Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse

Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.

Oscillatory Waveform Shape and Temporal Spike Correlations Differ across Bat Frontal and Auditory Cortex

Neural oscillations are associated with diverse computations in the mammalian brain. The waveform shape of oscillatory activity measured in the cortex relates to local physiology and can be informative about aberrant or dynamically changing states. However, how waveform shape differs across distant yet functionally and anatomically related cortical regions is largely unknown. In this study, we capitalize on simultaneous recordings of local field potentials (LFPs) in the auditory and frontal cortices of awake, male Carollia perspicillata bats to examine, on a cycle-by-cycle basis, waveform shape differences across cortical regions. We find that waveform shape differs markedly in the fronto-auditory circuit even for temporally correlated rhythmic activity in comparable frequency ranges (i.e., in the delta and gamma bands) during spontaneous activity. In addition, we report consistent differences between areas in the variability of waveform shape across individual cycles. A conceptual model predicts higher spike-spike and spike-LFP correlations in regions with more asymmetric shapes, a phenomenon that was observed in the data: spike-spike and spike-LFP correlations were higher in the frontal cortex. The model suggests a relationship between waveform shape differences and differences in spike correlations across cortical areas. Altogether, these results indicate that oscillatory activity in the frontal and auditory cortex possesses distinct dynamics related to the anatomical and functional diversity of the fronto-auditory circuit.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.

Propagating population activity patterns during spontaneous slow waves in the thalamus of rodents

Slow waves (SWs) represent the most prominent electrophysiological events in the thalamocortical system under anesthesia and during deep sleep. Recent studies have revealed that SWs have complex spatiotemporal dynamics and propagate across neocortical regions. However, it is still unclear whether neuronal activity in the thalamus exhibits similar propagation properties during SWs. Here, we report propagating population activity in the thalamus of ketamine/xylazine-anesthetized rats and mice visualized by high-density silicon probe recordings. In both rodent species, propagation of spontaneous thalamic activity during up-states was most frequently observed in dorsal thalamic nuclei such as the higher order posterior (Po), lateral posterior (LP) or laterodorsal (LD) nuclei. The preferred direction of thalamic activity spreading was along the dorsoventral axis, with over half of the up-states exhibiting a gradual propagation in the ventral-to-dorsal direction. Furthermore, simultaneous neocortical and thalamic recordings collected under anesthesia demonstrated that there is a weak but noticeable interrelation between propagation patterns observed during cortical up-states and those displayed by thalamic population activity. In addition, using chronically implanted silicon probes, we detected propagating activity patterns in the thalamus of naturally sleeping rats during slow-wave sleep. However, in comparison to propagating up-states observed under anesthesia, these propagating patterns were characterized by a reduced rate of occurrence and a faster propagation speed. Our findings suggest that the propagation of spontaneous population activity is an intrinsic property of the thalamocortical network during synchronized brain states such as deep sleep or anesthesia. Additionally, our data implies that the neocortex may have partial control over the formation of propagation patterns within the dorsal thalamus under anesthesia.

Thalamic Foxp2 regulates output connectivity and sensory-motor impairments in a model of Huntington's Disease

BACKGROUND

Huntington's Disease (HD) is a disorder that affects body movements. Altered glutamatergic innervation of the striatum is a major hallmark of the disease. Approximately 30% of those glutamatergic inputs come from thalamic nuclei. Foxp2 is a transcription factor involved in cell differentiation and reported low in patients with HD. However, the role of the Foxp2 in the thalamus in HD remains unexplored.

METHODS

We used two different mouse models of HD, the R6/1 and the HdhQ111 mice, to demonstrate a consistent thalamic Foxp2 reduction in the context of HD. We used in vivo electrophysiological recordings, microdialysis in behaving mice and rabies virus-based monosynaptic tracing to study thalamo-striatal and thalamo-cortical synaptic connectivity in R6/1 mice. Micro-structural synaptic plasticity was also evaluated in the striatum and cortex of R6/1 mice. We over-expressed Foxp2 in the thalamus of R6/1 mice or reduced Foxp2 in the thalamus of wild type mice to evaluate its role in sensory and motor skills deficiencies, as well as thalamo-striatal and thalamo-cortical connectivity in such mouse models.

RESULTS

Here, we demonstrate in a HD mouse model a clear and early thalamo-striatal aberrant connectivity associated with a reduction of thalamic Foxp2 levels. Recovering thalamic Foxp2 levels in the mouse rescued motor coordination and sensory skills concomitant with an amelioration of neuropathological features and with a repair of the structural and functional connectivity through a restoration of neurotransmitter release. In addition, reduction of thalamic Foxp2 levels in wild type mice induced HD-like phenotypes.

CONCLUSIONS

In conclusion, we show that a novel identified thalamic Foxp2 dysregulation alters basal ganglia circuits implicated in the pathophysiology of HD.

Entorhinal cortical delta oscillations drive memory consolidation

Long-term memories are formed by creating stable memory representations via memory consolidation, which mainly occurs during sleep following the encoding of labile memories in the hippocampus during waking. The entorhinal cortex (EC) has intricate connections with the hippocampus, but its role in memory consolidation is largely unknown. Using cell-type- and input-specific in vivo neural activity recordings, here we show that the temporoammonic pathway neurons in the EC, which directly innervate the output area of the hippocampus, exhibit potent oscillatory activities during anesthesia and sleep. Using in vivo individual and populational neuronal activity recordings, we demonstrate that a subpopulation of the temporoammonic pathway neurons, which we termed sleep cells, generate delta oscillations via hyperpolarization-activated cyclic-nucleotide-gated channels during sleep. The blockade of these oscillations significantly impaired the consolidation of hippocampus-dependent memory. Together, our findings uncover a key driver of delta oscillations and memory consolidation that are found in the EC.

Astrocytes adjust the dynamic range of cortical network activity to control modality-specific sensory information processing

Cortical neuron-astrocyte communication in response to peripheral sensory stimulation occurs in a topographic-, frequency-, and intensity-dependent manner. However, the contribution of this functional interaction to the processing of sensory inputs and consequent behavior remains unclear. We investigate the role of astrocytes in sensory information processing at circuit and behavioral levels by monitoring and manipulating astrocytic activity in vivo. We show that astrocytes control the dynamic range of the cortical network activity, optimizing its responsiveness to incoming sensory inputs. The astrocytic modulation of sensory processing contributes to setting the detection threshold for tactile and thermal behavior responses. The mechanism of such astrocytic control is mediated through modulation of inhibitory transmission to adjust the gain and sensitivity of responding networks. These results uncover a role for astrocytes in maintaining the cortical network activity in an optimal range to control behavior associated with specific sensory modalities.

Modeling Parkinson's disease-related symptoms in alpha-synuclein overexpressing mice

BACKGROUND

Intracellular deposition of alpha-synuclein (α-syn) as Lewy bodies and Lewy neurites is a central event in the pathogenesis of Parkinson's disease (PD) and other α-synucleinopathies. Transgenic mouse models overexpressing human α-syn, are useful research tools in preclinical studies of pathogenetic mechanisms. Such mice develop α-syn inclusions as well as neurodegeneration with a topographical distribution that varies depending on the choice of promoter and which form of α-syn that is overexpressed. Moreover, they display motor symptoms and cognitive disturbances that to some extent resemble the human conditions.

PURPOSE

One of the main motives for assessing behavior in these mouse models is to evaluate the potential of new treatment strategies, including their impact on motor and cognitive symptoms. However, due to a high within-group variability with respect to such features, the behavioral studies need to be applied with caution. In this review, we discuss how to make appropriate choices in the experimental design and which tests that are most suitable for the evaluation of PD-related symptoms in such studies.

METHODS

We have evaluated published results on two selected transgenic mouse models overexpressing wild type (L61) and mutated (A30P) α-syn in the context of their validity and utility for different types of behavioral studies.

CONCLUSIONS

By applying appropriate behavioral tests, α-syn transgenic mouse models provide an appropriate experimental platform for studies of symptoms related to PD and other α-synucleinopathies.

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.

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Complexity of isoflurane-induced slow waves reliably determines anesthesia level

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In deep anesthesia, the propagation strictly alternates between front-back-front patterns

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In light anesthesia, there is a continuum of directions and faster propagation

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Local excitability underpins the cortical reorganization in fading anesthesia

Energy-Based Hierarchical Clustering of Cortical Slow Waves in Multi-Electrode Recordings

The recent development of novel multi-electrode recording technologies has revealed the existence of traveling patterns of cortical activity in many species and under different states of awareness. Among these, slow activation waves occurring under sleep and anesthesia have been widely investigated as they provide unique insights into network features such as excitability, connectivity, structure, and dynamics of the cerebral cortex. Such characterization is usually based on clustering methods which are constrained by a priori assumptions as to the number of clusters to be used or rely on wave-by-wave pattern reconstruction. Here, we introduce a new computational tool based on modal analysis of fluid flows which is robustly applied to multivariate electrophysiological data from cortical networks, namely the Energy-based Hierarchical Waves Clustering method (EHWC). EHWC is composed of three main steps: (1) detecting the occurrence of global waves; (2) reducing the data dimensionality via singular value decomposition; (3) clustering hierarchically the singled-out waves. The analysis does not require the single-channel contribution to the waves, which is a typical bottleneck in this kind of analysis due to the unavoidable intrinsic variability of locally recorded activity. For testing and validation, here we used in vivo extracellular recordings from mice cortex under three different levels of anesthesia. As a result, we found slow waves with an increasing number of propagation modes as the anesthesia level decreases, giving an estimate of the increasing complexity of network dynamics. This and other wave's features replicate and extend the findings from previous literature, paving the way to extend the same approach to non-invasive electrophysiological recordings like EEG and fMRI used clinically for the characterization of brain dynamics and clinical stratification in brain lesions.

Control of Brain State Transitions with a Photoswitchable Muscarinic Agonist

The ability to control neural activity is essential for research not only in basic neuroscience, as spatiotemporal control of activity is a fundamental experimental tool, but also in clinical neurology for therapeutic brain interventions. Transcranial-magnetic, ultrasound, and alternating/direct current (AC/DC) stimulation are some available means of spatiotemporal controlled neuromodulation. There is also light-mediated control, such as optogenetics, which has revolutionized neuroscience research, yet its clinical translation is hampered by the need for gene manipulation. As a drug-based light-mediated control, the effect of a photoswitchable muscarinic agonist (Phthalimide-Azo-Iper (PAI)) on a brain network is evaluated in this study. First, the conditions to manipulate M2 muscarinic receptors with light in the experimental setup are determined. Next, physiological synchronous emergent cortical activity consisting of slow oscillations-as in slow wave sleep-is transformed into a higher frequency pattern in the cerebral cortex, both in vitro and in vivo, as a consequence of PAI activation with light. These results open the way to study cholinergic neuromodulation and to control spatiotemporal patterns of activity in different brain states, their transitions, and their links to cognition and behavior. The approach can be applied to different organisms and does not require genetic manipulation, which would make it translational to humans.

Attractor competition enriches cortical dynamics during awakening from anesthesia

Slow oscillations (≲ 1 Hz), a hallmark of slow-wave sleep and deep anesthesia across species, arise from spatiotemporal patterns of activity whose complexity increases as wakefulness is approached and cognitive functions emerge. The arousal process constitutes an open window to the unknown mechanisms underlying the emergence of such dynamical richness in awake cortical networks. Here, we investigate the changes in network dynamics as anesthesia fades out in the rat visual cortex. Starting from deep anesthesia, slow oscillations gradually increase their frequency, eventually expressing maximum regularity. This stage is followed by the abrupt onset of an infra-slow (~0.2 Hz) alternation between sleep-like oscillations and activated states. A population rate model reproduces this transition driven by an increased excitability that brings it to periodically cross a critical point. Based on our model, dynamical richness emerges as a competition between two metastable attractor states, a conclusion strongly supported by the data.

