5-MeO-DMT induces sleep-like LFP spectral signatures in the hippocampus and prefrontal cortex of awake rats.

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a potent classical psychedelic known to induce changes in locomotion, behaviour, and sleep in rodents. However, there is limited knowledge regarding its acute neurophysiological effects. Local field potentials (LFPs) are commonly used as a proxy for neural activity, but previous studies investigating psychedelics have been hindered by confounding effects of behavioural changes and anaesthesia, which alter these signals. To address this gap, we investigated acute LFP changes in the hippocampus (HP) and medial prefrontal cortex (mPFC) of freely behaving rats, following 5-MeO-DMT administration. 5-MeO-DMT led to an increase of delta power and a decrease of theta power in the HP LFPs, which could not be accounted for by changes in locomotion. Furthermore, we observed a dose-dependent reduction in slow (20-50 Hz) and mid (50-100 Hz) gamma power, as well as in theta phase modulation, even after controlling for the effects of speed and theta power. State map analysis of the spectral profile of waking behaviour induced by 5-MeO-DMT revealed similarities to electrophysiological states observed during slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. Our findings suggest that the psychoactive effects of classical psychedelics are associated with the integration of waking behaviours with sleep-like spectral patterns in LFPs.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Coordinating brain-distributed network activities in memory resistant to extinction.

Certain memories resist extinction to continue invigorating maladaptive actions. The robustness of these memories could depend on their widely distributed implementation across populations of neurons in multiple brain regions. However, how dispersed neuronal activities are collectively organized to underpin a persistent memory-guided behavior remains unknown. To investigate this, we simultaneously monitored the prefrontal cortex, nucleus accumbens, amygdala, hippocampus, and ventral tegmental area (VTA) of the mouse brain from initial recall to post-extinction renewal of a memory involving cocaine experience. We uncover a higher-order pattern of short-lived beta-frequency (15-25 Hz) activities that are transiently coordinated across these networks during memory retrieval. The output of a divergent pathway from upstream VTA glutamatergic neurons, paced by a slower (4-Hz) oscillation, actuates this multi-network beta-band coactivation; its closed-loop phase-informed suppression prevents renewal of cocaine-biased behavior. Binding brain-distributed neural activities in this temporally structured manner may constitute an organizational principle of robust memory expression.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

The medial septum controls hippocampal supra-theta oscillations.

Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.

Over and above frequency: Gamma oscillations as units of neural circuit operations.

Gamma oscillations (∼30-150 Hz) are widespread correlates of neural circuit functions. These network activity patterns have been described across multiple animal species, brain structures, and behaviors, and are usually identified based on their spectral peak frequency. Yet, despite intensive investigation, whether gamma oscillations implement causal mechanisms of specific brain functions or represent a general dynamic mode of neural circuit operation remains unclear. In this perspective, we review recent advances in the study of gamma oscillations toward a deeper understanding of their cellular mechanisms, neural pathways, and functional roles. We discuss that a given gamma rhythm does not per se implement any specific cognitive function but rather constitutes an activity motif reporting the cellular substrates, communication channels, and computational operations underlying information processing in its generating brain circuit. Accordingly, we propose shifting the attention from a frequency-based to a circuit-level definition of gamma oscillations.

Adult-born dentate granule cells promote hippocampal population sparsity.

The dentate gyrus (DG) gates neocortical information flow to the hippocampus. Intriguingly, the DG also produces adult-born dentate granule cells (abDGCs) throughout the lifespan, but their contribution to downstream firing dynamics remains unclear. Here, we show that abDGCs promote sparser hippocampal population spiking during mnemonic processing of novel stimuli. By combining triple-(DG-CA3-CA1) ensemble recordings and optogenetic interventions in behaving mice, we show that abDGCs constitute a subset of high-firing-rate neurons with enhanced activity responses to novelty and strong modulation by theta oscillations. Selectively activating abDGCs in their 4-7-week post-birth period increases sparsity of hippocampal population patterns, whereas suppressing abDGCs reduces this sparsity, increases principal cell firing rates and impairs novel object recognition with reduced dimensionality of the network firing structure, without affecting single-neuron spatial representations. We propose that adult-born granule cells transiently support sparser hippocampal population activity structure for higher-dimensional responses relevant to effective mnemonic information processing.

