Sleep Slow Oscillation-Spindle Coupling Precedes Spindle-Ripple Coupling During Development

STUDY OBJECTIVES

Sleep supports systems memory consolidation through the precise temporal coordination of specific oscillatory events during slow-wave sleep (SWS), i.e., the neocortical slow oscillations (SOs), thalamic spindles, and hippocampal ripples. Beneficial effects of sleep on memory are also observed in infants, although the contributing regions, especially hippocampus and frontal cortex, are immature. Here, we examined in rats the development of these oscillatory events and their coupling during early life.

METHODS

EEG and hippocampal local field potentials (LFPs) were recorded during sleep in male rats at postnatal days (PD)26 and 32, roughly corresponding to early (1-2 years) and late (9-10 years) human childhood, and in a group of adult rats (14-18 weeks, corresponding to ~22-29 years in humans).

RESULTS

SO and spindle amplitudes generally increased from PD26 to PD32. In parallel, frontocortical EEG spindles increased in density and frequency, while changes in hippocampal ripples remained non-significant. The proportion of SOs co-occurring with spindles also increased from PD26 to PD32. Whereas parietal cortical spindles were phase-locked to the depolarizing SO-upstate already at PD26, over frontal cortex SO-spindle phase-locking emerged not until PD32. Co-occurrence of hippocampal ripples with spindles was higher during childhood than in adult rats, but significant phase-locking of ripples to the excitable spindle troughs was observed only in adult rats.

CONCLUSIONS

Results indicate a protracted development of synchronized thalamocortical processing specifically in frontocortical networks (i.e., frontal SO-spindle coupling). However, synchronization within thalamocortical networks generally precedes synchronization of thalamocortical with hippocampal processing as reflected by the delayed occurrence of spindle-ripple phase-coupling.

Impact of single-trial avoidance learning on subsequent sleep

Both non-rapid eye movement (NonREM) sleep and rapid eye movement (REM) sleep, as well as sleep spindle and ripple oscillations, are important for memory formation. Through cortical EEG recordings of prefrontal cortex and hippocampus during and after an inhibitory avoidance task, we analysed the dynamic changes in the amounts of sleep, spindle and ripple oscillations related to memory formation. The total amount of NonREM sleep was reduced during the first hour after learning. Moreover, significant decrease of the total spindle and ripple counts was observed at the first hour after learning as well. In addition, foot shock alone, with no associated learning, produced little effect on the dynamics of sleep oscillations, indicating that the learning experience is necessary for these changes to occur.

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.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

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.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Calbindin-Expressing CA1 Pyramidal Neurons Encode Spatial Information More Efficiently

Hippocampal pyramidal neurons (PNs) are traditionally conceptualized as homogeneous population. For the past few years, cumulating evidence has revealed the structural and functional heterogeneity of hippocampal pyramidal neurons. But the in vivo neuronal firing pattern of molecularly identified pyramidal neuron subclasses is still absent. In this study, we investigated the firing patterns of hippocampal PNs based on different expression profile of Calbindin (CB) during a spatial shuttle task in free moving male mice. We found that CB+ place cells can represent spatial information more efficiently than CB- place cells, albeit lower firing rates during running epochs. Furthermore, a subset of CB+ PNs shifted their theta firing phase during rapid-eye movement (REM) sleep states compared with running states. Although CB- PNs are more actively engaged in ripple oscillations, CB+ PNs showed stronger ripple modulation during slow-wave sleep (SWS). Our results pointed out the heterogeneity in neuronal representation between hippocampal CB+ and CB- PNs. Particularly, CB+ PNs encode spatial information more efficiently, which might be contributed by stronger afferents from the lateral entorhinal cortex to CB+ PNs.

The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources

OBJECTIVE

To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.

METHODS

Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality.

RESULTS

Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01).

CONCLUSION

This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

A region-specific modulation of sleep slow waves on interictal epilepsy markers in focal epilepsy

OBJECTIVE

Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy.

METHODS

Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs.

RESULTS

Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusively irritative zones outside SOZ (EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extent than those in SOZ. The type II and type I ripples in EIZ were modulated by SSWs in different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio.

SIGNIFICANCE

SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization.

Surface nanopatterning by ion beam irradiation: compositional effects

Surface nanopatterning induced by ion beam irradiation (IBI) has emerged as an effective nanostructuring technique since it induces patterns on large areas of a wide variety of materials, in short time, and at low cost. Nowadays, two main subfields can be distinguished within IBI nanopatterning depending on the irrelevant or relevant role played by the surface composition. In this review, we give an up-dated account of the progress reached when surface composition plays a relevant role, with a main focus on IBI surface patterning with simultaneous co-deposition of foreign atoms. In addition, we also review the advances in IBI of compound surfaces as well as IBI systems where the ion employed is not a noble gas species. In particular, for the IBI with concurrent metal co-deposition, we detail the chronological evolution of these studies because it helps us to clarify some contradictory early reports. We describe the main patterns obtained with this technique as a function of the foreign atom deposition pathway, also focusing in those systematic studies that have contributed to identify the main mechanisms leading to the surface pattern formation and development. Likewise, we explain the main theoretical models aimed at describing these nanopattern formation processes. Finally, we address two main special features of the patterns induced by this technique, namely, the enhanced pattern ordering and the possibility to produce both morphological and chemical patterns.

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.

Modulation of lateral septal and dorsomedial striatal neurons by hippocampal sharp-wave ripples, theta rhythm, and running speed

Single units were recorded in hippocampus, lateral septum (LS), and dorsomedial striatum (DMS) while freely behaving rats (n = 3) ran trials in a T‐maze task and rested in a holding bucket between trials. In LS, 28% (64/226) of recorded neurons were excited and 14% (31/226) were inhibited during sharp wave ripples (SWRs). LS neurons that were excited during SWRs fired preferentially on the downslope of hippocampal theta rhythm and had firing rates that were positively correlated with running speed; LS neurons that were inhibited during SWRs fired preferentially on the upslope of hippocampal theta rhythm and had firing rates that were negatively correlated with running speed. In DMS, only 3.3% (12/366) of recorded neurons were excited and 5.7% (21/366) were inhibited during SWRs. As in LS, DMS neurons that were excited by SWRs tended to have firing rates that were positively modulated by running speed, whereas DMS neurons that were inhibited by SWRs tended to have firing rates that were negatively modulated by running speed. But in contrast with LS, these two DMS subpopulations did not clearly segregate their spikes to different phases of the theta cycle. Based on these results and a review of prior findings, we discuss how concurrent activation of spatial trajectories in hippocampus and motor representations in LS and DMS may contribute to neural computations that support reinforcement learning and value‐based decision making.

A Novel Versatile Approach for Underwater Conformal Volumetric Array Design

In this study, we present a novel approach to the design of a conformal volumetric array composed of M × N convex subarrays in two orthogonal curvilinear directions for underwater acoustic imaging for mine detection. Our design targets require that the proposed array transducer has three-dimensional half-power beamwidths of 85° and 25° in either of its convex subarray parts, while also reaching a peak transmitting voltage response above 147 dB. The radiated sound pressure of the subarrays was independently derived as a function of their geometrical parameters. The resulting directional factors were then combined to analyze the beam profile of the entire array. The design was finally optimized to minimize the ripple level. To validate this theoretical design, the structure was modeled and analyzed using the finite element method. The comparison between the resulting beam pattern from the finite element analysis and the analytical computation showed an excellent compliance. The method advanced is a simple and systematic analytical model to facilitate the development of new conformal volumetric arrays for underwater mine detection.

Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window

AIMS

To improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic.

METHODS

306-channel simulated or real clinical MEG is estimated as a lower-dimensional tensor by Tucker decomposition based on Higher-order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80-250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole-fitting method.

RESULTS

The experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole-fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole-fitting method. For both shallow and deep sources, the proposed method provided effective performance.

CONCLUSION

Tucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.