Medium spiny neurons activity reveals the discrete segregation of mouse dorsal striatum

Behavioral studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioral-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.

Up and Down States During Slow Oscillations in Slow-Wave Sleep and Different Levels of Anesthesia

Slow oscillations are a pattern of synchronized network activity generated by the cerebral cortex. They consist of Up and Down states, which are periods of activity interspersed with periods of silence, respectively. However, even when this is a unique dynamic regime of transitions between Up and Down states, this pattern is not constant: there is a range of oscillatory frequencies (0.1-4 Hz), and the duration of Up vs. Down states during the cycles is variable. This opens many questions. Is there a constant relationship between the duration of Up and Down states? How much do they vary across conditions and oscillatory frequencies? Are there different sub regimes within the slow oscillations? To answer these questions, we aimed to explore a concrete aspect of slow oscillations, Up and Down state durations, across three conditions: deep anesthesia, light anesthesia, and slow-wave sleep (SWS), in the same chronically implanted rats. We found that light anesthesia and SWS have rather similar properties, occupying a small area of the Up and Down state duration space. Deeper levels of anesthesia occupy a larger region of this space, revealing that a large variety of Up and Down state durations can emerge within the slow oscillatory regime. In a network model, we investigated the network parameters that can explain the different points within our bifurcation diagram in which slow oscillations are expressed.

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.

Early Disruption of Cortical Sleep-Related Oscillations in a Mouse Model of Dementia With Lewy Bodies (DLB) Expressing Human Mutant (A30P) Alpha-Synuclein

Changes in sleep behavior and sleep-related cortical activity have been reported in conditions associated with abnormal alpha-synuclein (α-syn) expression, in particular Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Notably, changes can occur in patients years before the onset of cognitive decline. Sleep-related network oscillations play a key role in memory function, but how abnormal α-syn impacts the generation of such activity is currently unclear. To determine whether early changes in sleep-related network activity could also be observed, prior to any previously reported cognitive dysfunction, we used mice that over-express human mutant α-syn (A30P). Recordings in vivo were performed under urethane anesthesia in the medial prefrontal cortex (mPFC) and CA1 region of the hippocampus in young male (2.5 – 4 months old) A30P and age-matched wild type (WT) mice. We found that the slow oscillation (SO) < 1 Hz frequency was significantly faster in both the mPFC and hippocampus in A30P mice, and Up-state-associated fast oscillations at beta (20 – 30 Hz) and gamma (30 – 80 Hz) frequencies were delayed relative to the onset of the Up-state. Spindle (8 – 15 Hz) activity in the mPFC was also altered in A30P mice, as spindles were shorter in duration and had reduced density compared to WT. These changes demonstrate that dysregulation of sleep-related oscillations occurs in young A30P mice long before the onset of cognitive dysfunction. Our data suggest that, as seen in patients, changes in sleep-related oscillations are an early consequence of abnormal α-syn aggregation in A30P mice.

Alterations of specific cortical GABAergic circuits underlie abnormal network activity in a mouse model of Down syndrome

Down syndrome (DS) results in various degrees of cognitive deficits. In DS mouse models, recovery of behavioral and neurophysiological deficits using GABA_AR antagonists led to hypothesize an excessive activity of inhibitory circuits in this condition. Nonetheless, whether over-inhibition is present in DS and whether this is due to specific alterations of distinct GABAergic circuits is unknown. In the prefrontal cortex of Ts65Dn mice (a well-established DS model), we found that the dendritic synaptic inhibitory loop formed by somatostatin-positive Martinotti cells (MCs) and pyramidal neurons (PNs) was strongly enhanced, with no alteration in their excitability. Conversely, perisomatic inhibition from parvalbumin-positive (PV) interneurons was unaltered, but PV cells of DS mice lost their classical fast-spiking phenotype and exhibited increased excitability. These microcircuit alterations resulted in reduced pyramidal-neuron firing and increased phase locking to cognitive-relevant network oscillations in vivo. These results define important synaptic and circuit mechanisms underlying cognitive dysfunctions in DS.

Down syndrome is a genetic disorder caused by the presence of a third copy of chromosome 21. Affected individuals show delayed growth, characteristic facial features, altered brain development; with mild to severe intellectual disability. The exact mechanisms underlying the intellectual disability in Down syndrome are unclear, although studies in mice have provided clues. Drugs that reduce the inhibitory activity in the brain improve cognition in a mouse model of Down syndrome. This suggests that excessive inhibitory activity may contribute to the cognitive impairments.

Many different neural circuits generate inhibitory activity in the brain. These circuits contain cells called interneurons. Sub-types of interneurons act via different mechanisms to reduce the activity of neurons. Identifying the interneurons that are affected in Down syndrome would thus improve our understanding of the brain basis of the disorder.

Zorrilla de San Martin et al. compared mice with Down syndrome to unaffected control mice. The results revealed an increased activity in two types of inhibitory brain circuits in Down syndrome. The first contains interneurons called Martinotti cells. These help the brain to combine inputs from different sources. The second contains interneurons called parvalbumin-positive basket cells. These help different areas of the brain to synchronize their activity, which in turn makes it easier for those areas to exchange information.

By mapping the changes in inhibitory circuits in Down syndrome, Zorrilla de San Martin et al. have provided new insights into the biological basis of the disorder. Future studies should examine whether targeting specific circuits with pharmacological treatments could ultimately help reduce the associated impairments.

