K
Keith J. Worsley
Researcher at McGill University
Publications - 128
Citations - 39367
Keith J. Worsley is an academic researcher from McGill University. The author has contributed to research in topics: Random field & Parametric statistics. The author has an hindex of 69, co-authored 128 publications receiving 37641 citations. Previous affiliations of Keith J. Worsley include University of Chicago & Hammersmith Hospital.
Papers
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Journal ArticleDOI
Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
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A unified statistical approach for determining significant signals in images of cerebral activation.
TL;DR: A unified statistical theory for assessing the significance of apparent signal observed in noisy difference images is presented and an estimate of the P‐value for local maxima of Gaussian, t, χ2 and F fields over search regions of any shape or size in any number of dimensions is estimated.
Journal ArticleDOI
Analysis of fMRI time-series revisited--again.
Keith J. Worsley,Karl J. Friston +1 more
TL;DR: Correct results are presented that replace those of the previous paper and solve the same problem without recourse to heuristic arguments and a proper and unbiased estimator for the error terms are introduced.
Journal ArticleDOI
Assessing the significance of focal activations using their spatial extent
TL;DR: The results mean that detecting significant activations no longer depends on a fixed threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region.
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A three-dimensional statistical analysis for CBF activation studies in human brain.
TL;DR: A simple method for determining an approximate p value for the global maximum based on the theory of Gaussian random fields is described, which focuses on the Euler characteristic of the set of voxels with a value larger than a given threshold.