Cargando…

Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models

Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic su...

Descripción completa

Detalles Bibliográficos
Autores principales: Shen, Yuan, Mayhew, Stephen D., Kourtzi, Zoe, Tiňo, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066951/
https://www.ncbi.nlm.nih.gov/pubmed/24041873
http://dx.doi.org/10.1016/j.neuroimage.2013.09.003
_version_ 1782322242284158976
author Shen, Yuan
Mayhew, Stephen D.
Kourtzi, Zoe
Tiňo, Peter
author_facet Shen, Yuan
Mayhew, Stephen D.
Kourtzi, Zoe
Tiňo, Peter
author_sort Shen, Yuan
collection PubMed
description Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's “region of influence” through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs.
format Online
Article
Text
id pubmed-4066951
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Academic Press
record_format MEDLINE/PubMed
spelling pubmed-40669512014-06-25 Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models Shen, Yuan Mayhew, Stephen D. Kourtzi, Zoe Tiňo, Peter Neuroimage Article Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's “region of influence” through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs. Academic Press 2014-01-01 /pmc/articles/PMC4066951/ /pubmed/24041873 http://dx.doi.org/10.1016/j.neuroimage.2013.09.003 Text en © 2013 Published by Elsevier Inc. https://creativecommons.org/licenses/by/3.0/This work is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/) .
spellingShingle Article
Shen, Yuan
Mayhew, Stephen D.
Kourtzi, Zoe
Tiňo, Peter
Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title_full Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title_fullStr Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title_full_unstemmed Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title_short Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models
title_sort spatial–temporal modelling of fmri data through spatially regularized mixture of hidden process models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066951/
https://www.ncbi.nlm.nih.gov/pubmed/24041873
http://dx.doi.org/10.1016/j.neuroimage.2013.09.003
work_keys_str_mv AT shenyuan spatialtemporalmodellingoffmridatathroughspatiallyregularizedmixtureofhiddenprocessmodels
AT mayhewstephend spatialtemporalmodellingoffmridatathroughspatiallyregularizedmixtureofhiddenprocessmodels
AT kourtzizoe spatialtemporalmodellingoffmridatathroughspatiallyregularizedmixtureofhiddenprocessmodels
AT tinopeter spatialtemporalmodellingoffmridatathroughspatiallyregularizedmixtureofhiddenprocessmodels