Generalized likelihood ratio tests for complex fMRI data

Conference paper (2004)
Department
Delft Centre for Systems and Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: ©2004 Sijbers, J., Den Dekker, A.J.
Generalized likelihood ratio test Functional magnetic resonance imaging Statistical parameter maps Magnitude data Complex data
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Published Date

30-04-2004

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Source:

Proceedings of SPIE, 2004 vol. 5369

ISSN:

0277-786X

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Centre for Systems and Control

Abstract

Functional magnetic resonance imaging (fMRI) intends to detect significant neural activity by means of statistical data processing. Commonly used statistical tests include the Student-t test, analysis of variance, and the generalized linear model test. A key assumption underlying these methods is that the data are Gaussian distributed. Moreover, although MR data are intrinsically complex valued, fMRI data analysis is usually performed on single valued magnitude data. Whereas complex MRI data are Gaussian distributed, magnitude data are Rician distributed. In this paper, we describe five Generalized Likelihood Ratio Tests (GLRTs) that fully exploit the knowledge of the distribution of the data: one is based on Rician distributed magnitude data and two are based on Gaussian distributed complex valued data. By means of Monte Carlo simulations, the performance of the GLRTs is compared with the classical statistical tests.