Subthreshold membrane potential dynamics of posterior parietal cortical neurons coupled with hippocampal ripples

During behavioral states of immobility, sleep, and anesthesia, the hippocampus generates high-frequency oscillations called ripples. Ripples occur simultaneously with synchronous neuronal activity in the neocortex, known as slow waves, and contribute to memory consolidation. During these ripples, various neocortical regions exhibit modulations in spike rates and local field activity irrespective of whether they receive direct synaptic inputs from the hippocampus. However, little is known about the subthreshold dynamics of the membrane potentials of neocortical neurons during ripples. We patch-clamped layer 2/3 pyramidal cells in the posterior parietal cortex (PPC), a neocortical region that is involved in allocentric spatial representation of behavioral exploration and sequential series of relevant action potentials during ripples. We simultaneously monitored the membrane potentials of post hoc-identified PPC neurons and the local field potentials of the hippocampus in anesthetized mice. More than 50% of the recorded PPC neurons exhibited significant depolarizations and/or hyperpolarizations during ripples. Histological inspections of the recorded neurons revealed that the ripple-modulated PPC neurons were distributed in the PPC in a spatially non-biased fashion. These results suggest that hippocampal ripples are widely but selectively associated with the subthreshold dynamics of the membrane potentials of PPC neurons even though there is no monosynaptic connectivity between the hippocampus and the PPC.

Prioritized experience replays on a hippocampal predictive map for learning

The hippocampus is crucial for spatial learning but their neuronal mechanisms remain unknown. Here, we found that hippocampal cell ensembles preferentially replayed salient reward-related locations as rats learned a new behavioral trajectory for reward. The contents of these hippocampal replays progressively varied with learning. Especially during learning, hippocampal neurons even replayed an optimized path that had never been exploited by the animals. The learning-related hippocampal activity was necessary for the stabilization of spatial behavior. These results suggest prioritized replays of significant experiences on a predictive map by hippocampal neurons.

Hippocampal cells are central to spatial and predictive representations, and experience replays by place cells are crucial for learning and memory. Nonetheless, how hippocampal replay patterns dynamically change during the learning process remains to be elucidated. Here, we designed a spatial task in which rats learned a new behavioral trajectory for reward. We found that as rats updated their behavioral strategies for a novel salient location, hippocampal cell ensembles increased theta-sequences and sharp wave ripple-associated synchronous spikes that preferentially replayed salient locations and reward-related contexts in reverse order. The directionality and contents of the replays progressively varied with learning, including an optimized path that had never been exploited by the animals, suggesting prioritized replays of significant experiences on a predictive map. Online feedback blockade of sharp wave ripples during a learning process inhibited stabilizing optimized behavior. These results implicate learning-associated experience replays that act to learn and reinforce specific behavioral strategies.

Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

Neural activity during ripples has attracted great theoretical and experimental attention over the last three decades. Perhaps one reason for such interest is that ripples occur during quiet waking moments and during sleep, times when we reflect and dream about what has just occurred and what we expect to happen next. The hope is that understanding such 'offline' activity may yield insights into reflection, planning, and the purposes of sleep. This review focuses on the mechanisms by which neurons organize during these high-frequency events. In studying ripples, broader principles have emerged that relate intrinsic neural properties, network topology and synaptic plasticity in controlling neural activity. Ripples, therefore, serve as an excellent model for studying how properties of a neural network relate to neural dynamics. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

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.

The relationship between cryptocurrencies and COVID-19 pandemic

We examine the relationship between cryptocurrencies (namely Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP)) and COVID-19 cases/deaths. This will help explore whether cryptocurrencies can serve as a hedge against COVID-19. The wavelet coherence analysis indicates that there is initially a negative relationship between Bitcoin and the number of reported cases and deaths; however, the relationship becomes positive during the later period. The findings for Ethereum and Ripple are also similar but with weaker interactions. This supports the hedging role of cryptocurrencies against the uncertainty raised by COVID-19.

Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Ripple-related firing of identified deep CA1 pyramidal cells in chronic temporal lobe epilepsy in mice

OBJECTIVE

Temporal lobe epilepsy (TLE) is often associated with memory deficits. Reactivation of memory traces in the hippocampus occurs during sharp-wave ripples (SWRs; 140-250 Hz). To better understand the mechanisms underlying high-frequency oscillations and cognitive comorbidities in epilepsy, we evaluated how rigorously identified deep CA1 pyramidal cells (dPCs) discharge during SWRs in control and TLE mice.

METHODS

We used the unilateral intraamygdala kainate model of TLE in video-electroencephalography (EEG) verified chronically epileptic adult mice. Local field potential and single-cell recordings were performed using juxtacellular recordings from awake control and TLE mice resting on a spherical treadmill, followed by post hoc identification of the recorded cells.

RESULTS

Hippocampal SWRs in TLE mice occurred with increased intraripple frequency compared to control mice. The frequency of SWR events was decreased, whereas the overall frequency of SWRs, interictal epileptiform discharges, and high-frequency ripples (250-500 Hz) together was not altered. CA1 dPCs in TLE mice showed significantly increased firing during ripples as well as between the ripple events. The strength of ripple modulation of dPC discharges increased in TLE without alteration of the preferred phase of firing during the ripple waves.

SIGNIFICANCE

These juxtacellular electrophysiology data obtained from identified CA1 dPCs from chronically epileptic mice are in general agreement with recent findings indicating distortion of normal firing patterns during offline SWRs as a mechanism underlying deficits in memory consolidation in epilepsy. Because the primary seizure focus in our experiments was in the amygdala and we recorded from the CA1 region, these results are also in agreement with the presence of altered high-frequency oscillations in areas of secondary seizure spread.

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.

Ripple Effect of Lifestyle Interventions During Pregnancy on Untreated Partners' Weight

OBJECTIVE

Weight-loss interventions have a positive "ripple effect" on untreated partners' weight, but ripple effects in pregnancy are unknown. The objective of this study was to determine whether prenatal lifestyle interventions that reduce gestational weight gain in pregnant women have a positive ripple effect on untreated partners' weight.

METHODS

Two clinical trials with the same outcome measures randomly assigned pregnant women to a lifestyle intervention or usual care. Untreated partners were randomly assigned according to their pregnant partner's group allocation and were assessed at study entry (~13 weeks' gestation), 35 weeks' gestation, and 6 and 12 months after delivery.

RESULTS

A total of 122 partners (100% male, 23% Hispanic, 82% married, and 48% with obesity) were randomly assigned to the intervention (n = 59) or usual care (n = 63). There was no intervention or intervention-by-time interaction effect on partner weight (P = 0.795). Partner weight changes were not statistically significant (P = 0.120) from study entry to 35 weeks' gestation (mean 0.19 kg; 95% CI: -0.73 to 1.24) or to 12 months after delivery (mean 0.82 kg; 95% CI: -0.26 to 1.91).

CONCLUSIONS

There was no evidence of a ripple effect on partner weight. In a self-selected sample, partners of pregnant women appeared not to experience sympathy weight gain.

Introducing Well-Defined Nanowrinkles in CVD Grown Graphene

The control of graphene’s topography at the nanoscale level opens up the possibility to greatly improve the surface functionalization, change the doping level or create nanoscale reservoirs. However, the ability to control the modification of the topography of graphene on a wafer scale is still rather challenging. Here we present an approach to create well-defined nanowrinkles on a wafer scale using nitrocellulose as the polymer to transfer chemical vapor deposition grown graphene from the copper foil to a substrate. During the transfer process, the complex tertiary nitrocellulose structure is imprinted into the graphene area layer. When the graphene layer is put onto a substrate this will result in a well-defined nanowrinkle pattern, which can be subsequently further processed. Using atomic force and Raman microscopy, we characterized the generated nanowrinkles in graphene.

CA2 neuronal activity controls hippocampal low gamma and ripple oscillations

Hippocampal oscillations arise from coordinated activity among distinct populations of neurons and are associated with cognitive functions. Much progress has been made toward identifying the contribution of specific neuronal populations in hippocampal oscillations, but less is known about the role of hippocampal area CA2, which is thought to support social memory. Furthermore, the little evidence on the role of CA2 in oscillations has yielded conflicting conclusions. Therefore, we sought to identify the contribution of CA2 to oscillations using a controlled experimental system. We used excitatory and inhibitory DREADDs to manipulate CA2 neuronal activity and studied resulting hippocampal-prefrontal cortical network oscillations. We found that modification of CA2 activity bidirectionally regulated hippocampal and prefrontal cortical low-gamma oscillations and inversely modulated hippocampal ripple oscillations in mice. These findings support a role for CA2 in low-gamma generation and ripple modulation within the hippocampus and underscore the importance of CA2 in extrahippocampal oscillations.

Strain Mapping of Two-Dimensional Heterostructures with Subpicometer Precision

Next-generation, atomically thin devices require in-plane, one-dimensional heterojunctions to electrically connect different two-dimensional (2D) materials. However, the lattice mismatch between most 2D materials leads to unavoidable strain, dislocations, or ripples, which can strongly affect their mechanical, optical, and electronic properties. We have developed an approach to map 2D heterojunction lattice and strain profiles with subpicometer precision and the ability to identify dislocations and out-of-plane ripples. We collected diffraction patterns from a focused electron beam for each real-space scan position with a high-speed, high dynamic range, momentum-resolved detector-the electron microscope pixel array detector (EMPAD). The resulting four-dimensional (4D) phase space data sets contain the full spatially resolved lattice information on the sample. By using this technique on tungsten disulfide (WS₂) and tungsten diselenide (WSe₂) lateral heterostructures, we have mapped lattice distortions with 0.3 pm precision across multimicron fields of view and simultaneously observed the dislocations and ripples responsible for strain relaxation in 2D laterally epitaxial structures.

The content of hippocampal "replay"

One of the most striking features of the hippocampal network is its ability to self-generate neuronal sequences representing temporally compressed, spatially coherent paths. These brief events, often termed "replay" in the scientific literature, are largely confined to non-exploratory states such as sleep or quiet rest. Early studies examining the content of replay noted a strong correlation between the encoded spatial information and the animal's prior behavior; thus, replay was initially hypothesized to play a role in memory formation and/or systems-level consolidation via "off-line" reactivation of previous experiences. However, recent findings indicate that replay may also serve as a memory retrieval mechanism to guide future behavior or may be an incidental reflection of pre-existing network assemblies. Here, I will review what is known regarding the content of replay events and their correlation with past and future actions, and I will discuss how this knowledge might inform or constrain models which seek to explain the circuit-level mechanisms underlying these events and their role in mnemonic processes.

Parvalbumin-positive interneurons mediate neocortical-hippocampal interactions that are necessary for memory consolidation

Following learning, increased coupling between spindle oscillations in the medial prefrontal cortex (mPFC) and ripple oscillations in the hippocampus is thought to underlie memory consolidation. However, whether learning-induced increases in ripple-spindle coupling are necessary for successful memory consolidation has not been tested directly. In order to decouple ripple-spindle oscillations, here we chemogenetically inhibited parvalbumin-positive (PV⁺) interneurons, since their activity is important for regulating the timing of spiking activity during oscillations. We found that contextual fear conditioning increased ripple-spindle coupling in mice. However, inhibition of PV⁺ cells in either CA1 or mPFC eliminated this learning-induced increase in ripple-spindle coupling without affecting ripple or spindle incidence. Consistent with the hypothesized importance of ripple-spindle coupling in memory consolidation, post-training inhibition of PV⁺ cells disrupted contextual fear memory consolidation. These results indicate that successful memory consolidation requires coherent hippocampal-neocortical communication mediated by PV⁺ cells.

Caffeine Increases Hippocampal Sharp Waves in Vitro

Caffeine promotes memory consolidation. Memory consolidation is thought to depend at least in part on hippocampal sharp waves (SWs). In the present study, we investigated the effect of bath-application of caffeine in spontaneously occurring SWs in mouse acute hippocampal slices. Caffeine induced an about 100% increase in the event frequency of SWs at concentrations of 60 and 200 µM. The effect of caffeine was reversible after washout of caffeine and was mimicked by an adenosine A₁ receptor antagonist, but not by an A_2A receptor antagonist. Caffeine increased SWs even in dentate-CA3 mini-slices without the CA2 regions, in which adenosine A₁ receptors are abundantly expressed in the hippocampus. Thus, caffeine facilitates SWs by inhibiting adenosine A₁ receptors in the hippocampal CA3 region or the dentate gyrus.

Role of Hippocampal CA2 Region in Triggering Sharp-Wave Ripples

Sharp-wave ripples (SPW-Rs) in the hippocampus are implied in memory consolidation, as shown by observational and interventional experiments. However, the mechanism of their generation remains unclear. Using two-dimensional silicon probe arrays, we investigated the propagation of SPW-Rs across the hippocampal CA1, CA2, and CA3 subregions. Synchronous activation of CA2 ensembles preceded SPW-R-related population activity in CA3 and CA1 regions. Deep CA2 neurons gradually increased their activity prior to ripples and were suppressed during the population bursts of CA3-CA1 neurons (ramping cells). Activity of superficial CA2 cells preceded the activity surge in CA3-CA1 (phasic cells). The trigger role of the CA2 region in SPW-R was more pronounced during waking than sleeping. These results point to the CA2 region as an initiation zone for SPW-Rs.

