A unified statistical approach for determining significant signals in images of cerebral activation

K J Worsley; S Marrett; P Neelin; A C Vandal; K J Friston; A C Evans

doi:10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O

A unified statistical approach for determining significant signals in images of cerebral activation

Hum Brain Mapp. 1996;4(1):58-73. doi: 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O.

Authors

K J Worsley¹, S Marrett, P Neelin, A C Vandal, K J Friston, A C Evans

Affiliation

¹ Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada H3A 2K6.

PMID: 20408186
DOI: 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O

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.