TY - CHAP
T1 - A BAYESIAN MODEL FOR ACTIVATION AND CONNECTIVITY IN TASK-RELATED FMRI DATA
AU - Yu, Zhe
AU - Prado, Raquel
AU - Cramer, Steve C.
AU - Quinlan, Erin B.
AU - Ombao, Hernando
N1 - KAUST Repository Item: Exported on 2022-11-18
PY - 2019/8/30
Y1 - 2019/8/30
N2 - We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network. 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).
AB - We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network. 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).
UR - http://hdl.handle.net/10754/685834
UR - https://www.emerald.com/insight/content/doi/10.1108/S0731-90532019000040A006/full/html
UR - http://www.scopus.com/inward/record.url?scp=85141178750&partnerID=8YFLogxK
U2 - 10.1108/S0731-90532019000040A006
DO - 10.1108/S0731-90532019000040A006
M3 - Chapter
SP - 91
EP - 132
BT - Advances in Econometrics
PB - Emerald Publishing Limited
ER -