Generalized likelihood ratio tests for complex fMRI data: a simulation study.

J Sijbers; A J den Dekker

doi:10.1109/tmi.2005.844075

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Generalized likelihood ratio tests for complex fMRI data: a simulation study.

J Sijbers·A J den Dekker

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Abstract

Statistical tests developed for the analysis of (intrinsically complex valued) functional magnetic resonance time series, are generally applied to the data's magnitude components. However, during the past five years, new tests were developed that incorporate the complex nature of fMRI data. In particular, a generalized likelihood ratio test (GLRT) was proposed based on a constant phase model. In this work, we evaluate the sensitivity of GLRTs for complex data to small misspecifications of the phase model by means of simulation experiments. It is argued that, in practical situations, GLRTs based on magnitude data are likely to perform better compared to GLRTs based on complex data in terms of detection rate and constant false alarm rate properties.

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Algorithms Artificial Intelligence Brain Mapping Computer Simulation Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Likelihood Functions Magnetic Resonance Imaging Models, Neurological Models, Statistical Reproducibility of Results Sensitivity and Specificity Comparative Study Evaluation Study Journal Article Validation Study

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J Sijbers, A J den Dekker. 2005. Generalized likelihood ratio tests for complex fMRI data: a simulation study.. https://doi.org/10.1109/tmi.2005.844075

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