Ricean over Gaussian modelling in magnitude fMRI analysis—added complexity with negligible practical benefits

Date
2013-12-01
Authors
Adrian, Daniel
Maitra, Ranjan
Rowe, Daniel
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Altmetrics
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Statistics
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Abstract

It is well known that Gaussian modelling of functional magnetic resonance imaging (fMRI) magnitude time-course data, which are truly Rice distributed, constitutes an approximation, especially at low signal-to-noise ratios (SNRs). Based on this fact, previous work has argued that Rice-based activation tests show superior performance over their Gaussian-based counterparts at low SNRs and should be preferred in spite of the attendant additional computational and estimation burden. Here, we revisit these past studies and, after identifying and removing their underlying limiting assumptions and approximations, provide a more comprehensive comparison. Our experimental evaluations using Receiver Operating Characteristic (ROC) curve methodology show that tests derived using Ricean modelling are substantially superior over the Gaussian-based activation tests only for SNRs below 0.6, that is, SNR values far lower than those encountered in fMRI as currently practiced

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This is the peer reviewed version of the following article: Stat 2 (2013): 303, doi: 10.1002/sta4.34, which has been published in final form at http://dx.doi.org/10.1002/sta4.34. This article may be used for non-commerical purposes in accordance with Wiley Terms and Conditions for self-archiving

Keywords
EM algorithm, fMRI, Likelihood Ratio Test, Maximum likelihood estimate, Newton- Raphson, Rice distribution, ROC curve, signal-to-noise ratio
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