Assessing brain activity through spatial bayesian variable selection
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- 作者：Smith ; Michael ; Pü ; tz ; Benno ; Auer ; Dorothee ; Fahrmeir ; Ludwig
- 关键词：Activation probabilities ; Anatomical prior information ; Bayesian spatial modeling ; Edge-preservation ; fMRI experiments ; Ising prior ; Markov chain Monte Carlo sampling
- 刊名：NeuroImage
- 出版年：2003
- 出版时间：October, 2003
- 年：2003
- 卷：20
- 期：2
- 页码：802-815
- 全文大小：674 K
文摘
Statistical parametric mapping (SPM), relying on the general linear model and classical hypothesis testing, is a benchmark tool for assessing human brain activity using data from fMRI experiments. Friston et al. discuss some limitations of this frequentist approach and point out promising Bayesian perspectives. In particular, a Bayesian formulation allows explicit modeling and estimation of activation probabilities. In this study, we directly address this issue and develop a new regression based approach using spatial Bayesian variable selection. Our method has several advantages. First, spatial correlation is directly modeled for activation probabilities and indirectly for activation amplitudes. As a consequence, there is no need for spatial adjustment in a postprocessing step. Second, anatomical prior information, such as the distribution of grey matter or expert knowledge, can be included as part of the model. Third, the method has superior edge-preservation properties as well as being fast to compute. When applied to data from a simple visual experiment, the results demonstrate improved sensitivity for detecting activated cortical areas and for better preserving details of activated structures.