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, chi(2) 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.

Keywords

VoxelCerebral blood flowGaussianRange (aeronautics)Pattern recognition (psychology)Computer scienceMaxima and minimaPixelArtificial intelligenceSIGNAL (programming language)MathematicsPhysicsMathematical analysisMedicine

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Publication Info

Year
1996
Type
article
Volume
4
Issue
1
Pages
58-73
Citations
2813
Access
Closed

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Keith J. Worsley, Sean Marrett, P. Neelin et al. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping , 4 (1) , 58-73. https://doi.org/10.1002/(sici)1097-0193(1996)4:1<58::aid-hbm4>3.0.co;2-o

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DOI
10.1002/(sici)1097-0193(1996)4:1<58::aid-hbm4>3.0.co;2-o