Modulation of cortical slow oscillatory rhythm by GABAB receptors: an in vitro experimental and computational study

KEY POINTS

We confirm that GABAB receptors (GABAB -Rs) are involved in the termination of Up-states; their blockade consistently elongates Up-states. GABAB -Rs also modulate Down-states and the oscillatory cycle, thus having an impact on slow oscillation rhythm and its regularity. The most frequent effect of GABAB -R blockade is elongation of Down-states and subsequent decrease of oscillatory frequency, with an increased regularity. In a quarter of cases, GABAB -R blockade shortened Down-states and increased oscillatory frequency, changes that are independent of firing rates in Up-states. Our computer model provides mechanisms for the experimentally observed dynamics following blockade of GABAB -Rs, for Up/Down durations, oscillatory frequency and regularity. The time course of excitation, inhibition and adaptation can explain the observed dynamics of the network. This study brings novel insights into the role of GABAB -R-mediated slow inhibition on the slow oscillatory activity, which is considered the default activity pattern of the cortical network.

ABSTRACT

Slow wave oscillations (SWOs) dominate cortical activity during deep sleep, anaesthesia and in some brain lesions. SWOs are composed of periods of activity (Up states) interspersed with periods of silence (Down states). The rhythmicity expressed during SWOs integrates neuronal and connectivity properties of the network and is often altered under pathological conditions. Adaptation mechanisms as well as synaptic inhibition mediated by GABAB receptors (GABAB -Rs) have been proposed as mechanisms governing the termination of Up states. The interplay between these two mechanisms is not well understood, and the role of GABAB -Rs controlling the whole cycle of the SWO has not been described. Here we contribute to its understanding by combining in vitro experiments on spontaneously active cortical slices and computational techniques. GABAB -R blockade modified the whole SWO cycle, not only elongating Up states, but also affecting the subsequent Down state duration. Furthermore, while adaptation tends to yield a rather regular behaviour, we demonstrate that GABAB -R activation desynchronizes the SWOs. Interestingly, variability changes could be accomplished in two different ways: by either shortening or lengthening the duration of Down states. Even when the most common observation following GABAB -Rs blocking is the lengthening of Down states, both changes are expressed experimentally and also in numerical simulations. Our simulations suggest that the sluggishness of GABAB -Rs to follow the excitatory fluctuations of the cortical network can explain these different network dynamics modulated by GABAB -Rs.

Electrical stimulation of the ventral tegmental area evokes sleep-like state transitions under urethane anaesthesia in the rat medial prefrontal cortex via dopamine D1 -like receptors

The role of dopamine in regulating sleep-state transitions during, both natural sleep and under anaesthesia, is still unclear. Recording in vivo in the rat mPFC under urethane anaesthesia, we observed predominantly slow wave activity (SWA) of <1 Hz in the local field potential interrupted by occasional spontaneous transitions to a low-amplitude-fast (LAF) pattern of activity. During periods of SWA, transitions to LAF activity could be rapidly and consistently evoked by electrical stimulation of the ventral tegmental area (VTA). Spontaneous LAF activity, and that evoked by stimulation of the VTA, consisted of fast oscillations similar to those seen in the rapid eye movement (REM)-like sleep state. Spontaneous and VTA stimulation-evoked LAF activity occurred simultaneously along the dorsoventral extent of all mPFC subregions. Evoked LAF activity depended on VTA stimulation current and could be elicited using either regular (25-50 Hz) or burst stimulation patterns and was reproducible upon repeated stimulation. Simultaneous extracellular single-unit recordings showed that during SWA, presumed pyramidal cells fired phasically and almost exclusively on the Up state, while during both spontaneous and VTA-evoked LAF activity, they fired tonically. The transition to LAF activity evoked by VTA stimulation depended on dopamine D1 -like receptor activation as it was almost completely blocked by systemic administration of the D1 -like receptor antagonist SCH23390. Overall, our data demonstrate that activation of dopamine D1 -like receptors in the mPFC is important for regulating sleep-like state transitions.

Brain states govern the spatio-temporal dynamics of resting-state functional connectivity

Previously, using simultaneous resting-state functional magnetic resonance imaging (fMRI) and photometry-based neuronal calcium recordings in the anesthetized rat, we identified blood oxygenation level-dependent (BOLD) responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized correlate of locally recorded neuronal activity (Schwalm et al., 2017). Here, using the same techniques, we investigate two distinct cortical activity states: persistent activity, in which compartmentalized network dynamics were observed; and slow wave activity, dominated by a cortex-wide BOLD component, suggesting a strong functional coupling of inter-cortical activity. During slow wave activity, we find a correlation between the occurring slow wave events and the strength of functional connectivity between different cortical areas. These findings suggest that down-up transitions of neuronal excitability can drive cortex-wide functional connectivity. This study provides further evidence that changes in functional connectivity are dependent on the brain's current state, directly linked to the generation of slow waves.

The (Un)Conscious Mouse as a Model for Human Brain Functions: Key Principles of Anesthesia and Their Impact on Translational Neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca2+ imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species.

Slow Waves in Cortical Slices: How Spontaneous Activity is Shaped by Laminar Structure

Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during deep sleep and anesthesia and are also expressed in isolated brain slices and cortical slabs. We lack full understanding of how SO integrate the different structural levels underlying local excitability of cell assemblies and their mutual interaction. Here, we focus on ongoing slow waves (SWs) in cortical slices reconstructed from a 16-electrode array designed to probe the neuronal activity at multiple spatial scales. In spite of the variable propagation patterns observed, we reproducibly found a smooth strip of loci leading the SW fronts, overlapping cortical layers 4 and 5, along which Up states were the longest and displayed the highest firing rate. Propagation modes were uncorrelated in time, signaling a memoryless generation of SWs. All these features could be modeled by a multimodular large-scale network of spiking neurons with a specific balance between local and intermodular connectivity. Modules work as relaxation oscillators with a weakly stable Down state and a peak of local excitability to model layers 4 and 5. These conditions allow for both optimal sensitivity to the network structure and richness of propagation modes, both of which are potential substrates for dynamic flexibility in more general contexts.

