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2018 | OriginalPaper | Buchkapitel

19. Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging

verfasst von : Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones, Donald R. Musgrove

Erschienen in: Handbook of Big Data Analytics

Verlag: Springer International Publishing

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Abstract

We consider recent developments in Bayesian spatiotemporal models for detecting neuronal activation in fMRI experiment. A Bayesian approach typically results in complicated posterior distributions that can be of enormous dimension for a whole-brain analysis, thus posing a formidable computational challenge. Recently developed Bayesian approaches to detecting local activation have proved computationally efficient while requiring few modeling compromises. We review two such methods and implement them on a data set from the Human Connectome Project in order to show that, contrary to popular opinion, careful implementation of Markov chain Monte Carlo methods can be used to obtain reliable results in a matter of minutes.

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Vorheriges Kapitel Functional Data Analysis for Big Data: A Case Study on California Temperature Trends

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Titel: Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging
verfasst von: Martin Bezener
Lynn E. Eberly
John Hughes
Galin Jones
Donald R. Musgrove
Verlag: Springer International Publishing
Buch: Handbook of Big Data Analytics
Print ISBN: 978-3-319-18283-4

Electronic ISBN: 978-3-319-18284-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-18284-1_19

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