Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF.
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: (1) the localization of cerebral regions that elicit evoked activity and (2) the estimation of activation dynamics also known as Hemodynamic Response Function (HRF) recovery. 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 (1), the classical voxelwise GLM procedure is also available through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models are concerned with HRF estimation (2) and are specifically implemented in pyhrf. Several parcellation tools are also integrated such as spatial and functional clustering. 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 volume-based data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets, which are used in this paper to compare the outcome of the different available approaches. The artificial fMRI data generator is also described to illustrate how to simulate different activation configurations, HRF shapes or nuisance components. To cope with the high computational needs for inference, pyhrf handles distributing computing by exploiting cluster units as well as multi-core machines. Finally, a dedicated viewer is presented, which handles n-dimensional images and provides suitable features to explore whole brain hemodynamics (time series, maps, ROI mask overlay).
INRIA, MISTIS, LJK, Grenoble University Grenoble, France ; UNATI/INRIA Saclay, Parietal, CEA/DSV/I2BM NeuroSpin center Gif-sur-Yvette, France.
Thomas
T
Vincent
UNATI/INRIA Saclay, Parietal, CEA/DSV/I2BM NeuroSpin center Gif-sur-Yvette, France ; INRIA, Parietal, NeuroSpin center Gif-sur-Yvette, France.
Solveig
S
Badillo
UNATI/INRIA Saclay, Parietal, CEA/DSV/I2BM NeuroSpin center Gif-sur-Yvette, France ; CNRS, UMR 5219, Statistics and Probability Team, Toulouse Mathematics Institute Toulouse, France.
Laurent
L
Risser
INRIA, MISTIS, LJK, Grenoble University Grenoble, France ; INP-ENSEEIHT/CNRS UMR 5505, TCI, IRIT, University of Toulouse Toulouse, France.
Lotfi
L
Chaari
INRIA, MISTIS, LJK, Grenoble University Grenoble, France.
Christine
C
Bakhous
INRIA, MISTIS, LJK, Grenoble University Grenoble, France.
Florence
F
Forbes
UNATI/INRIA Saclay, Parietal, CEA/DSV/I2BM NeuroSpin center Gif-sur-Yvette, France ; INRIA, Parietal, NeuroSpin center Gif-sur-Yvette, France.
Philippe
P
Ciuciu
Bayesian inference; fMRI; medical imaging analysis; python; scientific computing