Title
Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling
Abstract
Multi-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on within-subject studies, which are usually conducted using a massively univariate approach. In this paper, we investigate the impact of a novel within-subject analysis on group studies. Our approach is based on the use of spatial mixture models (SMM) in a. joint detection-estimation framework (JDE) [1]. This setting allows us to characterise the hemodynamic filter at a regional scale and therefore to account for its spatial variability. As the subject- specific BOLD effects enter as input parameters in the computation of group statistics, we then compare two kinds of Random effect analyses (RFX). The first one takes the estimated BOLD effects computed by SPM1 as inputs while the second one considers the results of our JDE scheme. We finally show on a real dataset of 15 subjects that brain activations appear more spatially resolved using SMM instead of SPM and that a better sensitivity is achieved. Moreover, the JDE framework allows to assess the regional inter-subject variability of the brain dynamics.
Year
DOI
Venue
2008
10.1109/ISBI.2008.4541233
Paris
Keywords
Field
DocType
biomedical MRI,data analysis,medical image processing,BOLD effects,FMRI group study,SPM1 computation,functional magnetic resonance imaging,joint detection estimation framework,nonparametric bold signal modeling,random effect analyses,spatial mixture models,RFX analysis,detection-estimation,fMRI
Computer vision,Signal processing,Random effects model,Pattern recognition,Functional magnetic resonance imaging,Computer science,Nonparametric statistics,Artificial intelligence,Spatial variability,Neuroimaging,Univariate,Mixture model
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4244-2003-2
3
PageRank 
References 
Authors
0.45
7
4
Name
Order
Citations
PageRank
Philippe Ciuciu145250.82
Thomas Vincent232027.52
Anne-Laure Fouque3101.68
Alexis Roche413913.01