Hemodynamically informed parcellation of cerebral FMRI data

TL;DR

This paper introduces a novel hemodynamically informed parcellation method for fMRI data that improves the accuracy of detecting and estimating brain activity by integrating activation levels into the parcellation process.

Contribution

It proposes a new parcellation approach based on hemodynamic features and Gaussian Mixture models that enhances detection-estimation of brain activity in fMRI analysis.

Findings

Improved accuracy in HRF estimation across brain regions

Enhanced detection of brain activity using the proposed parcellation

Better handling of non-active regions in fMRI data

Abstract

Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories. This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. An hemodynamically-based parcellation method is proposed, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics.