Investigation of two neural mass models for DCM‐based effective connectivity inference in temporal epilepsy

•The complete Physiology-Based Model (cPBM) allows to generate multiple Power Spectral Densities (PSDs) peaks and highly contributes to a better understanding of epileptic patterns than PBM.•To the best of our knowledge, cPBM has never been introduced in the context of DCM. Bayesian model comparison was used to establish the identifiability of models of effective connectivity in the setting of DCM with cPBM.•cPBM offers lower computational complexity better estimation accuracy compared to PBM. Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.