Bayesian fusion and multimodal DCM for EEG and fMRI

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the us...
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Autor*in:	Huilin Wei [verfasserIn] Amirhossein Jafarian [verfasserIn] Peter Zeidman [verfasserIn] Vladimir Litvak [verfasserIn] Adeel Razi [verfasserIn] Dewen Hu [verfasserIn] Karl J. Friston [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain

Übergeordnetes Werk:	In: NeuroImage - Elsevier, 2020, 211(2020), Seite 116595-
Übergeordnetes Werk:	volume:211 ; year:2020 ; pages:116595-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.neuroimage.2020.116595

Katalog-ID:	DOAJ037418769

Internformat


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spelling	10.1016/j.neuroimage.2020.116595 doi (DE-627)DOAJ037418769 (DE-599)DOAJf51c96c840c04c2fafdc700971c52c34 DE-627 ger DE-627 rakwb eng RC321-571 Huilin Wei verfasserin aut Bayesian fusion and multimodal DCM for EEG and fMRI 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain Neurosciences. Biological psychiatry. Neuropsychiatry Amirhossein Jafarian verfasserin aut Peter Zeidman verfasserin aut Vladimir Litvak verfasserin aut Adeel Razi verfasserin aut Dewen Hu verfasserin aut Karl J. Friston verfasserin aut In NeuroImage Elsevier, 2020 211(2020), Seite 116595- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:211 year:2020 pages:116595- https://doi.org/10.1016/j.neuroimage.2020.116595 kostenfrei https://doaj.org/article/f51c96c840c04c2fafdc700971c52c34 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811920300823 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 211 2020 116595-
allfields_unstemmed	10.1016/j.neuroimage.2020.116595 doi (DE-627)DOAJ037418769 (DE-599)DOAJf51c96c840c04c2fafdc700971c52c34 DE-627 ger DE-627 rakwb eng RC321-571 Huilin Wei verfasserin aut Bayesian fusion and multimodal DCM for EEG and fMRI 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain Neurosciences. Biological psychiatry. Neuropsychiatry Amirhossein Jafarian verfasserin aut Peter Zeidman verfasserin aut Vladimir Litvak verfasserin aut Adeel Razi verfasserin aut Dewen Hu verfasserin aut Karl J. Friston verfasserin aut In NeuroImage Elsevier, 2020 211(2020), Seite 116595- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:211 year:2020 pages:116595- https://doi.org/10.1016/j.neuroimage.2020.116595 kostenfrei https://doaj.org/article/f51c96c840c04c2fafdc700971c52c34 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811920300823 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 211 2020 116595-
allfieldsGer	10.1016/j.neuroimage.2020.116595 doi (DE-627)DOAJ037418769 (DE-599)DOAJf51c96c840c04c2fafdc700971c52c34 DE-627 ger DE-627 rakwb eng RC321-571 Huilin Wei verfasserin aut Bayesian fusion and multimodal DCM for EEG and fMRI 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain Neurosciences. Biological psychiatry. Neuropsychiatry Amirhossein Jafarian verfasserin aut Peter Zeidman verfasserin aut Vladimir Litvak verfasserin aut Adeel Razi verfasserin aut Dewen Hu verfasserin aut Karl J. Friston verfasserin aut In NeuroImage Elsevier, 2020 211(2020), Seite 116595- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:211 year:2020 pages:116595- https://doi.org/10.1016/j.neuroimage.2020.116595 kostenfrei https://doaj.org/article/f51c96c840c04c2fafdc700971c52c34 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811920300823 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 211 2020 116595-
allfieldsSound	10.1016/j.neuroimage.2020.116595 doi (DE-627)DOAJ037418769 (DE-599)DOAJf51c96c840c04c2fafdc700971c52c34 DE-627 ger DE-627 rakwb eng RC321-571 Huilin Wei verfasserin aut Bayesian fusion and multimodal DCM for EEG and fMRI 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain Neurosciences. Biological psychiatry. Neuropsychiatry Amirhossein Jafarian verfasserin aut Peter Zeidman verfasserin aut Vladimir Litvak verfasserin aut Adeel Razi verfasserin aut Dewen Hu verfasserin aut Karl J. Friston verfasserin aut In NeuroImage Elsevier, 2020 211(2020), Seite 116595- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:211 year:2020 pages:116595- https://doi.org/10.1016/j.neuroimage.2020.116595 kostenfrei https://doaj.org/article/f51c96c840c04c2fafdc700971c52c34 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811920300823 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 211 2020 116595-
language	English
source	In NeuroImage 211(2020), Seite 116595- volume:211 year:2020 pages:116595-
sourceStr	In NeuroImage 211(2020), Seite 116595- volume:211 year:2020 pages:116595-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	NeuroImage
authorswithroles_txt_mv	Huilin Wei @@aut@@ Amirhossein Jafarian @@aut@@ Peter Zeidman @@aut@@ Vladimir Litvak @@aut@@ Adeel Razi @@aut@@ Dewen Hu @@aut@@ Karl J. Friston @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	268125503
id	DOAJ037418769
language_de	englisch
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callnumber-first	R - Medicine
author	Huilin Wei
spellingShingle	Huilin Wei misc RC321-571 misc Multimodal data misc Bayesian belief updating misc Dynamic causal modelling misc Canonical microcircuit neural mass model misc Information gain misc Neurosciences. Biological psychiatry. Neuropsychiatry Bayesian fusion and multimodal DCM for EEG and fMRI
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topic_title	RC321-571 Bayesian fusion and multimodal DCM for EEG and fMRI Multimodal data Bayesian belief updating Dynamic causal modelling Canonical microcircuit neural mass model Information gain
topic	misc RC321-571 misc Multimodal data misc Bayesian belief updating misc Dynamic causal modelling misc Canonical microcircuit neural mass model misc Information gain misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Multimodal data misc Bayesian belief updating misc Dynamic causal modelling misc Canonical microcircuit neural mass model misc Information gain misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Bayesian fusion and multimodal DCM for EEG and fMRI
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title_full	Bayesian fusion and multimodal DCM for EEG and fMRI
author_sort	Huilin Wei
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author_browse	Huilin Wei Amirhossein Jafarian Peter Zeidman Vladimir Litvak Adeel Razi Dewen Hu Karl J. Friston
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title_sort	bayesian fusion and multimodal dcm for eeg and fmri
callnumber	RC321-571
title_auth	Bayesian fusion and multimodal DCM for EEG and fMRI
abstract	This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.
abstractGer	This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.
abstract_unstemmed	This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.
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title_short	Bayesian fusion and multimodal DCM for EEG and fMRI
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Bayesian fusion and multimodal DCM for EEG and fMRI

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