Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia.

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

Modulation of cortical slow oscillations and complexity across anesthesia levels.

The ability of different groups of cortical neurons to engage in causal interactions that are at once differentiated and integrated results in complex dynamic patterns. Complexity is low during periods of unconsciousness (deep sleep, anesthesia, unresponsive wakefulness syndrome) in which the brain tends to generate a stereotypical pattern consisting of alternating active and silent periods of neural activity-slow oscillations- and is high during wakefulness. But how is cortical complexity built up? Is it a continuum? An open question is whether cortical complexity can vary within the same brain state. Here we recorded with 32-channel multielectrode arrays from the cortical surface of the mouse and used both spontaneous dynamics (wave propagation entropy and functional complexity) and a perturbational approach (a variation of the perturbation complexity index) to measure complexity at different anesthesia levels. Variations in anesthesia level within the bistable regime of slow oscillations (0.1-1.5 Hz) resulted in a modulation of the slow oscillation frequency. Both perturbational and spontaneous complexity increased with decreasing anesthesia levels, in correlation with the decrease in coherence of the underlying network. Changes in complexity level are related to, but not dependent on, changes in excitability. We conclude that cortical complexity can vary within a single brain state dominated by slow oscillations, building up to the higher complexity associated with consciousness.

Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses.

In the neocortex, synaptic inhibition shapes all forms of spontaneous and sensory evoked activity. Importantly, inhibitory transmission is highly plastic, but the functional role of inhibitory synaptic plasticity is unknown. In the mouse barrel cortex, activation of layer (L) 2/3 pyramidal neurons (PNs) elicits strong feedforward inhibition (FFI) onto L5 PNs. We find that FFI involving parvalbumin (PV)-expressing cells is strongly potentiated by postsynaptic PN burst firing. FFI plasticity modifies the PN excitation-to-inhibition (E/I) ratio, strongly modulates PN gain, and alters information transfer across cortical layers. Moreover, our LTPi-inducing protocol modifies firing of L5 PNs and alters the temporal association of PN spikes to γ-oscillations both in vitro and in vivo. All of these effects are captured by unbalancing the E/I ratio in a feedforward inhibition circuit model. Altogether, our results indicate that activity-dependent modulation of perisomatic inhibitory strength effectively influences the participation of single principal cortical neurons to cognition-relevant network activity.

Analysis Pipeline for Extracting Features of Cortical Slow Oscillations.

Cortical slow oscillations (≲1 Hz) are an emergent property of the cortical network that integrate connectivity and physiological features. This rhythm, highly revealing of the characteristics of the underlying dynamics, is a hallmark of low complexity brain states like sleep, and represents a default activity pattern. Here, we present a methodological approach for quantifying the spatial and temporal properties of this emergent activity. We improved and enriched a robust analysis procedure that has already been successfully applied to both in vitro and in vivo data acquisitions. We tested the new tools of the methodology by analyzing the electrocorticography (ECoG) traces recorded from a custom 32-channel multi-electrode array in wild-type isoflurane-anesthetized mice. The enhanced analysis pipeline, named SWAP (Slow Wave Analysis Pipeline), detects Up and Down states, enables the characterization of the spatial dependency of their statistical properties, and supports the comparison of different subjects. The SWAP is implemented in a data-independent way, allowing its application to other data sets (acquired from different subjects, or with different recording tools), as well as to the outcome of numerical simulations. By using the SWAP, we report statistically significant differences in the observed slow oscillations (SO) across cortical areas and cortical sites. Computing cortical maps by interpolating the features of SO acquired at the electrode positions, we give evidence of gradients at the global scale along an oblique axis directed from fronto-lateral toward occipito-medial regions, further highlighting some heterogeneity within cortical areas. The results obtained using the SWAP will be essential for producing data-driven brain simulations. A spatial characterization of slow oscillations will also trigger a discussion on the role of, and the interplay between, the different regions in the cortex, improving our understanding of the mechanisms of generation and propagation of delta rhythms and, more generally, of cortical properties.