Gravitational-Like Lens Based on Graphene Ripple

We conducted a semiclassical study on carrier movement in curved graphene. A previous attempt was made to show that curved graphene is a readily available and cheap laboratory material used to study general relativity effects, especially if the electron energies satisfy 4μeV ≪ |E| ≪ 3eV. Furthermore, a gravitational-like lens can be constructed based on a special graphene ripple; this lens has neither chromatic nor cometic aberration. One can design an ideal electron lens using a graphene ripple.

Ripplocations in van der Waals layers

Dislocations are topological line defects in three-dimensional crystals. Same-sign dislocations repel according to Frank's rule |b1 + b2|(2) > |b1|(2) + |b2|(2). This rule is broken for dislocations in van der Waals (vdW) layers, which possess crystallographic Burgers vector as ordinary dislocations but feature "surface ripples" due to the ease of bending and weak vdW adhesion of the atomic layers. We term these line defects "ripplocations" in accordance to their dual "surface ripple" and "crystallographic dislocation" characters. Unlike conventional ripples on noncrystalline (vacuum, amorphous, or fluid) substrates, ripplocations tend to be very straight, narrow, and crystallographically oriented. The self-energy of surface ripplocations scales sublinearly with |b|, indicating that same-sign ripplocations attract and tend to merge, opposite to conventional dislocations. Using in situ transmission electron microscopy, we directly observed ripplocation generation and motion when few-layer MoS2 films were lithiated or mechanically processed. Being a new subclass of elementary defects, ripplocations are expected to be important in the processing and defect engineering of vdW layers.

Task-related preparatory modulations multiply with acoustic processing in monkey auditory cortex

We characterised task-related top-down signals in monkey auditory cortex cells by comparing single-unit activity during passive sound exposure with neuronal activity during a predictable and unpredictable reaction-time task for a variety of spectral-temporally modulated broadband sounds. Although animals were not trained to attend to particular spectral or temporal sound modulations, their reaction times demonstrated clear acoustic spectral-temporal sensitivity for unpredictable modulation onsets. Interestingly, this sensitivity was absent for predictable trials with fast manual responses, but re-emerged for the slower reactions in these trials. Our analysis of neural activity patterns revealed a task-related dynamic modulation of auditory cortex neurons that was locked to the animal's reaction time, but invariant to the spectral and temporal acoustic modulations. This finding suggests dissociation between acoustic and behavioral signals at the single-unit level. We further demonstrated that single-unit activity during task execution can be described by a multiplicative gain modulation of acoustic-evoked activity and a task-related top-down signal, rather than by linear summation of these signals.

Multineuronal spike sequences repeat with millisecond precision

Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging (fMCI) technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and non-spiking neurons. Multineuronal spike sequences (MSSs) were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

Disrupting neural activity related to awake-state sharp wave-ripple complexes prevents hippocampal learning

Oscillations in hippocampal local-field potentials (LFPs) reflect the crucial involvement of the hippocampus in memory trace formation: theta (4–8 Hz) oscillations and ripples (~200 Hz) occurring during sharp waves are thought to mediate encoding and consolidation, respectively. During sharp wave-ripple complexes (SPW-Rs), hippocampal cell firing closely follows the pattern that took place during the initial experience, most likely reflecting replay of that event. Disrupting hippocampal ripples using electrical stimulation either during training in awake animals or during sleep after training retards spatial learning. Here, adult rabbits were trained in trace eyeblink conditioning, a hippocampus-dependent associative learning task. A bright light was presented to the animals during the inter-trial interval (ITI), when awake, either during SPW-Rs or irrespective of their neural state. Learning was particularly poor when the light was presented following SPW-Rs. While the light did not disrupt the ripple itself, it elicited a theta-band oscillation, a state that does not usually coincide with SPW-Rs. Thus, it seems that consolidation depends on neuronal activity within and beyond the hippocampus taking place immediately after, but by no means limited to, hippocampal SPW-Rs.