Altered Neocortical Dynamics in a Mouse Model of Williams-Beuren Syndrome

Williams-Beuren syndrome (WBS) is a rare neurodevelopmental disorder characterized by moderate intellectual disability and learning difficulties alongside behavioral abnormalities such as hypersociability. Several structural and functional brain alterations are characteristic of this syndrome, as well as disturbed sleep and sleeping patterns. However, the detailed physiological mechanisms underlying WBS are mostly unknown. Here, we characterized the cortical dynamics in a mouse model of WBS previously reported to replicate most of the behavioral alterations described in humans. We recorded the laminar local field potential generated in the frontal cortex during deep anesthesia and characterized the properties of the emergent slow oscillation activity. Moreover, we performed micro-electrocorticogram recordings using multielectrode arrays covering the cortical surface of one hemisphere. We found significant differences between the cortical emergent activity and functional connectivity between wild-type mice and WBS model mice. Slow oscillations displayed Up states with diminished firing rate and lower high-frequency content in the gamma range. Lower firing rates were also recorded in the awake WBS animals while performing a marble burying task and could be associated with the decreased spine density and thus synaptic connectivity in this cortical area. We also found an overall increase in functional connectivity between brain areas, reflected in lower clustering and abnormally high integration, especially in the gamma range. These results expand previous findings in humans, suggesting that the cognitive deficits characterizing WBS might be associated with reduced excitability, plus an imbalance in the capacity to functionally integrate and segregate information.

Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques

Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).

Spatio-temporal properties of sleep slow waves and implications for development

Objective sleep quality can be measured by electroencephalography (EEG), a non-invasive technique to quantify electrical activity generated by the brain. With EEG, sleep depth is measured by appearance and an increase in slow wave activity (scalp-SWA). EEG slow waves (scalp-SW) are the manifestation of underlying synchronous membrane potential transitions between silent (DOWN) and active (UP) states. This bistable periodic rhythm is defined as slow oscillation (SO). During its "silent state" cortical neurons are hyperpolarized and appear inactive, while during its "active state" cortical neurons are depolarized, fire spikes and exhibit continuous synaptic activity, excitatory and inhibitory. In adults, data from high-density EEG revealed that scalp-SW propagate across the cortical mantle in complex patterns. However, scalp-SW propagation undergoes modifications across development. We present novel data from children, indicating that scalp-SW originate centro-parietally, and emerge more frontally by adolescence. Based on the concept that SO and SW could actively modify neuronal connectivity, we discuss whether they fulfill a key purpose in brain development by actively conveying modifications of the maturing brain.

Cholinergic Switch between Two Types of Slow Waves in Cerebral Cortex

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory versus excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.

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.

Scaling of a Large-Scale Simulation of Synchronous Slow-Wave and Asynchronous Awake-Like Activity of a Cortical Model With Long-Range Interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192 × 192 modules, each composed of 1,250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40 GHz clock rate. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1,024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3 × 109 and 4.1 × 109 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.

Frequency-dependent exacerbation of Alzheimer's disease neuropathophysiology

Neuronal activity patterns are disrupted in neurodegenerative disorders, including Alzheimer’s disease (AD). One example is disruption of corticothalamic slow oscillations responsible for sleep-dependent memory consolidation. Slow waves are periodic oscillations in neuronal activity occurring at frequencies of <1 Hz. The power, but not the frequency of slow oscillations is altered in a mouse model of AD. Optogenetic rescue of slow oscillations by increasing activity in cortical pyramidal neurons at the frequency of slow waves restores slow wave power, halts deposition of amyloid plaques and prevents neuronal calcium dysregulation. Here we determined whether driving this circuit at an increased rate would exacerbate the amyloid-dependent calcium dyshomeostasis in transgenic mice. Doubling the frequency of slow waves for one month with optogenetics resulted in increased amyloid beta - dependent disruptions in neuronal calcium homeostasis and loss of synaptic spines. Therefore, while restoration of physiological circuit dynamics is sufficient to abrogate the progression of Alzheimer’s disease pathology and should be considered an avenue for clinical treatment of AD patients with sleep disorders, pathophysiological stimulation of neuronal circuits leads to activity - dependent acceleration of amyloid production, aggregation and downstream neuronal dysfunction.

Outer Brain Oscillations in Down Syndrome

The present article reviews the relationship between sleep and oscillatory activity in Down Syndrome (DS), as well as the featuring emergent rhythmic activity across different brain states. A comprehensive discussion of the data from electroencephalographic studies in DS humans and transgenic/trisomic mouse models is provided, as well as data from signals collected from local field potentials (LFP) and intracellular recordings in DS mouse models. The first sections focus specially on the alpha phenotype consistently observed in DS subjects, as well as its description in DS childhood and aging. Subsequently, a review of the data reported in DS mouse models is presented with the aim to deepen on the mechanisms underlying altered rhythmic patterns. Further sections situate the state-of-the-art of the field, with a discussion on the possible circuit alterations that may underlie impaired alpha and gamma oscillatory activity. A further aim is to highlight the importance of studying network oscillatory activity in mouse models to infer alterations in the underlying circuits related to cognition, such as in intellectual disability. In this direction, a view of alpha and gamma rhythms generated by the cerebral cortex as a tool for evaluating an unbalance between excitation and inhibition in DS is claimed, which points out toward an over-inhibited network. A final aim is to situate oscillatory activity as a key phenomenon that may be used as a biomarker for monitoring as well the effect of novel therapeutic strategies.

Control of cortical oscillatory frequency by a closed-loop system

BACKGROUND

We present a closed-loop system able to control the frequency of slow oscillations (SO) spontaneously generated by the cortical network in vitro. The frequency of SO can be controlled by direct current (DC) electric fields within a certain range. Here we set out to design a system that would be able to autonomously bring the emergent oscillatory activity to a target frequency determined by the experimenter.

METHODS

The cortical activity was recorded through an electrode and was analyzed online. Once a target frequency was set, the frequency of the slow oscillation was steered through the injection of DC of variable intensity that generated electric fields of proportional amplitudes in the brain slice. To achieve such closed-loop control, we designed a custom programmable stimulator ensuring low noise and accurate tuning over low current levels. For data recording and analysis, we relied on commercial acquisition and software tools.