Strong preference for autaptic self-connectivity of neocortical PV interneurons facilitates their tuning to γ-oscillations.

Parvalbumin (PV)-positive interneurons modulate cortical activity through highly specialized connectivity patterns onto excitatory pyramidal neurons (PNs) and other inhibitory cells. PV cells are autoconnected through powerful autapses, but the contribution of this form of fast disinhibition to cortical function is unknown. We found that autaptic transmission represents the most powerful inhibitory input of PV cells in neocortical layer V. Autaptic strength was greater than synaptic strength onto PNs as a result of a larger quantal size, whereas autaptic and heterosynaptic PV-PV synapses differed in the number of release sites. Overall, single-axon autaptic transmission contributed to approximately 40% of the global inhibition (mostly perisomatic) that PV interneurons received. The strength of autaptic transmission modulated the coupling of PV-cell firing with optogenetically induced γ-oscillations, preventing high-frequency bursts of spikes. Autaptic self-inhibition represents an exceptionally large and fast disinhibitory mechanism, favoring synchronization of PV-cell firing during cognitive-relevant cortical network activity.

Possible Implication of the CA2 Hippocampal Circuit in Social Cognition Deficits Observed in the Neuroligin 3 Knock-Out Mouse, a Non-Syndromic Animal Model of Autism.

Autism spectrum disorders (ASDs) comprise a heterogeneous group of neuro-developmental abnormalities with a strong genetic component, characterized by deficits in verbal and non-verbal communication, impaired social interactions, and stereotyped behaviors. In a small percentage of cases, ASDs are associated with alterations of genes involved in synaptic function. Among these, relatively frequent are mutations/deletions of genes encoding for neuroligins (NLGs). NLGs are postsynaptic adhesion molecules that, interacting with their presynaptic partners neurexins, ensure the cross talk between pre- and postsynaptic specializations and synaptic stabilization, a condition needed for maintaining a proper excitatory/inhibitory balance within local neuronal circuits. We have focused on mice lacking NLG3 (NLG3 knock-out mice), animal models of a non-syndromic form of autism, which exhibit deficits in social behavior reminiscent of those found in ASDs. Among different brain areas involved in social cognition, the CA2 region of the hippocampus has recently emerged as a central structure for social memory processing. Here, in vivo recordings from anesthetized animals and ex vivo recordings from hippocampal slices have been used to assess the dynamics of neuronal signaling in the CA2 hippocampal area. In vivo experiments from NLG3-deficient mice revealed a selective impairment of spike-related slow wave activity in the CA2 area and a significant reduction in oscillatory activity in the theta and gamma frequencies range in both CA2 and CA3 regions of the hippocampus. These network effects were associated with an increased neuronal excitability in the CA2 hippocampal area. Ex vivo recordings from CA2 principal cells in slices obtained from NLG3 knock-out animals unveiled a strong excitatory/inhibitory imbalance in this region accompanied by a strong reduction of perisomatic inhibition mediated by CCK-containing GABAergic interneurons. These data clearly suggest that the selective alterations in network dynamics and GABAergic signaling observed in the CA2 hippocampal region of NLG3 knock-out mice may account for deficits in social memory reminiscent of those observed in autistic patients.

Autaptic self-inhibition of cortical GABAergic neurons: synaptic narcissism or useful introspection?

Fast synaptic inhibition sculpts all forms of cortical activity by means of a specialized connectivity pattern between highly heterogeneous inhibitory interneurons and principal excitatory cells. Importantly, inhibitory neurons connect also to each other extensively, following a detailed blueprint, and, indeed, specific forms of disinhibition affect important behavioral functions. Here we discuss a peculiar form of cortical disinhibition: the massive autaptic self-inhibition of parvalbumin-(PV) positive basket cells. Despite being described long ago, autaptic inhibition onto PV basket cells is rarely included in cortical circuit diagrams, perhaps because of its still elusive function. We propose here a potential dual role of autaptic feedback inhibition in temporally coordinating PV basket cells during cortical network activity.

