Pruebas de Hipótesis Regu larizadas en Campos Aleatorios con Aplicaciones a Neuroimágenes
DOI:
https://doi.org/10.17488/RMIB.41.2.2Palabras clave:
Prueba de hipótesis regularizada, campo aleatorio Markoviano, estimación Bayesiana, Imágenes de Resonancia Magnética FuncionalResumen
In several scientific areas there appears the problem of determining in which elements of a random field (e.g., pixels in an image) a certain null hypothesis may be rejected. In this paper we present a new method for performing this task, focusing on applications to neuroimaging research. The proposed method is based on the formulation of the hypothesis testing task as a Bayesian estimation problem, with a Markov Random Field prior, which allows to incorporate local spatial information. The proposed method is flexible enough to accept different types of noise models including spatially correlated noise. Additionaly in this work, we address the problem of parameter selection by maximizing the true positive rate while keeping control of false positive rate. Simulation studies confirm the excellent performance of the proposed method compared with state of the art methodologies. We illustrate this performance on an experiment with real Functional Magnetic Imaging (fMRI) data and show the robustness of the proposed method when reducing the signal to noise ratio, obtaining better performance than other methods.
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Derechos de autor 2020 Oscar Dalmau
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