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Combining voxel intensity and cluster extent with permutation test framework

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Hayasaka, Satoru and Nichols, Thomas E.. (2004) Combining voxel intensity and cluster extent with permutation test framework. NeuroImage, Vol.23 (No.1). pp. 54-63. ISSN 10538119

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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2004.04.035

Abstract

In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined. Various ways of combining voxel intensity and cluster extent are possible, and a few such combining methods have been proposed. Poline et al.'s [NeuroImage 16 (1997) 83] minimum P value approach is sensitive to signals whose either intensity or extent is significant. Bullmore et al.'s [IEEE Trans. Med. Imag. 18 (1999) 32] cluster mass method can detect signals whose intensity and extent are sufficiently large, even when they are not significant by intensity or extent alone. In this work, we study such combined inference methods using combining functions (Pesarin, F., 2001. Multivariate Permutation Tests. Wiley, New York) and permutation framework [Holmes et al., J. Cereb. Blood Flow Metab. 16 (1996) 7], which allow us to examine different ways of combining voxel intensity and cluster extent information without knowing their distribution. We also attempt to calibrate combined inference by using weighted combining functions, which adjust the test according to signals of interest. Furthermore, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into a single statistic. We found that combined tests are able to detect signals that are not detected by voxel or cluster size test alone. We also found that the weighted combining functions can calibrate the combined test according to the signals of interest, emphasizing either intensity or extent as appropriate. Though not necessarily more sensitive than individual combining functions, the meta-combining function is sensitive to all types of signals and thus can be used as a single test summarizing all the combining functions.

Item Type:

Journal Article

Subjects:

Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry

Divisions:

Faculty of Science > Statistics

Library of Congress Subject Headings (LCSH):

Brain -- Imaging -- Data processing, Brain -- Imaging -- Statistical methods, Permutations, Cluster analysis, Mathematical statistics

Journal or Publication Title:

NeuroImage

Publisher:

Elsevier

ISSN:

10538119

Official Date:

22 July 2004

Dates:

Date	Event
22 July 2004	Published

Volume:

Vol.23

Number:

No.1

Page Range:

pp. 54-63

Identification Number:

10.1016/j.neuroimage.2004.04.035

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Restricted or Subscription Access

Version or Related Resource:

Hayasaka, S. and Nichols, T.E. (2004). Combining voxel intensity and cluster extent with a permutation test framework. In: Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium, 1, Arlington, VA, 15-18 April 2004.

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