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Section: Software and Platforms

The PyHRF software

Participants : Christine Bakhous, Florence Forbes, Thomas Vincent.

Joint work with: Philippe Ciuciu and Solveig Badillo from Parietal Team Inria and CEA NeuroSpin, Lotfi Chaari and Laurent Risser from Toulouse University.

As part of fMRI data analysis, the PyHRF package ( ) provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (i) the localization of cerebral regions that elicit evoked activity and (ii) the estimation of the activation dynamics also referenced to as the recovery of the Hemodynamic Response Function (HRF). To tackle these two problems, PyHRF implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With respect to the sole detection issue (i), the classical voxelwise GLM procedure is also available through NIPY, whereas Finite Impulse Response (FIR) and temporally regularized FIR models are implemented to deal with the HRF estimation concern (ii). Several parcellation tools are also integrated such as spatial and functional clusterings. Parcellations may be used for spatial averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates in the JDE approach. These analysis procedures can be applied either to volumic data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets. To cope with the high computational needs for inference, PyHRF handles distributing computing by exploiting cluster units as well as multiple cores computers. Finally, a dedicated viewer is available which handles n-dimensional images and provides suitable features for exploring whole brain hemodynamics (display of time series, maps, ROI mask overlay). A paper under revision for Frontiers in Neuroinformatics gives more details on the current PyHRF functionalities.