Parametric inference for functional information mapping

Dennis Leech, Robert Leech and Anna Simmonds
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Robert Leech: Division of Neuroscience and Mental Health, Imperial College London
Anna Simmonds: MRC Clinical Sciences Center, Imperial College London

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

Abstract: An 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.

Date: 2009
New Economics Papers: this item is included in nep-ecm and nep-neu
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:899

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