RESULTS

The result is a flexible and reliable system that ensures control over SO frequency in vitro. The system guarantees artifact removal, minimal gaps in data acquisition and robustness in spite of slice heterogeneity.

CONCLUSIONS

Our tool opens new possibilities for the investigation of dynamics of cortical slow oscillations-an activity pattern that is associated with cognitive processes such as memory consolidation, and that is altered in several neurological conditions-and also for potential applications of this technology.

High-Density Porous Graphene Arrays Enable Detection and Analysis of Propagating Cortical Waves and Spirals

Cortical propagating waves have recently attracted significant attention by the neuroscience community. These travelling waves have been suggested to coordinate different brain areas and play roles in assisting neural plasticity and learning. However, it is extremely challenging to record them with very fine spatial scales over large areas to investigate their effect on neural dynamics or network connectivity changes. In this work, we employ high-density porous graphene microelectrode arrays fabricated using laser pyrolysis on flexible substrates to study the functional network connectivity during cortical propagating waves. The low-impedance porous graphene arrays are used to record cortical potentials during theta oscillations and drug-induced seizures in vivo. Spatiotemporal analysis on the neural recordings reveal that theta oscillations and epileptiform activities have distinct characteristics in terms of both synchronization and resulting propagating wave patterns. To investigate the network connectivity during the propagating waves, we perform network analysis. The results show that the propagating waves are consistent with the functional connectivity changes in the neural circuits, suggesting that the underlying network states are reflected by the cortical potential propagation patterns.

An ultra-compact integrated system for brain activity recording and stimulation validated over cortical slow oscillations in vivo and in vitro

The understanding of brain processing requires monitoring and exogenous modulation of neuronal ensembles. To this end, it is critical to implement equipment that ideally provides highly accurate, low latency recording and stimulation capabilities, that is functional for different experimental preparations and that is highly compact and mobile. To address these requirements, we designed a small ultra-flexible multielectrode array and combined it with an ultra-compact electronic system. The device consists of a polyimide microelectrode array (8 µm thick and with electrodes measuring as low as 10 µm in diameter) connected to a miniaturized electronic board capable of amplifying, filtering and digitalizing neural signals and, in addition, of stimulating brain tissue. To evaluate the system, we recorded slow oscillations generated in the cerebral cortex network both from in vitro slices and from in vivo anesthetized animals, and we modulated the oscillatory pattern by means of electrical and visual stimulation. Finally, we established a preliminary closed-loop algorithm in vitro that exploits the low latency of the electronics (<0.5 ms), thus allowing monitoring and modulating emergent cortical activity in real time to a desired target oscillatory frequency.

Does Impedance Matter When Recording Spikes With Polytrodes?

Extracellular microelectrodes have been widely used to measure brain activity, yet there are still basic questions about the requirements for a good extracellular microelectrode. One common source of confusion is how much an electrode's impedance affects the amplitude of extracellular spikes and background noise. Here we quantify the effect of an electrode's impedance on data quality in extracellular recordings, which is crucial for both the detection of spikes and their assignment to the correct neurons. This study employs commercial polytrodes containing 32 electrodes (177 μm2) arranged in a dense array. This allowed us to directly compare, side-by-side, the same extracellular signals measured by modified low impedance (∼100 kΩ) microelectrodes with unmodified high impedance (∼1 MΩ) microelectrodes. We begin with an evaluation of existing protocols to lower the impedance of the electrodes. The poly (3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT-PSS) electrodeposition protocol is a simple, stable, and reliable method for decreasing the impedance of a microelectrode up to 10-fold. We next record in vivo using polytrodes that are modified in a 'chess board' pattern, such that the signal of one neuron is detected by multiple coated and non-coated electrodes. The performance of the coated and non-coated electrodes is then compared on measures of background noise and amplitude of the detected action potentials. If the proper recording system is used, then the impedance of a microelectrode within the range of standard polytrodes (∼0.1 to 2 MΩ) does not greatly affect data quality and spike sorting. This study should encourage neuroscientists to stop worrying about one more unknown.

Cell Type- and Layer-Specific Muscarinic Potentiation of Excitatory Synaptic Drive onto Parvalbumin Neurons in Mouse Prefrontal Cortex

Cholinergic neuromodulation is thought to shape network activity in the PFC, and thus PFC-dependent cognitive functions. ACh may modulate the activity of parvalbumin-positive (PV+) neurons, which critically regulate cortical network function. However, the mechanisms of cholinergic regulation of PV+ neuron activity, and particularly of the basket cell (BC) versus chandelier cell (ChC) subtypes, are unclear. Using patch clamp recordings in acute slices, we examined the effects of the ACh receptor (AChR) agonist carbachol on the excitatory synaptic drive onto BCs or ChCs in layers 2 to 6 of mouse PFC. Carbachol increased the frequency and amplitude of spontaneous EPSCs (sEPSCs) recorded from PV+ BCs in layers 3-6, but not in BCs from layer 2. Moreover, carbachol did not change the sEPSCs in ChCs, which were located exclusively in layer 2. The potentiation of sEPSCs in layers 3-6 BCs was prevented by the Na+ channel blocker tetrodotoxin and was abolished by the M1-selective muscarinic AChR antagonist pirenzepine. Thus, carbachol potentiates the activity-dependent excitatory drive onto PV+ neurons via M1-muscarinic AChR activation in a cell type- and layer-specific manner. In current clamp recordings with synaptic transmission blocked, carbachol directly evoked firing in deep layer pyramidal neurons (PNs). In contrast, carbachol elicited deep layer BC firing indirectly, via glutamate-mediated synaptic drive. Our data suggest that ACh powerfully regulates PFC microcircuit function by facilitating the firing of PNs that synaptically recruit deep layer PV+ BC activity, possibly shaping the patterns of network activity that contribute to cognitive function.

