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Published in:
2008 | OriginalPaper | Chapter
Statistical Baselines from Random Matrix Theory
Authors : Marotesa Voultsidou, J. Michael Herrmann
Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Publisher: Springer Berlin Heidelberg
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Quantitative descriptors of intrinsic properties of imaging data can be obtained from the theory of random matrices (RMT). Based on theoretical results for standardized data, RMT offers a systematic approach to surrogate data which allows us to evaluate the significance of deviations from the random baseline. Considering exemplary fMRI data sets recorded at a visuo-motor task and rest, we show their distinguishability by RMT-based quantities and demonstrate that the degree of sparseness and of localization can be evaluated in a strict way, provided that the data are sufficiently well described by the pairwise cross-correlations.