Hindiael Belchior

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.

Spike avalanches exhibit universal dynamics across the sleep-wake cycle.

Background Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. Methodology/Principal Findings To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus. Conclusions/Significance Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

On high-frequency field oscillations (>100 Hz) and the spectral leakage of spiking activity.

Recent reports converge to the idea that high-frequency oscillations in local field potentials (LFPs) represent multiunit activity. In particular, the amplitude of LFP activity above 100 Hz—commonly referred to as “high-gamma” or “epsilon” band—was found to correlate with firing rate. However, other studies suggest the existence of true LFP oscillations at this frequency range that are different from the well established ripple oscillations. Using multisite recordings of the hippocampus of freely moving rats, we show here that high-frequency LFP oscillations can represent either the spectral leakage of spiking activity or a genuine rhythm, depending on recording location. Both spike-leaked, spurious activity and true fast oscillations couple to theta phase; however, the two phenomena can be clearly distinguished by other key features, such as preferred coupling phase and spectral signatures. Our results argue against the idea that all high-frequency LFP activity stems from spike contamination and suggest avoiding defining brain rhythms solely based on frequency range.

Cross-modal responses in the primary visual cortex encode complex objects and correlate with tactile discrimination

Cortical areas that directly receive sensory inputs from the thalamus were long thought to be exclusively dedicated to a single modality, originating separate labeled lines. In the past decade, however, several independent lines of research have demonstrated cross-modal responses in primary sensory areas. To investigate whether these responses represent behaviorally relevant information, we carried out neuronal recordings in the primary somatosensory cortex (S1) and primary visual cortex (V1) of rats as they performed whisker-based tasks in the dark. During the free exploration of novel objects, V1 and S1 responses carried comparable amounts of information about object identity. During execution of an aperture tactile discrimination task, tactile recruitment was slower and less robust in V1 than in S1. However, V1 tactile responses correlated significantly with performance across sessions. Altogether, the results support the notion that primary sensory areas have a preference for a given modality but can engage in meaningful cross-modal processing depending on task demand.

Increase in Hippocampal Theta Oscillations during Spatial Decision Making

The processing of spatial and mnemonic information is believed to depend on hippocampal theta oscillations (5–12 Hz). However, in rats both the power and the frequency of the theta rhythm are modulated by locomotor activity, which is a major confounding factor when estimating its cognitive correlates. Previous studies have suggested that hippocampal theta oscillations support decision‐making processes. In this study, we investigated to what extent spatial decision making modulates hippocampal theta oscillations when controlling for variations in locomotion speed. We recorded local field potentials from the CA1 region of rats while animals had to choose one arm to enter for reward (goal) in a four‐arm radial maze. We observed prominent theta oscillations during the decision‐making period of the task, which occurred in the center of the maze before animals deliberately ran through an arm toward goal location. In speed‐controlled analyses, theta power and frequency were higher during the decision period when compared to either an intertrial delay period (also at the maze center), or to the period of running toward goal location. In addition, theta activity was higher during decision periods preceding correct choices than during decision periods preceding incorrect choices. Altogether, our data support a cognitive function for the hippocampal theta rhythm in spatial decision making.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent . Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

New Insights into the Role of Respiratory Inputs in Hippocampal Oscillations

The mammalian hippocampus generates oscillatory patterns as a result of synchronized activity of its neuronal populations. Over the last several years, it has been suggested that such rhythmic activity could have a mechanistic role for neural computations, constituting a temporal framework for

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 Shannons 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.

Reactivation of time cell sequences in the hippocampus

The temporal order of an experience is a fundamental property of episodic memories, yet the mechanism for the consolidation of temporal sequences in long-term memory is still unknown. A potential mechanism for memory consolidation depends on the reactivation of neuronal sequences in the hippocampus. Despite abundant evidence of sequence reactivation in the formation of spatial memory, the reactivation of hippocampal neuronal sequences carrying non-spatial information has been much less explored. In this work, we recorded the activity of time cell sequences while rats performed multiple 15-s treadmill runnings during the intertrial intervals of a spatial alternation memory task. We observed forward and reverse reactivations of time cell sequences often occurring during sharp-wave ripple events following reward consumption. Surprisingly, the reactivation events specifically engaged cells coding temporal information. The reactivation of time cell sequences may thus reflect the organization of temporal order required for episodic memory formation.

Rats hippocampal field potentials feature extraction of wake and sleep stages in Euclidean space

This paper presents a new methodology of feature extraction of sleep and wake stages of a freely behaving rat based on Continuous Wavelet Transform (CWT). The automatic separation of those stages is very useful for experiments related to learning and memory consolidation since recent scientific evidence indicates that sleep is strongly involved with offline reprocessing of acquired information during waking. Our approach transforms hippocampal Local Field Potentials (LFP) in data vectors that describe the energy distribution pattern of the signal on scaled Morlet wavelets projections. Results indicate that the mathematical analysis used in this work can sensibly describe brain signal patterns that correlate to states of behaviour and that our method can be used for a wider range of applications in neuroscience research.