Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks

In medication-resistant epilepsy, the objective of epilepsy surgery is to render a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) in the epilepsy monitoring unit (EMU) by an expert epileptologist, and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used support vector machines (SVMs) to compare the resection volumes of actual resections, defined using the clinical standard of care, with virtual resections of fast ripple (FR 350-600 Hz) networks. Cross-validation of the SVM that labeled patients as seizure free or not seizure free using FR metrics as factors demonstrated an accuracy of 0.78. In all the patients rendered seizure free after surgery, we found that the virtual resection, defined using FR generator sites with highest rate and greatest autonomy, was larger than the actual resection. In the patients who experienced seizures after the actual resection, a virtual resection that included the SOZ and other FR generating regions rendered half of these patients virtually seizure free. We also examined patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR. We used the simulations to investigate if the likelihood of a RNS super response (>90% seizure reduction) would be increased.

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.

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.

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

Objective

To test the feasibility and reliability of intracranial electrophysiological recordings in an acute status epilepticus model on laboratory swine.

Method

Intrahippocampal injection of kainic acid (KA) was performed on 17 male Bama pigs (Sus scrofa domestica) weighing between 25 and 35 kg. Two stereoelectroencephalography (SEEG) electrodes with a total of 16 channels were implanted bilaterally along the sensorimotor cortex to the hippocampus. Brain electrical activity was recorded 2 h daily for 9-28 days. Three KA dosages were tested to evaluate the quantities capable of evoking status epilepticus. Local field potentials (LFPs) were recorded and compared before and after the KA injection. We quantified the epileptic patterns, including the interictal spikes, seizures, and high-frequency oscillations (HFOs), up to 4 weeks after the KA injection. Test-retest reliability using intraclass correlation coefficients (ICCs) were performed on interictal HFO rates to evaluate the recording stability of this model.

Results

The KA dosage test suggested that a 10 μl (1.0 μg/μl) intrahippocampal injection could successfully evoke status epilepticus lasting from 4 to 12 h. At this dosage, eight pigs (50% of total) had prolonged epileptic events (tonic-chronic seizures + interictal spikes n = 5, interictal spikes alone n = 3) in the later 4 weeks of the video-SEEG recording period. Four pigs (25% of total) had no epileptic activities, and another four (25%) had lost the cap or did not complete the experiments. Animals that showed epileptiform events were grouped as E + (n = 8) and the four animals showing no signs of epileptic events were grouped as E- (n = 4). A total of 46 electrophysiological seizures were captured in the 4-week post-KA period from 4 E + animals, with the earliest onset on day 9. The seizure durations ranged from 12 to 45 s. A significant increase of hippocampal HFOs rate (num/min) was observed in the E+ group during the post-KA period (weeks 1, 2,4, p < 0.05) compared to the baseline. But the E-showed no change or a decrease (in week 2, p = 0.43) compared to their baseline rate. The between-group comparison showed much higher HFO rates in E + vs. E - (F = 35, p < 0.01). The high ICC value [ICC (1, k) = 0.81, p < 0.05] quantified from the HFO rate suggested that this model had a stable measurement of HFOs during the four-week post-KA periods.

Significance

This study measured intracranial electrophysiological activity in a swine model of KA-induced mesial temporal lobe epilepsy (mTLE). Using the clinical SEEG electrode, we distinguished abnormal EEG patterns in the swine brain. The high test-retest reliability of HFO rates in the post-KA period suggests the utility of this model for studying mechanisms of epileptogenesis. The use of swine may provide satisfactory translational value for clinical epilepsy research.

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.

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.

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.

Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography

The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.

Spike and wave discharges and fast ripples during posttraumatic epileptogenesis

OBJECTIVE

The goal of the present study was to determine whether spike and wave discharges (SWDs) and SWDs with superimposed fast ripples (SWDFRs) could be biomarkers of posttraumatic epileptogenesis.

METHODS

Fluid percussion injury was conducted on 13-14-week old male Sprague Dawley rats. Immediately after traumatic brain injury (TBI), they were implanted with microelectrodes in the neocortex, hippocampus, and striatum bilaterally. Age-matched sham rats with the same electrode implantation montage acted as controls. Wideband brain electrical activity was recorded intermittently from Day 1 of TBI, and continued from 2 to 21 weeks after TBI. SWD and SWDFR analysis was performed during the first 2 weeks to investigate whether the occurrence of this pattern predicted development of epilepsy. The remaining 3-21 weeks were used for identifying which rats became epileptic (E+ group) and which did not (E- group).

RESULTS

The E+ group (n = 9) showed a significant increase in SWD rate in prefrontal cortex during Weeks 1 and 2 after TBI. The E- group showed a significant increase in SWD rate only in the second week. One hundred percent of rats in the E+ group displayed SWDFRs beginning from the first week after TBI. The SWDFR pattern was observed in all recorded brain areas: prefrontal and perilesional cortices, hippocampus, and striatum. None of rats in the E- group showed coexistence of fast ripples with SWDs.

SIGNIFICANCE

Occurrence of SWDFRs after TBI, but not an increase in the rate of SWDs, could be a noninvasive electroencephalographic biomarker of posttraumatic epileptogenesis.

Topographical reorganization of brain functional connectivity during an early period of epileptogenesis

OBJECTIVE

The current study aims to investigate functional brain network representations during the early period of epileptogenesis.

