Low-voltage fast seizures in humans begin with increased interneuron firing.

OBJECTIVE: Intracellular recordings from cells in entorhinal cortex tissue slices show that low-voltage fast (LVF) onset seizures are generated by inhibitory events. Here, we determined whether increased firing of interneurons occurs at the onset of spontaneous mesial-temporal LVF seizures recorded in patients. METHODS: The seizure onset zone (SOZ) was identified using visual inspection of the intracranial electroencephalogram. We used wavelet clustering and temporal autocorrelations to characterize changes in single-unit activity during the onset of LVF seizures recorded from microelectrodes in mesial-temporal structures. Action potentials generated by principal neurons and interneurons (ie, putative excitatory and inhibitory neurons) were distinguished using waveform morphology and K-means clustering. RESULTS: From a total of 200 implanted microelectrodes in 9 patients during 13 seizures, we isolated 202 single units; 140 (69.3%) of these units were located in the SOZ, and 40 (28.57%) of them were classified as inhibitory. The waveforms of both excitatory and inhibitory units remained stable during the LVF epoch (p > > 0.05). In the mesial-temporal SOZ, inhibitory interneurons increased their firing rate during LVF seizure onset (p < 0.01). Excitatory neuron firing rates peaked 10 seconds after the inhibitory neurons (p < 0.01). During LVF spread to the contralateral mesial temporal lobe, an increase in inhibitory neuron firing rate was also observed (p < 0.01). INTERPRETATION: Our results suggest that seizure generation and spread during spontaneous mesial-temporal LVF onset events in humans may result from increased inhibitory neuron firing that spawns a subsequent increase in excitatory neuron firing and seizure evolution. Ann Neurol 2018;84:588-600.

Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding.

BACKGROUND: We sought to determine if ripple oscillations (80-120 Hz), detected in intracranial electroencephalogram (iEEG) recordings of patients with epilepsy, correlate with an enhancement or disruption of verbal episodic memory encoding. METHODS: We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch and included the seizure onset zone (SOZ) as a covariate in the LRMs. RESULTS: We detected events during 58,312 word presentation trials from 7630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the SOZ (p < 0.04). In the left temporal neocortex, RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however, this effect only met significance in the SOZ (OR of word recall: 0.71, 95% confidence interval (CI): 0.59-0.85, n = 158 events, adaptive Hochberg, p < 0.01). Ripple on oscillation (RonO) events that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR: 0.52, 95% CI: 0.34-0.80, n = 140, adaptive Hochberg, p < 0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus (MTG) correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p < 0.01). CONCLUSION: Ripples and spikes generated in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Although physiological and pathological ripple oscillations were not distinguished during cognitive tasks, our results show an association of undifferentiated ripples with impaired encoding. The effect was sometimes specific to regions outside the SOZ, suggesting that widespread effects of epilepsy outside the SOZ may contribute to cognitive impairment.

Insulinoma as a Potential Insidious Presenter in Medical Refractory Epilepsy.

BACKGROUND: Insulinoma as a cause of epileptic seizure has been thoroughly described but often not considered in differentials for previously established diagnoses of seizure disorder. Hypoglycemic symptoms can mimic neurological disorders such as epilepsy. CASE PRESENTATION: A 52-year-old woman presented with a history of epilepsy on anti-epileptic drugs (AEDs) developed repeated episodes consisting of seizures and neuropsychiatric symptoms with no predisposing factors for epilepsy at age 52. She had received full AED treatment before the possibility of hypoglycemia was considered. Following a clinical diagnosis of insulinoma, distal pancreatectomy was performed, and her seizures did not occur again. CONCLUSION: The early diagnosis of insulinoma requires vigilance, not only for hypoglycemia in patients with neuropsychiatric symptoms, but also for the possible masking effects of a history of epilepsy and preceding AED usage.

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). METHODS: We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. RESULTS: More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. CONCLUSION: This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized.