PyHRF: A Python Library for the Analysis of fMRI Data Based on Local Estimation of the Hemodynamic Response Function
Jaime Arias
Philippe Ciuciu
Michel Dojat
Florence Forbes
Aina Frau-Pascual
Thomas Perret
Jan M. Warnking
Video: https://youtu.be/Usyvds1o4lQ
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.
BOLD response, fMRI, hemodynamic response function
DOI10.25080/shinma-7f4c6e7-006