Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer.

OBJECTIVE: To confirm and investigate why pathological high-frequency oscillations (pHFOs), including ripples (80-200 Hz) and fast ripples (200-600 Hz), are generated during the UP-DOWN transition of the slow wave and if information transmission mediated by ripple temporal coupling is disrupted in the seizure-onset zone (SOZ). METHODS: We isolated 217 total units from 175.95 intracranial electroencephalography (iEEG) contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the SOZ, HFOs and associated action potentials (APs) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross-correlograms. RESULTS: At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p < < .001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p < < .001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d = .11-.83) and fast ripples (d = .36-.90) at the UP-DOWN transition (p < .05 f.d.r. corrected), respectively. By comparison, also in the SOZ, 6.6% (d = .14-.30) and 8.5% (d = .33-.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows that ripple and fast ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by >50% in the SOZ compared to the non-SOZ (N = 3). SIGNIFICANCE: The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. Ripple temporal correlations across brain regions may be important in memory consolidation and are disrupted in the SOZ, perhaps by pHFO generation.

Delta oscillation coupled propagating fast ripples precede epileptiform discharges in patients with focal epilepsy.

Epileptiform spikes are used to localize epileptogenic brain tissue. The mechanisms that spontaneously trigger epileptiform discharges are not yet elucidated. Pathological fast ripple (FR, 200-600 Hz) are biomarkers of epileptogenic brain, and we postulated that FR network interactions are involved in generating epileptiform spikes. Using macroelectrode stereo intracranial EEG (iEEG) recordings from a cohort of 46 patients we found that, in the seizure onset zone (SOZ), propagating FR were more often followed by an epileptiform spike, as compared with non-propagating FR (p < 0.05). Propagating FR had a distinct frequency and larger power (p < 1e-10) and were more strongly phase coupled to the peak of iEEG delta oscillation, which likely correspond with the DOWN states during non-REM sleep (p < 1e-8), than non-propagating FR. While FR propagation was rare, all FR occurred with the highest probability within +/- 400 msec of epileptiform spikes with superimposed high-frequency oscillations (p < 0.05). Thus, a sub-population of epileptiform spikes in the SOZ, are preceded by propagating FR that are coordinated by the DOWN state during non-REM sleep.

Temporal lobe interictal spikes disrupt encoding and retrieval of verbal memory: A subregion analysis.

OBJECTIVE: The medial temporal lobe (MTL) encodes and recalls memories and can be a predominant site for interictal spikes (IS) in patients with focal epilepsy. It is unclear whether memory deficits are due to IS in the MTL producing a transient decline. Here, we investigated whether IS in the MTL subregions and lateral temporal cortex impact episodic memory encoding and recall. METHODS: Seventy-eight participants undergoing presurgical evaluation for medically refractory focal epilepsy with depth electrodes placed in the temporal lobe participated in a verbal free recall task. IS were manually annotated during the pre-encoding, encoding, and recall epochs. We examined the effect of IS on word recall using mixed-effects logistic regression. RESULTS: IS in the left hippocampus (odds ratio [OR] = .73, 95% confidence interval [CI] = .63-.84, p < .001) and left middle temporal gyrus (OR = .46, 95% CI = .27-.78, p < .05) during word encoding decreased subsequent recall performance. Within the left hippocampus, this effect was specific for area CA1 (OR = .76, 95% CI = .66-.88, p < .01) and dentate gyrus (OR = .74, 95% CI = .62-.89, p < .05). IS in other MTL subregions or inferior and superior temporal gyrus and IS occurring during the prestimulus window did not affect word encoding (p > .05). IS during retrieval in right hippocampal (OR = .22, 95% CI = .08-.63, p = .01) and parahippocampal regions (OR = .24, 95% CI = .07-.8, p < .05) reduced the probability of recalling a word. SIGNIFICANCE: IS in medial and lateral temporal cortex contribute to transient memory decline during verbal episodic memory.

Contribution of left supramarginal and angular gyri to episodic memory encoding: An intracranial EEG study.