High-frequency oscillations (HFOs) in clinical epilepsy

Epilepsy is one of the most frequent neurological diseases. In focal medically refractory epilepsies, successful surgical treatment largely depends on the identification of epileptogenic zone. High-frequency oscillations (HFOs) between 80 and 500Hz, which can be recorded with EEG, may be novel markers of the epileptogenic zone. This review discusses the clinical importance of HFOs as markers of epileptogenicity and their application in different types of epilepsies. HFOs are clearly linked to the seizure onset zone, and the surgical removal of regions generating them correlates with a seizure free post-surgical outcome. Moreover, HFOs reflect the seizure-generating capability of the underlying tissue, since they are more frequent after the reduction of antiepileptic drugs. They can be successfully used in pediatric epilepsies such as epileptic spasms and help to understand the generation of this specific type of seizures. While mostly recorded on intracranial EEGs, new studies suggest that identification of HFOs on scalp EEG or magnetoencephalography (MEG) is possible as well. Thus not only patients with refractory epilepsies and invasive recordings but all patients might profit from the analysis of HFOs. Despite these promising results, the analysis of HFOs is not a routine clinical procedure; most results are derived from relatively small cohorts of patients and many aspects are not yet fully understood. Thus the review concludes that even if HFOs are promising biomarkers of epileptic tissue, there are still uncertainties about mechanisms of generation, methods of analysis, and clinical applicability. Large multicenter prospective studies are needed prior to widespread clinical application.

Clinical utility of interictal high-frequency oscillations recorded with subdural macroelectrodes in partial epilepsy

BACKGROUND AND PURPOSE

There is growing interest in high-frequency oscillations (HFO) as electrophysiological biomarkers of the epileptic brain. We evaluated the clinical utility of interictal HFO events, especially their occurrence rates, by comparing the spatial distribution with a clinically determined epileptogenic zone by using subdural macroelectrodes.

METHODS

We obtained intracranial electroencephalogram data with a high temporal resolution (2000 Hz sampling rate, 0.05-500 Hz band-pass filter) from seven patients with medically refractory epilepsy. Three epochs of 5-minute, artifact-free data were selected randomly from the interictal period. HFO candidates were first detected by an automated algorithm and subsequently screened to discard false detections. Validated events were further categorized as fast ripple (FR) and ripple (R) according to their spectral profiles. The occurrence rate of HFOs was calculated for each electrode contact. An HFO events distribution map (EDM) was constructed for each patient to allow visualization of the spatial distribution of their HFO events.

RESULTS

The subdural macroelectrodes were capable of detecting both R and FR events from the epileptic neocortex. The occurrence rate of HFO events, both FR and R, was significantly higher in the seizure onset zone (SOZ) than in other brain regions. Patient-specific HFO EDMs can facilitate the identification of the location of HFO-generating tissue, and comparison with findings from ictal recordings can provide additional useful information regarding the epileptogenic zone.

CONCLUSIONS

The distribution of interictal HFOs was reasonably consistent with the SOZ. The detection of HFO events and construction of spatial distribution maps appears to be useful for the presurgical mapping of the epileptogenic zone.

Complex spectral interactions encoded by auditory cortical neurons: relationship between bandwidth and pattern

The focus of most research on auditory cortical neurons has concerned the effects of rather simple stimuli, such as pure tones or broad-band noise, or the modulation of a single acoustic parameter. Extending these findings to feature coding in more complex stimuli such as natural sounds may be difficult, however. Generalizing results from the simple to more complex case may be complicated by non-linear interactions occurring between multiple, simultaneously varying acoustic parameters in complex sounds. To examine this issue in the frequency domain, we performed a parametric study of the effects of two global features, spectral pattern (here ripple frequency) and bandwidth, on primary auditory (A1) neurons in awake macaques. Most neurons were tuned for one or both variables and most also displayed an interaction between bandwidth and pattern implying that their effects were conditional or interdependent. A spectral linear filter model was able to qualitatively reproduce the basic effects and interactions, indicating that a simple neural mechanism may be able to account for these interdependencies. Our results suggest that the behavior of most A1 neurons is likely to depend on multiple parameters, and so most are unlikely to respond independently or invariantly to specific acoustic features.

