Conference Paper

FMRI analysis through Bayesian variable selection with a spatial prior

Dept. of Stat., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
DOI: 10.1109/ISBI.2009.5193147 Conference: Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Source: IEEE Xplore


This paper presents a novel spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation of functional magnetic resonance imaging (fMRI) data. A Bayesian variable selection approach is used to induce shrinkage and sparsity, with a spatial prior on latent variables representing activated hemodynamic response components. Then, the activation map is generated from the full spectrum of posterior inference constructed through a Markov chain Monte Carlo scheme, and HRFs at different voxels are estimated non-parametrically with information pooling from neighboring voxels. By integrating functional activation detection and HRFs estimation in a unified framework, our method is more robust to noise and less sensitive to model mis-specification.

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    • "Moreover, FIR-based inference may lack robustness and often yields disrupted shapes that are difficult to interpret, due to over-fitting problems. Hence, to cope with this issue, temporal [22] [12] [36] [6] but also spatial regularization [21] [54] [11] [34] [42] [17] [28] [14] [7] [53] have been introduced in models to improve estimation accuracy. Such estimation methods can be categorized according to three main criteria: i.) the HRF model they rely on (parametric [14] [30] or FIR [12] [11] [1] [6] [35] [53]), ii.) the type of inference that they perform (univariate [25] [14] [1] vs. multivariate [53] [51]) and iii.) the adaptability degree of regularization they introduce, namely the supervised [30] vs. unsupervised [14] [53] [51] setting of hyper-parameters. "
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