Abstract
Multivariate analytical routines have become increasingly popular in the study of cerebral function in health and in disease states. Spatial covariance analysis of functional neuroimaging data has been used to identify and validate characteristic topographies associated with specific brain disorders. Voxel-wise correlations can be used to assess similarities and differences that exist between covariance topographies. While the magnitude of the resulting topographical correlations is critical, statistical significance can be difficult to determine in the setting of large data vectors (comprised of over 100,000 voxel weights) and substantial autocorrelation effects. Here, we propose a novel method to determine the p-value of such correlations using pseudo-random network simulations.
Highlights
Spatial covariance analysis of scans of resting cerebral function provides a useful way to characterize specific network abnormalities in a variety of neurodegenerative disorders [1,2,3,4,5]. This approach has been valuable in elucidating the systemslevel changes in cerebral function that underlie hypokinetic movement disorders such as Parkinson’s disease (PD) [1,5], as well as atypical variant conditions such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) [6,7]
Topographical Correlation Similarities/differences between the PD-related metabolic covariance pattern (PDRP) [13], MSA-related pattern (MSARP) [6,7] and PSP-related pattern (PSPRP) [6], and PDRPs from four different countries (i.e., USA, Netherlands, China and India) [5] were evaluated by computing the percent of the overall variance shared (r2) between the non-zero voxel weights on each pair of topographies [10,11,15]
The voxel-level topographical correlation between PDRP and either parkinsonian syndrome-related patterns were not significant (p.0.05) (Table 2). It did not survive the correction for multiple comparisons, a moderate-level of topographical similarity was observed between PSPRP and MSARP
Summary
Spatial covariance analysis of scans of resting cerebral function provides a useful way to characterize specific network abnormalities in a variety of neurodegenerative disorders [1,2,3,4,5]. The source of the autocorrelation comes from regional intrinsic connectivity and remote functional connectivity, which may be elevated in the preprocessing procedures such as spatial normalization and smoothing To adjust for such effects in the assessment of correlations between very large data vectors (.100,000 voxel pairs), we simulated 1,000 pseudorandom volume pairs containing a degree of autocorrelation (measured by Moran’s I [16]) that was similar to those measured for each of the actual pattern topographies [cf 17]. This method allowed for the non-parametric computation of an adjusted pvalue with which to assess the significance of the observed topographic correlations. We compared PDRPs derived from five different PET centers from USA, Netherlands, China, India and South Korea
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