High frequency oscillations and seizure frequency in patients with focal epilepsy

High frequency oscillations (HFOs) have been associated with epileptogenicity. In rats, the extent of HFOs (>200 Hz) is correlated with seizure frequency. We studied whether the same applies to patients with focal epilepsy. Thirty-nine patients with intracerebral EEG sampled at 2000 Hz were studied for interictal ripples (80-250 Hz), fast ripples (FR, 250-500 Hz) and spikes. Seizure frequency before implantation was compared to numbers of channels with HFOs (>1/min). Analyses were repeated for HFO rates of >5, >10 and >20. Separate analyses were done for 25 patients with temporal lobe epilepsy only and for a selection of similar unilateral temporal channels in 12 patients. No linear correlation or trend was found relating the number of channels with HFOs and seizure frequency. There was a linear positive correlation between the number of channels with more than 20 FRs/min and seizure frequency. The hypothesis that the more tissue generating HFOs, the higher the seizure frequency, was not confirmed, though there might be a correlation for high FR rates.

Characterizing the dynamic frequency structure of fast oscillations in the rodent hippocampus

Fast oscillations or “ripples” are found in the local field potential (LFP) of the rodent hippocampus during awake and sleep states. Ripples have been found to correlate with memory related neural processing, however, the functional role of the ripple has yet to be fully established. We applied a Kalman smoother based estimator of instantaneous frequency (iFreq) and frequency modulation (FM) to ripple oscillations recorded in-vivo from region CA1 of the rat and mouse hippocampus during slow wave sleep. We found that (1) ripples exhibit stereotypical frequency dynamics that are consistent in the rat and mouse, (2) instantaneous frequency information may be used as an additional dimension in the classification of ripple events, and (3) the instantaneous frequency structure of ripples may be used to improve the detection of ripple events by reducing Type I and Type II errors. Based on our results, we propose that high temporal and spectral resolution estimates of frequency dynamics may be used to help elucidate the mechanisms of ripple generation and memory related processing.

Cell type-specific firing during ripple oscillations in the hippocampal formation of humans

High-frequency field ripples occur in the rodent hippocampal formation and are assumed to depend on interneuron type-specific firing patterns, structuring the activity of pyramidal cells. Ripples with similar characteristics are also present in humans, yet their underlying cellular correlates are still unknown. By in vivo recording interneurons and pyramidal cells in the human hippocampal formation, we find that cell type-specific firing patterns and phase-locking on a millisecond timescale can be distinguished during ripples. In particular, pyramidal cells fired preferentially at the highest amplitude of the ripple, but interneurons began to discharge earlier than pyramidal cells. Furthermore, a large fraction of cells were phase-locked to the ripple cycle, but the preferred phase of discharge of interneurons followed the maximum discharge probability of pyramidal neurons. These relationships between human ripples and unit activity are qualitatively similar to that observed in vivo in the rodents, suggesting that their underlying mechanisms are similar.

High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings

Neuronal oscillations span a wide range of spatial and temporal scales that extend beyond traditional clinical EEG. Recent research suggests that high-frequency oscillations (HFO), in the ripple (80-250 Hz) and fast ripple (250-1000 Hz) frequency range, may be signatures of epileptogenic brain and involved in the generation of seizures. However, most research investigating HFO in humans comes from microwire recordings, whose relationship to standard clinical intracranial EEG (iEEG) has not been explored. In this study iEEG recordings (DC - 9000 Hz) were obtained from human medial temporal lobe using custom depth electrodes containing both microwires and clinical macroelectrodes. Ripple and fast-ripple HFO recorded from both microwires and clinical macroelectrodes were increased in seizure generating brain regions compared to control regions. The distribution of HFO frequencies recorded from the macroelectrodes was concentrated in the ripple frequency range, compared to a broad distribution of HFO frequencies recorded from microwires. The average frequency of ripple HFO recorded from macroelectrodes was lower than that recorded from microwires (143.3 +/- 49.3 Hz versus 116.3 +/- 38.4, Wilcoxon rank sum P<0.0001). Fast-ripple HFO were most often recorded on a single microwire, supporting the hypothesis that fast-ripple HFO are primarily generated by highly localized, sub-millimeter scale neuronal assemblies that are most effectively sampled by microwire electrodes. Future research will address the clinical utility of these recordings for localizing epileptogenic networks and understanding seizure generation.