METHODS

Eighteen rats with the intrahippocampal kainate model of mesial temporal lobe epilepsy were used for this experiment. Functional magnetic resonance imaging (fMRI) measurements were made 1 week after status epilepticus, followed by 2-4-month electrophysiological and video monitoring. Animals were identified as having (1) developed epilepsy (E+, n = 9) or (2) not developed epilepsy (E-, n = 6). Nine additional animals served as controls. Graph theory analysis was performed on the fMRI data to quantify the functional brain networks in all animals prior to the development of epilepsy. Spectrum clustering with the network features was performed to estimate their predictability in epileptogenesis.

RESULTS

Our data indicated that E+ animals showed an overall increase in functional connectivity strength compared to E- and control animals. Global network features and small-worldness of E- rats were similar to controls, whereas E+ rats demonstrated increased small-worldness, including increased reorganization degree, clustering coefficient, and global efficiency, with reduced shortest pathlength. A notable classification of the combined brain network parameters was found in E+ and E- animals. For the local network parameters, the E- rats showed increased hubs in sensorimotor cortex, and decreased hubness in hippocampus. The E+ rats showed a complete loss of hippocampal hubs, and the appearance of new hubs in the prefrontal cortex. We also observed that lesion severity was not related to epileptogenesis.

SIGNIFICANCE

Our data provide a view of the reorganization of topographical functional brain networks in the early period of epileptogenesis and how it can significantly predict the development of epilepsy. The differences from E- animals offer a potential means for applying noninvasive neuroimaging tools for the early prediction of epilepsy.

Scalp EEG interictal high frequency oscillations as an objective biomarker of infantile spasms

OBJECTIVE

To investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.

METHODS

We retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs).

RESULTS

Twenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure.

CONCLUSIONS

Increased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms.

SIGNIFICANCE

Scalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.

Ripples Have Distinct Spectral Properties and Phase-Amplitude Coupling With Slow Waves, but Indistinct Unit Firing, in Human Epileptogenic Hippocampus

Ripple oscillations (80-200 Hz) in the normal hippocampus are involved in memory consolidation during rest and sleep. In the epileptic brain, increased ripple and fast ripple (200-600 Hz) rates serve as a biomarker of epileptogenic brain. We report that both ripples and fast ripples exhibit a preferred phase angle of coupling with the trough-peak (or On-Off) state transition of the sleep slow wave in the hippocampal seizure onset zone (SOZ). Ripples on slow waves in the hippocampal SOZ also had a lower power, greater spectral frequency, and shorter duration than those in the non-SOZ. Slow waves in the mesial temporal lobe modulated the baseline firing rate of excitatory neurons, but did not significantly influence the increased firing rate associated with ripples. In summary, pathological ripples and fast ripples occur preferentially during the On-Off state transition of the slow wave in the epileptogenic hippocampus, and ripples do not require the increased recruitment of excitatory neurons.

ORTHOSTATIC STABILITY OF THE CIRCULATORY SYSTEM AND ITS REGULATION IN GIRLS WITH DIFFERENT PHYSICAL ACTIVITY LEVELS

Aim. The article deals with studying the orthostatic stability of cardiac chronotropic function, arterial pressure, and heart rate regulation in girls aged 11–13 with different physical acti­vity levels. Materials and methods. We formed two groups: the first group consisted of girls with a relatively low level of physical activity; the second group included girls involved in track-and-field (middle-distance running). The parameters of the circulatory system were registered in the supine position and during active orthostasis with the help of Kentavr II PC. The analysis  of slow wave heart rate variability was performed by using the Biospectrum program. Results. Regardless of physical activity levels in the girls of this age group, the transition to active orthostasis is accompanied by the hypertonic reaction of arterial pressure. In girls with a low phy­sical activity level, there is a pronounced lability of heart rate regulation with the predominance of neurovisceral effect at rest (high general PC variability, LF and HF power) with the mechanisms of orthostatic stability based on the decreased parasympathetic effects on the circulatory system. In girls involved in track-and-field, there is a predominance of the sympathetic nervous system (high LF) at rest with a higher humoral and metabolic effects on heart rate which provide the orthostatic stability of the circulatory system. Conclusion. Despite the absence of significant differences in the parameters of the circulatory system in girls aged 10–13 with different physical activity levels, we registered the peculiarities of heart rate regulation at rest and in orthostasis.

Localizing epileptogenic regions using high-frequency oscillations and machine learning

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

Unit firing and oscillations at seizure onset in epileptic rodents

Epileptic seizures result from a variety of pathophysiological processes, evidenced by different electrographic ictal onset patterns, as seen on direct brain recordings. The two most common electrographic patterns of focal ictal onset in patients are hypersynchronous (HYP) and low-voltage fast (LVF). Whereas LVF ictal onsets were believed to result from disinhibition; based on similarities with absence seizures, focal HYP ictal onsets were believed to result from increased synchronizing inhibition. Recent findings, however, suggest the differences between these seizure onset types are more complicated and, in some cases, the opposite of these concepts are true. The following review presents evidence that a reduction of tonic inhibition on small pathologically interconnected neuron (PIN) clusters generating pathological high-frequency oscillations (pHFOs), which reflect abnormal synchronously bursting neurons may be the cause of HYP ictal onsets. Increased inhibition preceding LVF ictal onsets are discussed in other reviews in this issue. We postulate that neuronal cell loss following epileptogenic insults can result in structural reorganization, giving rise to small PIN clusters, which generate pHFOs. These clusters have a heterogeneous distribution and are spatially stable over time. Studies have demonstrated that a transient reduction in tonic inhibition causes these clusters to increase in size. This could result in consolidation and synchronization of pHFOs until a critical mass leads to propagation of HYP ictal discharges. Viewed within a network neuroscience framework, local disturbances such as PIN clusters are likely to contribute to large-scale brain network alterations: a better understanding of these epileptogenic networks promises to elucidate mechanisms of ictogenesis, epileptogenesis, and certain comorbidities of epilepsy.

