Synaptic transmission modulates while non-synaptic processes govern the transition from pre-ictal to seizure activity in vitro

Abstract

It is well established that non-synaptic mechanisms can generate electrographic seizures after blockade of synaptic function. We investigated the interaction of intact synaptic activity with nonsynaptic mechanisms in the isolated CA1 region of rat hippocampal slices using the "elevated-K + " model of epilepsy. Elevated K + ictal bursts share waveform features with other models of electrographic seizures, including non-synaptic models where chemical synaptic transmission is suppressed, such as the low-Ca 2+ model. These features include a prolonged (several seconds) negative field shift associated with neuronal depolarization and superimposed population spikes. When population spikes are disrupted for up to several seconds, intracellular recording demonstrated that the prolonged suppression of population spikes during ictal activity was due to depolarization block of neurons. Elevated-K + ictal bursts were often preceded by a build-up of "pre-ictal" epileptiform discharges that were characterized as either "slow-transition" (localized and with a gradual increase in population spike amplitude, reminiscent non-synaptic neuronal aggregate formation, presumed mediated by extracellular K + concentrations ([K + ])o accumulation), or "fast-transition" (with a sudden increase in population spike amplitude, presumed mediated by field effects). When ictal activity had a fast-transition it was preceded by fast-transition pre-ictal activity; otherwise population spikes developed gradually at ictal event onset. Addition of bicuculline, a GABAA receptor antagonist, suppressed population spike generation during ictal activity, reduced pre-ictal activity, and increased the frequency of ictal discharges. Nipecotic acid and NNC-711, both of which block GABA reuptake, increased population spike amplitude during ictal bursts and promoted the generation of preictal activity. By contrast, addition of ionotropic glutamate-receptor antagonists (NBQX, D-APV) had no consistent effect on ictal burst waveform or frequency and did not fully suppress pre-ictal activity. Similarly, CGP 55848, a GABAB receptor antagonist, has no significant effect on pre-ictal activity or burst frequency (although it did increase burst duration slightly). Our results are consistent with the hypothesis that non-synaptic mechanisms underpin the generation of ictal bursts in CA1 and that GABAA synaptic mechanisms can shape event development by delaying event initiation and counteracting depolarization block.

Modeling seizures: From single neurons to networks

“…Ephaptic interactions may for instance synchronize atin potential propagation in tightly packed fiber bundles [30]. Slow oscillations can emerge from non-synaptic interactions, enabling the transitions towards seizure-like states [28,29,31]. But most studies done at the network scale take into account the interconnection between neurons through models of synapses [32][33][34][35][36][37][38].…”

Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures

“…To distinguish between the differential involvement of synaptic changes in driving seizure state transitions, we investigated 3 parameters in our model: 1) network connectivity – the number of connections between neurons defined by parameter m , 2) synaptic weights – the strength of synapses between neurons defined by parameter r , and 3) intrinsic excitability – the propensity of individual neurons to spike defined by parameter V th ( Figure 8 ). While a diversity of other parameters can drive epileptic seizures, including neuronal subtype ( Khoshkhoo et al, 2017 ; Magloire et al, 2019 ; Sessolo et al, 2015 ), non-synaptic ( Bikson et al, 2018 ; Dudek et al, 1998 ; Magloire et al, 2022 ) and non-neuronal mechanisms ( Coulter & Steinhäuser, 2015 ; Diaz Verdugo et al, 2019 ; Vezzani et al, 2022 ; Wu et al, 2020 ), we restricted our model to 3 simplified plasticity parameters, in order to relate predictions from criticality theory with observed seizure avalanche dynamics. Importantly, such simplified plasticity models make few assumptions about the underlying dynamics, and if they can explain observed data, can demonstrate the sufficiency of general synaptic mechanisms in driving seizure activity and critical dynamics.…”

“…Having identified key neuronal parameters driving generalised seizure dynamics, we next investigated the functional implications of such changes. Interestingly, during generalised seizures patients regularly exhibit a complete loss of awareness ( Bikson et al, 2018 ; Dudek et al, 1998 ; Magloire et al, 2022 ; Shimoda et al, 2020). Given that generalised seizures emerge as a loss of criticality, we theorised that co-occuring cognitive dysfunction would be caused by the suboptimal computational capacities of networks away from criticality.…”