Diversity of sharp-wave-ripple LFP signatures reveals differentiated brain-wide dynamical events

Laminar pattern of sensory-evoked dynamic high-frequency oscillatory activity in the macaque auditory cortex

High-frequency (>60 Hz) neuroelectric signals likely have functional roles distinct from low-frequency (<30 Hz) signals. While high-gamma activity (>60 Hz) does not simply equate to neuronal spiking, they are highly correlated, having similar information encoding. High-gamma activity is typically considered broadband and poorly phase-locked to sensory stimuli and thus is typically analyzed after transformations into absolute amplitude or spectral power. However, those analyses discard signal polarity, compromising the interpretation of neuroelectric events that are essentially dipolar. In the spectrotemporal profiles of field potentials in auditory cortex, we show high-frequency spectral peaks not phase-locked to sound onset, which follow the broadband peak of phase-locked onset responses. Isolating the signal components comprising the high-frequency peaks reveals narrow-band high-frequency oscillatory events, whose instantaneous frequency changes rapidly from >150 to 60 Hz, which may underlie broadband high-frequency spectral peaks in previous reports. The laminar amplitude distributions of the isolated activity had two peak positions, while the laminar phase patterns showed a counterphase relationship between those peaks, indicating the formation of dipoles. Our findings suggest that nonphase-locked HGA arises in part from oscillatory or recurring activity of supragranular-layer neuronal ensembles in auditory cortex.

Brain oscillations: Hippocampal-prefrontal ripples unfolded

Memory consolidation is the process of translating memory traces from the hippocampus to the cortex. Hippocampal ripples are key in driving this transfer. A new study now shows that independent cortical ripples can suppress this communication. What could be the underlying mechanisms?

Prefrontal cortical ripples mediate top-down suppression of hippocampal reactivation during sleep memory consolidation

Consolidation of initially encoded hippocampal representations in the neocortex through reactivation is crucial for long-term memory formation and is facilitated by the coordination of hippocampal sharp-wave ripples (SWRs) with cortical slow and spindle oscillations during non-REM sleep. Recent evidence suggests that high-frequency cortical ripples can also coordinate with hippocampal SWRs in support of consolidation; however, the contribution of cortical ripples to reactivation remains unclear. We used high-density, continuous recordings in the hippocampus (area CA1) and prefrontal cortex (PFC) over the course of spatial learning and show that independent PFC ripples dissociated from SWRs are prevalent in NREM sleep and predominantly suppress hippocampal activity. PFC ripples paradoxically mediate top-down suppression of hippocampal reactivation rather than coordination, and this suppression is stronger for assemblies that are reactivated during coordinated CA1-PFC ripples for consolidation of recent experiences. Further, we show non-canonical, serial coordination of independent cortical ripples with slow and spindle oscillations, which are known signatures of memory consolidation. These results establish a role for prefrontal cortical ripples in top-down regulation of behaviorally relevant hippocampal representations during consolidation.

Open Data In Neurophysiology: Advancements, Solutions & Challenges

Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress1,2. For example, structural biology3 and genomics4,5 have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began (e.g.6). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential7 to facilitate and expand the still limited adoption of open science practices in our field8. Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

Lactate as a supplemental fuel for synaptic transmission and neuronal network oscillations: Potentials and limitations

Lactate shuttled from the blood circulation, astrocytes, oligodendrocytes or even activated microglia (resident macrophages) to neurons has been hypothesized to represent a major source of pyruvate compared to what is normally produced endogenously by neuronal glucose metabolism. However, the role of lactate oxidation in fueling neuronal signaling associated with complex cortex function, such as perception, motor activity, and memory formation, is widely unclear. This issue has been experimentally addressed using electrophysiology in hippocampal slice preparations (ex vivo) that permit the induction of different neural network activation states by electrical stimulation, optogenetic tools or receptor ligand application. Collectively, these studies suggest that lactate in the absence of glucose (lactate only) impairs gamma (30-70 Hz) and theta-gamma oscillations, which feature high energy demand revealed by the cerebral metabolic rate of oxygen (CMRO2, set to 100%). The impairment comprises oscillation attenuation or moderate neural bursts (excitation-inhibition imbalance). The bursting is suppressed by elevating the glucose fraction in energy substrate supply. By contrast, lactate can retain certain electric stimulus-induced neural population responses and intermittent sharp wave-ripple activity that features lower energy expenditure (CMRO2 of about 65%). Lactate utilization increases the oxygen consumption by about 9% during sharp wave-ripples reflecting enhanced adenosine-5'-triphosphate (ATP) synthesis by oxidative phosphorylation in mitochondria. Moreover, lactate attenuates neurotransmission in glutamatergic pyramidal cells and fast-spiking, γ-aminobutyric acid (GABA)ergic interneurons by reducing neurotransmitter release from presynaptic terminals. By contrast, the generation and propagation of action potentials in the axon is regular. In conclusion, lactate is less effective than glucose and potentially detrimental during neural network rhythms featuring high energetic costs, likely through the lack of some obligatory ATP synthesis by aerobic glycolysis at excitatory and inhibitory synapses. High lactate/glucose ratios might contribute to central fatigue, cognitive impairment, and epileptic seizures partially seen, for instance, during exhaustive physical exercise, hypoglycemia and neuroinflammation.

A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species

The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

Awake ripples enhance emotional memory encoding in the human brain

Enhanced memory for emotional experiences is hypothesized to depend on amygdala-hippocampal interactions during memory consolidation. Here we show using intracranial recordings from the human amygdala and the hippocampus during an emotional memory encoding and discrimination task increased awake ripples after encoding of emotional, compared to neutrally-valenced stimuli. Further, post-encoding ripple-locked stimulus similarity is predictive of later memory discrimination. Ripple-locked stimulus similarity appears earlier in the amygdala than in hippocampus and mutual information analysis confirms amygdala influence on hippocampal activity. Finally, the joint ripple-locked stimulus similarity in the amygdala and hippocampus is predictive of correct memory discrimination. These findings provide electrophysiological evidence that post-encoding ripples enhance memory for emotional events.