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.

Nonictal EEG biomarkers for diagnosis and treatment

There are no reliable nonictal biomarkers for epilepsy, electroencephalography (EEG) or otherwise, but efforts to identify biomarkers that would predict the development of epilepsy after a potential epileptogenic insult, diagnose the existence of epilepsy, or assess the effects of antiseizure or antiepileptogenic interventions are relying heavily on electrophysiology. The most promising EEG biomarkers to date are pathologic high‐frequency oscillations (pHFOs), brief EEG events in the range of 100 to 600 Hz, which are believed to reflect summated action potentials from synchronously bursting neurons. Studies of patients with epilepsy, and experimental animal models, have been based primarily on direct brain recording, which makes pHFOs potentially useful for localizing the epileptogenic zone for surgical resection, but application for other diagnostic and therapeutic purposes is limited. Consequently, recent efforts have involved identification of HFOs recorded with scalp electrodes, and with magnetoencephalography, which may reflect the same pathophysiologic mechanisms as pHFOs recorded directly from the brain. The search is also on for other EEG changes that might serve as epilepsy biomarkers, and candidates include arcuate rhythms, which may reflect repetitive pHFOs, reduction in theta rhythm, which correlates with epileptogenesis in several rodent models of epilepsy, and shortened sleep spindles that correlate with ictogenesis.

Remote Changes in Cortical Excitability after Experimental Traumatic Brain Injury and Functional Reorganization

Although cognitive and behavioral deficits are well known to occur following traumatic brain injury (TBI), motor deficits that occur even after mild trauma are far less known, yet are equally persistent. This study was aimed at making progress toward determining how the brain reorganizes in response to TBI. We used the adult rat controlled cortical impact injury model to study the ipsilesional forelimb map evoked by electrical stimulation of the affected limb, as well as the contralesional forelimb map evoked by stimulation of the unaffected limb, both before injury and at 1, 2, 3, and 4 weeks after using functional magnetic resonance imaging (fMRI). End-point c-FOS immunohistochemistry data following 1 h of constant stimulation of the unaffected limb were acquired in the same rats to avoid any potential confounds due to altered cerebrovascular coupling. Single and paired-pulse sensory evoked potential (SEP) data were recorded from skull electrodes over the contralesional cortex in a parallel series of rats before injury, at 3 days, and at 1, 2, 3, and 4 weeks after injury in order to determine whether alterations in cortical excitability accompanied reorganization of the cortical map. The results show a transient trans-hemispheric shift in the ipsilesional cortical map as indicated by fMRI, remote contralesional increases in cortical excitability that occur in spatially similar regions to altered fMRI activity and greater c-FOS activation, and reduced or absent ipsilesional cortical activity chronically. The contralesional changes also were indicated by reduced SEP latency within 3 days after injury, but not by blood oxygenation level-dependent fMRI until much later. Detailed interrogation of cortical excitability using paired-pulse electrophysiology showed that the contralesional cortex undergoes both an early and a late post-injury period of hyper-excitability in response to injury, interspersed by a period of relatively normal activity. From these data, we postulate a cross-hemispheric mechanism by which remote cortex excitability inhibits ipsilesional activation by rebalanced cortical excitation-inhibition.

Electrophysiological biomarkers of epileptogenicity after traumatic brain injury

Post-traumatic epilepsy is the architype of acquired epilepsies, wherein a brain insult initiates an epileptogenic process culminating in an unprovoked seizure after weeks, months or years. Identifying biomarkers of such process is a prerequisite for developing and implementing targeted therapies aimed at preventing the development of epilepsy. Currently, there are no validated electrophysiological biomarkers of post-traumatic epileptogenesis. Experimental EEG studies using the lateral fluid percussion injury model have identified three candidate biomarkers of post-traumatic epileptogenesis: pathological high-frequency oscillations (HFOs, 80-300 Hz); repetitive HFOs and spikes (rHFOSs); and reduction in sleep spindle duration and dominant frequency at the transition from stage III to rapid eye movement sleep. EEG studies in humans have yielded conflicting data; recent evidence suggests that epileptiform abnormalities detected acutely after traumatic brain injury carry a significantly increased risk of subsequent epilepsy. Well-designed studies are required to validate these promising findings, and ultimately establish whether there are post-traumatic electrophysiological features which can guide the development of 'antiepileptogenic' therapies.

Extrahippocampal high-frequency oscillations during epileptogenesis

The current study aimed to investigate the spatial and temporal patterns of high-frequency oscillations (HFOs) in the intra-/extrahippocampal areas during epileptogenesis. Local field potentials were bilaterally recorded from hippocampus (CA1), thalamus, motor cortex, and prefrontal cortex in 13 rats before and after intrahippocampal kainic acid (KA) lesions. HFOs in the ripple (100-200 Hz) and fast ripple (250-500 Hz) ranges were detected and their rates were computed during different time periods (1-5 weeks) after KA-induced status epilepticus (SE). Recurrent spontaneous seizures were observed in 7 rats after SE, and the other 6 rats did not develop epilepsy. During the latent period, the rate of hippocampal HFOs increased at the ipsilateral site of the KA lesion in both groups, and the HFO rate was significantly higher in the animals that later developed epilepsy. Animals that later developed epilepsy also demonstrated widespread appearance of HFOs, in both the ripple and the fast ripple range, whereas animals that did not develop epilepsy only exhibited changes in the ipsilateral intrahippocampal HFO rate. This study demonstrates an association between an increased rate of widespread HFOs and the later development of epilepsy, suggesting the formation of large-scale distributed pathological networks during epileptogenesis.

