Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathologic or even protective IEDs. Recent studies suggest that highly pathologic IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these ‘HFO-IEDs’ at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from ‘non-HFO-IEDs’. Analyzing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. During non-HFO-IEDs, no such imbalance was observed. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and moreover identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathologic IEDs.

The mammalian hippocampus expresses highly organized patterns of neuronal activity which form a neuronal correlate of spatial memories. These memory-encoding neuronal ensembles form on top of different network oscillations which entrain neurons in a state- and experience-dependent manner. The mechanisms underlying activation, timing and selection of participating neurons are incompletely understood. Here we studied the synaptic mechanisms underlying one prominent network pattern called sharp wave-ripple complexes (SPW-R) which are involved in memory consolidation during sleep. We recorded SPW-R with extracellular electrodes along the different layers of area CA1 in mouse hippocampal slices. Contribution of glutamatergic excitation and GABAergic inhibition, respectively, was probed by local application of receptor antagonists into s. radiatum, pyramidale and oriens. Laminar profiles of field potentials show that GABAergic potentials contribute substantially to sharp waves and superimposed ripple oscillations in s. pyramidale. Inhibitory inputs to s. pyramidale and s. oriens are crucial for action potential timing by ripple oscillations, as revealed by multiunit-recordings in the pyramidal cell layer. Glutamatergic afferents, on the other hand, contribute to sharp waves in s. radiatum where they also evoke a fast oscillation at ~200 Hz. Surprisingly, field ripples in s. radiatum are slightly slower than ripples in s. pyramidale, resulting in a systematic shift between dendritic and somatic oscillations. This complex interplay between dendritic excitation and perisomatic inhibition may be responsible for the precise timing of discharge probability during the time course of SPW-R. Together, our data illustrate a complementary role of spatially confined excitatory and inhibitory transmission during highly ordered network patterns in the hippocampus.

Objective Identification of children at risk of developing epilepsy after a first unprovoked seizure can be challenging. Interictal epileptiform discharges are associated with higher risk but have limited sensitivity and specificity. High frequency oscillations (HFOs) are newer biomarkers for epileptogenesis. We prospectively evaluated the predictive value of HFOs for developing epilepsy in scalp electroencephalogram (EEG) of children after a first unprovoked seizure. Methods After their first seizure, 56 children were followed prospectively over 12 months and then grouped in “epilepsy” or “no epilepsy.” Initial EEGs were visually analyzed for spikes, spike ripples, and ripples. Inter‐group comparisons of spike‐rates and HFO‐rates were done by Mann–Whitney U test. Predictive values and optimal thresholds were calculated by receiver operating characteristic (ROC) curves. Results In the epilepsy group (n = 26, 46%), mean rates of ripples (0.3 vs 0.09 / minute, p < 0.0001) and spike ripples (0.6 vs 0.06 / minute, p < 0.05) were significantly higher, with no difference in spike rates (1.7 vs 3.0 / minute, p = 0.38). Of those 3 markers, ripples showed the best predictive value (area under the curve [AUC]ripples = 0.88). The optimal threshold for ripples was calculated to be ≥ 0.125 / minute with a sensitivity of 87% and specificity of 85%. Ripple rates were negatively correlated to days passing before epilepsy‐diagnosis (R = −0.59, p < 0.0001) and time to a second seizure (R = −0.64, 95% confidence interval [CI] = −0.77 to 0.43, p < 0.0001). Interpretation We could show that in a cohort of children with a first unprovoked seizure, ripples predict the development of epilepsy better than spikes or spike ripples and might be useful biomarkers in the estimation of prognosis and question of treatment. ANN NEUROL 2021;89:134–142

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