Bayesian fusion and multimodal DCM for EEG and fMRI.

Wei, Huilin; Jafarian, Amirhossein; Zeidman, Peter; Litvak, Vladimir; Razi, Adeel; Hu, Dewen; Friston, Karl J

Wei, Huilin; Jafarian, Amirhossein; Zeidman, Peter; Litvak, Vladimir; Razi, Adeel; Hu, Dewen; Friston, Karl J.

Afiliación

Wei H; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
Jafarian A; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
Zeidman P; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
Litvak V; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
Razi A; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Austral
Hu D; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China. Electronic address: dwhu@nudt.edu.cn.
Friston KJ; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom. Electronic address: k.friston@ucl.ac.uk.

Neuroimage ; 211: 116595, 2020 05 01.

Article en En | MEDLINE | ID: mdl-32027965

RESUMEN

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.

Asunto(s)

Encéfalo/diagnóstico por imagen; Encéfalo/fisiología; Electroencefalografía/métodos; Neuroimagen Funcional/métodos; Imagen por Resonancia Magnética/métodos; Modelos Teóricos; Imagen Multimodal/métodos; Acoplamiento Neurovascular/fisiología; Teorema de Bayes; Simulación por Computador; Electroencefalografía/normas; Neuroimagen Funcional/normas; Humanos; Imagen por Resonancia Magnética/normas; Imagen Multimodal/normas; Prueba de Estudio Conceptual

Palabras clave

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

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Imagen por Resonancia Magnética / Electroencefalografía / Imagen Multimodal / Neuroimagen Funcional / Acoplamiento Neurovascular / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google