Temporal Dynamics of Distinct CA1 Cell Populations during Unconscious State Induced by Ketamine

Ketamine is a widely used dissociative anesthetic which can induce some psychotic-like symptoms and memory deficits in some patients during the post-operative period. To understand its effects on neural population dynamics in the brain, we employed large-scale in vivo ensemble recording techniques to monitor the activity patterns of simultaneously recorded hippocampal CA1 pyramidal cells and various interneurons during several conscious and unconscious states such as awake rest, running, slow wave sleep, and ketamine-induced anesthesia. Our analyses reveal that ketamine induces distinct oscillatory dynamics not only in pyramidal cells but also in at least seven different types of CA1 interneurons including putative basket cells, chandelier cells, bistratified cells, and O-LM cells. These emergent unique oscillatory dynamics may very well reflect the intrinsic temporal relationships within the CA1 circuit. It is conceivable that systematic characterization of network dynamics may eventually lead to better understanding of how ketamine induces unconsciousness and consequently alters the conscious mind

Phase Coupled Firing of Prefrontal Parvalbumin Interneuron With High Frequency Oscillations

The prefrontal cortex (PFC) plays a central role in executive functions and inhibitory control over many cognitive behaviors. Dynamic changes in local field potentials (LFPs), such as gamma oscillation, have been hypothesized to be important for attentive behaviors and modulated by local interneurons such as parvalbumin (PV) cells. However, the precise relationships between the firing patterns of PV interneurons and temporal dynamics of PFC activities remains elusive. In this study, by combining in vivo electrophysiological recordings with optogenetics, we investigated the activities of prefrontal PV interneurons and categorized them into three subtypes based on their distinct firing rates under different behavioral states. Interestingly, all the three subtypes of interneurons showed strong phase-locked firing to cortical high frequency oscillations (HFOs), but not to theta or gamma oscillations, despite of behavior states. Moreover, we showed that sustained optogenetic stimulation (over a period of 10 s) of PV interneurons can consequently modulate the activities of local pyramidal neurons. Interestingly, such optogenetic manipulations only showed moderate effects on LFPs in the PFC. We conclude that prefrontal PV interneurons are consist of several subclasses of cells with distinct state-dependent modulation of firing rates, selectively coupled to HFOs

Large-scale neural ensemble recording in the brains of freely behaving mice

Abstract With the availability of sophisticated genetic techniques, the mouse is a valuable mammalian model to study the molecular and cellular basis of cognitive behaviors. However, the small size of mice makes it difficult for a systematic investigation of activity patterns of neural networks in vivo. Here we report the development and construction of a high-density ensemble recording array with up to 128-recording channels that can be formatted as single electrodes, stereotrodes, or tetrodes. This high-density recording array is capable of recording from hundreds of individual neurons simultaneously in the hippocampus of the freely behaving mice. This large-scale in vivo ensemble recording techniques, once coupled with mouse genetics, should be valuable to the study of complex relationship between the genes, neural network, and cognitive behaviors

Prediction of rat behavior outcomes in memory tasks using functional connections among neurons.

BACKGROUND: Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved. METHODOLOGY/PRINCIPAL FINDINGS: This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively. CONCLUSIONS/SIGNIFICANCE: The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior

Ensemble <i>in vivo</i> recording in the CA1 region of the mouse hippocampus.

<p>(<b>A</b>) Histological confirmation of the electrodes position. The top panel shows the atlas of the mouse brain, the brown bars illustrate the position of electrode arrays with the tips in the pyramidal cell layer. The lower panel shows Nissl staining in a hippocampal slice; the small holes indicating the actual position of the electrode bundle marked by a small amount of current. (<b>B</b>) The characteristic oscillations also confirm the recording happened in the hippocampal CA1 region. The top panel shows an example of LFP recorded from one channel during SWS, and the filtered LFP shows high-frequency ripple (100–250 Hz). The middle and lower panels show the LFP recorded when the animal was in REM and running, and LFP_theta indicates the characteristic theta oscillations (4–12 Hz). Vertical scale bar represents 500 mV for LFP, horizontal scale bar represents 0.1 sec time. (<b>C</b>, <b>D</b>) Stable recordings were confirmed as judged by the waveforms of recorded cells at the beginning (left subpanel), during (middle), and end (right) of the experiments. One representative putative pyramidal cell (<b>C</b>) and one representative putative interneuron (<b>D</b>) are shown here. The waveforms were plotted from a 100-sec recording in the beginning (upper row) and end (lower row) of recordings.</p

Properties of type-4 interneurons (putative O-LM cell).

<p>(<b>A</b>) Spike rasters of the putative O-LM cell (red) and simultaneously recorded pyramidal cells (blue) after ketamine injection, during running and slow-wave sleep are shown (left subpanels). The cross-correlation functions of the interneuron and pyramidal cell joint-activities are plotted on the right. The black line is the cross-correlation during the awake rest state. This unit has negative correlation with pyramidal cells at time 0 in the cross-correlation plot (upper right panel). (<b>B</b>) The basic properties of this O-LM cell. The most upper left panel shows the average spike waveforms. The middle left panel illustrates that this unit has strongly tendency to fire in the valleys of theta oscillations (p<0.001, Rayleigth's test). The lower left panel shows the firing rate is significantly decreased during ripple epochs. (<b>C</b>) Autocorrelograms in different states: after ketamine injection, during running, and during sleep respectively. Autocorrelograms are shown in two time scales: 0–200 ms and 0–1.5 sec.</p

Properties of type-5 interneurons.

<p>(<b>A</b>) Spike rasters of silmultanously recorded pyramidal cells (blue) and type-5 interneuron (an example unit plotted is in red, and other type-5 interneurons in orange) after ketamine injection, during running and slow-wave sleep are shown (left subpanels). The cross-correlation functions of the interneuron and pyramidal cell joint-activities are plotted on the right. The black line is the cross-correlation during the awake rest state. (<b>B</b>) The basic properties of this type-5 unit. The most upper left panel shows the average spike waveforms. Firing probability histograms suggest that the neuronal firing timing showed slight correlation with theta oscillations but was not coupled to ripple oscillations (middle left and lower left panels). (<b>C</b>) Autocorrelograms in different states: after ketamine injection, during running, and during sleep respectively. Autocorrelograms are shown in two time scales: 0–50 ms and 0–1.5 sec. Note the slow oscillatory dynamics-induced by ketamine is evident in both cross-correlation plot (the top right corner plot in A) and the longer time scale autocorrelogram (bottom left corner plot in C).</p

Properties of type-2 interneurons (putative <i>bistratified cells</i>).

<p>(<b>A</b>) Spike rasters of silmultanously recorded pyramidal cells (blue) and bistratified cells (an example unit plotted here in red and remaining units in orange) after ketamine injection, during running and slow-wave sleep are shown (left panels). The cross-correlation functions of the interneuron and pyramidal cell joint-activities are plotted on the right. The black line in each plot is the cross-correlation during the awake rest state. (<b>B</b>) The basic properties of this example basket cell. The most upper left panel shows the average spike waveforms. The middle left panel illustrates the spike timing of this unit is negatively coupled with theta oscillations (p<0.001, Rayleigth's test). The lower left panel shows the significantly elevated firing rate throughout the entire ripple epochs. (<b>C</b>) Autocorrelograms in different states: after ketamine injection, during running, and during sleep respectively. Autocorrelograms are shown in two time scales: 0–200 ms and 0–1.5 sec.</p