Testing for spatial heterogeneity in functional MRI using the multivariate general linear model

Robert Leech and Dennis Leech
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Robert Leech: The Computational, Cognitive and Clinical Neuroimaging Laboratory, The Division of Experimental Medicine, Imperial College London

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

Abstract: Much current research in functional MRI employs multivariate machine learning approaches (e.g., support vector machines) to detect fine-scale spatial patterns from the temporal fluctuations of the neural signal. The aim of many studies is not classification, however, but investigation of multivariate spatial patterns, which pattern classifiers detect only indirectly. Here we propose a direct statistical measure for the existence of fine-scale spatial patterns (or spatial heterogeneity) applicable for fMRI datasets. We extend the univariate general linear model (typically used in fMRI analysis) to a multivariate case. We demonstrate that contrasting maximum likelihood estimations of different restrictions on this multivariate model can be used to estimate the extent of spatial heterogeneity in fMRI data. Under asymptotic assumptions inference can be made with reference to the X2 distribution. The test statistic is then assessed using simulated timecourses derived from real fMRI data. This demonstrates the utility of the proposed measure of heterogeneity as well as considerations in its application. Measuring spatial heterogeneity in fMRI has important theoretical implications in its own right and has potential uses for better characterising neurological conditions such as stroke and Alzheimer’s disease.

Keywords: wordsNeuroimaging; Multivariate pattern analysis; Maximum likelihood estimation; Seemingly unrelated regression (search for similar items in EconPapers)
Date: 2010
New Economics Papers: this item is included in nep-ecm and nep-neu
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:938

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