This Python package gives the pipeline used to process the MRI data obtained in the Individual brain Charting Project. More info on the data can be found at IBC public protocols and IBC webpage .
The raw data are available here: OpenfMRI
The result sof typical analysis are give here: NeuroVault
These script make it possible to preprocess the data
- run topup distortion correction
- run motion correction
- run coregistration of the fMRI scans to the individual T1 image
- run spatial normalization of the data
- run a general linear model to obtain brain activity maps for the main contrasts of the experiment.
The core scripts are in the processing
folder
pipeline.py
lunches the full analysis on fMRI data (pre-processing + GLM)glm_only.py
launches GLM analyses on the datasurface_based_analyses
launches surface extraction and registration with Freesurfer; it also projects fMRI data to the surfacesurface_glm_analysis.py
runs glm analyses on the surfacedmri_preprocessing
(WIP) is for diffusion daat. It relies on dipy.anatomical mapping
(WIP) yields T1w, T2w and MWF surrogates from anatomical acquisitions.script_retino.py
yields some post-processing for retinotopic acquisitions (derivation of retinotopic representations from fMRI maps)
Dependences are :
- FSL (topup)
- SPM12 for preprocessing
- Freesurfer for sueface-based analysis
- nipype to call SPM12 functions
- pypreprocess to generate preprocessing reports
- Nilearn for various functions
- Nistats to run general Linear models.
The scripts have been used with the following versions of software and environment:
- FSL v5.0.9
- SPM12 rev 7219
- Nipype v0.14.0
- Pypreprocess v0.0.1.dev
- Nilearn v0.4.0
- Nistats v0.0.1.a
- Python 2.7.2
- Ubuntu 16.04
- More high-level analyses scripts
- Scripts for additional datasets not yet available
- scripts for surface-based analysis
Please feel free to report any issue and propose improvements on Github.
Licensed under simplified BSD.
Bertrand Thirion, 2015 - present