Seizure development in the acute intrahippocampal epileptic focus

Currently, an epileptic seizure is considered to involve a temporary network that exists for a finite period of time. Formation of this network evolves through spread of epileptiform activity from a seizure onset zone (SOZ). Propagation of seizures evoked by kainic acid injection in hippocampus to different brain areas was analyzed at macro- and micro-intervals. The mean latency of seizure occurrence in different brain areas varied between 0.5 sec and 85 sec (mean 14.9 ± 14.5 (SD)), and it increased after each consecutive seizure in areas located contralateral to the area of injection, but not in the ipsilateral sites. We have shown that only 41% of epileptic individual events in target brain areas were driven by epileptic events generated in the SOZ once the seizure began. Fifty-nine percent of epileptiform events in target areas occurred one millisecond before or after events in the SOZ. These data illustrate that during seizure maintenance, only some individual epileptiform events in areas outside of SOZ could be consistently triggered by the SOZ; and the majority must be triggered by a driver located outside the SOZ or brain areas involved in ictal activity could be coupled to each other via an unknown mechanism such as stochastic resonance.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

OBJECTIVE

To develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ).

METHODS

iEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection.

RESULTS

The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001).

CONCLUSIONS

Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone.

SIGNIFICANCE

Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

OBJECTIVE

To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG.

METHODS

A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals.

RESULTS

The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated.

CONCLUSIONS

Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing.

SIGNIFICANCE

Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.

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.

The progression of electrophysiologic abnormalities during epileptogenesis after experimental traumatic brain injury

OBJECTIVE

Posttraumatic epilepsy (PTE) accounts for 20% of acquired epilepsies. Experimental models are important for studying epileptogenesis. We previously reported that repetitive high-frequency oscillations with spikes (rHFOSs) occur early after lateral fluid percussion injury (FPI) and may be a biomarker for PTE. The objective of this study was to use multiple electrodes in rat hippocampal and neocortical regions to describe the long-term electroencephalographic and behavioral evolution of rHFOSs and epileptic seizures after traumatic brain injury (TBI).

METHODS

Adult male rats underwent mild, moderate, or severe FPI or sham injury followed by video-electroencephalography (EEG) recordings with a combination of 16 neocortical and hippocampal electrodes at an early, intermediate, or late time-point after injury, up to 52 weeks. Recordings were analyzed for the presence of rHFOSs and seizures.

RESULTS

Analysis was done on 28 rats with FPI and 7 shams. Perilesional rHFOSs were recorded in significantly more rats after severe (70.3%) than mild (20%) injury or shams (14.3%). Frequency of occurrence was significantly highest in the early (10.8/h) versus late group (3.2/h). Late focal seizures originating from the same electrodes were recorded in significantly more rats in the late (87.5%) versus early period (22.2%), occurring almost exclusively in injured rats. Seizure duration increased significantly over time, averaging 19 s at the beginning of the early period and 27 s at the end of the late period. Seizure frequency also increased significantly over time, from 4.4 per week in the early group to 26.4 per week in the late group. Rarely, rats displayed early seizures or generalized seizures.

SIGNIFICANCE

FPI results in early rHFOSs and later spontaneous focal seizures arising from peri-lesional neocortex, supporting its use as a model for PTE. Epilepsy severity increased over time and was related to injury severity. The association between early rHFOSs and later focal seizures suggests that rHFOSs may be a potential noninvasive biomarker of PTE.

Pathologic electrographic changes after experimental traumatic brain injury

OBJECTIVE

To investigate possible electroencephalography (EEG) correlates of epileptogenesis after traumatic brain injury (TBI) using the fluid percussion model.

METHODS

Experiments were conducted on adult 2- to 4-month-old male Sprague-Dawley rats. Two groups of animals were studied: (1) the TBI group with depth and screw electrodes implanted immediately after the fluid percussion injury (FPI) procedure, and (2) a naive age-matched control group with the same electrode implantation montage. Pairs of tungsten microelectrodes (50 μm outer diameter) and screw electrodes were implanted in neocortex inside the TBI core, areas adjacent to TBI, and remote areas. EEG activity, recorded on the day of FPI, and continuously for 2 weeks, was analyzed for possible electrographic biomarkers of epileptogenesis. Video-EEG monitoring was also performed continuously in the TBI group to capture electrographic and behavioral seizures until the caps came off (28-189 days), and for 1 week, at 2, 3, and 6 months of age, in the control group.

RESULTS

Pathologic high-frequency oscillations (pHFOs) with a central frequency between 100 and 600 Hz, were recorded from microelectrodes, beginning during the first two post-FPI weeks, in 7 of 12 animals in the TBI group (58%) and never in the controls. pHFOs only occurred in cortical areas within or adjacent to the TBI core. These were associated with synchronous multiunit discharges and popSpikes, duration 15-40 msec. Repetitive pHFOs and EEG spikes (rHFOSs) formed paroxysmal activity, with a unique arcuate pattern, in the frequency band 10-16 Hz in the same areas as isolated pHFOs, and these events were also recorded by screw electrodes. Although loss of caps prevented long-term recordings from all rats, pHFOs and rHFOSs occurred during the first 2 weeks in all four animals that later developed seizures, and none of the rats without these events developed late seizures.

SIGNIFICANCE

pHFOs, similar to those associated with epileptogenesis in the status rat model of epilepsy, may also reflect epileptogenesis after FPI. rHFOSs could be noninvasive biomarkers of epileptogenesis.