The role of the left ventral lateral parietal cortex (VPC) in episodic memory is hypothesized to include bottom-up attentional orienting to recalled items, according to the dual-attention model (Cabeza et al., 2008). However, its role in memory encoding could be further clarified, with studies showing both positive and negative subsequent memory effects (SMEs). Furthermore, few studies have compared the relative contributions of sub-regions in this functionally heterogeneous area, specifically the anterior VPC (supramarginal gyrus/BA40) and the posterior VPC (angular gyrus/BA39), on a within-subject basis. To elucidate the role of the VPC in episodic encoding, we compared SMEs in the intracranial EEG across multiple frequency bands in the supramarginal gyrus (SmG) and angular gyrus (AnG), as twenty-four epilepsy patients with indwelling electrodes performed a free recall task. We found a significant SME of decreased theta power and increased high gamma power in the VPC overall, and specifically in the SmG. Furthermore, SmG exhibited significantly greater spectral tilt SME from 0.5 to 1.6 s post-stimulus, in which power spectra slope differences between recalled and unrecalled words were greater than in the AnG (p = 0.04). These results affirm the contribution of VPC to episodic memory encoding, and suggest an anterior-posterior dissociation within VPC with respect to its electrophysiological underpinnings.

Spatial and temporal profile of high-frequency oscillations in posttraumatic epileptogenesis.

We studied the role of temporal and spatial changes in high-frequency oscillation (HFO, 80-500 Hz) generation in epileptogenesis following traumatic brain injury (TBI). Experiments were conducted on adult male Sprague Dawley rats. For the TBI group, fluid percussion injury (FPI) on the left sensorimotor area was performed to induce posttraumatic epileptogenesis. For the sham control group, only the craniotomy was performed. After TBI, 8 bipolar micro-electrodes were implanted bilaterally in the prefrontal cortex, perilesional area and homotopic contralateral site, striatum, and hippocampus. Long-term video/local field potential (LFP) recordings were performed for up to 21 weeks to identify and characterize seizures and capture HFOs. The electrode tip locations and the volume of post TBI brain lesions were further estimated by ex-vivo MRI scans. HFOs were detected during slow-wave sleep and categorized as ripple (80-200 Hz) and fast ripple (FR, 250-500 Hz) events. HFO rates and the HFO peak frequencies were compared in the 8 recording locations and across 8-weeks following TBI. Data from 48 rats (8 sham controls and 40 TBI rats) were analyzed. Within the TBI group, 22 rats (55%) developed recurrent spontaneous seizures (E+ group), at an average of 62.2 (+17.1) days, while 18 rats (45%) did not (E- group). We observed that the HFOs in the E+ group had a higher mean peak frequency than the E- group and the sham group (P < 0.05). Furthermore, the FR rate of the E+ group showed a significant increase compared to the E-group (P < 0.01) and sham control group (P < 0.01), specifically in the perilesional area, homotopic contralateral site, bilateral hippocampus, and to a lesser degree bilateral striatum. When compared across time, the increased FR rate in the E+ group occurred immediately after the insult and remained stable across the duration of the experiment. In addition, lesion size was not statistically different in the E+ and E- group and was not correlated with HFO rates. Our results suggest that TBI results in the formation of a widespread epileptogenic network. FR rates serve as a biomarker of network formation and predict the future development of epilepsy, however FR are not a temporally specific biomarker of TBI sequelae responsible for epileptogenesis. These results suggest that in patients, future risk of post-TBI epilepsy can be predicted early using FR.

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.

Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy.

OBJECTIVE: Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. METHODS: Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. RESULTS: We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p < 0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p < 0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and non-SOZ (NSOZ) were compared. We found that in the frontal (spindle, p = 0.009; theta, p = 0.006, slow, p = 0.004) and parietal lobe (theta, p = 0.007, delta, p = 0.002, slow, p = 0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased. SIGNIFICANCE: Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathologic and physiologic events in patients with frontal and parietal SOZ.

Ripples on spikes show increased phase-amplitude coupling in mesial temporal lobe epilepsy seizure-onset zones.