Brain neural patterns and the memory function of sleep.

Sleep is crucial for healthy cognition, including memory. The two main phases of sleep, REM (rapid eye movement) and non-REM sleep, are associated with characteristic electrophysiological patterns that are recorded using surface and intracranial electrodes. These patterns include sharp-wave ripples, cortical slow oscillations, delta waves, and spindles during non-REM sleep and theta oscillations during REM sleep. They reflect the precisely timed activity of underlying neural circuits. Here, we review how these electrical signatures have been guiding our understanding of the circuits and processes sustaining memory consolidation during sleep, focusing on hippocampal theta oscillations and sharp-wave ripples and how they coordinate with cortical patterns. Finally, we highlight how these brain patterns could also sustain sleep-dependent homeostatic processes and evoke several potential future directions for research on the memory function of sleep.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

The nonsinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single-cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time series using masked empirical mode decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency, and phase) with instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase grid space, makes it possible to compare cycles of different durations and shapes. "Normalized shapes" can then be constructed with high temporal detail while accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks nonsinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average yet exhibit high variability on a cycle-by-cycle basis. We show how principal component analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration, and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of inquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.NEW & NOTEWORTHY We propose a novel analysis approach quantifying nonsinusoidal waveform shape. The approach isolates oscillations with empirical mode decomposition before waveform shape is quantified using phase-aligned instantaneous frequency. This characterizes the full shape profile of individual cycles while accounting for between-cycle differences in duration, amplitude, and timing. We validated in simulations before applying to identify a range of data-driven nonsinusoidal shape motifs in hippocampal theta oscillations.

EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python.

The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature analysis. These implementations are supported by online documentation containing a range of practical tutorials.

An emergent neural coactivity code for dynamic memory.

Neural correlates of external variables provide potential internal codes that guide an animal's behavior. Notably, first-order features of neural activity, such as single-neuron firing rates, have been implicated in encoding information. However, the extent to which higher-order features, such as multineuron coactivity, play primary roles in encoding information or secondary roles in supporting single-neuron codes remains unclear. Here, we show that millisecond-timescale coactivity among hippocampal CA1 neurons discriminates distinct, short-lived behavioral contingencies. This contingency discrimination was unrelated to the tuning of individual neurons, but was instead an emergent property of their coactivity. Contingency-discriminating patterns were reactivated offline after learning, and their reinstatement predicted trial-by-trial memory performance. Moreover, optogenetic suppression of inputs from the upstream CA3 region during learning impaired coactivity-based contingency information in the CA1 and subsequent dynamic memory retrieval. These findings identify millisecond-timescale coactivity as a primary feature of neural firing that encodes behaviorally relevant variables and supports memory retrieval.

Integrating new memories into the hippocampal network activity space.

By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.

Spike sorting with Gaussian mixture models.

The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant features using a combination of common techniques (e.g., principal components, wavelets) and GMM fitting parameters (e.g., Gaussian distances). Then, we developed an approach to perform unsupervised clustering using GMMs, estimating cluster properties in a data-driven way. We found the proposed GMM-based framework outperforms previously established methods in simulated and real extracellular recordings. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface to run our algorithm, which allows manual adjustments.

A Hippocampus-Accumbens Tripartite Neuronal Motif Guides Appetitive Memory in Space.

Retrieving and acting on memories of food-predicting environments are fundamental processes for animal survival. Hippocampal pyramidal cells (PYRs) of the mammalian brain provide mnemonic representations of space. Yet the substrates by which these hippocampal representations support memory-guided behavior remain unknown. Here, we uncover a direct connection from dorsal CA1 (dCA1) hippocampus to nucleus accumbens (NAc) that enables the behavioral manifestation of place-reward memories. By monitoring neuronal ensembles in mouse dCA1→NAc pathway, combined with cell-type selective optogenetic manipulations of input-defined postsynaptic neurons, we show that dCA1 PYRs drive NAc medium spiny neurons and orchestrate their spiking activity using feedforward inhibition mediated by dCA1-connected parvalbumin-expressing fast-spiking interneurons. This tripartite cross-circuit motif supports spatial appetitive memory and associated NAc assemblies, being independent of dorsal subiculum and dispensable for both spatial novelty detection and reward seeking. Our findings demonstrate that the dCA1→NAc pathway instantiates a limbic-motor interface for neuronal representations of space to promote effective appetitive behavior.

Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior.

Theta oscillations reflect rhythmic inputs that continuously converge to the hippocampus during exploratory and memory-guided behavior. The theta-nested operations that organize hippocampal spiking could either occur regularly from one cycle to the next or be tuned on a cycle-by-cycle basis. To resolve this, we identified spectral components nested in individual theta cycles recorded from the mouse CA1 hippocampus. Our single-cycle profiling revealed theta spectral components associated with different firing modulations and distinguishable ensembles of principal cells. Moreover, novel co-firing patterns of principal cells in theta cycles nesting mid-gamma oscillations were the most strongly reactivated in subsequent offline sharp-wave/ripple events. Finally, theta-nested spectral components were differentially altered by behavioral stages of a memory task; the 80-Hz mid-gamma component was strengthened during learning, whereas the 22-Hz beta, 35-Hz slow gamma, and 54-Hz mid-gamma components increased during retrieval. We conclude that cycle-to-cycle variability of theta-nested spectral components allows parsing of theta oscillations into transient operating modes with complementary mnemonic roles.

Extracting information from the shape and spatial distribution of evoked potentials.

BACKGROUND: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. NEW METHOD: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses. RESULTS: Using simulations and real data from four experiments, we show that the proposed approach outperforms standard supervised analyses based on peak amplitude estimation. Moreover, the method can extract information using the raw data from all recorded channels using no a priori knowledge or pre-processing steps. COMPARISON WITH EXISTING METHOD(S): We show that traditional approaches often disregard important features of the signal such as the shape of EEG waveforms. Also, other approaches often require some form of a priori knowledge for feature selection and lead to problems of multiple comparisons. CONCLUSIONS: This approach offers a new and complementary framework to design experiments that go beyond the traditional analyses of ERPs. Potentially, it allows a wide usage beyond basic research; such as for clinical diagnosis, brain-machine interfaces, and neurofeedback applications requiring single-trial analyses.

Extracting information in spike time patterns with wavelets and information theory.

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information.

Beta2 oscillations (23-30 Hz) in the mouse hippocampus during novel object recognition.

The oscillatory activity of hippocampal neuronal networks is believed to play a role in memory acquisition and consolidation. Particular focus has been given to characterising theta (4-12 Hz), gamma (40-100 Hz) and ripple (150-250 Hz) oscillations. Beyond these well-described network states, few studies have investigated hippocampal beta2 (23-30 Hz) activity in vivo and its link to behaviour. A previous sudy showed that the exploration of novel environments may lead to the appearance of beta2 oscillations in the mouse hippocampus. In the present study we characterised hippocampal beta2 oscillations in mice during an object recognition task. We found prominent bursts of beta2 oscillations in the beginning of novel exploration sessions (four new objects), which could be readily observed by spectral analysis and visual inspection of local field potentials. Beta2 modulated hippocampal but not neocortical neurons and its power decreased along the session. We also found increased beta2 power in the beginning of a second exploration session performed 24 h later in a slightly modified environment (two new, two familiar objects), but to a lesser extent than in the first session. However, the increase in beta2 power in the second exploration session became similar to the first session when we pharmacologically impaired object recognition in a new set of experiments performed 1 week later. Our results suggest that hippocampal beta2 activity is associated with a dynamic network state tuned for novelty detection and which may allow new learning to occur.

Local field potentials in the gustatory cortex carry taste information.

It has been recently shown that local field potentials (LFPs) from the auditory and visual cortices carry information about sensory stimuli, but whether this is a universal property of sensory cortices remains to be determined. Moreover, little is known about the temporal dynamics of sensory information contained in LFPs following stimulus onset. Here we investigated the time course of the amount of stimulus information in LFPs and spikes from the gustatory cortex of awake rats subjected to tastants and water delivery on the tongue. We found that the phase and amplitude of multiple LFP frequencies carry information about stimuli, which have specific time courses after stimulus delivery. The information carried by LFP phase and amplitude was independent within frequency bands, since the joint information exhibited neither synergy nor redundancy. Tastant information in LFPs was also independent and had a different time course from the information carried by spikes. These findings support the hypothesis that the brain uses different frequency channels to dynamically code for multiple features of a stimulus.