Ictal onset patterns of local field potentials, high frequency oscillations, and unit activity in human mesial temporal lobe epilepsy

OBJECTIVE

To characterize local field potentials, high frequency oscillations, and single unit firing patterns in microelectrode recordings of human limbic onset seizures.

METHODS

Wide bandwidth local field potential recordings were acquired from microelectrodes implanted in mesial temporal structures during spontaneous seizures from six patients with mesial temporal lobe epilepsy.

RESULTS

In the seizure onset zone, distinct epileptiform discharges were evident in the local field potential prior to the time of seizure onset in the intracranial EEG. In all three seizures with hypersynchronous (HYP) seizure onset, fast ripples with incrementally increasing power accompanied epileptiform discharges during the transition to the ictal state (p < 0.01). In a single low voltage fast (LVF) onset seizure a triad of evolving HYP LFP discharges, increased single unit activity, and fast ripples of incrementally increasing power were identified ~20 s prior to seizure onset (p < 0.01). In addition, incrementally increasing fast ripples occurred after seizure onset just prior to the transition to LVF activity (p < 0.01). HYP onset was associated with an increase in fast ripple and ripple rate (p < 0.05) and commonly each HYP discharge had a superimposed ripple followed by a fast ripple. Putative excitatory and inhibitory single units could be distinguished during limbic seizure onset, and heterogeneous shifts in firing rate were observed during LVF activity.

SIGNIFICANCE

Epileptiform activity is detected by microelectrodes before it is detected by depth macroelectrodes, and the one clinically identified LVF ictal onset was a HYP onset at the local level. Patterns of incrementally increasing fast ripple power are consistent with observations in rats with experimental hippocampal epilepsy, suggesting that limbic seizures arise when small clusters of synchronously bursting neurons increase in size, coalesce, and reach a critical mass for propagation.

Variants of Porphyromonas gingivalis lipopolysaccharide alter lipidation of autophagic protein, microtubule-associated protein 1 light chain 3, LC3

Porphyromonas gingivalis often subverts host cell autophagic processes for its own survival. Our previous studies document the association of the cargo sorting protein, melanoregulin (MREG), with its binding partner, the autophagic protein, microtubule-associated protein 1 light chain 3 (LC3) in macrophages incubated with P. gingivalis (strain 33277). Differences in the lipid A moiety of lipopolysaccharide (LPS) affect the virulence of P. gingivalis; penta-acylated LPS₁₆₉₀ is a weak Toll-like receptor 4 agonist compared with Escherichia coli LPS, whereas tetra-acylated LPS_1435/1449 acts as an LPS₁₆₉₀ antagonist. To determine how P. gingivalis LPS₁₆₉₀ affects autophagy we assessed LC3-dependent and MREG-dependent processes in green fluorescent protein (GFP)-LC3-expressing Saos-2 cells. LPS₁₆₉₀ stimulated the formation of very large LC3-positive vacuoles and MREG puncta. This LPS₁₆₉₀ -mediated LC3 lipidation decreased in the presence of LPS_1435/1449 . When Saos-2 cells were incubated with P. gingivalis the bacteria internalized but did not traffic to GFP-LC3-positive structures. Nevertheless, increases in LC3 lipidation and MREG puncta were observed. Collectively, these results suggest that P. gingivalis internalization is not necessary for LC3 lipidation. Primary human gingival epithelial cells isolated from patients with periodontitis showed both LC3II and MREG puncta whereas cells from disease-free individuals exhibited little co-localization of these two proteins. These results suggest that the prevalence of a particular LPS moiety may modulate the degradative capacity of host cells, so influencing bacterial survival.

Molecular alterations in areas generating fast ripples in an animal model of temporal lobe epilepsy

The molecular basis of epileptogenesis is poorly characterized. Studies in humans and animal models have identified an electrophysiological signature that precedes the onset of epilepsy, which has been termed fast ripples (FRs) based on its frequency. Multiple lines of evidence implicate regions generating FRs in epileptogenesis, and FRs appear to demarcate the seizure onset zone, suggesting a role in ictogenesis as well. We performed gene expression analysis comparing areas of the dentate gyrus that generate FRs to those that do not generate FRs in a well-characterized rat model of epilepsy. We identified a small cohort of genes that are differentially expressed in FR versus non-FR brain tissue and used quantitative PCR to validate some of those that modulate neuronal excitability. Gene expression network analysis demonstrated conservation of gene co-expression between non-FR and FR samples, but examination of gene connectivity revealed changes that were most pronounced in the cm-40 module, which contains several genes associated with synaptic function and the differentially expressed genes Kcna4, Kcnv1, and Npy1r that are down-regulated in FRs. We then demonstrate that the genes within the cm-40 module are regulated by seizure activity and enriched for the targets of the RNA binding protein Elavl4. Our data suggest that seizure activity induces co-expression of genes associated with synaptic transmission and that this pattern is attenuated in areas displaying FRs, implicating the failure of this mechanism in the generation of FRs.

Single-unit activities during epileptic discharges in the human hippocampal formation

Between seizures the brain of patients with epilepsy generates pathological patterns of synchronous activity, designated as interictal epileptiform discharges (ID). Using microelectrodes in the hippocampal formations of 8 patients with drug-resistant temporal lobe epilepsy, we studied ID by simultaneously analyzing action potentials from individual neurons and the local field potentials (LFPs) generated by the surrounding neuronal network. We found that ~30% of the units increased their firing rate during ID and 40% showed a decrease during the post-ID period. Surprisingly, 30% of units showed either an increase or decrease in firing rates several hundred of milliseconds before the ID. In 4 patients, this pre-ID neuronal firing was correlated with field high-frequency oscillations at 40-120 Hz. Finally, we observed that only a very small subset of cells showed significant coincident firing before or during ID. Taken together, we suggested that, in contrast to traditional views, ID are generated by a sparse neuronal network and followed a heterogeneous synchronization process initiated over several hundreds of milliseconds before the paroxysmal discharges.

