Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG
This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically i...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2020
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| _version_ | 1797059394703196160 |
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| author | Jafarian, A Litvak, V Cagnan, H Friston, KJ Zeidman, P |
| author_facet | Jafarian, A Litvak, V Cagnan, H Friston, KJ Zeidman, P |
| author_sort | Jafarian, A |
| collection | OXFORD |
| description | This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison - asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software. |
| first_indexed | 2024-03-06T20:03:37Z |
| format | Journal article |
| id | oxford-uuid:283065aa-0645-46e2-9192-c52a3eaf7274 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:03:37Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:283065aa-0645-46e2-9192-c52a3eaf72742022-03-26T12:11:19ZComparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEGJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:283065aa-0645-46e2-9192-c52a3eaf7274EnglishSymplectic ElementsElsevier2020Jafarian, ALitvak, VCagnan, HFriston, KJZeidman, PThis technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison - asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software. |
| spellingShingle | Jafarian, A Litvak, V Cagnan, H Friston, KJ Zeidman, P Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title | Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title_full | Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title_fullStr | Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title_full_unstemmed | Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title_short | Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG |
| title_sort | comparing dynamic causal models of neurovascular coupling with fmri and eeg meg |
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