Direct alteration of a specific inhibitory circuit of the hippocampus by antidepressants.

Correct brain functioning relies on the precise activity of a myriad of synapses assembling neurons in complex networks. In the hippocampus, highly diverse inhibitory circuits differently govern several physiologically relevant network activities. Particularly, perisomatic inhibition provided by specific interneurons was proposed to control emotional states, and could therefore be affected by mood disorders and their therapy. We found that both chronic and acute administration of two major antidepressants, imipramine and fluoxetine, strongly and directly altered GABA-mediated (GABAergic) hippocampal neurotransmission in mice and rats, independently of their effects on amine reuptake systems. These drugs affected GABA release from synapses formed by fast-spiking cells, but not interneurons expressing cannabinoid receptor type 1, resulting in the disruption of γ oscillations. This differential effect, shared by two types of antidepressants, suggests a new mechanism of action of these medications, and a possible role of perisomatic inhibition in depressive disorders.

Viewing strategy of Cebus monkeys during free exploration of natural images.

Humans and other primates move their eyes several times per second to foveate at different locations of a visual scene. What features of a scene guide eye movements in natural vision? We recorded eye movements of three monkeys during free exploration of natural scenes and propose a simple model to explain their dynamics. We use the spatial clustering of fixation positions to define the monkeys' subjective regions-of-interest (ROI) in natural scenes. For most images the subjective ROIs match significantly the computed saliency of the natural scene, except when the image contains human or primate faces. We also investigated the temporal sequence of eye movements by computing the probability that a fixation will be made inside or outside of the ROI, given the current fixation position. We fitted a Markov chain model to the sequence of fixation positions, and find that fixations made inside a ROI are more likely to be followed by another fixation in the same ROI. This is true, independent of the image saliency in the area of the ROI. Our results show that certain regions in a natural scene are explored locally before directing the focus to another local region. This strategy could allow for quick integration of the visual features that constitute an object, and efficient segmentation of objects from other objects and the background during free viewing of natural scenes.

Effectiveness of systematic spike dithering depends on the precision of cortical synchronization.

Spike synchronization is a candidate mechanism of cortical information processing. The widely used method of dithering randomly perturbs the spike times of experimental data to construct a distribution of coincidence counts enabling an assessment of the significance of the original data set. The precision of any existing synchrony, however, is limited by the biophysics of the neural system and detection methods are designed to tolerate an adjustable temporal spread. Previous works have independently studied the detectability of jittered spike coincidences and the destruction of precise coincidences by dithering. Here we derive for the first time how dithering interacts with temporally jittered coincidences. We demonstrate that the probability of detecting a spike coincidence characteristically decays with the applied dither interval. This unique relationship enables us to determine the precision of synchronization in cortical spike data of a freely viewing monkey based on the analysis for a single setting of tolerated temporal spread.

Robustness of the significance of spike synchrony with respect to sorting errors.

The aim of spike sorting is to reconstruct single unit spike times from extracellular multi-unit recordings. Failure in the identification of a spike (false negative) or assignment of a spike to a wrong unit (false positive) are typical examples of sorting errors. Their influence on cross-correlation measures has been addressed and it has been shown that correlation analysis of multi-unit signals may lead to incorrect interpretations. We formulate a model to study the influence of sorting errors on the significance of synchronized spikes, and thus are able to study if and how the significance changes in case of imperfect sorting. Here we explore the case of pairwise analysis of simultaneously recorded neurons. Interestingly, a decrease in the significance is observed in the presence of false positives, as well as for false negatives. Furthermore, false negative errors reduce the significance of synchronized spikes more strongly than false positives. Thus, conservative sorting strategies have a stronger tendency to lead to a loss of the significance of synchronization. We demonstrate that a detailed understanding of sorting techniques and their possible effects on subsequent data analyses is important in order to rule out inconsistencies in the interpretation of results.