Dynamics of recovery from anaesthesia-induced unconsciousness across primate neocortex.

How the brain recovers from general anaesthesia is poorly understood. Neurocognitive problems during anaesthesia recovery are associated with an increase in morbidity and mortality in patients. We studied intracortical neuronal dynamics during transitions from propofol-induced unconsciousness into consciousness by directly recording local field potentials and single neuron activity in a functionally and anatomically interconnecting somatosensory (S1, S2) and ventral premotor (PMv) network in primates. Macaque monkeys were trained for a behavioural task designed to determine trial-by-trial alertness and neuronal response to tactile and auditory stimulation. We found that neuronal dynamics were dissociated between S1 and higher-order PMv prior to return of consciousness. The return of consciousness was distinguishable by a distinctive return of interregionally coherent beta oscillations and disruption of the slow-delta oscillations. Clustering analysis demonstrated that these state transitions between wakefulness and unconsciousness were rapid and unstable. In contrast, return of pre-anaesthetic task performance was observed with a gradual increase in the coherent beta oscillations. We also found that recovery end points significantly varied intra-individually across sessions, as compared to a rather consistent loss of consciousness time. Recovery of single neuron multisensory responses appeared to be associated with the time of full performance recovery rather than the length of recovery time. Similar to loss of consciousness, return of consciousness was identified with an abrupt shift of dynamics and the regions were dissociated temporarily during the transition. However, the actual dynamics change during return of consciousness is not simply an inverse of loss of consciousness, suggesting a unique process.

Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization.

OBJECTIVE: The treatment of focal epilepsies is largely predicated on the concept that there is a "focus" from which the seizure emanates. Yet, the physiological context that determines if and how ictal activity starts and propagates remains poorly understood. To delineate these phenomena more completely, we studied activity outside the seizure-onset zone prior to and during seizure initiation. METHODS: Stereotactic depth electrodes were implanted in 17 patients with longstanding pharmacoresistant epilepsy for lateralization and localization of the seizure-onset zone. Only seizures with focal onset in mesial temporal structures were used for analysis. Spectral analyses were used to quantify changes in delta, theta, alpha, beta, gamma, and high gamma frequency power, in regions inside and outside the area of seizure onset during both preictal and seizure initiation periods. RESULTS: In the 78 seizures examined, an average of 9.26% of the electrode contacts outside of the seizure focus demonstrated changes in power at seizure onset. Of interest, seizures that were secondarily generalized, on average, showed power changes in a greater number of extrafocus electrode contacts at seizure onset (16.7%) compared to seizures that remained focal (3.8%). The majority of these extrafocus changes occupied the delta and theta bands in electrodes placed in the ipsilateral, lateral temporal lobe. Preictally, we observed extrafocal high-frequency power decrements, which also correlated with seizure spread. SIGNIFICANCE: This widespread activity at and prior to the seizure-onset time further extends the notion of the ictogenic focus and its relationship to seizure spread. Further understanding of these extrafocus, periictal changes might help identify the neuronal dynamics underlying the initiation of seizures and how therapies can be devised to control seizure activity.

Dynamics of Propofol-Induced Loss of Consciousness Across Primate Neocortex.

UNLABELLED: The precise neural mechanisms underlying transitions between consciousness and anesthetic-induced unconsciousness remain unclear. Here, we studied intracortical neuronal dynamics leading to propofol-induced unconsciousness by recording single-neuron activity and local field potentials directly in the functionally interconnecting somatosensory (S1) and frontal ventral premotor (PMv) network during a gradual behavioral transition from full alertness to loss of consciousness (LOC) and on through a deeper anesthetic level. Macaque monkeys were trained for a behavioral task designed to determine the trial-by-trial alertness and neuronal response to tactile and auditory stimulation. We show that disruption of coherent beta oscillations between S1 and PMv preceded, but did not coincide with, the LOC. LOC appeared to correspond to pronounced but brief gamma-/high-beta-band oscillations (lasting ∼3 min) in PMv, followed by a gamma peak in S1. We also demonstrate that the slow oscillations appeared after LOC in S1 and then in PMv after a delay, together suggesting that neuronal dynamics are very different across S1 versus PMv during LOC. Finally, neurons in both S1 and PMv transition from responding to bimodal (tactile and auditory) stimulation before LOC to only tactile modality during unconsciousness, consistent with an inhibition of multisensory integration in this network. Our results show that propofol-induced LOC is accompanied by spatiotemporally distinct oscillatory neuronal dynamics across the somatosensory and premotor network and suggest that a transitional state from wakefulness to unconsciousness is not a continuous process, but rather a series of discrete neural changes. SIGNIFICANCE STATEMENT: How information is processed by the brain during awake and anesthetized states and, crucially, during the transition is not clearly understood. We demonstrate that neuronal dynamics are very different within an interconnecting cortical network (primary somatosensory and frontal premotor area) during the loss of consciousness (LOC) induced by propofol in nonhuman primates. Coherent beta oscillations between these regions are disrupted before LOC. Pronounced but brief gamma-band oscillations appear to correspond to LOC. In addition, neurons in both of these cortices transition from responding to both tactile and auditory stimulation before LOC to only tactile modality during unconsciousness. We demonstrate that propofol-induced LOC is accompanied by spatiotemporally distinctive neuronal dynamics in this network with concurrent changes in multisensory processing.

