A personalizable autonomous neural mass model of epileptic seizures

Autor: Edmundo Lopez-Sola, Roser Sanchez-Todo, Èlia Lleal, Elif Köksal-Ersöz, Maxime Yochum, Julia Makhalova, Borja Mercadal, Maria Guasch-Morgades, Ricardo Salvador, Diego Lozano-Soldevilla, Julien Modolo, Fabrice Bartolomei, Fabrice Wendling, Pascal Benquet, Giulio Ruffini
Přispěvatelé: Neuroelectrics Barcelona, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital de la Timone [CHU - APHM] (TIMONE), This work has received funding from FET European Union’s Horizon 2020 research and innovation programme (grant agreement No 101017716), Jonchère, Laurent
Rok vydání: 2021
Předmět:
Zdroj: Journal of Neural Engineering
Journal of Neural Engineering, 2022, 19 (5), pp.055002. ⟨10.1088/1741-2552/ac8ba8⟩
ISSN: 1741-2560
1741-2552
DOI: 10.1101/2021.12.24.474090
Popis: 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.
Databáze: OpenAIRE
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