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Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework

  • L. Chaari
  • F. Forbes
  • T. Vincent
  • P. Ciuciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)

Abstract

Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool.

Keywords

Mean Square Error fMRI Data Hemodynamic Response Function Spatial Support fMRI Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • L. Chaari
    • 1
  • F. Forbes
    • 1
  • T. Vincent
    • 1
  • P. Ciuciu
    • 2
  1. 1.Mistis TeamInria Grenoble and LJKFrance
  2. 2.CEA/DSV/I2BM/NeurospinLNAOGif-Sur-YvetteFrance

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