Open AccessA unified statistical approach for determining significant signals in images of cerebral activation
Author(s) -
Worsley K. J.,
Marrett S.,
Neelin P.,
Vandal A. C.,
Friston K. J.,
Evans A. C.
Publication year - 1996
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/(sici)1097-0193(1996)4:1<58::aid-hbm4>3.0.co;2-o
Subject(s) - voxel , cerebral blood flow , gaussian , range (aeronautics) , pattern recognition (psychology) , computer science , maxima and minima , pixel , artificial intelligence , signal (programming language) , mathematics , physics , mathematical analysis , medicine , materials science , quantum mechanics , cardiology , composite material , programming language
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 , χ 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. © 1996 Wiley‐Liss, Inc.