In recent years, state of the art brain imaging techniques like Functional Magnetic Resonance Imaging (fMRI), have raised new challenges to the statistical community, which is asked to provide new frameworks for modeling and data analysis. Here, motivated by resting state fMRI data, which can be seen as a collection of spatially dependent functional observations among brain regions, we propose a parsimonious but flexible representation of their dependence structure leveraging a Bayesian time-dependent latent factor model. Adopting an assumption of separability of the covariance structure in space and time, we are able to substantially reduce the computational cost and, at the same time, provide interpretable results. Theoretical properties of the model along with identifiability conditions are discussed. For model fitting, we propose a mcmc algorithm to enable posterior inference. We illustrate our work through an application to a dataset coming from the enkirs project, discussing the estimated covariance structure and also performing model selection along with network analysis. Our modeling is preliminary but offers ideas for developing fully Bayesian fMRI models, incorporating a plausible space and time dependence structure.

Caponera, A., Denti, F., Rigon, T., Sottosanti, A., Gelfand, A., Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data, in Antonio Canale, D. D. L. P. B. S. (ed.), Studies in Neural Data Science, Springer New York LLC, N/A 2018: <<SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS>>, 257 111- 130. 10.1007/978-3-030-00039-4_7 [http://hdl.handle.net/10807/201727]

Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data

Denti, Francesco;
2018

Abstract

In recent years, state of the art brain imaging techniques like Functional Magnetic Resonance Imaging (fMRI), have raised new challenges to the statistical community, which is asked to provide new frameworks for modeling and data analysis. Here, motivated by resting state fMRI data, which can be seen as a collection of spatially dependent functional observations among brain regions, we propose a parsimonious but flexible representation of their dependence structure leveraging a Bayesian time-dependent latent factor model. Adopting an assumption of separability of the covariance structure in space and time, we are able to substantially reduce the computational cost and, at the same time, provide interpretable results. Theoretical properties of the model along with identifiability conditions are discussed. For model fitting, we propose a mcmc algorithm to enable posterior inference. We illustrate our work through an application to a dataset coming from the enkirs project, discussing the estimated covariance structure and also performing model selection along with network analysis. Our modeling is preliminary but offers ideas for developing fully Bayesian fMRI models, incorporating a plausible space and time dependence structure.
2018
Inglese
Studies in Neural Data Science
978-3-030-00038-7
Springer New York LLC
257
Caponera, A., Denti, F., Rigon, T., Sottosanti, A., Gelfand, A., Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data, in Antonio Canale, D. D. L. P. B. S. (ed.), Studies in Neural Data Science, Springer New York LLC, N/A 2018: <<SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS>>, 257 111- 130. 10.1007/978-3-030-00039-4_7 [http://hdl.handle.net/10807/201727]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/201727
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