Accounting for spatial dependence in the analysis of SPECT brain imaging data

Jeffrey S. Spence, Patrick S. Carmack, Richard F. Gunst, William R. Schucany, Wayne A. Woodward, Robert W. Haley

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

The size and complexity of brain imaging databases confront statistical analysts with a variety of issues when assessing brain activation differences between groups of subjects. Detecting small group differences in activation is compounded by the need to analyze hundreds of thousands of spatially correlated measurements per image. These analyses are especially problematic when, as is typical, the number of subjects in each group is small. In this article a comprehensive analysis of single-photon emission computed tomography (SPECT) brain images demonstrates that spatial modeling can increase the sensitivity of group comparisons. The key statistical approach for increasing the sensitivity of group comparisons is the spatial modeling of intervoxel correlations. Correlations among normalized SPECT counts in 2×2×2 mm3 voxels are shown to be very large in neighboring voxels and to decrease in magnitude until they become negligible among those approximately 5-7 voxels (10-14 mm) apart. Exploiting this correlation structure, blocks of contiguous voxels are defined within each of several structures within the deep brain so that the geometric centers of the blocks are no closer than the range at which voxel counts can be considered uncorrelated. Using kriging methods, block averages and their prediction variances are calculated. For each structure of interest, the block averages within the structure are weighted by their prediction variances, producing a structure average for each subject. The subject averages and their prediction variances are used in a linear model to compare group effects. This analysis is shown to be more sensitive to group mean differences than the voxel-by-voxel analysis commonly used by medical researchers. The procedures are applied to comparisons of SPECT brain imaging data from four groups of subjects, three of which have variants of the 1991 Gulf War syndrome and one of which is a control group. Commonly used voxel-by-voxel group comparisons do not identify any brain structures that are significantly different for the syndrome and control groups in the analysis of cholinergic response to a physostigmine drug challenge. Spatial modeling and analyses of these data do identify regions of the deep brain that exhibit statistically significant group differences. These results are consistent with medical evidence that these structures might have been affected by Gulf War chemical exposures.

Original languageEnglish (US)
Pages (from-to)464-473
Number of pages10
JournalJournal of the American Statistical Association
Volume102
Issue number478
DOIs
StatePublished - Jun 1 2007

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Keywords

  • Kriging
  • Multiple comparisons
  • Semivariogram
  • Single-photon emission computed tomography
  • War syndrome

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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