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 x2 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