Functional connectivity between brain areas estimated by analysis of gamma waves

The goal of this study is to investigate functional connectivity between different brain regions by analyzing the temporal relationship of the maxima of gamma waves recorded in multiple brain areas. Local field potentials were recorded from motor cortex, hippocampus, entorhinal cortex and piriform cortex of rats. Gamma activity was filtered and separated into two bands; high (65-90Hz) and low (30-55Hz) gamma. Maxima for gamma activity waves were detected and functional connectivity between different brain regions was determined using Shannon entropy for perievent histograms for each pair channels. Significant Shannon entropy values were reported as connectivity factors. We defined a connectivity matrix based the connectivity factors between different regions. We found that maxima of low and high frequency gamma occur in strong temporal relationship between some brain areas, indicating the existence of functional connections between these areas. The spatial pattern of functional connections between brain areas was different for slow wave sleep and waking states. However for each behavioral state in the same animal the pattern of functional connections was stable over time within 30min of continuous analysis and over a 5 day period. With the same electrode montage the pattern of functional connectivity varied from one subject to another. Analysis of the temporal relationship of maxima of gamma waves between various brain areas could be a useful tool for investigation of functional connections between these brain areas. This approach could be applied for analysis of functional alterations occurring in these connections during different behavioral tasks and during processes related to learning and memory. The specificity in the connectivity pattern from one subject to another can be explained by the existence of unique functional networks for each subject.

Patterns of the UP-Down state in normal and epileptic mice

Goal of this manuscript is to investigate whether changes that exist in epileptic brain generating spontaneous seizures are reflected in the pattern of the UP-Down state (UDS) recorded from the neocortex and dentate gyrus. Experiments were carried out on naive and epileptic mice under urethane anesthesia. Local field potentials were recorded with chronically implanted microelectrodes and single unit activity was recorded with glass microelectrodes. Recorded neurons were labeled by neurobiotin and identified later as granular cells or interneurons in histological sections. The following major features differentiate the pattern of UDS in epilepsy from normal. (1) The duration of UP and Down phases is significantly longer. (2) Recovery of network excitability after termination of the UP phase is longer. (3) UP-spikes occur during the UP phase, which transiently interrupt the development of the normal electrographic pattern of UP phase. Our data provide evidence that UP-spikes result from gigantic EPSPs generated in response to afferent activity. UP-spikes in the neocortex and dentate gyrus occur in close temporal relationship indicating the existence of direct or indirect pathological functional connections between these areas. Changes in the duration of UP and Down phases as well increased time of recovery of excitability of epileptic brain after termination of UP phase suggest alterations in the homeostatic properties of neuronal network in epileptic brain. We suggest that the existence of UP-spikes in epileptic brain may be an additional electrographic pattern indicating epileptogenicity. Unraveling the neuronal substrates of UP-spikes may further improve our understanding of the mechanisms of epilepsy.

Novel Cell and Tissue Acquisition System (CTAS): microdissection of live and frozen brain tissues

We developed a novel, highly accurate, capillary based vacuum-assisted microdissection device CTAS - Cell and Tissue Acquisition System, for efficient isolation of enriched cell populations from live and freshly frozen tissues, which can be successfully used in a variety of molecular studies, including genomics and proteomics. Specific diameter of the disposable capillary unit (DCU) and precisely regulated short vacuum impulse ensure collection of the desired tissue regions and even individual cells. We demonstrated that CTAS is capable of dissecting specific regions of live and frozen mouse and rat brain tissues at the cellular resolution with high accuracy. CTAS based microdissection avoids potentially harmful physical treatment of tissues such as chemical treatment, laser irradiation, excessive heat or mechanical cell damage, thus preserving primary functions and activities of the dissected cells and tissues. High quality DNA, RNA, and protein can be isolated from CTAS-dissected samples, which are suitable for sequencing, microarray, 2D gel-based proteomic analyses, and Western blotting. We also demonstrated that CTAS can be used to isolate cells from native living tissues for subsequent recultivation of primary cultures without affecting cellular viability, making it a simple and cost-effective alternative for laser-assisted microdissection.

Inhibition of aminoacylase 3 protects rat brain cortex neuronal cells from the toxicity of 4-hydroxy-2-nonenal mercapturate and 4-hydroxy-2-nonenal

4-Hydroxy-2-nonenal (4HNE) and acrolein (ACR) are highly reactive neurotoxic products of lipid peroxidation that are implicated in the pathogenesis and progression of Alzheimer's and Parkinson's diseases. Conjugation with glutathione (GSH) initiates the 4HNE and ACR detoxification pathway, which generates the mercapturates of 4HNE and ACR that can be excreted. Prior work has shown that the efficiency of the GSH-dependent renal detoxification of haloalkene derived mercapturates is significantly decreased upon their deacetylation because of rapid transformation of the deacetylated products into toxic compounds mediated by β-lyase. The enzymes of the GSH-conjugation pathway and β-lyases are expressed in the brain, and we hypothesized that a similar toxicity mechanism may be initiated in the brain by the deacetylation of 4HNE- and ACR-mercapturate. The present study was performed to identify an enzyme(s) involved in 4HNE- and ACR-mercapturate deacetylation, characterize the brain expression of this enzyme and determine whether its inhibition decreases 4HNE and 4HNE-mercapturate neurotoxicity. We demonstrated that of two candidate deacetylases, aminoacylases 1 (AA1) and 3 (AA3), only AA3 efficiently deacetylates both 4HNE- and ACR-mercapturate. AA3 was further localized to neurons and blood vessels. Using a small molecule screen we generated high-affinity AA3 inhibitors. Two of them completely protected rat brain cortex neurons expressing AA3 from the toxicity of 4HNE-mercapturate. 4HNE-cysteine (4HNE-Cys) was also neurotoxic and its toxicity was mostly prevented by a β-lyase inhibitor, aminooxyacetate. The results suggest that the AA3 mediated deacetylation of 4HNE-mercapturate may be involved in the neurotoxicity of 4HNE.