Beta and Gamma Oscillations in Prefrontal Cortex During NMDA Hypofunction: An In Vitro Model of Schizophrenia Features

NMDA receptor (NMDAr) hypofunction has been widely used as a schizophrenia model. Decreased activation of NMDAr is associated with a disrupted excitation/inhibition balance in the prefrontal cortex and with alterations in gamma synchronization. Our aim was to investigate whether this phenomenon could be reproduced in the spontaneous oscillatory activity generated by the local prefrontal network in vitro and, if so, to explore the effects of antipsychotics on the resulting activity. Extracellular recordings were obtained from prefrontal cortex slices bathed in in vivo-like ACSF solution. Slow (<1 Hz) oscillations consisting of interspersed Up (active) and Down (silent) states spontaneously emerged. Fast-frequency oscillations (15-90 Hz) occurred during Up states. We explored the effects of the NMDAr antagonist MK-801 on the spontaneously generated activity. Bath-applied MK-801 induced a dose-dependent decrease in Up-state duration and in the frequency of Up states. However, the beta/gamma power during Up states significantly increased; this increase was in turn prevented by the antipsychotic drug clozapine. The increased beta/gamma power with NMDAr blockade implies that NMDAr activation in physiological conditions prevents hypersynchronization in this frequency range. High-frequency hypersynchronization following NMDAr blockade occurring in cortical slices suggests that-at least part of-the underlying mechanisms of this schizophrenia feature persist in the local cortical circuit, even in the absence of long-range cortical or subcortical inputs. The observed action of clozapine decreasing hypersynchronization in the local circuit may be one of the mechanisms of action of clozapine in preventing schizophrenia symptoms derived from NMDA hypofunction.

EEG 40 Hz Coherence Decreases in REM Sleep and Ketamine Model of Psychosis

Cognitive processes are carried out during wakefulness by means of extensive interactions between cortical and subcortical areas. In psychiatric conditions, such as psychosis, these processes are altered. Interestingly, REM sleep where most dreams occurs, shares electrophysiological, pharmacological, and neurochemical features with psychosis. Because of this fact, REM sleep is considered a natural model of psychosis. Ketamine is a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that at sub-anesthetic dose induces psychotomimetic-like effects in humans and animals, and is employed as a pharmacological model of psychosis. Oscillations in the gamma frequency band of the electroencephalogram (EEG), mainly at about 40 Hz, have been involved in cognitive functions. Hence, the present study was conducted to analyze the EEG low gamma (30-45 Hz) band power and coherence of the cat, in natural (REM sleep) and pharmacological (sub-anesthetic doses of ketamine) models of psychosis. These results were compared with the gamma activity during alert (AW) and quiet wakefulness (QW), as well as during non-REM (NREM) sleep. Five cats were chronically prepared for polysomnographic recordings, with electrodes in different cortical areas. Basal recordings were obtained and ketamine (5, 10, and 15 mg/kg, i.m.) was administrated. Gamma activity (power and coherence) was analyzed in the abovementioned conditions. Compared to wakefulness and NREM sleep, following ketamine administration gamma coherence decreased among all cortical regions studied; the same coherence profile was observed during REM sleep. On the contrary, gamma power was relatively high under ketamine, and similar to QW and REM sleep. We conclude that functional interactions between cortical areas in the gamma frequency band decrease in both experimental models of psychosis. This uncoupling of gamma frequency activity may be involved in the cognitive features shared by dreaming and psychosis.

Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro

Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.

Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses.

When a person is in a deep non-dreaming sleep, neurons in their brain alternate slowly between periods of silence and periods of activity. This gives rise to low-frequency brain rhythms called slow waves, which are thought to help stabilize memories. Slow wave activity can be detected on multiple scales, from the pattern of electrical impulses sent by an individual neuron to the collective activity of the brain’s entire outer layer, the cortex. But does slow wave activity in an individual group of neurons in the cortex affect the activity of the rest of the brain?

To find out, Schwalm, Schmid, Wachsmuth et al. took advantage of the fact that slow waves also occur under general anesthesia, and placed anesthetized rats inside miniature whole-brain scanners. A small region of cortex in each rat had been injected with a dye that fluoresces whenever the neurons in that region are active. An optical fiber was lowered into the rat’s brain to transmit the fluorescence signals to a computer. Monitoring these signals while the animals lay inside the scanner revealed that slow-wave activity in any one group of cortical neurons was accompanied by slow-wave activity across the cortex as a whole. This relationship was seen only for slow waves, and not for other brain rhythms.

Slow waves seem to occur in all species of animal with a backbone, and in both healthy and diseased brains. While it is not possible to inject fluorescent dyes into the human brain, it is possible to monitor neuronal activity using electrodes. Comparing local electrode recordings with measures of whole-brain activity from scanners could thus allow similar experiments to be performed in people. There is growing evidence – from animal models and from studies of patients – that slow waves may be altered in Alzheimer’s disease. Further work is required to determine whether detecting these changes could help diagnose disease at earlier stages, and whether reversing them may have therapeutic potential.

UP-DOWN cortical dynamics reflect state transitions in a bistable network

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

Shaping the Default Activity Pattern of the Cortical Network

Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states. We propose that slow oscillations provide a robust unifying paradigm for the study of cortical function.

Sex differences in endogenous cortical network activity: spontaneously recurring Up/Down states

BACKGROUND

Several molecular and cellular processes in the vertebrate brain exhibit differences between males and females, leading to sexual dimorphism in the formation of neural circuits and brain organization. While studies on large-scale brain networks provide ample evidence for both structural and functional sex differences, smaller-scale local networks have remained largely unexplored. In the current study, we investigate sexual dimorphism in cortical dynamics by means of spontaneous Up/Down states, a type of network activity that is exhibited during slow-wave sleep, quiet wakefulness, and anesthesia and is thought to represent the default activity of the cortex.

METHODS

Up state activity was monitored by local field potential recordings in coronal brain slices of male and female mice across three ages with distinct secretion profiles of sex hormones: (i) pre-puberty (17-21 days old), (ii) 3-9 adult (months old), and (iii) old (19-24 months old).

