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Bayesian Comparison of Neurovascular Coupling Models Using EEG-fMRI

Figure 4

LEV model inversion.

Here we adopted a ‘multi-step’ approach as opposed to inverting the model in a single step. a) Single-step approach: the EEG and fMRI data are used to estimate the neuronal and hemodynamic parameters ( and ) simultaneously. At each iteration the model equations are integrated at a small time scale matching that of neuronal activity, , for the entire time interval, . b) Multi-step method: here the inversion is performed in three main steps. (1) First the neuronal parameters, , are estimated (using iterations) from the EEG data with a fine temporal resolution, , but for a smaller period, (2 seconds). (2) In the second step these parameter estimates are used to integrate the neuronal equations of the LEV model, , with the same temporal resolution but entire time interval . (3) In the last step we use the BOLD data to estimate (using iterations) only the hemodynamic parameters, , with a lower time resolution of over the full time interval, . The total number of time steps, , for each approach is displayed in each gray box.

doi: https://doi.org/10.1371/journal.pcbi.1002070.g004