A cognitive map for value-guided choice in ventromedial prefrontal cortex

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

Cortical ripples mediate top-down suppression of hippocampal reactivation during sleep memory consolidation

Consolidation of initially encoded hippocampal representations in the neocortex through reactivation is crucial for long-term memory formation, and is facilitated by the coordination of hippocampal sharp-wave ripples (SWRs) with cortical oscillations during non-REM sleep. However, the contribution of high-frequency cortical ripples to consolidation is still unclear. We used continuous recordings in the hippocampus and prefrontal cortex (PFC) over the course of spatial learning and show that independent PFC ripples, when dissociated from SWRs, predominantly suppress hippocampal activity in non-REM sleep. PFC ripples paradoxically mediate top-down suppression of hippocampal reactivation, which is inversely related to reactivation strength during coordinated CA1-PFC ripples. Further, we show non-canonical, serial coordination of ripples with cortical slow and spindle oscillations. These results establish a role for cortical ripples in regulating consolidation.

Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations

The reactivation of experience-based neural activity patterns in the hippocampus is crucial for learning and memory. These reactivation patterns and their associated sharp-wave ripples (SWRs) are highly variable. However, this variability is missed by commonly used spectral methods. Here, we use topological and dimensionality reduction techniques to analyze the waveform of ripples recorded at the pyramidal layer of CA1. We show that SWR waveforms distribute along a continuum in a low-dimensional space, which conveys information about the underlying layer-specific synaptic inputs. A decoder trained in this space successfully links individual ripples with their expected sinks and sources, demonstrating how physiological mechanisms shape SWR variability. Furthermore, we found that SWR waveforms segregated differently during wakefulness and sleep before and after a series of cognitive tasks, with striking effects of novelty and learning. Our results thus highlight how the topological analysis of ripple waveforms enables a deeper physiological understanding of SWRs.

A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species

The study of sharp-wave ripples (SWRs) has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy and Alzheimer's disease is considered a biomarker of dysfunction. SWRs exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine learning (ML) models for automatic detection and analysis of SWRs. The ML architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of SWR features recorded in the dorsal hippocampus of mice. When applied to data from the macaque hippocampus, these models were able to generalize detection and revealed shared SWR properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize SWR research, lowering the threshold for its adoption in biomedical applications.

Uncovering the organization of neural circuits with Generalized Phase Locking Analysis

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Fast network oscillations during non-REM sleep support memory consolidation

The neocortex is disconnected from the outside world during sleep, which has been hypothesized to be relevant for synaptic reorganization involved in memory consolidation. Fast network oscillations, such as hippocampal sharp-wave ripples, cortical ripples, and amygdalar high-frequency oscillations, are prominent during non-REM sleep. Although these oscillations are thought to be generated by local circuit mechanisms, their occurrence rates and amplitudes are modulated by thalamocortical spindles and neocortical slow oscillations during non-REM sleep, suggesting that fast network oscillations and slower oscillations cooperatively work to facilitate memory consolidation. This review discusses the recent progress in understanding the generation, coordination, and functional roles of fast network oscillations. Further, it outlines how fast network oscillations in distinct brain regions synergistically support memory consolidation and retrieval by hosting cross-regional coactivation of memory-related neuronal ensembles.

Disrupting ripples: Methods, results, and caveats in closed-loop approaches in rodents

Hippocampal ripple oscillations have been associated with memory reactivations during wake and sleep. These reactivations should contribute to working memory and memory consolidation respectively. In the past decade studies have moved from being observational to actively disrupting ripple-related activity in closed-loop approaches to enable causal investigations into their function. All together these studies have been able to provide evidence that wake, task-related ripple activity is important for working memory and planning but less important for stabilisation of spatial representations. Rest and sleep-related ripple activity, in contrast, is important for long-term memory performance and thus memory consolidation. In this review, we summarise results from different closed-loop approaches in rodents. Further, we highlight differences in detection and stimulation methods as well as controls and discuss how these differences could influence outcomes.

Internally Triggered Experiences of Hedonic Valence in Nonhuman Animals: Cognitive and Welfare Considerations

Do any nonhuman animals have hedonically valenced experiences not directly caused by stimuli in their current environment? Do they, like us humans, experience anticipated or previously experienced pains and pleasures as respectively painful and pleasurable? We review evidence from comparative neuroscience about hippocampus-dependent simulation in relation to this question. Hippocampal sharp-wave ripples and theta oscillations have been found to instantiate previous and anticipated experiences. These hippocampal activations coordinate with neural reward and fear centers as well as sensory and cortical areas in ways that are associated with conscious episodic mental imagery in humans. Moreover, such hippocampal “re- and preplay” has been found to contribute to instrumental decision making, the learning of value representations, and the delay of rewards in rats. The functional and structural features of hippocampal simulation are highly conserved across mammals. This evidence makes it reasonable to assume that internally triggered experiences of hedonic valence (IHVs) are pervasive across (at least) all mammals. This conclusion has important welfare implications. Most prominently, IHVs act as a kind of “welfare multiplier” through which the welfare impacts of any given experience of pain or pleasure are increased through each future retrieval. However, IHVs also have practical implications for welfare assessment and cause prioritization.

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.

