Combined spatial and non-spatial prior for inference on MRI time-series.
When modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can b...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2009
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| _version_ | 1797073947499429888 |
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| author | Groves, A Chappell, M Woolrich, M |
| author_facet | Groves, A Chappell, M Woolrich, M |
| author_sort | Groves, A |
| collection | OXFORD |
| description | When modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can be incorporated in a principled manner; however, this requirement for a fixed non-spatial prior on a parameter would normally preclude using spatial regularization on that same parameter. We have developed a Gaussian-process-based prior to apply adaptive spatial regularization while still ensuring that the fixed biophysical prior is correctly applied on each voxel. A parameterized covariance matrix provides separate control over the variance (the diagonal elements) and the between-voxel correlation (due to off-diagonal elements). Analysis proceeds using evidence optimization (EO), with variational Bayes (VB) updates used for some parameters. The method can also be applied to non-linear forward models by using a linear Taylor expansion centred on the latest parameter estimates. Applying the method to FMRI with a constrained haemodynamic response function (HRF) shape model shows improved fits in simulations, compared to using either the non-spatial or spatial-smoothness prior alone. We also analyse multi-inversion arterial spin labelling data using a non-linear perfusion model to estimate cerebral blood flow and bolus arrival time. By combining both types of prior information, this new prior performs consistently well across a wider range of situations than either prior alone, and provides better estimates when both types of prior information are relevant. |
| first_indexed | 2024-03-06T23:29:14Z |
| format | Journal article |
| id | oxford-uuid:6b7097d3-50bb-4657-9a58-ecff930b9f42 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:29:14Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:6b7097d3-50bb-4657-9a58-ecff930b9f422022-03-26T19:04:05ZCombined spatial and non-spatial prior for inference on MRI time-series.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6b7097d3-50bb-4657-9a58-ecff930b9f42EnglishSymplectic Elements at Oxford2009Groves, AChappell, MWoolrich, MWhen modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can be incorporated in a principled manner; however, this requirement for a fixed non-spatial prior on a parameter would normally preclude using spatial regularization on that same parameter. We have developed a Gaussian-process-based prior to apply adaptive spatial regularization while still ensuring that the fixed biophysical prior is correctly applied on each voxel. A parameterized covariance matrix provides separate control over the variance (the diagonal elements) and the between-voxel correlation (due to off-diagonal elements). Analysis proceeds using evidence optimization (EO), with variational Bayes (VB) updates used for some parameters. The method can also be applied to non-linear forward models by using a linear Taylor expansion centred on the latest parameter estimates. Applying the method to FMRI with a constrained haemodynamic response function (HRF) shape model shows improved fits in simulations, compared to using either the non-spatial or spatial-smoothness prior alone. We also analyse multi-inversion arterial spin labelling data using a non-linear perfusion model to estimate cerebral blood flow and bolus arrival time. By combining both types of prior information, this new prior performs consistently well across a wider range of situations than either prior alone, and provides better estimates when both types of prior information are relevant. |
| spellingShingle | Groves, A Chappell, M Woolrich, M Combined spatial and non-spatial prior for inference on MRI time-series. |
| title | Combined spatial and non-spatial prior for inference on MRI time-series. |
| title_full | Combined spatial and non-spatial prior for inference on MRI time-series. |
| title_fullStr | Combined spatial and non-spatial prior for inference on MRI time-series. |
| title_full_unstemmed | Combined spatial and non-spatial prior for inference on MRI time-series. |
| title_short | Combined spatial and non-spatial prior for inference on MRI time-series. |
| title_sort | combined spatial and non spatial prior for inference on mri time series |
| work_keys_str_mv | AT grovesa combinedspatialandnonspatialpriorforinferenceonmritimeseries AT chappellm combinedspatialandnonspatialpriorforinferenceonmritimeseries AT woolrichm combinedspatialandnonspatialpriorforinferenceonmritimeseries |