RESULTS

Female mice of all ages exhibited longer and more frequent Up states compared to aged-matched male mice. Power spectrum analysis revealed sex differences in the relative power of Up state events, with female mice showing reduced power in the delta range (1-4 Hz) and increased power in the theta range (4-8 Hz) compared to male mice. No sex differences were found in the characteristics of Up state peak voltage and latency.

CONCLUSIONS

The present study revealed for the first time sex differences in intracortical network activity, using an ex vivo paradigm of spontaneously occurring Up/Down states. We report significant sex differences in Up state properties that are already present in pre-puberty animals and are maintained through adulthood and old age.

High-Throughput Analysis of in-vitro LFP Electrophysiological Signals: A validated workflow/software package

Synchronized brain activity in the form of alternating epochs of massive persistent network activity and periods of generalized neural silence, has been extensively studied as a fundamental form of circuit dynamics, important for many cognitive functions including short-term memory, memory consolidation, or attentional modulation. A key element in such studies is the accurate determination of the timing and duration of those network events. The local field potential (LFP) is a particularly attractive method for recording network activity, because it allows for long and stable recordings from multiple sites, allowing researchers to estimate the functional connectivity of local networks. Here, we present a computational method for the automatic detection and quantification of in-vitro LFP events, aiming to overcome the limitations of current approaches (e.g. slow analysis speed, arbitrary threshold-based detection and lack of reproducibility across and within experiments). The developed method is based on the implementation of established signal processing and machine learning approaches, is fully automated and depends solely on the data. In addition, it is fast, highly efficient and reproducible. The performance of the software is compared against semi-manual analysis and validated by verification of prior biological knowledge.

Slow and Fast Neocortical Oscillations in the Senescence-Accelerated Mouse Model SAMP8

The senescence-accelerated mouse prone 8 (SAMP8) model is characterized by accelerated, progressive cognitive decline as well as Alzheimer’s disease (AD)-like neurodegenerative changes, and resembles the etiology of multicausal, sporadic late-onset/age-related AD in humans. Our aim was to find whether these AD-like pathological features, together with the cognitive deficits present in the SAMP8 strain, are accompanied by disturbances in cortical network activity with respect to control mice (SAM resistance 1, SAMR1) and, if so, how the alterations in cortical activity progress with age. For this purpose, we characterized the extracellular spontaneous oscillatory activity in different regions of the cerebral cortex of SAMP8 and SAMR1 mice under ketamine anesthesia at 5 and 7 months of age. Under these conditions, slow oscillations and fast rhythms generated in the cortical network were recorded and different parameters of these oscillations were quantified and compared between SAMP8 and their control, SAMR1 mice. The average frequency of slow oscillations in SAMP8 mice was decreased with respect to the control mice at both studied ages. An elongation of the silent periods or Down states was behind the decreased slow oscillatory frequency while the duration of active or Up states remained stable. SAMP8 mice also presented increased cycle variability and reduced high frequency components during Down states. During Up states, the power peak in the gamma range was displaced towards lower frequencies in all the cortical areas of SAMP8 with respect to control mice suggesting that the spectral profile of SAMP8 animals is shifted towards lower frequencies. This shift is reminiscent to one of the principal hallmarks of electroencephalography (EEG) abnormalities in patients with Alzheimer’s disease, and adds evidence in support of the suitability of the SAMP8 mouse as a model of this disease. Although some of the differences between SAMP8 and control mice were emphasized with age, the evolution of the studied parameters as SAMR1 mice got older indicates that the SAMR1 phenotype tends to converge with that of SAMP8 animals. To our knowledge, this is the first systematic characterization of the cortical slow and fast rhythms in the SAMP8 strain and it provides useful insights about the cellular and synaptic mechanisms underlying the reported alterations.

State-Dependent Modulation of Slow Wave Motifs towards Awakening

Slow cortical waves that propagate across the cerebral cortex forming large-scale spatiotemporal propagation patterns are a hallmark of non-REM sleep and anesthesia, but also occur during resting wakefulness. To investigate how the spatial temporal properties of slow waves change with the depth of anesthetic, we optically imaged population voltage transients generated by mouse layer 2/3 pyramidal neurons across one or two cortical hemispheres dorsally with a genetically encoded voltage indicator (GEVI). From deep barbiturate anesthesia to light barbiturate sedation, depolarizing wave events recruiting at least 50% of the imaged cortical area consistently appeared as a conserved repertoire of distinct wave motifs. Toward awakening, the incidence of individual motifs changed systematically (the motif propagating from visual to motor areas increased while that from somatosensory to visual areas decreased) and both local and global cortical dynamics accelerated. These findings highlight that functional endogenous interactions between distant cortical areas are not only constrained by anatomical connectivity, but can also be modulated by the brain state.

Ginsenoside Rg1 modulates medial prefrontal cortical firing and suppresses the hippocampo-medial prefrontal cortical long-term potentiation

Background

Panax ginseng is one of the most commonly used medicinal herbs worldwide for a variety of therapeutic properties including neurocognitive effects. Ginsenoside Rg1 is one of the most abundant active chemical constituents of this herb with known neuroprotective, anxiolytic, and cognition improving effects.

Methods

We investigated the effects of Rg1 on the medial prefrontal cortex (mPFC), a key brain region involved in cognition, information processing, working memory, and decision making. In this study, the effects of systemic administration of Rg1 (1 mg/kg, 3 mg/kg, or 10 mg/kg) on (1) spontaneous firing of the medial prefrontal cortical neurons and (2) long-term potentiation (LTP) in the hippocampal–medial prefrontal cortical (HP–mPFC) pathway were investigated in male Sprague–Dawley rats.

Results

The spontaneous neuronal activity of approximately 50% the recorded pyramidal cells in the mPFC was suppressed by Rg1. In addition, Rg1 attenuated LTP in the HP–mPFC pathway. These effects were not dose-dependent.

Conclusion

This report suggests that acute treatment of Rg1 impairs LTP in the HP–mPFC pathway, perhaps by suppressing the firing of a subset of mPFC neurons that may contribute to the neurocognitive effects of Rg1.