A personalizable autonomous neural mass model of epileptic seizures. (1st October 2022)

• Record Type: Journal Article
• Authors: Lopez-Sola, Edmundo; Sanchez-Todo, Roser; Lleal, Èlia; Köksal-Ersöz, Elif; Yochum, Maxime; Makhalova, Julia; Mercadal, Borja; Guasch-Morgades, Maria; Salvador, Ricardo; Lozano-Soldevilla, Diego; Modolo, Julien; Bartolomei, Fabrice; Wendling, Fabrice; Benquet, Pascal; Ruffini, Giulio
• Is Part Of: Journal of neural engineering. Volume 19:Number 5(2022)
• Publication Date: 2022-10-01
• Subjects: neural mass model -- SEEG recordings -- depolarizing GABA -- epilepsy; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac8ba8
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.