Imaging brain neuronal activity using functionalized magnetonanoparticles and MRI

This study explored the use of non-radioactive 2-deoxy glucose (2DG)-labeled magnetonanoparticles (MNP) and magnetic resonance imaging (MRI) to detect functional activity during rest, peripheral stimulation, and epileptic seizures, in animal models. Non-radioactive 2DG was covalently attached to magnetonanoparticles composed of iron oxide and dextran and intravenous (tail) injections were performed. 2DG-MNP was injected in resting and stimulated naïve rodents and the subsequent MRI was compared to published (14)C-2DG autoradiography data. Reproducibility and statistical significance was established in one studied model. Negative contrast enhancement (NCE) in acute seizures and chronic models of epilepsy were investigated. MRI NCE due to 2DG-MNP particles was compared to that of plain (unconjugated) MNP in one animal. NCE due to 2DG-MNP particles at 3 T, which is approved for human use, was also investigated. Histology showed presence of MNP (following intravenous injection) in the brain tissues of resting naïve animal. 2DG-MNP intraparenchymal uptake was visible on MRI and histology. The locations of NCE agreed with published results of 2DG autoradiography in resting and stimulated animals and epileptic rats. Localization of epileptogenicity was confirmed by subsequent depth-electrode EEG (iEEG). Non-radioactive 2DG-MNP can cross the blood-brain barrier (BBB) and may accurately localize areas of increased activity. Although, this proof-of-principle study involves only a limited number of animals, and much more research and quantification are necessary to demonstrate that 2DG-MNP, or MNPs conjugated with other ligands, could eventually be used to image localized cerebral function with MRI in humans, this MNP-MRI approach is potentially applicable to the use of many bioactive molecules as ligands for imaging normal and abnormal localized cerebral functions.

Mechanisms of physiological and epileptic HFO generation

High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-of-phase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.

High-frequency oscillations and other electrophysiological biomarkers of epilepsy: underlying mechanisms

In the normal mammalian brain, neuronal synchrony occurs on a spatial scale of submillimeters to centimeters and temporal scale of submilliseconds to seconds that is reflected in the occurrence of high-frequency oscillations, physiological sharp waves and slow wave sleep oscillations referred to as Up-Down states. In the epileptic brain, the well-studied pathologic counterparts to these physiological events are pathological high-frequency oscillations and interictal spikes that could be electrophysiological biomarkers of epilepsy. Establishing these abnormal events as biomarkers of epilepsy will largely depend on a better understanding of the mechanisms underlying their generation, which will not only help distinguish pathological from physiological events, but will also determine what roles these pathological events play in epileptogenesis and epileptogenicity. This article focuses on the properties and neuronal mechanisms supporting the generation of high-frequency oscillations and interictal spikes, and introduces a new phenomenon called Up-spikes.

Non-linear classification of heart rate parameters as a biomarker for epileptogenesis

PURPOSE

To characterize a biomarker for epileptogenesis based on cardiac interbeat interval characteristics.

METHODS

Electrocardiograph (ECG) and electroencephalogram (EEG) signals were recorded from freely moving rats (n = 23) before status epilepticus (SE) induced by i.p. pilocarpine (PILO) injection as baseline, and on days 1, 3 and 7 after SE. We assessed several features from cardiac interbeat intervals, including linear, non-linear and frequency parameters of interbeat intervals, and power spectra of interpolated intervals during epileptogenesis. After thresholding, the altered values were applied to a non-linear classifier. The non-linear classifier divided animals into two groups; with and without epilepsy, based on all collected data.

RESULTS

We found that none of the single altered parameters in cardiac activity emerged as a sole biomarker for epileptogenesis. However, the non-linear classifier distinguished animals that later developed from those and did not develop epilepsy. The non-linear classification was performed on preliminary findings from 23 animals; six did not develop epilepsy and the rest did. The average positive predictive value (precision rate) was 78%. This was calculated based on the average sensitivity and specificity, which were 80.6% and 35.2% respectively, for the 100 classification passes. We also showed that these numbers would have increased as the number of subjects increased.

CONCLUSION

Changes to the brain caused by status epilepticus that lead to epileptogenesis have systemic effects, and alter cardiac activity. A non-linear classifier performed on several extracted features of cardiac interbeat intervals may be useful as a biomarker to identify animals with low and high probability of developing epilepsy after status epilepticus.

Absence of CNTNAP2 leads to epilepsy, neuronal migration abnormalities, and core autism-related deficits

Although many genes predisposing to autism spectrum disorders (ASD) have been identified, the biological mechanism(s) remain unclear. Mouse models based on human disease-causing mutations provide the potential for understanding gene function and novel treatment development. Here, we characterize a mouse knockout of the Cntnap2 gene, which is strongly associated with ASD and allied neurodevelopmental disorders. Cntnap2(-/-) mice show deficits in the three core ASD behavioral domains, as well as hyperactivity and epileptic seizures, as have been reported in humans with CNTNAP2 mutations. Neuropathological and physiological analyses of these mice before the onset of seizures reveal neuronal migration abnormalities, reduced number of interneurons, and abnormal neuronal network activity. In addition, treatment with the FDA-approved drug risperidone ameliorates the targeted repetitive behaviors in the mutant mice. These data demonstrate a functional role for CNTNAP2 in brain development and provide a new tool for mechanistic and therapeutic research in ASD.

