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November 1999 The geometry of correlation fields with an application to functional connectivity of the brain
Jin Cao, Keith Worsley
Ann. Appl. Probab. 9(4): 1021-1057 (November 1999). DOI: 10.1214/aoap/1029962864


We introduce two new types of random field. The cross correlation field $R(\mathbf{s}, \mathbf{t})$ is the usual sample correlation coefficient for a set of pairs of Gaussian random fields, one sampled at point $s \epsilon \Re^M$, the other sampled at point $\mathbf{t} \epsilon \Re^N$. The homologous correlation field is defined as $R(\mathbf{t}) = R(\mathbf{t}, \mathbf{t})$, that is, the "diagonal" of the cross correlation field restricted to the same location $\mathbf{s} = \mathbf{t}$. Although the correlation coefficient can be transformed pointwise to a t-statistic, neither of the two correlation fields defined above can be transformed to a t-field, defined as a standard Gaussian field divided by the root mean square of i.i.d. standard Gaussian fields. For this reason, new results are derived for the geometry of the excursion set of these correlation fields that extend those of Adler. The results are used to detect functional connectivity (regions of high correlation) in three-dimensional positron emission tomography (PET) images of human brain activity.


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Jin Cao. Keith Worsley. "The geometry of correlation fields with an application to functional connectivity of the brain." Ann. Appl. Probab. 9 (4) 1021 - 1057, November 1999.


Published: November 1999
First available in Project Euclid: 21 August 2002

zbMATH: 0961.60052
MathSciNet: MR1727913
Digital Object Identifier: 10.1214/aoap/1029962864

Primary: 60G15

Keywords: Euler characteristic , image analysis , Random field

Rights: Copyright © 1999 Institute of Mathematical Statistics


Vol.9 • No. 4 • November 1999
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