P118 Simulating interictal epileptiform discharges in a whole-brain neural mass model with calcium-mediated bidirectional plasticity
MehmetAlihanKayabas1, Elif Köksal-Ersöz2,3,Linda-Iris JosephTomy1,Pascal Benquet1, Isabelle Merlet1, Fabrice Wendling1 ¹UnivRennes, INSERM, LTSI – UMR 1099, Rennes F-35000, France ²InriaLyonResearchCentre, Villeurbanne 69603, France ³CophyTeam, Lyon NeuroscienceResearchCenter, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Bron 69500, France *Email:malihankayabas@gmail.com Introduction
Whole-brainmodeling of interictal epileptiform dischargesoffers a promising approach to optimize transcranial direct current stimulation (tDCS) protocols, byidentifyingspecific regions of the brain involved in the epileptic network [1,2]. In this study, we investigated the synaptic plasticity induced bytDCSin a whole-brain network of connected neural mass models (NMMs) [3, 4], which we extended by implementing the calcium-mediated synaptic plasticity mechanisms based on our recent study [5]. We studied the impact oftwolocal parameters:synapticdepression(θd)andpotentiation(θp)thresholds, on long-term depression (LTD) and long-term potentiation (LTP) undertDCS.
Methods The activity of each node of the network was simulated by NMMs including excitatory and inhibitory neuronal subpopulations. The nodes are interconnected by a structural connectivity matrix fromthe HumanConnectome Project [6]. We tuned parameters of NMMstosimulateinterictal epileptiformdischarges (IEDs), alpha-band activity (8-12 Hz), and background activity. We assumed that the electrical stimulation affects the mean membrane potential of excitatory neuronal subpopulations. We varied thedepression andpotentiation threshold parametersin different subnetworks and simulated the system for 15 min for each condition.Two metrics were evaluated:Functional connectivity calculated using a non-linear correlation coefficient, and mean amplitude per channel. Results Under most conditions both the signal amplitude and node strength decreased.(Fig. 1).Except forall_nodes_θdconditionwherethedepression thresholdwas increasedacross all nodeswhichreduced LTD activity, resulting in an increase in strength of epileptic nodes.Regionally,the parietal nodesshowedthemost significantreductionswhile the frontal nodesthe least significantvariations. An increase in potentiation threshold across all nodes (all_nodes_θpcondition) resulted inthehighestreduction in bothamplitude and strength.,When bothθdandθpwere increased simultaneously,the decrease in strength of epileptic nodes was even more pronounced, while increasingθpalone in the occipital nodes did not yield a reduction in epileptic node strength. Discussion Variation insynapticplasticity thresholds alters whole-brain network dynamics. In nodesexhibitingalpha-band activity, decreased node strength lowers signal amplitude without changing frequency. Inepileptogenic nodes, reduced node strength leads to lower IED frequency anddesynchronization between two regions of the epileptogenic zone, while increased strength has the opposite effect.As of this day, there is no consensus in the literature on the effect oftDCSon alpha-band[7,8] or on the IED frequency [9, 10].In future studies, we willstudyour model further to elucidate the mechanisms and role oftDCStreatmentin focal epilepsy.
Figure 1. (A) percentage difference in node strength relative to basal level. (B) percentage difference in amplitude relative to basal level. (C) Examples of LFP signals for left lateral occipital (alpha) and precentral (epileptic) nodes before (blue) and after (red) the increase in potentiation threshold. θp: Potentiation threshold and θd: Depotentiation threshold.* Denotes p-value < 0.05 Kruskal-Wallis Acknowledgements This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (No 855109). References 1.https://doi.org/10.1093/med/9780199746545.003.0017 2.https://doi.org/10.1093/brain/awz269 3.https://doi.org/10.1016/j.softx.2024.101924 4. https://doi.org/10.1088/1741-2552/ac8fb4 5.https://doi.org/10.1371/journal.pcbi.1012666 6.https://doi.org/10.1016/j.neuroimage.2021.118543 7.https://doi.org/10.3389/fnhum.2013.00529 8.https://doi.org/10.1038/s41598-020-75861-5 9.https://10.1016/j.brs.2016.12.005 10.https://doi.org/10.1016/j.eplepsyres.2024.107320