Cell-type-specific silence in thalamocortical circuits precedes hippocampal sharp-wave ripples

Evidence suggests that the hippocampus conveys memory-related neural patterns across distributed cortical circuits during high-frequency oscillations called sharp-wave ripples (SWRs). We investigate how circuit activity in the retrosplenial cortex (RSC), a primary hippocampal target, could aid in processing SWR-related input. Using patch-clamp recordings from awake mice, we find that SWR-aligned membrane potential modulation is widespread but weak, and that spiking responses are sparse. However, using cell-type-specific two-photon Ca²⁺ imaging and optogenetics, we show that, 1-2 s before SWRs, superficial inhibition and thalamocortical input in RSC is reduced. We propose that pyramidal dendrites experience decreased local inhibition and subcortical interference in a seconds-long time window preceding SWRs. This may aid communication of weak and sparse SWR-aligned excitation between the hippocampus and neocortex and promote the strengthening of memory-related connections.

Brain-wide interactions during hippocampal sharp wave ripples

During periods of disengagement from the environment, transient population bursts, known as sharp wave ripples (SPW-Rs), occur sporadically. While numerous experiments have characterized the bidirectional relationship between SPW-Rs and activity in chosen brain areas, the topographic relationship between different segments of the hippocampus and brain-wide target areas has not been studied at high temporal and spatial resolution. Yet, such knowledge is necessary to infer the direction of communication. We analyzed two publicly available datasets with simultaneous high-density silicon probe recordings from across the mouse forebrain. We found that SPW-Rs coincide with a transient brain-wide increase in functional connectivity. In addition, we show that the diversity in SPW-R features, such as their incidence, magnitude, and intrahippocampal topography in the septotemporal axis, are correlated with slower excitability fluctuations in cortical and subcortical areas. Further, variations in SPW-R features correlated with the timing, sign, and magnitude of downstream responses with large-amplitude SPW-Rs followed by transient silence in extrahippocampal structures. Our findings expand on previous results and demonstrate that the activity patterns in extrahippocampal structures depend both on the intrahippocampal topographic origin and magnitude of hippocampal SPW-Rs.

Dentate gyrus and CA3 GABAergic interneurons bidirectionally modulate signatures of internal and external drive to CA1

Specific classes of GABAergic neurons play specific roles in regulating information processing in the brain. In the hippocampus, two major classes, parvalbumin-expressing (PV⁺) and somatostatin-expressing (SST⁺), differentially regulate endogenous firing patterns and target subcellular compartments of principal cells. How these classes regulate the flow of information throughout the hippocampus is poorly understood. We hypothesize that PV⁺ and SST⁺ interneurons in the dentate gyrus (DG) and CA3 differentially modulate CA3 patterns of output, thereby altering the influence of CA3 on CA1. We find that while suppressing either interneuron class increases DG and CA3 output, the effects on CA1 were very different. Suppressing PV⁺ interneurons increases local field potential signatures of coupling from CA3 to CA1 and decreases signatures of coupling from entorhinal cortex to CA1; suppressing SST⁺ interneurons has the opposite effect. Thus, DG and CA3 PV⁺ and SST⁺ interneurons bidirectionally modulate the flow of information through the hippocampal circuit.

Towards threshold invariance in defining hippocampal ripples

Objective.Hippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials (LFPs), and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the 'ripple' could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. In addition, LFPs are often influenced by myoelectric noise arising from animal movement, making it difficult to distinguish ripples from high-frequency noises. These problems have to be overcome.Approach.We extracted ripple candidates under few constraints and labeled them as binary or stochastic 'true' or 'false' ripples using Gaussian mixed model clustering and a deep convolutional neural network (CNN) in a weakly supervised fashion.Main results.Our automatic method separated ripples and myoelectric noise and was able to detect ripples even when the animals were moving. Moreover, we confirmed that a CNN detected ripples defined by our method. Leave-one-animal-out cross-validation estimated the accuracy, the area under the precision-recall curve, the receiver operating characteristic curve to be 0.88, 0.99 and 0.96, respectively.Significance.Our automatic ripple detection method will reduce time spent on performing laborious experiments and analyses.

Prodromal dysfunction of α5GABA-A receptor modulated hippocampal ripples occurs prior to neurodegeneration in the TgF344-AD rat model of Alzheimer's disease

Decades of research attempting to slow the onset of Alzheimer's disease (AD) indicates that a better understanding of memory will be key to the discovery of effective therapeutic approaches. Here, we ask whether prodromal neural network dysfunction might occur in the hippocampal trisynaptic circuit by using α5IA (an established memory enhancer and selective negative allosteric modulator of extrasynaptic tonically active α5GABA-A receptors) as a probe drug in TgF344-AD transgenic rats, a model for β-amyloid induced early onset AD. The results demonstrate that orally bioavailable α5IA increases CA1 pyramidal cell mean firing rates during foraging and peak ripple amplitude during wakeful immobility in wild type F344 rats in a familiar environment. We further demonstrate that CA1 ripples in TgF344-AD rats are nonresponsive to α5IA by 9 months of age, prior to the onset of AD-like pathology and memory dysfunction. TgF344-AD rats express human β-amyloid precursor protein (with the Swedish mutation) and human presenilin-1 (with a Δ exon 9 mutation) and we found high serum Aβ42 and Aβ40 levels by 3 months of age. When taken together, this demonstrates, to the best of our knowledge, the first evidence for prodromal α5GABA-A receptor dysfunction in the ripple-generating hippocampal trisynaptic circuit of AD-like transgenic rats. As α5GABA-A receptors are found at extrasynaptic and synaptic contacts, we posit that negative modulation of α5GABA-A receptor mediated tonic as well as phasic inhibition augments CA1 ripples and memory consolidation but that this modulatory mechanism is lost at an early stage of AD onset.

Theta Oscillations Coincide with Sustained Hyperpolarization in CA3 Pyramidal Cells, Underlying Decreased Firing

Brain states modulate the membrane potential dynamics of neurons, influencing the functional repertoire of the network. Pyramidal cells (PCs) in the hippocampal CA3 are necessary for rapid memory encoding, which preferentially occurs during exploratory behavior in the high-arousal theta state. However, the relationship between the membrane potential dynamics of CA3 PCs and theta has not been explored. Here we characterize the changes in the membrane potential of PCs in relation to theta using electrophysiological recordings in awake mice. During theta, most PCs behave in a stereotypical manner, consistently hyperpolarizing time-locked to the duration of theta. Additionally, PCs display lower membrane potential variance and a reduced firing rate. In contrast, during large irregular activity, PCs show heterogeneous changes in membrane potential. This suggests coordinated hyperpolarization of PCs during theta, possibly caused by increased inhibition. This could lead to a higher signal-to-noise ratio in the small population of PCs active during theta, as observed in ensemble recordings.

Subiculum as a generator of sharp wave-ripples in the rodent hippocampus

Sharp wave-ripples (SWRs) represent synchronous discharges of hippocampal neurons and are believed to play a major role in memory consolidation. A large body of evidence suggests that SWRs are exclusively generated in the CA3-CA2 network. In contrast, here, we provide several lines of evidence showing that the subiculum can function as a secondary SWRs generator. SWRs with subicular origin propagate forward into the entorhinal cortex as well as backward into the hippocampus proper. Our findings suggest that the output structures of the hippocampus are not only passively facilitating the transfer of SWRs to the cortex, but they also can actively contribute to the genesis of SWRs. We hypothesize that SWRs with a subicular origin may be important for the consolidation of information conveyed to the hippocampus via the temporoammonic pathway.

Imbrosci et al. show that the subiculum can work as a secondary generator of sharp wave-ripples (SWRs). SWRs with their origin in subiculum can propagate to the entorhinal cortex and backward to CA1 and CA3.

Phasic oxygen dynamics confounds fast choline-sensitive biosensor signals in the brain of behaving rodents

Cholinergic fast time-scale modulation of cortical physiology is critical for cognition, but direct local measurement of neuromodulators in vivo is challenging. Choline oxidase (ChOx)-based electrochemical biosensors have been used to capture fast cholinergic signals in behaving animals. However, these transients might be biased by local field potential and O₂-evoked enzymatic responses. Using a novel Tetrode-based Amperometric ChOx (TACO) sensor, we performed highly sensitive and selective simultaneous measurement of ChOx activity (COA) and O₂. In vitro and in vivo experiments, supported by mathematical modeling, revealed that non-steady-state enzyme responses to O₂ give rise to phasic COA dynamics. This mechanism accounts for most of COA transients in the hippocampus, including those following locomotion bouts and sharp-wave/ripples. Our results suggest that it is unfeasible to probe phasic cholinergic signals under most behavioral paradigms with current ChOx biosensors. This confound is generalizable to any oxidase-based biosensor, entailing rigorous controls and new biosensor designs.

RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

Neuromodulation of the mind-wandering brain state: the interaction between neuromodulatory tone, sharp wave-ripples and spontaneous thought

Mind-wandering has become a captivating topic for cognitive neuroscientists. By now, it is reasonably well described in terms of its phenomenology and the large-scale neural networks that support it. However, we know very little about what neurobiological mechanisms trigger a mind-wandering episode and sustain the mind-wandering brain state. Here, we focus on the role of ascending neuromodulatory systems (i.e. acetylcholine, noradrenaline, serotonin and dopamine) in shaping mind-wandering. We advance the hypothesis that the hippocampal sharp wave-ripple (SWR) is a compelling candidate for a brain state that can trigger mind-wandering episodes. This hippocampal rhythm, which occurs spontaneously in quiescent behavioural states, is capable of propagating widespread activity in the default network and is functionally associated with recollective, associative, imagination and simulation processes. The occurrence of the SWR is heavily dependent on hippocampal neuromodulatory tone. We describe how the interplay of neuromodulators may promote the hippocampal SWR and trigger mind-wandering episodes. We then identify the global neuromodulatory signatures that shape the evolution of the mind-wandering brain state. Under our proposed framework, mind-wandering emerges due to the interplay between neuromodulatory systems that influence the transitions between brain states, which either facilitate, or impede, a wandering mind. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Coupling of hippocampal theta and ripples with pontogeniculooccipital waves

The hippocampus has a major role in encoding and consolidating long-term memories, and undergoes plastic changes during sleep¹. These changes require precise homeostatic control by subcortical neuromodulatory structures². The underlying mechanisms of this phenomenon, however, remain unknown. Here, using multi-structure recordings in macaque monkeys, we show that the brainstem transiently modulates hippocampal network events through phasic pontine waves known as pontogeniculooccipital waves (PGO waves). Two physiologically distinct types of PGO wave appear to occur sequentially, selectively influencing high-frequency ripples and low-frequency theta events, respectively. The two types of PGO wave are associated with opposite hippocampal spike-field coupling, prompting periods of high neural synchrony of neural populations during periods of ripple and theta instances. The coupling between PGO waves and ripples, classically associated with distinct sleep stages, supports the notion that a global coordination mechanism of hippocampal sleep dynamics by cholinergic pontine transients may promote systems and synaptic memory consolidation as well as synaptic homeostasis.

Early Hippocampal Sharp-Wave Ripple Deficits Predict Later Learning and Memory Impairments in an Alzheimer's Disease Mouse Model

Alzheimer’s disease (AD) is characterized by progressive memory loss, and there is a pressing need to identify early pathophysiological alterations that predict subsequent memory impairment. Hippocampal sharp-wave ripples (SWRs)—electrophysiological signatures of memory reactivation in the hippocampus—are a compelling candidate for this purpose. Mouse models of AD show reductions in both SWR abundance and associated slow gamma (SG) power during aging, but these alterations have yet to be directly linked to memory impairments. In aged apolipoprotein E4 knockin (apoE4-KI) mice—a model of the major genetic risk factor for AD—we find that reduced SWR abundance and associated CA3 SG power predicted spatial memory impairments measured 1–2 months later. Importantly, SWR-associated CA3 SG power reduction in young apoE4-KI mice also predicted spatial memory deficits measured 10 months later. These results establish features of SWRs as potential functional biomarkers of memory impairment in AD.

