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Parametric inference for functional information mapping

Author

Listed:
  • Leech, Dennis

    (Department of Economics, University of Warwick)

  • Leech, Robert

    (Division of Neuroscience and Mental Health, Imperial College London)

  • Simmonds, Anna

    (MRC Clinical Sciences Center, Imperial College London)

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.

Suggested Citation

  • Leech, Dennis & Leech, Robert & Simmonds, Anna, 2009. "Parametric inference for functional information mapping," The Warwick Economics Research Paper Series (TWERPS) 899, University of Warwick, Department of Economics.
    • Handle: RePEc:wrk:warwec:899
    as

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    References listed on IDEAS

    as
    1. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
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