Statistical Baselines from Random Matrix Theory

被引:0
|
作者
Voultsidou, Marotesa [1 ]
Herrmann, J. Michael [2 ]
机构
[1] Univ Crete, Dept Phys, POB 2208, Iraklion, Crete, Greece
[2] Univ Edinburgh, Inst Percept Action & Behaviour Informat Forum, Edinburgh EH8 9YL, Midlothian, Scotland
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暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页码:362 / +
页数:2
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