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).
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
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