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A Bayesian Model for Activation and Connectivity in Task-related fMRI Data

aUniversity of California Irvine, United States
bUniversity of California Santa Cruz, United States
cUniversity of California Irvine, United States
dKing's College London, United Kingdom
eKing Abdullah University of Science and Technology, Saudi Arabia

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A

ISBN: 978-1-78973-242-9, eISBN: 978-1-78973-241-2

ISSN: 0731-9053

Publication date: 30 August 2019

Abstract

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

Keywords

Citation

Yu, Z., Prado, R., Cramer, S.C., Quinlan, E.B. and Ombao, H. (2019), "A Bayesian Model for Activation and Connectivity in Task-related fMRI Data", Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A (Advances in Econometrics, Vol. 40A), Emerald Publishing Limited, Bingley, pp. 91-132. https://doi.org/10.1108/S0731-90532019000040A006

Publisher

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Emerald Publishing Limited

Copyright © 2019 Emerald Publishing Limited