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Objective High frequency oscillations (HFOs) in intraoperative electrocorticography (ioECoG) are thought to be generated by hyperexcitable neurons. Inflammation may promote neuronal hyperexcitability. We investigated the relation between HFOs and inflammation in tumor-related epilepsy. Methods We identified HFOs (ripples 80–250 Hz, fast ripples 250–500 Hz) in the preresection ioECoG of 32 patients with low-grade tumors. Localization of recorded HFOs was classified based on magnetic resonance imaging reconstructions: in tumor, in resected non-tumorous area and outside the resected area. We tested if the following inflammatory markers in the tumor or peritumoral tissue were related to HFOs: activated microglia, cluster of differentiation 3 (CD3)-positive T-cells, interleukin 1-beta (IL1β), toll-like receptor 4 (TLR4) and high mobility group box 1 protein (HMGB1). Results Tumors that generated ripples were infiltrated by more CD3-positive cells than tumors without ripples. Ripple rate outside the resected area was positively correlated with IL1β/TLR4/HMGB1 pathway activity in peritumoral area. These two areas did not directly overlap. Conclusions Ripple rates may be associated with inflammatory processes. Significance Our findings support that ripple generation and spread might be associated with synchronized fast firing of hyperexcitable neurons due to certain inflammatory processes. This pilot study provides arguments for further investigations in HFOs and inflammation.

Objective Up to 30% of people with juvenile myoclonic epilepsy (JME) have photoparoxysmal responses (PPR). Recent studies report on structural and pathophysiological differences between people with JME with (JME+PPR) and without PPR (JME−PPR). We investigated whether electrophysiological features outside photic stimulation differ between these subtypes. Methods We analysed EEG recordings of people with JME at a tertiary epilepsy centre and an academic hospital. Photosensitivity was assessed in a drug-naïve condition. We compared the occurrence and involvement of posterior electrodes for focal abnormalities and generalised spike-wave activity in the EEG outside photic stimulation between JME+PPR and JME−PPR. Results We included EEG recordings of 18 people with JME+PPR and 21 with JME−PPR. People with JME−PPR had less focal abnormalities in the posterior brain regions than people with JME+PPR (19% vs 55%, p

OBJECTIVE: High frequency oscillations (HFOs) and in particular fast ripples (FRs) in the post-resection electrocorticogram (ECoG) have recently been shown to be highly specific predictors of outcome of epilepsy surgery. FR visual marking is time consuming and prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome. METHODS: Pre-resection ECoG dataset (N=14 patients) with visually marked HFOs were used to optimize the detector's parameters in the time-frequency domain. The optimized detector was then applied on a larger post-resection ECoG dataset (N=54) and the output was compared with visual markings and seizure outcome. The analysis was conducted separately for ripples (80-250Hz) and FRs (250-500Hz). RESULTS: Channel-wise comparison showed a high association between automatic detection and visual marking (p

Objective: Single Pulse Electrical Stimulation (SPES) probes epileptogenic cortex during electrocorticography. Two SPES responses are described: pathological delayed responses (DR, >100 ms) associated with the seizure onset zone (SOZ) and physiological early responses (ER, 80 Hz, in the SOZ and seizure propagation areas. Methods: We used data from 12 refractory epilepsy patients. SPES consisted of 10 pulses of 1 ms, 4–8 mA and 5 s interval on adjacent electrodes pairs. Data were available at 2048 samples/s for six and 512 samples/s (22 bits) for eight patients and analyzed in the time–frequency (TF) and time-domain (TD). Results: Electrodes with ERs were stronger associated with SOZ than non-SOZ electrodes. ERs with frequency content >80 Hz exist and are specific for SOZ channels. ERs evoked by stimulation of seizure onset electrodes were associated with electrodes involved in seizure propagation. Conclusion: Analysis of ERs can reveal aspects of pathology, manifested by association with seizure propagation and areas with high ER numbers that coincide with the SOZ. Significance: Not only DRs, but also ERs could have clinical value for mapping epileptogenic cortex and help to unravel aspects of the epileptic network.

Objective Ripples (80–250 Hz) have been shown to be a more specific biomarker for the epileptogenic zone than epileptic spikes in intracranial EEG and even surface EEG. Ripples often co-occur with spikes. We investigated the spatiotemporal relation between spikes and ripples, and differences between spikes that do and do not co-occur with ripples. Methods We marked 50 time points with spikes in bipolar surface EEG during NREM sleep in patients with focal or multifocal epilepsy. We marked ripples that occurred with spikes and calculated parameters relating spikes and ripples: the duration, amplitude and slope of spikes, the timing of the start of ripples and spikes and the proportion of overlap. Results In total 219 ripples and 5995 individual spikes were marked in 31 patients. Spikes with ripples were on average shorter, had higher amplitude and higher slope than spikes without ripples. 64% of ripples started before spikes started. Spikes occurred on 13 (5–26) channels per patient, and ripples on 3 (0–14) channels, which were also spike channels. Conclusion Ripples precede rather than follow spikes, so ripples are unlikely to result from spikes. Significance Ripples and spikes seem not one-on-one coupled, but certain states of the brain can accommodate both.

Objective High frequency oscillations (HFOs) are a biomarker of epileptogenicity. Visual marking of HFOs is highly time-consuming and inevitably subjective, making automatic detection necessary. We compare four existing detectors on the same dataset. Methods HFOs and baselines were identified by experienced reviewers in intracerebral EEGs from 20 patients. A new feature of our detector to deal with channels where baseline cannot be found is presented. The original and an optimal configuration are implemented. Receiver operator curves, false discovery rate, and channel ranking are used to evaluate performance. Results All detectors improve performance with the optimal configuration. Our detector had higher sensitivity, lower false positives than the others, and similar false detections. The main difference in performance was in very active channels. Conclusions Each detector was developed for different recordings and with different aims. Our detector performed better in this dataset, but was developed on data similar to the test data. Moreover, optimizing on a particular data type improves performance in any detector. Significance Automatic HFO detection is crucial to propel their clinical use as biomarkers of epileptogenic tissue. Comparing detectors on a single dataset is important to analyze their performance and to emphasize the issues involved in validation.