Abstract
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).
Highlights
As Magnetic Resonance Imaging (MRI) is a growing imaging modality in neuroscience, the need for powerful tools to explore the increasing amount of data is more and more significant
As the employed methodology mainly uses linear systems, dynamic processes are summarized within the so-called Hemodynamic Response Function (HRF), which is the impulse response that links neuronal activity to the Functional MRI (fMRI) signal
The package offers various tools to analyze evoked fMRI data ranging from spatial mappings such as those provided by the General Linear Model (GLM) framework (Friston et al, 1995) to finer hemodynamics models as provided by the joint detectionestimation (JDE) approach described in Makni et al (2005, 2008) and Vincent et al (2010)
Summary
As Magnetic Resonance Imaging (MRI) is a growing imaging modality in neuroscience, the need for powerful tools to explore the increasing amount of data is more and more significant. The higher temporal resolution combined with higher SNR allows for a better recovery of dynamic processes so that we are no longer limited to only static mappings of cerebral activity. In this context, pyhrf aims to extract dynamic features from fMRI data, especially the Blood Oxygenated Level Dependent (BOLD) modality (Ogawa et al, 1990). As the employed methodology mainly uses linear systems, dynamic processes are summarized within the so-called Hemodynamic Response Function (HRF), which is the impulse response that links neuronal activity to the fMRI signal. The JDE approach is a spatially adaptive GLM built on unknown parcel-dependent HRFs with spatio-temporal regularization
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