File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Book Chapter: Spatio-Temporal Modeling of fMRI Data

TitleSpatio-Temporal Modeling of fMRI Data
Authors
Issue Date2016
PublisherSpringer International Publishing
Citation
Spatio-Temporal Modeling of fMRI Data. In Rojas, I & Pomares, H (Eds.), Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference, p. 293-311. Switzerland: Springer International Publishing, 2016 How to Cite?
AbstractFunctional magnetic resonance imaging (fMRI) uses fast MRI techniques to enable studies of dynamic physiological processes at a time scale of seconds. This can be used for spatially localizing dynamic processes in the brain, such as neuronal activity. However, to achieve this we need to be able to infer on models of four-dimensional data. Predominantly, for statistical and computational simplicity, analysis of fMRI data is performed in two-stages. Firstly, the purely temporal nature of the fMRI data is modeled at each voxel independently, before considering spatial modeling on summary statistics from the purely temporal analysis. Clearly, it would be preferable to incorporate the spatial and temporal modeling into one all encompassing model. This would allow for correct propagation of uncertainty between temporal and spatial model parameters. In this paper, the strengths and the weaknesses of currently available methods will be discussed based on hemodynamic response (HRF) signal modeling and spatio-temporal noise modeling. Specific application to a medical study will also be described.
Persistent Identifierhttp://hdl.handle.net/10722/233463
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChen, W-
dc.contributor.authorShen, H-
dc.contributor.authorTruong, YK-
dc.date.accessioned2016-09-20T05:36:58Z-
dc.date.available2016-09-20T05:36:58Z-
dc.date.issued2016-
dc.identifier.citationSpatio-Temporal Modeling of fMRI Data. In Rojas, I & Pomares, H (Eds.), Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference, p. 293-311. Switzerland: Springer International Publishing, 2016-
dc.identifier.isbn978-3-319-28723-2-
dc.identifier.urihttp://hdl.handle.net/10722/233463-
dc.description.abstractFunctional magnetic resonance imaging (fMRI) uses fast MRI techniques to enable studies of dynamic physiological processes at a time scale of seconds. This can be used for spatially localizing dynamic processes in the brain, such as neuronal activity. However, to achieve this we need to be able to infer on models of four-dimensional data. Predominantly, for statistical and computational simplicity, analysis of fMRI data is performed in two-stages. Firstly, the purely temporal nature of the fMRI data is modeled at each voxel independently, before considering spatial modeling on summary statistics from the purely temporal analysis. Clearly, it would be preferable to incorporate the spatial and temporal modeling into one all encompassing model. This would allow for correct propagation of uncertainty between temporal and spatial model parameters. In this paper, the strengths and the weaknesses of currently available methods will be discussed based on hemodynamic response (HRF) signal modeling and spatio-temporal noise modeling. Specific application to a medical study will also be described.-
dc.languageeng-
dc.publisherSpringer International Publishing-
dc.relation.ispartofTime Series Analysis and Forecasting: Selected Contributions from the ITISE Conference-
dc.titleSpatio-Temporal Modeling of fMRI Data-
dc.typeBook_Chapter-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityShen, H=rp02082-
dc.identifier.doi10.1007/978-3-319-28725-6_22-
dc.identifier.hkuros263841-
dc.identifier.spage293-
dc.identifier.epage311-
dc.publisher.placeSwitzerland-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats