Name
Abstract | ||
|---|---|---|
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM de- inition at the subject level and makes also the classical strategy of spatial Gaussian filtering deprecated. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TMI.2010.2042064 | Medical Imaging, IEEE Transactions |
Keywords | Field | DocType |
Bayes methods,bioelectric potentials,biomedical MRI,brain,extrapolation,haemodynamics,physiological models,time series,Bayesian formalism,Ising fields,brain,detection-estimation framework,evoked activity,extrapolation technique,fMRI,general linear model,hemodynamic response estimation,independent mixture models,signal-to-noise ratio,spatial mixture models,spatially adaptive mixture modeling,supervised SMM,Bayesian analysis,Ising field,MCMC,Markov random field (MRF),estimation of partition function,functional magnetic resonance imaging (fMRI),joint detection estimation,spatial mixture models | Active shape model,Time series,Spatial correlation,Pattern recognition,Computer science,Filter (signal processing),Nonparametric statistics,Gaussian,Artificial intelligence,Mixture model,Bayesian probability | Journal |
Volume | Issue | ISSN |
29 | 4 | 0278-0062 |
Citations | PageRank | References |
43 | 1.85 | 44 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Vincent | 1 | 320 | 27.52 |
| Laurent Risser | 2 | 43 | 1.85 |
| P. Ciuciu | 3 | 99 | 6.15 |