Parametric inference for functional information mapping
AbstractAn increasing trend in functional MRI experiments involves discriminating between experimental conditions on the basis of fine-grained spatial patterns extending across many voxels. Typically, these approaches have used randomized resampling to derive inferences. Here, we introduce an analytical method for drawing inferences from multivoxel patterns. This approach extends the general linear model to the multivoxel case resulting in a variant of the Mahalanobis distance statistic which can be evaluated on the !2 distribution. We apply this parametric inference to a single-subject fMRI dataset and consider how the approach is both computationally more efficient and more sensitive than resampling inference.
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Bibliographic InfoPaper provided by University of Warwick, Department of Economics in its series The Warwick Economics Research Paper Series (TWERPS) with number 899.
Date of creation: 2009
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-02 (All new papers)
- NEP-ECM-2009-05-02 (Econometrics)
- NEP-NEU-2009-05-02 (Neuroeconomics)
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