRoNTo).
