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

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.

An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI

OBJECTIVE

Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we sought to provide an open-source, validated ripple detector for the scientific community.

APPROACH

We developed and validated a ripple detection plugin integrated into the Open Ephys GUI. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device.

MAIN RESULTS

To determine the accuracy of the detection algorithm, we first carried out simulations in Matlab with real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and RMS block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 ± 0.5 ms. The embedded movement monitoring was effective in reducing false positives and the plugin's flexibility to detect pathological events was also verified.

SIGNIFICANCE

Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.

A minimally invasive flexible electrode array for simultaneous recording of ECoG signals from multiple brain regions

The minimal invasiveness of electrocorticography (ECoG) enabled its widespread use in clinical areas as well as in neuroscience research. However, most existing ECoG arrays require that the entire surface area of the brain that is to be recorded be exposed through a large craniotomy. We propose a device that overcomes this limitation, i.e., a minimally invasive, polyimide-based flexible array of electrodes that can enable the recording of ECoG signals in multiple regions of the brain with minimal exposure of the surface of the brain. Magnetic force-assisted positioning of a flexible electrode array enables recording from distant brain regions with a small cranial window. Also, a biodegradable organic compound used for attaching a magnet on the electrodes allows simple retrieval of the magnet. We demonstrate with an in vivo chronic recording that an implanted ECoG electrode array can record ECoG signals from the visual cortex and the motor cortex during a rat's free behavior. Our results indicate that the proposed device induced minimal damage to the animal. We expect the proposed device to be utilized for experiments for large-scale brain circuit analyses as well as clinical applications for intra-operative monitoring of epileptic activity.

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'.

Impairment of Sharp-Wave Ripples in a Murine Model of Dravet Syndrome

Dravet syndrome (DS) is a severe early-onset epilepsy associated with heterozygous loss-of-function mutations in SCN1A Animal models of DS with global Scn1a haploinsufficiency recapitulate the DS phenotype, including seizures, premature death, and impaired spatial memory performance. Spatial memory requires hippocampal sharp-wave ripples (SPW-Rs), which consist of high-frequency field potential oscillations (ripples, 100-260 Hz) superimposed on a slower SPW. Published in vitro electrophysiologic recordings in DS mice demonstrate reduced firing of GABAergic inhibitory neurons, which are essential for the formation of SPW-R complexes. Here, in vivo electrophysiologic recordings of hippocampal local field potential in both male and female mice demonstrate that Scn1a haploinsufficiency slows intrinsic ripple frequency and reduces the rate of SPW-R occurrence. In DS mice, peak ripple-band power is shifted to lower frequencies, average intertrough intervals of individually detected ripples are slower, and the rate of SPW-R generation is reduced, while SPW amplitude remains unaffected. These alterations in SPW-R properties, in combination with published reductions in interneuron function in DS, suggest a direct link between reduced inhibitory neuron excitability and impaired SPW-R function. A simple interconnected, conductance-based in silico interneuron network model was used to determine whether reduced sodium conductance is sufficient to slow ripple frequency, and stimulation with a modeled SPW demonstrates that reduced sodium conductance alone is sufficient to slow oscillatory frequencies. These findings forge a potential mechanistic link between impaired SPW-R generation and Scn1a mutation in DS mice, expanding the set of disorders in which SPW-R dysfunction contributes to impaired memory.SIGNIFICANCE STATEMENT Disruption of sharp-wave ripples, a characteristic hippocampal rhythm coordinated by the precise timing of GABAergic interneurons, impairs spatial learning and memory. Prior in vitro patch-clamp recordings in brain slices from genetic mouse models of Dravet syndrome (DS) reveal reduced sodium current and excitability in GABAergic interneurons but not excitatory cells, suggesting a causal role for impaired interneuron activity in seizures and cognitive impairment. Here, heterozygous Scn1a mutation in DS mice reduces hippocampal sharp-wave ripple occurrence and slows internal ripple frequency in vivo and a simple in silico model demonstrates reduction in interneuron function alone is sufficient to slow model oscillations. Together, these findings provide a plausible pathophysiologic mechanism for Scn1a gene mutation to impair spatial memory.

Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators

Hippocampal ripples are involved in memory consolidation, but the mechanisms underlying their generation remain unclear. Models relying on interneuron networks in the CA1 region disagree on the predominant source of excitation to interneurons: either "direct," via the Schaffer collaterals that provide feedforward input from CA3 to CA1, or "indirect," via the local pyramidal cells in CA1, which are embedded in a recurrent excitatory-inhibitory network. Here, we used physiologically constrained computational models of basket-cell networks to investigate how they respond to different conditions of transient, noisy excitation. We found that direct excitation of interneurons could evoke ripples (140-220 Hz) that exhibited intraripple frequency accommodation and were frequency-insensitive to GABA modulators, as previously shown in in vitro experiments. In addition, the indirect excitation of the basket-cell network enabled the expression of intraripple frequency accommodation in the fast-gamma range (90-140 Hz), as in vivo In our model, intraripple frequency accommodation results from a hysteresis phenomenon in which the frequency responds differentially to the rising and descending phases of the transient excitation. Such a phenomenon predicts a maximum oscillation frequency occurring several milliseconds before the peak of excitation. We confirmed this prediction for ripples in brain slices from male mice. These results suggest that ripple and fast-gamma episodes are produced by the same interneuron network that is recruited via different excitatory input pathways, which could be supported by the previously reported intralaminar connectivity bias between basket cells and functionally distinct subpopulations of pyramidal cells in CA1. Together, our findings unify competing inhibition-first models of rhythm generation in the hippocampus.SIGNIFICANCE STATEMENT The hippocampus is a part of the brain of humans and other mammals that is critical for the acquisition and consolidation of memories. During deep sleep and resting periods, the hippocampus generates high-frequency (∼200 Hz) oscillations called ripples, which are important for memory consolidation. The mechanisms underlying ripple generation are not well understood. A prominent hypothesis holds that the ripples are generated by local recurrent networks of inhibitory neurons. Using computational models and experiments in brain slices from rodents, we show that the dynamics of interneuron networks clarify several previously unexplained characteristics of ripple oscillations, which advances our understanding of hippocampus-dependent memory consolidation.

Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning

Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.

Pyramidal cell-interneuron interactions underlie hippocampal ripple oscillations

High-frequency ripple oscillations, observed most prominently in the hippocampal CA1 pyramidal layer, are associated with memory consolidation. The cellular and network mechanisms underlying the generation, frequency control, and spatial coherence of the rhythm are poorly understood. Using multisite optogenetic manipulations in freely behaving rodents, we found that depolarization of a small group of nearby pyramidal cells was sufficient to induce high-frequency oscillations, whereas closed-loop silencing of pyramidal cells or activation of parvalbumin- (PV) or somatostatin-immunoreactive interneurons aborted spontaneously occurring ripples. Focal pharmacological blockade of GABAA receptors abolished ripples. Localized PV interneuron activation paced ensemble spiking, and simultaneous induction of high-frequency oscillations at multiple locations resulted in a temporally coherent pattern mediated by phase-locked interneuron spiking. These results constrain competing models of ripple generation and indicate that temporally precise local interactions between excitatory and inhibitory neurons support ripple generation in the intact hippocampus.

Theta-associated high-frequency oscillations (110-160Hz) in the hippocampus and neocortex

We review recent evidence for a novel type of fast cortical oscillatory activity that occurs circumscribed between 110 and 160Hz, which we refer to as high-frequency oscillations (HFOs). HFOs characteristically occur modulated by theta phase in the hippocampus and neocortex. HFOs can co-occur with gamma oscillations nested in the same theta cycle, in which case they typically peak at different theta phases. Despite the overlapping frequency ranges, HFOs differ from hippocampal ripple oscillations in some key characteristics, including amplitude, region of occurrence, associated behavioral state, and activity time-course (sustained vs intermittent). Recent in vitro evidence suggests that HFOs depend on fast GABAergic transmission and may also depend on axonal gap junctions. The functional role of HFOs is currently unclear. Both hippocampal and neocortical theta-HFO coupling increase during REM sleep, suggesting a role for HFOs in memory processing.

Theta phase modulates multiple layer-specific oscillations in the CA1 region

It was recently proposed that fast gamma oscillations (60-150 Hz) convey spatial information from the medial entorhinal cortex (EC) to the CA1 region of the hippocampus. However, here we describe 2 functionally distinct oscillations within this frequency range, both coupled to the theta rhythm during active exploration and rapid eye movement sleep: an oscillation with peak activity at ∼80 Hz and a faster oscillation centered at ∼140 Hz. The 2 oscillations are differentially modulated by the phase of theta depending on the CA1 layer; theta-80 Hz coupling is strongest at stratum lacunosum-moleculare, while theta-140 Hz coupling is strongest at stratum oriens-alveus. This laminar profile suggests that the ∼80 Hz oscillation originates from EC inputs to deeper CA1 layers, while the ∼140 Hz oscillation reflects CA1 activity in superficial layers. We further show that the ∼140 Hz oscillation differs from sharp wave-associated ripple oscillations in several key characteristics. Our results demonstrate the existence of novel theta-associated high-frequency oscillations and suggest a redefinition of fast gamma oscillations.

Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: influence of dentate and entorhinal cortical activity

Hippocampal sharp waves (SPWs) and associated fast ("ripple") oscillations (SPW-Rs) in the CA1 region are among the most synchronous physiological patterns in the mammalian brain. Using two-dimensional arrays of electrodes for recording local field potentials and unit discharges in freely moving rats, we studied the emergence of ripple oscillations (140-220 Hz) and compared their origin and cellular-synaptic mechanisms with fast gamma oscillations (90-140 Hz). We show that (1) hippocampal SPW-Rs and fast gamma oscillations are quantitatively distinct patterns but involve the same networks and share similar mechanisms; (2) both the frequency and magnitude of fast oscillations are positively correlated with the magnitude of SPWs; (3) during both ripples and fast gamma oscillations the frequency of network oscillation is higher in CA1 than in CA3; and (4) the emergence of CA3 population bursts, a prerequisite for SPW-Rs, is biased by activity patterns in the dentate gyrus and entorhinal cortex, with the highest probability of ripples associated with an "optimum" level of dentate gamma power. We hypothesize that each hippocampal subnetwork possesses distinct resonant properties, tuned by the magnitude of the excitatory drive.

An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

This work presents a connectionist model of the semantic-lexical system. Model assumes that the lexical and semantic aspects of language are memorized in two distinct stores, and are then linked together on the basis of previous experience, using physiological learning mechanisms. Particular characteristics of the model are: (1) the semantic aspects of an object are described by a collection of features, whose number may vary between objects. (2) Individual features are topologically organized to implement a similarity principle. (3) Gamma-band synchronization is used to segment different objects simultaneously. (4) The model is able to simulate the formation of categories, assuming that objects belong to the same category if they share some features. (5) Homosynaptic potentiation and homosynaptic depression are used within the semantic network, to create an asymmetric pattern of synapses; this allows a different role to be assigned to shared and distinctive features during object reconstruction. (6) Features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. (7) Features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that, after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurred during learning, according to a grounded cognition viewpoint. Several words and their conceptual description can coexist in the lexical-semantic system exploiting gamma-band time division. Schematic exempla are shown, to illustrate the possibility to distinguish between words representing a category, and words representing individual members and to evaluate the role of gamma-band synchronization in priming. Finally, the model is used to simulate patients with focalized lesions, assuming a damage of synaptic strength in specific feature areas. Results are critically discussed in view of future model extensions and application to real objects. The model represents an original effort to incorporate many basic ideas, found in recent conceptual theories, within a single quantitative scaffold.

α5GABAA receptors regulate hippocampal sharp wave-ripple activity in vitro

Sharp waves and ripples (SWRs) are a basic endogenous network activity of the hippocampus. Growing evidence from in vivo studies suggests that this activity plays a crucial role in the process of memory consolidation. Generation of SWR activity requires an intricate interaction between pyramidal cells and specific classes of GABAergic interneurons. Though GABA(A)R-mediated transmission is required for generation of SWRs little is known about the possible implication of different subtypes of GABA(A)R in SWRs. One of the most abundant subtypes of GABA(A) receptor in the hippocampus contains the α5 subunit. This subtype is specifically located on pyramidal cells preferably mediating tonic inhibition and is implicated in memory processes. Using hippocampal slices of adult rats we investigated the effects of etomidate and L-655,708, two substances that display opposite effects on the α5 subunit-containing GABA(A) receptor (α5GABA(A)R), in the generation of spontaneous SWRs. We found that the two drugs at concentrations assumed to display preferential interaction with the α5GABA(A)Rs had opposite effects on: a) the probability of generation of SWRs in episodes of multiple consecutive events (i.e. clusters), b) the timing of generation of consecutive events in clusters, c) the strength of ripple oscillation and d) the ability of the network to initiate episodes of SWRs. Most of the opposite drug effects on SWRs were also observed at higher concentrations. The present finding demonstrates a crucial involvement of the α5GABA(A)Rs in the SWR activity suggesting that distinct facets of the GABA(A)R-mediated transmission are implicated in particular features of the SWRs activity. In addition, the present results are consistent with the known opposite effects of the two drugs on memory performance.

Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100-250Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.