Currently, there are no functional biomarkers that can predict progression to Alzheimer’s disease before cognitive decline begins. Jones et al. demonstrate that sharp-wave ripple and associated slow gamma deficits predict memory impairments in aged apoE4 mice. Slow gamma deficits in young apoE4 mice predict memory impairment onset 10+ months later.

Lactate Attenuates Synaptic Transmission and Affects Brain Rhythms Featuring High Energy Expenditure

Lactate shuttled from blood, astrocytes, and/or oligodendrocytes may serve as the major glucose alternative in brain energy metabolism. However, its effectiveness in fueling neuronal information processing underlying complex cortex functions like perception and memory is unclear. We show that sole lactate disturbs electrical gamma and theta-gamma oscillations in hippocampal networks by either attenuation or neural bursts. Bursting is suppressed by elevating the glucose fraction in substrate supply. By contrast, lactate does not affect electrical sharp wave-ripple activity featuring lower energy use. Lactate increases the oxygen consumption during the network states, reflecting enhanced oxidative ATP synthesis in mitochondria. Finally, lactate attenuates synaptic transmission in excitatory pyramidal cells and fast-spiking, inhibitory interneurons by reduced neurotransmitter release from presynaptic terminals, whereas action potential generation in the axon is regular. In conclusion, sole lactate is less effective and potentially harmful during gamma-band rhythms by omitting obligatory ATP delivery through fast glycolysis at the synapse.

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Lactate fuels network oscillations featuring low energy expenditure

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Lactate can disturb the neuronal excitation-inhibition balance

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Lactate attenuates neurotransmission at glutamatergic and GABAergic synapses

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Lactate increases oxygen consumption, whereas neural activity can even decrease

Three brain states in the hippocampus and cortex

Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet, despite this critique, the stimulus-driven paradigm still dominates-possibly because a convincing alternative has not been clear. Here, we review a series of findings suggesting such an alternative. These findings indicate that neural activity in the hippocampus occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different functional roles in cognition. This three-state framework also indicates that neural representations in the hippocampus follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in the cortex, suggesting shared principles and brain-wide organization of intrinsic neural 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'.

Propagation of hippocampal ripples to the neocortex by way of a subiculum-retrosplenial pathway

Bouts of high frequency activity known as sharp wave ripples (SPW-Rs) facilitate communication between the hippocampus and neocortex. However, the paths and mechanisms by which SPW-Rs broadcast their content are not well understood. Due to its anatomical positioning, the granular retrosplenial cortex (gRSC) may be a bridge for this hippocampo-cortical dialogue. Using silicon probe recordings in awake, head-fixed mice, we show the existence of SPW-R analogues in gRSC and demonstrate their coupling to hippocampal SPW-Rs. gRSC neurons reliably distinguished different subclasses of hippocampal SPW-Rs according to ensemble activity patterns in CA1. We demonstrate that this coupling is brain state-dependent, and delineate a topographically-organized anatomical pathway via VGlut2-expressing, bursty neurons in the subiculum. Optogenetic stimulation or inhibition of bursty subicular cells induced or reduced responses in superficial gRSC, respectively. These results identify a specific path and underlying mechanisms by which the hippocampus can convey neuronal content to the neocortex during SPW-Rs.

Memory reactivation in rat medial prefrontal cortex occurs in a subtype of cortical UP state during slow-wave sleep

Interaction between hippocampal sharp-wave ripples (SWRs) and UP states, possibly by coordinated reactivation of memory traces, is conjectured to play an important role in memory consolidation. Recently, it was reported that SWRs were differentiated into multiple subtypes. However, whether cortical UP states can also be classified into subtypes is not known. Here, we analysed neural ensemble activity from the medial prefrontal cortex from rats trained to run a spatial sequence-memory task. Application of the hidden Markov model (HMM) with three states to epochs of UP-DOWN oscillations identified DOWN states and two subtypes of UP state (UP-1 and UP-2). The two UP subtypes were distinguished by differences in duration, with UP-1 having a longer duration than UP-2, as well as differences in the speed of population vector (PV) decorrelation, with UP-1 decorrelating more slowly than UP-2. Reactivation of recent memory sequences predominantly occurred in UP-2. Short-duration reactivating UP states were dominated by UP-2 whereas long-duration ones exhibit transitions from UP-1 to UP-2. Thus, recent memory reactivation, if it occurred within long-duration UP states, typically was preceded by a period of slow PV evolution not related to recent experience, and which we speculate may be related to previously encoded information. If that is the case, then the transition from UP-1 to UP-2 subtypes may help gradual integration of recent experience with pre-existing cortical memories by interleaving the two in the same UP state. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Potential factors influencing replay across CA1 during sharp-wave ripples

Sharp-wave ripples are complex neurophysiological events recorded along the trisynaptic hippocampal circuit (i.e. from CA3 to CA1 and the subiculum) during slow-wave sleep and awake states. They arise locally but scale brain-wide to the hippocampal target regions at cortical and subcortical structures. During these events, neuronal firing sequences are replayed retrospectively or prospectively and in the forward or reverse order as defined by experience. They could reflect either pre-configured firing sequences, learned sequences or an option space to inform subsequent decisions. How can different sequences arise during sharp-wave ripples? Emerging data suggest the hippocampal circuit is organized in different loops across the proximal (close to dentate gyrus) and distal (close to entorhinal cortex) axis. These data also disclose a so-far neglected laminar organization of the hippocampal output during sharp-wave events. Here, I discuss whether by incorporating cell-type-specific mechanisms converging on deep and superficial CA1 sublayers along the proximodistal axis, some novel factors influencing the organization of hippocampal sequences could be unveiled. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Organization of Propagated Intrinsic Brain Activity in Individual Humans

Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks-altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples

A prevalent model is that sharp-wave ripples (SWR) arise ‘spontaneously’ in CA3 and propagate recent memory traces outward to the neocortex to facilitate memory consolidation there. Using voltage and extracellular glutamate transient recording over widespread regions of mice dorsal neocortex in relation to CA1 multiunit activity (MUA) and SWR, we find that the largest SWR-related modulation occurs in retrosplenial cortex; however, contrary to the unidirectional hypothesis, neocortical activation exhibited a continuum of activation timings relative to SWRs, varying from leading to lagging. Thus, contrary to the model in which SWRs arise ‘spontaneously’ in the hippocampus, neocortical activation often precedes SWRs and may thus constitute a trigger event in which neocortical information seeds associative reactivation of hippocampal ‘indices’. This timing continuum is consistent with a dynamics in which older, more consolidated memories may in fact initiate the hippocampal-neocortical dialog, whereas reactivation of newer memories may be initiated predominantly in the hippocampus.

Deficit in hippocampal ripples does not preclude spatial memory formation in APP/PS1 mice

General theory of declarative memory formation posits a cortical-hippocampal dialog during which hippocampal ripple oscillations support information transfer and long-term consolidation of hippocampus dependent memories. Brain dementia, as Alzheimer disease (AD), is accompanied by memory loss and inability to form new memories. A large body of work has shown variety of mechanisms acting at cellular and molecular levels which can putatively play an important role in the impairment of memory formation. However, far less is known about changes occurring at the network-level activity patterns that support memory processing. Using freely moving APP/PS1 mice, a model of AD, we undertook a study to unravel the alterations of the activity of hippocampal and cortical circuits during generation of ripples in the transgenic and wild-type mice undergoing encoding and consolidation of spatial information. We report that APP/PS1 animals are able to consolidate spatial memory despite a major deficit of hippocampal ripples occurrence rate and learning dependent dynamics. We propose that these impairments may be compensated by an increase of the occurrence of cortical ripples and reorganization of cortical-hippocampal interaction.

Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and Memory-Guided Decision Making

Spatial learning requires remembering and choosing paths to goals. Hippocampal place cells replay spatial paths during immobility in reverse and forward order, offering a potential mechanism. However, how replay supports both goal-directed learning and memory-guided decision making is unclear. We therefore continuously tracked awake replay in the same hippocampal-prefrontal ensembles throughout learning of a spatial alternation task. We found that, during pauses between behavioral trajectories, reverse and forward hippocampal replay supports an internal cognitive search of available past and future possibilities and exhibits opposing learning gradients for prediction of past and future behavioral paths, respectively. Coordinated hippocampal-prefrontal replay distinguished correct past and future paths from alternative choices, suggesting a role in recall of past paths to guide planning of future decisions for spatial working memory. Our findings reveal a learning shift from hippocampal reverse-replay-based retrospective evaluation to forward-replay-based prospective planning, with prefrontal readout of memory-guided paths for learning and decision making.

Global brain signal in awake rats

Although often used as a nuisance in resting-state functional magnetic resonance imaging (rsfMRI), the global brain signal in humans and anesthetized animals has important neural basis. However, our knowledge of the global signal in awake rodents is sparse. To bridge this gap, we systematically analyzed rsfMRI data acquired with a conventional single-echo (SE) echo planar imaging (EPI) sequence in awake rats. The spatial pattern of rsfMRI frames during peaks of the global signal exhibited prominent co-activations in the thalamo-cortical and hippocampo-cortical networks, as well as in the basal forebrain, hinting that these neural networks might contribute to the global brain signal in awake rodents. To validate this concept, we acquired rsfMRI data using a multi-echo (ME) EPI sequence and removed non-neural components in the rsfMRI signal. Consistent co-activation patterns were obtained in extensively de-noised ME-rsfMRI data, corroborating the finding from SE-rsfMRI data. Furthermore, during rsfMRI experiments, we simultaneously recorded neural spiking activities in the hippocampus using GCaMP-based fiber photometry. The hippocampal calcium activity exhibited significant correspondence with the global rsfMRI signal. These data collectively suggest that the global rsfMRI signal contains significant neural components that involve coordinated activities in the thalamo-cortical and hippocampo-cortical networks. These results provide important insight into the neural substrate of the global brain signal in awake rodents.

Improved identification and differentiation from epileptiform activity of human hippocampal sharp wave ripples during NREM sleep

In rodents, pyramidal cell firing patterns from waking may be replayed in nonrapid eye movement sleep (NREM) sleep during hippocampal sharp wave ripples (HC-SWR). In humans, HC-SWR have only been recorded with electrodes implanted to localize epileptogenicity. Here, we characterize human HC-SWR with rigorous rejection of epileptiform activity, requiring multiple oscillations and coordinated sharp waves. We demonstrated typical SWR in those rare HC recordings which lack interictal epileptiform spikes (IIS) and with no or minimal seizure involvement. These HC-SWR have a similar rate (~12 min^-1 on average, variable across NREM stages and anterior/posterior HC) and apparent intra-HC topography (ripple maximum in putative stratum pyramidale, slow wave in radiatum) as rodents, though with lower frequency (~85 Hz compared to ~140 Hz in rodents). Similar SWR are found in HC with IIS, but no significant seizure involvement. These SWR were modulated by behavior, being largely absent (<2 min^-1 ) except during NREM sleep in both Stage 2 (~9 min^-1 ) and Stage 3 (~15 min^-1 ), distinguishing them from IIS. This study quantifies the basic characteristics of a strictly selected sample of SWR recorded in relatively healthy human hippocampi.