OBJECTIVE: Ripples (80-150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathologic ripple (pRipple) event. METHODS: We quantified phase amplitude coupling (PAC) between epileptiform electroencephalography (EEG) spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by (1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a (2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure-onset zone (SOZ). RESULTS: Both the phasor transform and ripple-triggered averaging methods showed that ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was found more regularly inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode's location was within the SOZ (p < 0.01). The ratio of the rate of ripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (p < 0.05). Coupling strength correlated with an increase in mean normalized ripple amplitude (p < 0.01), and a decrease in mean ripple spectral frequency (p < 0.05). SIGNIFICANCE: Generation of low-frequency (80-150 Hz) pRipples in the SOZ involves coupling between epileptiform spike phase and ripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies.

Ictal high frequency oscillations distinguish two types of seizure territories in humans.

High frequency oscillations have been proposed as a clinically useful biomarker of seizure generating sites. We used a unique set of human microelectrode array recordings (four patients, 10 seizures), in which propagating seizure wavefronts could be readily identified, to investigate the basis of ictal high frequency activity at the cortical (subdural) surface. Sustained, repetitive transient increases in high gamma (80-150 Hz) amplitude, phase-locked to the low-frequency (1-25 Hz) ictal rhythm, correlated with strong multi-unit firing bursts synchronized across the core territory of the seizure. These repetitive high frequency oscillations were seen in recordings from subdural electrodes adjacent to the microelectrode array several seconds after seizure onset, following ictal wavefront passage. Conversely, microelectrode recordings demonstrating only low-level, heterogeneous neural firing correlated with a lack of high frequency oscillations in adjacent subdural recording sites, despite the presence of a strong low-frequency signature. Previously, we reported that this pattern indicates a failure of the seizure to invade the area, because of a feedforward inhibitory veto mechanism. Because multi-unit firing rate and high gamma amplitude are closely related, high frequency oscillations can be used as a surrogate marker to distinguish the core seizure territory from the surrounding penumbra. We developed an efficient measure to detect delayed-onset, sustained ictal high frequency oscillations based on cross-frequency coupling between high gamma amplitude and the low-frequency (1-25 Hz) ictal rhythm. When applied to the broader subdural recording, this measure consistently predicted the timing or failure of ictal invasion, and revealed a surprisingly small and slowly spreading seizure core surrounded by a far larger penumbral territory. Our findings thus establish an underlying neural mechanism for delayed-onset, sustained ictal high frequency oscillations, and provide a practical, efficient method for using them to identify the small ictal core regions. Our observations suggest that it may be possible to reduce substantially the extent of cortical resections in epilepsy surgery procedures without compromising seizure control.

High-gamma and beta bursts in the Left Supramarginal Gyrus can accurately differentiate verbal memory states and performance.

Positive and negative subsequent memory effects have been reported in the left supramarginal gyrus (LSMG) which is thought to mediate attention to memory. We performed a secondary analysis of 142 verbal delayed free recall experiments, in patients with medically refractory epilepsy, undergoing pre-surgical evaluation with electrode contacts implanted in the LSMG. Convolutional neural networks (CNNs), utilizing high-gamma and beta bursts in the LSMG during the word encoding epoch, had an area under the receiver operating curve (AUROC) for classifying remembered words of >0.6 in 16 experiments. These experiments were distinguished by higher peak amplitudes of high-gamma and beta bursts and electrode placement in more anterior and dorsal regions of the LSMG. Among these 16 experiments the CNN utilizing high-gamma and beta bursts was equivalent, or outperformed, the equivalent CNN utilizing raw intracranial electroencephalogram (iEEG). In 7 of these 16 experiments, we also trained and tested CNNs that differentiated verbal word encoding from recall, and better performance recall from poor performance recall. These CNNs also utilized the high-gamma and beta bursts from the respective iEEG epochs. For many of the experiments these CNNs had an AUROC >0.6 demonstrating proof of principle that memory state and performance biomarkers can be derived from highgamma and beta bursts in the iEEG recordings of the LSMG alone.

Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy.

High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.

Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction.

Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.

Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer.

