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Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations

Abstract

The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.

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Data availability

All the Python source files used for building the network and running the simulations are accessible on the ModelDB public repositories.

Notes

  1. For an alternative way of assessing the role of different parameters in a higher dimensional parameter space, see Aussel et al. (2021), where we analyzed the healthy hippocampus model.

  2. \(R^2=1-\frac{\sum _{i=1}^N (y_i-\hat{y}_i)^2}{\sum _{i=1}^N(y_i-\bar{y})^2}\) with y the observed values to estimate, \(\bar{y}\) the mean of the observed values, \(\hat{y}\) the modeled values and N the number of data points

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AA, LB and RR designed the study. SCC, LT and LM collected the sEEG data. AA built the computational model and performed the simulations. AA, LB, OA and RR analyzed the results. AA drafted the paper. All authors approved the final manuscript.

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Correspondence to Amélie Aussel.

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Aussel, A., Ranta, R., Aron, O. et al. Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations. J Comput Neurosci (2022). https://doi.org/10.1007/s10827-022-00829-5

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  • DOI: https://doi.org/10.1007/s10827-022-00829-5

Keywords

  • Hippocampus
  • Computational modeling
  • Epilepsy
  • Sleep-wake cycle
  • Realistic anatomy
  • Pathological connectivity