LFP clustering in cortex reveals a taxonomy of Up states and near-millisecond, ordered phase-locking in cortical neurons

During slow-wave sleep and anesthesia, mammalian cortex exhibits a synchronized state during which neurons shift from a largely nonfiring to a firing state, known as an Up-state transition. Up-state transitions may constitute the default activity pattern of the entire cortex (Neske GT. Front Neural Circuits 9: 88, 2016) and could be critical to understanding cortical function, yet the genesis of such transitions and their interaction with single neurons is not well understood. It was recently shown that neurons firing at rates >2 Hz fire spikes in a stereotyped order during Up-state transitions (Luczak A, McNaughton BL, Harris KD. Nat Rev Neurosci 16: 745-755, 2015), yet it is still unknown if Up states are homogeneous and whether spiking order is present in neurons with rates <2 Hz (the majority). Using extracellular recordings from anesthetized cats and mice and from naturally sleeping rats, we show for the first time that Up-state transitions can be classified into several types based on the shape of the local field potential (LFP) during each transition. Individual LFP events could be localized in time to within 1-4 ms, more than an order of magnitude less than in previous studies. The majority of recorded neurons synchronized their firing to within ±5-15 ms relative to each Up-state transition. Simultaneous electrophysiology and wide-field imaging in mouse confirmed that LFP event clusters are cortex-wide phenomena. Our findings show that Up states are of different types and point to the potential importance of temporal order and millisecond-scale signaling by cortical neurons.NEW & NOTEWORTHY During cortical Up-state transitions in sleep and anesthesia, neurons undergo brief periods of increased firing in an order similar to that occurring in awake states. We show that these transitions can be classified into distinct types based on the shape of the local field potential. Transition times can be defined to <5 ms. Most neurons synchronize their firing to within ±5-15 ms of the transitions and fire in a consistent order.

Coordination of Human Hippocampal Sharpwave Ripples during NREM Sleep with Cortical Theta Bursts, Spindles, Downstates, and Upstates

In rodents, waking firing patterns replay in NREM sleep during hippocampal sharpwave ripples (HC-SWRs), correlated with neocortical graphoelements (NC-GEs). NC-GEs include theta bursts, spindles, downstates, and upstates. In humans, consolidation during sleep is correlated with scalp-recorded spindles and downstates/upstates, but HC-SWRs cannot be recorded noninvasively. Here we show in humans of both sexes that HC-SWRs are highly correlated with NC-GEs during NREM, with significantly more related HC-SWRs/NC-GEs for downstates or upstates than theta bursts or spindles, in N2 than N3, in posterior than anterior HC, in frontal than occipital cortex, and ipsilaterally than contralaterally. The preferences interacted (e.g., frontal spindles co-occurred frequently with posterior HC-SWRs in N2). These preferred GEs, stages, and locations for HC-SWR/NC-GE interactions may index selective consolidation activity, although that was not tested in this study. SWR recorded in different HC regions seldom co-occurred, and were related to GE in different cortical areas, showing that HC-NC interact in multiple transient, widespread but discrete, networks. NC-GEs tend to occur with consistent temporal relationships to HC-SWRs, and to each other. Cortical theta bursts usually precede HC-SWRs, where they may help define cortical input triggering HC-SWR firing. HC-SWRs often follow cortical downstate onsets, surrounded by locally decreased broadband power, suggesting a mechanism synchronizing cortical, thalamic, and hippocampal activities. Widespread cortical upstates and spindles follow HC-SWRs, consistent with the hypothesized contribution by hippocampal firing during HC-SWRs to cortical firing-patterns during upstates and spindles. Overall, our results describe how hippocampal and cortical oscillations are coordinated in humans during events that are critical for memory consolidation in rodents.SIGNIFICANCE STATEMENT Hippocampal sharpwave ripples, essential for memory consolidation, mark when hippocampal neurons replay waking firing patterns. In rodents, cortical sleep waves coordinate the transfer of temporary hippocampal to permanent cortical memories, but their relationship with human hippocampal sharpwave ripples remains unclear. We show that human hippocampal sharpwave ripples co-occur with all varieties of cortical sleep waves, in all cortical regions, and in all stages of NREM sleep, but with overall preferences for each of these. We found that sharpwave ripples in different parts of the hippocampus usually occurred independently of each other, and preferentially interacted with different cortical areas. We found that sharpwave ripples typically occur after certain types of cortical waves, and before others, suggesting how the cortico-hippocampo-cortical interaction may be organized in time and space.

Posterior Hippocampal Spindle Ripples Co-occur with Neocortical Theta Bursts and Downstates-Upstates, and Phase-Lock with Parietal Spindles during NREM Sleep in Humans

