Hemodynamically informed parcellation of cerebral 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.
Keywords
Cite
@article{arxiv.1501.01121,
title = {Hemodynamically informed parcellation of cerebral FMRI data},
author = {Aina Frau-Pascual and Thomas Vincent and Florence Forbes and Philippe Ciuciu},
journal= {arXiv preprint arXiv:1501.01121},
year = {2015}
}