TY - JOUR
T1 - A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data
AU - Warnick, Ryan
AU - Guindani, Michele
AU - Erhardt, Erik
AU - Allen, Elena
AU - Calhoun, Vince
AU - Vannucci, Marina
N1 - Funding Information:
Michele Guindani and Marina Vannucci are partially supported by NSF SES-1659921 and NSF SES-1659925. Vince Calhoun is supported by NIH grants P20GM103472 and R01EB020407 and NSF grant # 1539067. Ryan Warnick is supported by NSF Graduate Fellowship DGE 1450681.
Publisher Copyright:
© 2018 American Statistical Association.
PY - 2018/1/2
Y1 - 2018/1/2
N2 - Dynamic functional connectivity, that is, the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task -based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
AB - Dynamic functional connectivity, that is, the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task -based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
KW - Bayesian modeling
KW - Brain connectivity
KW - Graphical models
KW - fMRI
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U2 - 10.1080/01621459.2017.1379404
DO - 10.1080/01621459.2017.1379404
M3 - Article
C2 - 30853734
AN - SCOPUS:85041736106
SN - 0162-1459
VL - 113
SP - 134
EP - 151
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 521
ER -