Figure 3.

Illustration of neurogenerative models for EEG-fMRI data analysis. This approach relies on mathematically modeling the dynamics of neural ensembles at various scales ranging from gross connectivity patterns to cellular events. In addition, biophysical forward models must specify the transformation of neural events to the measured EEG and fMRI signals. Based on these data, the result of a predefined neural activity pattern can be simulated (forward modeling) or, given a multimodal EEG-fMRI dataset, the most likely neural events can be inferred based on the observed EEG-fMRI signal properties (inverse modeling).