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PyHRF: A Python Library for the Analysis of fMRI Data Based on Local Estimation of the Hemodynamic Response Function

Jaime Arias
Univ. Grenoble Alpes, Inria, MISTIS, LJK, F-38000 Grenoble, France

Philippe Ciuciu
CEA/NeuroSpin and Inria Saclay, Parietal, France

Michel Dojat
Inserm, U1216, F-38000 Grenoble, France
Univ. Grenoble Alpes, GIN, F-38000 Grenoble, France

Florence Forbes
Univ. Grenoble Alpes, Inria, MISTIS, LJK, F-38000 Grenoble, France

Aina Frau-Pascual
Univ. Grenoble Alpes, Inria, MISTIS, LJK, F-38000 Grenoble, France

Thomas Perret
Univ. Grenoble Alpes, Inria, MISTIS, LJK, F-38000 Grenoble, France

Jan M. Warnking
Inserm, U1216, F-38000 Grenoble, France
Univ. Grenoble Alpes, GIN, F-38000 Grenoble, France

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique that allows the non-invasive study of brain function. It is based on the hemodynamic variations induced by changes in cerebral synaptic activity following sensory or cognitive stimulation. The measured signal depends on the variation of blood oxygenation level (BOLD signal) which is related to brain activity: a decrease in deoxyhemoglobin concentration induces an increase in BOLD signal. The BOLD signal is delayed with respect to changes in synaptic activity, which can be modeled as a convolution with the Hemodynamic Response Function (HRF) whose exact form is unknown and fluctuates with various parameters such as age, brain region or physiological conditions.

In this paper we present PyHRF, a software to analyze fMRI data using a Joint Detection-Estimation (JDE) approach. It jointly detects cortical activation and estimates the HRF. In contrast to existing tools, PyHRF estimates the HRF instead of considering it as a given constant in the entire brain. Here, we present an overview of the package and showcase its performance with a real case in order to demonstrate that PyHRF is a suitable tool for clinical applications.

Keywords

BOLD response, fMRI, hemodynamic response function

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