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.

Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes.

BACKGROUND: Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain. METHODS: We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects. RESULTS: APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers. CONCLUSIONS: Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.

Learning differentially shapes prefrontal and hippocampal activity during classical conditioning.

The ability to use sensory cues to inform goal-directed actions is a critical component of behavior. To study how sounds guide anticipatory licking during classical conditioning, we employed high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC) in mice. CA1 and PFC neurons undergo distinct learning-dependent changes at the single-cell level and maintain representations of cue identity at the population level. In addition, reactivation of task-related neuronal assemblies during hippocampal awake Sharp-Wave Ripples (aSWRs) changed within individual sessions in CA1 and over the course of multiple sessions in PFC. Despite both areas being highly engaged and synchronized during the task, we found no evidence for coordinated single cell or assembly activity during conditioning trials or aSWR. Taken together, our findings support the notion that persistent firing and reactivation of task-related neural activity patterns in CA1 and PFC support learning during classical conditioning.

Beta2 Oscillations in Hippocampal-Cortical Circuits During Novelty Detection.

Novelty detection is a core feature of behavioral adaptation and involves cascades of neuronal responses-from initial evaluation of the stimulus to the encoding of new representations-resulting in the behavioral ability to respond to unexpected inputs. In the past decade, a new important novelty detection feature, beta2 (~20-30 Hz) oscillations, has been described in the hippocampus (HC). However, the interactions between beta2 and the hippocampal network are unknown, as well as the role-or even the presence-of beta2 in other areas involved with novelty detection. In this work, we combined multisite local field potential (LFP) recordings with novelty-related behavioral tasks in mice to describe the oscillatory dynamics associated with novelty detection in the CA1 region of the HC, parietal cortex, and mid-prefrontal cortex. We found that transient beta2 power increases were observed only during interaction with novel contexts and objects, but not with familiar contexts and objects. Also, robust theta-gamma phase-amplitude coupling was observed during the exploration of novel environments. Surprisingly, bursts of beta2 power had strong coupling with the phase of delta-range oscillations. Finally, the parietal and mid-frontal cortices had strong coherence with the HC in both theta and beta2. These results highlight the importance of beta2 oscillations in a larger hippocampal-cortical circuit, suggesting that beta2 plays a role in the mechanism for detecting and modulating behavioral adaptation to novelty.

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.

On Information Metrics for Spatial Coding.

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice.

Asymmetry of the temporal code for space by hippocampal place cells.

Hippocampal place cells convey spatial information through spike frequency ("rate coding") and spike timing relative to the theta phase ("temporal coding"). Whether rate and temporal coding are due to independent or related mechanisms has been the subject of wide debate. Here we show that the spike timing of place cells couples to theta phase before major increases in firing rate, anticipating the animal's entrance into the classical, rate-based place field. In contrast, spikes rapidly decouple from theta as the animal leaves the place field and firing rate decreases. Therefore, temporal coding has strong asymmetry around the place field center. We further show that the dynamics of temporal coding along space evolves in three stages as the animal traverses the place field: phase coupling, sharp precession and phase decoupling. These results suggest that independent mechanisms may govern rate and temporal coding.

Dream characteristics in a Brazilian sample: an online survey focusing on lucid dreaming.

During sleep, humans experience the offline images and sensations that we call dreams, which are typically emotional and lacking in rational judgment of their bizarreness. However, during lucid dreaming (LD), subjects know that they are dreaming, and may control oneiric content. Dreaming and LD features have been studied in North Americans, Europeans and Asians, but not among Brazilians, the largest population in Latin America. Here we investigated dreams and LD characteristics in a Brazilian sample (n = 3,427; median age = 25 years) through an online survey. The subjects reported recalling dreams at least once a week (76%), and that dreams typically depicted actions (93%), known people (92%), sounds/voices (78%), and colored images (76%). The oneiric content was associated with plans for the upcoming days (37%), memories of the previous day (13%), or unrelated to the dreamer (30%). Nightmares usually depicted anxiety/fear (65%), being stalked (48%), or other unpleasant sensations (47%). These data corroborate Freudian notion of day residue in dreams, and suggest that dreams and nightmares are simulations of life situations that are related to our psychobiological integrity. Regarding LD, we observed that 77% of the subjects experienced LD at least once in life (44% up to 10 episodes ever), and for 48% LD subjectively lasted less than 1 min. LD frequency correlated weakly with dream recall frequency (r = 0.20, p < 0.01), and LD control was rare (29%). LD occurrence was facilitated when subjects did not need to wake up early (38%), a situation that increases rapid eye movement sleep (REMS) duration, or when subjects were under stress (30%), which increases REMS transitions into waking. These results indicate that LD is relatively ubiquitous but rare, unstable, difficult to control, and facilitated by increases in REMS duration and transitions to wake state. Together with LD incidence in USA, Europe and Asia, our data from Latin America strengthen the notion that LD is a general phenomenon of the human species.

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

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

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

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

Exploring preprocessing techniques in a three-class brain-machine interface.

In this work, we implemented a brain-machine interface (BMI) based on electroencephalographic (EEG) signals and used it to classify and separate three types of mental tasks: motor imagery with the right and left hands and simple arithmetic sums. In order to reduce dimension of variables and increase classification power, we used both PCA and ICA based algorithms for spectral analysis. Our results show that we were no able to reduce dimension without reducing classification performance.