Topic: neural masses

The characterization of brain activity has become a cornerstone of neuroscience to study cognition and neuropathologies. Here we focus on models of neural masses that aim to fit neurophysiological signals like EEG and MEG, in particular by optimizing interactions between brain regions, following previous work on the dynamic causal model (DCM) . We compare several modern Bayesian inference schemes for model inversion on simulated data . We benchmark the identifiability of local parameters governing the interplay between several neuronal populations in each region, as well as global parameters t

Context: Scalp electroencephalography (EEG) is the gold standard method for recording interictal and ictal activity in epileptic patients. Dynamic and topography of EEG signals are patient-specific. They depends on the individual's brain anatomy, type of the lesion, propagation of the epileptic activity within large-scale epileptic networks. How can we interpret epileptiform events from a neurophysiological perspective? To answer this, we developed a whole brain model based on neuronal physiology that can be used to interpret patient-specific EEG signals.Methods. We developed a whole brain neu