Human anterior and posterior hippocampus (aHC, pHC) differ in connectivity and behavioral correlates. Here we report physiological differences in humans of both sexes. During NREM sleep, the human hippocampus generates sharpwave ripples (SWRs) similar to those which in rodents mark memory replay. We show that while pHC generates SWRs, it also generates approximately as many spindle ripples (SSR: ripples phase-locked to local spindles). In contrast, SSRs are rare in aHC. Like SWRs, SSRs often co-occur with neocortical theta bursts (TBs), downstates (DSs), sleep spindles (SSs), and upstates (USs), which coordinate cortico-hippocampal interactions and facilitate consolidation in rodents. SWRs co-occur with these waves in widespread cortical areas, especially frontocentral. These waves typically occur in the sequence TB-DS-SS-US, with SWRs usually occurring before SS-US. In contrast, SSRs occur ∼350 ms later, with a strong preference for co-occurrence with posterior-parietal SSs. pHC-SSs were strongly phase-locked with parietal-SSs, and pHC-SSRs were phase-coupled with pHC-SSs and parietal-SSs. Human SWRs (and associated replay events, if any) are separated by ∼5 s on average, whereas ripples on successive SSR peaks are separated by only ∼80 ms. These distinctive physiological properties of pHC-SSR enable an alternative mechanism for hippocampal engagement with neocortex.SIGNIFICANCE STATEMENT Rodent hippocampal neurons replay waking events during sharpwave ripples (SWRs) in NREM sleep, facilitating memory transfer to a permanent cortical store. We show that human anterior hippocampus also produces SWRs, but spindle ripples predominate in posterior. Whereas SWRs typically occur as cortex emerges from inactivity, spindle ripples typically occur at peak cortical activity. Furthermore, posterior hippocampal spindle ripples are tightly coupled to posterior parietal locations activated by conscious recollection. Finally, multiple spindle ripples can recur within a second, whereas SWRs are separated by ∼5 s. The human posterior hippocampus is considered homologous to rodent dorsal hippocampus, which is thought to be specialized for consolidation of specific memory details. We speculate that these distinct physiological characteristics of posterior hippocampal spindle ripples may support a related function in humans.

The hippocampal sharp wave-ripple in memory retrieval for immediate use and consolidation

Various cognitive functions have long been known to require the hippocampus. Recently, progress has been made in identifying the hippocampal neural activity patterns that implement these functions. One such pattern is the sharp wave-ripple (SWR), an event associated with highly synchronous neural firing in the hippocampus and modulation of neural activity in distributed brain regions. Hippocampal spiking during SWRs can represent past or potential future experience, and SWR-related interventions can alter subsequent memory performance. These findings and others suggest that SWRs support both memory consolidation and memory retrieval for processes such as decision-making. In addition, studies have identified distinct types of SWR based on representational content, behavioural state and physiological features. These various findings regarding SWRs suggest that different SWR types correspond to different cognitive functions, such as retrieval and consolidation. Here, we introduce another possibility - that a single SWR may support more than one cognitive function. Taking into account classic psychological theories and recent molecular results that suggest that retrieval and consolidation share mechanisms, we propose that the SWR mediates the retrieval of stored representations that can be utilized immediately by downstream circuits in decision-making, planning, recollection and/or imagination while simultaneously initiating memory consolidation processes.

The hippocampal code for space in Mongolian gerbils

Large parts of our knowledge about the physiology of the hippocampus in the intact brain are derived from studies in rats and mice. While many of those findings fit well to the limited data available from humans and primates, there are also marked differences, for example, in hippocampal oscillation frequencies and in the persistence of theta oscillations. To test whether the distinct sensory specializations of the visual and auditory system of primates play a key role in explaining these differences, we recorded basic hippocampal physiological properties in Mongolian gerbils, a rodent species with high visual acuity, and good low-frequency hearing, similar to humans. We found that gerbils show only minor differences to rats regarding hippocampal place field activity, theta properties (frequency, persistence, phase precession, theta compression), and sharp wave ripple events. The only major difference between rats and gerbils was a considerably higher degree of head direction selectivity of gerbil place fields, which may be explained by their visual system being able to better resolve distant cues. Thus, differences in sensory specializations between rodent species only affect hippocampal circuit dynamics to a minor extent, which implies that differences to other mammalian lineages, such as bats and primates, cannot be solely explained by specialization in the auditory or visual system.

Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus

Forming reliable memories requires coordinated activity within distributed brain networks. At present, neural mechanisms underlying systems-level consolidation of declarative memory beyond the hippocampal-prefrontal interactions remain largely unexplored. The mediodorsal thalamic nucleus (MD) is reciprocally connected with the medial prefrontal cortex (mPFC) and also receives inputs from parahippocampal regions. The MD may thus modulate functional connectivity between the hippocampus and the mPFC at different stages of information processing. Here, we characterized, in freely behaving Sprague Dawley male rats, the MD neural activity around hippocampal ripples, indicators of memory replay and hippocampal-cortical information transfer. Overall, the MD firing rate was transiently (0.76 ± 0.06 s) decreased around ripples, with the MD activity suppression preceding the ripple onset for 0.41 ± 0.04 s (range, 0.01-0.95 s). The degree of MD modulation correlated with ripple amplitude, differed across behavioral states, and also depended on the dynamics of hippocampal-cortical population activity. The MD suppression was the strongest and the most consistent during awake ripples. During non-rapid eye movement sleep, MD firing rate decreased around spindle-uncoupled ripples, but increased around spindle-coupled ripples. Our results suggest a competitive interaction between the thalamocortical and hippocampal-cortical networks supporting "on-line" and "off-line" information processing, respectively. We hypothesize that thalamic activity suppression during spindle-uncoupled ripples is favorable for memory replay, as it reduces interference from sensory relay. In turn, the thalamic input during hippocampal-cortical communication, as indicated by spindle/ripple coupling, may contribute to selectivity and reliability of information transfer. Both predictions need to be tested in future experiments.SIGNIFICANCE STATEMENT Systems mechanisms of declarative memory consolidation beyond the hippocampal-prefrontal interactions remain largely unexplored. The connectivity of the mediodorsal thalamic nucleus (MD) with extrahippocampal regions and with medial prefrontal cortex underlies its role in execution of diverse cognitive functions. However, little is known about the MD involvement in "off-line" consolidation. We found that MD neural activity was transiently suppressed around hippocampal ripples, except for ripples co-occurring with sleep spindles, when the MD activity was elevated. The thalamic activity suppression at times of spindle-uncoupled ripples may be favorable for memory replay, as it reduces interference with sensory relay. In turn, the thalamic input during hippocampal-cortical communication, as indicated by spindle/ripple coupling, may contribute to selectivity and reliability of information transfer.