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Cortical and Thalamocortical circuits involved in Spike-and-Wave Discharges and Sleep Spindles

For decades, SWDs were considered as aberrant sleep spindles of the thalamocortical circuitry and shared circuits were thought to underlie SWD generation in absence seizures.


I am interested in this question because it applies to the differential detection of slow wave sleep (SWS) vs. Spike and wave discharge (SWD), vs. sleep spindles (SS), all markers of drug activity or involved in various safety risk assessments. Moreover, the question is important because absence seizure development correlates with the deterioration of slow-wave sleep (SWS) and sleep spindles (SS) and associated with negative cognitive effects, and is reversible with ethosuximide (ETX) and other drugs.


This post is highlighting a paper showing differences between SWDs and spindles, and network level aspects of their age dependence. In "Spike-and-Wave Discharges Are Not Pathological Sleep Spindles, Network-Level Aspects of Age-Dependent Absence Seizure Development in Rats", Kozak et al. investigates how the progression of SWD generation in Long Evans rats impacts physiologic sleep spindles, their overlap as well as the differences between the circuits generating and maintaining each pattern.


Spontaneous absence seizures characterized by a typical EEG pattern of SWDs were hypothesized to emerge from a hyperexcitable cortical focus which generalizes to the thalamocortical circuitry, yet it is largely unexplored how local ictal activity of the putative focus can evolve into global absence seizures. The thalamocortical network also gives rise to normal physiologic oscillations such as spindles and δ waves during sleep. This is one of the first studies to investigate the evolution of the physiologic thalamocortical oscillations in relation to the emerging seizure activity coexisting in the same circuitry, and the age dependence of these mechanisms.


Kozak et al. found that while SWDs and spindles look similar in young rats, they diverge with maturation and shift to appear in different brain states. Thus, despite being generated by the same network, they are likely two distinct manifestations of the thalamocortical activity. They show that while spindles are already mainly global oscillations, SWDs appear only focally in young. SWDs become capable to generalize later with maturation, when the out-of-focus brain regions develop a decreased inhibitory/excitatory balance. These results suggest that a hyperexcitable focus is not sufficient alone to drive generalized absence seizures. They also found the gradual age dependent disappearance of sleep spindles coincided with the simultaneous gradual emergence of spike and waves, which both could be reversed by the ethosuximide.


This work confirms what we already seen repeatedly in our lab, that spontaneous seizure occurrence correlates with changes in wakefulness. In the current paper, while the seizure probability was very high around transitions between wakefulness and light sleep, seizures were replaced by slow waves as sleep deepened and even rhythmic cortical activation failed to induce seizures in this state. Mechanistically, "arousal related brain states controlled locally via the locus coeruleus noradrenergic system are involved as well as increased synchrony among TRN (Thalamic Reticular) neurons during transition to SWS, which is responsible for gating the sensory information to the cortex. These mechanisms can be in favor of spreading highly synchronous activity of epileptic seizures. "It is possible that in maturing animals during wake-sleep transitions the thalamic synchrony is imbalanced in a way that the continuously decreasing arousal together with increasing TRN synchrony provides a window where the cortical excitability is still high enough to initiate a putative seizure and the TRN is already sufficiently synchronous to provide a sufficiently broad feedback to the cortex via the thalamocortical cells to frame seizure activity for the next cycle."


In my work assessing a seizure risk in preclinical models it is important to differentiate SWDs from SSs (not an easy tasks in some cases, the pattern is very similar) and correctly classify SWDs. I have seen enough EEGs to know that sleep transitions to SWS are at higher risk of drug-induced SWDs, not just spontaneously occurring, and in our lab we have developed algorithms that specifically target these periods to detect changes in morphology and spectral composition.


Reference:

Kozák, G., Földi, T., & Berényi, A. (2020). Spike-and-Wave Discharges Are Not Pathological Sleep Spindles, Network-Level Aspects of Age-Dependent Absence Seizure Development in Rats. eNeuro, 7(1). https://doi.org/10.1523/ENEURO.0253-19.2019

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