A unified statistical approach for determining significant signals in images of cerebral activation
Human brain mapping, 1996•Wiley Online Library
We present a unified statistical theory for assessing the significance of apparent signal
observed in noisy difference images. The results are usable in a wide range of applications,
including fMRI, but are discussed with particular reference to PET images which represent
changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main
result is 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. This unifies the P‐values …
observed in noisy difference images. The results are usable in a wide range of applications,
including fMRI, but are discussed with particular reference to PET images which represent
changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main
result is 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. This unifies the P‐values …
Abstract
We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is 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. This unifies the P‐values for large search areas in 2‐D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699) large search regions in 3‐D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) and the usual uncorrected P‐value at a single pixel or voxel. © 1996 Wiley‐Liss, Inc.
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