Objective: To confirm and investigate why pathological HFOs (pHFOs), including Ripples [80-200 Hz] and fast ripples [200-600 Hz], are generated during the UP-DOWN transition of the slow wave and if pHFOs interfere with information transmission. Methods: We isolated 217 total units from 175.95 iEEG contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (0.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the seizure onset zone (SOZ), HFOs and associated action potentials (AP) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross correlograms. Results: At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p<<0.001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p<<0.001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d=0.11-0.83) and fast ripples (d=0.36-0.90) at the UP-DOWN transition (p<0.05 f.d.r corrected), respectively. By comparison, also in the SOZ, 6.6% (d=0.14-0.30) and 8.5% (d=0.33-0.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by ~50-80% in the SOZ compared to the non-SOZ (N=3). Significance: The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. The pathological neurons and pHFOs disrupt ripple temporal correlations across brain regions that transfer information and may be important in memory consolidation.

Fast ripples reflect increased excitability that primes epileptiform spikes.

The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.

Fast ripples reflect increased excitability that primes epileptiform spikes.

The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.

An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.

OBJECTIVE: Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. METHODS: We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. RESULTS: The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of 0.95 ± 0.03 , with precision, recall, and F1 scores of 0.92 ± 0.05 , 0.99 ± 0.01 , and 0.94 ± 0.03 , respectively. For the HFO classification, our algorithm obtained an accuracy of 0.97 ± 0.02 . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was 0.94 ± 0.03 for HFO detection and 0.85 ± 0.04 for HFO classification. SIGNIFICANCE: Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.

Graph theoretical measures of fast ripples support the epileptic network hypothesis.

The epileptic network hypothesis and epileptogenic zone hypothesis are two theories of ictogenesis. The network hypothesis posits that coordinated activity among interconnected nodes produces seizures. The epileptogenic zone hypothesis posits that distinct regions are necessary and sufficient for seizure generation. High-frequency oscillations, and particularly fast ripples, are thought to be biomarkers of the epileptogenic zone. We sought to test these theories by comparing high-frequency oscillation rates and networks in surgical responders and non-responders, with no appreciable change in seizure frequency or severity, within a retrospective cohort of 48 patients implanted with stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye movement sleep and semi-automatically detected and quantified high-frequency oscillations. Each electrode contact was localized in normalized coordinates. We found that the accuracy of seizure onset zone electrode contact classification using high-frequency oscillation rates was not significantly different in surgical responders and non-responders, suggesting that in non-responders the epileptogenic zone partially encompassed the seizure onset zone(s) (P > 0.05). We also found that in the responders, fast ripple on oscillations exhibited a higher spectral content in the seizure onset zone compared with the non-seizure onset zone (P < 1 × 10-5). By contrast, in the non-responders, fast ripple had a lower spectral content in the seizure onset zone (P < 1 × 10-5). We constructed two different networks of fast ripple with a spectral content >350 Hz. The first was a rate-distance network that multiplied the Euclidian distance between fast ripple-generating contacts by the average rate of fast ripple in the two contacts. The radius of the rate-distance network, which excluded seizure onset zone nodes, discriminated non-responders, including patients not offered resection or responsive neurostimulation due to diffuse multifocal onsets, with an accuracy of 0.77 [95% confidence interval (CI) 0.56-0.98]. The second fast ripple network was constructed using the mutual information between the timing of the events to measure functional connectivity. For most non-responders, this network had a longer characteristic path length, lower mean local efficiency in the non-seizure onset zone, and a higher nodal strength among non-seizure onset zone nodes relative to seizure onset zone nodes. The graphical theoretical measures from the rate-distance and mutual information networks of 22 non- responsive neurostimulation treated patients was used to train a support vector machine, which when tested on 13 distinct patients classified non-responders with an accuracy of 0.92 (95% CI 0.75-1). These results indicate patients who do not respond to surgery or those not selected for resection or responsive neurostimulation can be explained by the epileptic network hypothesis that is a decentralized network consisting of widely distributed, hyperexcitable fast ripple-generating nodes.

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.