High-frequency activity in experimental and clinical epileptic foci

Pathological high-frequency electrographic activity (pHFA, >80Hz) represents one of the major discoveries in epilepsy research over the past few decades. In this review we focus on the high-frequency activity recorded in vivo in chronic models of epilepsy. The presence of HFA particularly of fast ripples (250-600Hz)reflects epileptogenic reorganization of brain tissue, endogenous epileptogenicity and ability to generate spontaneous seizures. The spatial distribution of epileptic HFA can be used to localize epileptic foci. In some regions of brain the localizing value of epileptic HFA is weakened by frequency overlap with physiological HFA. In this situation, only detailed knowledge of the regional physiological activity may provide relevant information which frequencies provide localizing information. In the epileptic hippocampus, the activity from 250Hz to 600Hz frequency band (fast ripples) is always epileptic and can be used as reliable marker of epileptic tissue in all hippocampal subregions. The localizing value of HFA in the identification of the epileptic focus is discussed from an experimental and clinical perspective; as the information provided by HFA can improve presurgical diagnosis and surgical outcome. Finally, research into HFA has contributed to improved understanding and new insights into the cellular and network organization of epileptic foci and the pathophysiology of epilepsy.

A systems level, functional genomics analysis of chronic epilepsy

Neither the molecular basis of the pathologic tendency of neuronal circuits to generate spontaneous seizures (epileptogenicity) nor anti-epileptogenic mechanisms that maintain a seizure-free state are well understood. Here, we performed transcriptomic analysis in the intrahippocampal kainate model of temporal lobe epilepsy in rats using both Agilent and Codelink microarray platforms to characterize the epileptic processes. The experimental design allowed subtraction of the confounding effects of the lesion, identification of expression changes associated with epileptogenicity, and genes upregulated by seizures with potential homeostatic anti-epileptogenic effects. Using differential expression analysis, we identified several hundred expression changes in chronic epilepsy, including candidate genes associated with epileptogenicity such as Bdnf and Kcnj13. To analyze these data from a systems perspective, we applied weighted gene co-expression network analysis (WGCNA) to identify groups of co-expressed genes (modules) and their central (hub) genes. One such module contained genes upregulated in the epileptogenic region, including multiple epileptogenicity candidate genes, and was found to be involved the protection of glial cells against oxidative stress, implicating glial oxidative stress in epileptogenicity. Another distinct module corresponded to the effects of chronic seizures and represented changes in neuronal synaptic vesicle trafficking. We found that the network structure and connectivity of one hub gene, Sv2a, showed significant changes between normal and epileptogenic tissue, becoming more highly connected in epileptic brain. Since Sv2a is a target of the antiepileptic levetiracetam, this module may be important in controlling seizure activity. Bioinformatic analysis of this module also revealed a potential mechanism for the observed transcriptional changes via generation of longer alternatively polyadenlyated transcripts through the upregulation of the RNA binding protein HuD. In summary, combining conventional statistical methods and network analysis allowed us to interpret the differentially regulated genes from a systems perspective, yielding new insight into several biological pathways underlying homeostatic anti-epileptogenic effects and epileptogenicity.

Further evidence that pathologic high-frequency oscillations are bursts of population spikes derived from recordings of identified cells in dentate gyrus

PURPOSE

To analyze activity of identified dentate gyrus granular cells and interneurons during pathologic high-frequency oscillations (pHFOs).

METHODS

Pilocarpine-treated epileptic mice were anesthetized with urethane and ketamine. Their heads were fixed in a stereotaxic frame. Extracellular unit activity was recoded with glass micropipettes, whereas multiunit and local field activity was simultaneously recorded with attached tungsten microelectrodes. After electrophysiologic experiments, recorded cells were labeled by neurobiotin and visualized by immunohistochemical methods. KEY FINDINGS AND SIGNIFICANCES: pHFOs containing more than three waves were recorded in our experiments, but pathologic single-population spikes also occurred. Identified granular cells discharged preferentially in synchrony with pHFOs and single population spikes, whereas interneurons decreased their discharge frequency during this time. These experiments provide additional confirmation that pHFOs in the dentate gyrus represent single or recurrent population spikes, which in turn reflect summated hypersynchronous discharges of principal cells.

High-frequency oscillations in epileptic brain

PURPOSE OF REVIEW

It has been 10 years since pathological high-frequency oscillations (pHFOs) were described in the brain of epileptic animals and patients. This review summarizes progress in research on mechanisms of their generation and potential clinical applications over that period.

RECENT FINDINGS

Initially, pHFOs were recorded with microelectrodes in the hippocampus of rodents and patients with mesial temporal lobe epilepsy (MTLE), but recently pHFOs have also been recorded with clinical depth and grid electrodes in multiple brain areas including the hippocampus and neocortex of patients with different types of epilepsy. One hypothesis is that pHFOs reflect fields of hypersynchronized action potentials (bursts of population spikes) within small discrete neuronal clusters responsible for seizure generation. Studies suggest that pHFOs can be used as a reliable biomarker for epileptogenesis, epileptogenicity, and the delineation of the epileptogenic region.

SUMMARY

Recording of pHFOs with clinical electrodes provides a means for further investigation of their functional role in the epileptic brain and as a potential biomarker of epileptogenesis and epileptogenicity and for presurgical mapping.