Slow Spatial Recruitment of Neocortex during Secondarily Generalized Seizures and Its Relation to Surgical Outcome.

Understanding the spatiotemporal dynamics of brain activity is crucial for inferring the underlying synaptic and nonsynaptic mechanisms of brain dysfunction. Focal seizures with secondary generalization are traditionally considered to begin in a limited spatial region and spread to connected areas, which can include both pathological and normal brain tissue. The mechanisms underlying this spread are important to our understanding of seizures and to improve therapies for surgical intervention. Here we study the properties of seizure recruitment-how electrical brain activity transitions to large voltage fluctuations characteristic of spike-and-wave seizures. We do so using invasive subdural electrode arrays from a population of 16 patients with pharmacoresistant epilepsy. We find an average delay of ∼30 s for a broad area of cortex (8 × 8 cm) to be recruited into the seizure, at an estimated speed of ∼4 mm/s. The spatiotemporal characteristics of recruitment reveal two categories of patients: one in which seizure recruitment of neighboring cortical regions follows a spatially organized pattern consistent from seizure to seizure, and a second group without consistent spatial organization of activity during recruitment. The consistent, organized recruitment correlates with a more regular, compared with small-world, connectivity pattern in simulation and successful surgical treatment of epilepsy. We propose that an improved understanding of how the seizure recruits brain regions into large amplitude voltage fluctuations provides novel information to improve surgical treatment of epilepsy and highlights the slow spread of massive local activity across a vast extent of cortex during seizure.

A biologically constrained, mathematical model of cortical wave propagation preceding seizure termination.

Epilepsy--the condition of recurrent, unprovoked seizures--manifests in brain voltage activity with characteristic spatiotemporal patterns. These patterns include stereotyped semi-rhythmic activity produced by aggregate neuronal populations, and organized spatiotemporal phenomena, including waves. To assess these spatiotemporal patterns, we develop a mathematical model consistent with the observed neuronal population activity and determine analytically the parameter configurations that support traveling wave solutions. We then utilize high-density local field potential data recorded in vivo from human cortex preceding seizure termination from three patients to constrain the model parameters, and propose basic mechanisms that contribute to the observed traveling waves. We conclude that a relatively simple and abstract mathematical model consisting of localized interactions between excitatory cells with slow adaptation captures the quantitative features of wave propagation observed in the human local field potential preceding seizure termination.

Neuronal ensemble synchrony during human focal seizures.

Seizures are classically characterized as the expression of hypersynchronous neural activity, yet the true degree of synchrony in neuronal spiking (action potentials) during human seizures remains a fundamental question. We quantified the temporal precision of spike synchrony in ensembles of neocortical neurons during seizures in people with pharmacologically intractable epilepsy. Two seizure types were analyzed: those characterized by sustained gamma (∼40-60 Hz) local field potential (LFP) oscillations or by spike-wave complexes (SWCs; ∼3 Hz). Fine (<10 ms) temporal synchrony was rarely present during gamma-band seizures, where neuronal spiking remained highly irregular and asynchronous. In SWC seizures, phase locking of neuronal spiking to the SWC spike phase induced synchrony at a coarse 50-100 ms level. In addition, transient fine synchrony occurred primarily during the initial ∼20 ms period of the SWC spike phase and varied across subjects and seizures. Sporadic coherence events between neuronal population spike counts and LFPs were observed during SWC seizures in high (∼80 Hz) gamma-band and during high-frequency oscillations (∼130 Hz). Maximum entropy models of the joint neuronal spiking probability, constrained only on single neurons' nonstationary coarse spiking rates and local network activation, explained most of the fine synchrony in both seizure types. Our findings indicate that fine neuronal ensemble synchrony occurs mostly during SWC, not gamma-band, seizures, and primarily during the initial phase of SWC spikes. Furthermore, these fine synchrony events result mostly from transient increases in overall neuronal network spiking rates, rather than changes in precise spiking correlations between specific pairs of neurons.

Cell-type-specific cholinergic control of granular retrosplenial cortex with implications for angular velocity coding across brain states.

Cholinergic receptor activation enables the persistent firing of cortical pyramidal neurons, providing a key cellular basis for theories of spatial navigation involving working memory, path integration, and head direction encoding. The granular retrosplenial cortex (RSG) is important for spatially-guided behaviors, but how acetylcholine impacts RSG neurons is unknown. Here, we show that a transcriptomically, morphologically, and biophysically distinct RSG cell-type - the low-rheobase (LR) neuron - has a very distinct expression profile of cholinergic muscarinic receptors compared to all other neighboring excitatory neuronal subtypes. LR neurons do not fire persistently in response to cholinergic agonists, in stark contrast to all other principal neuronal subtypes examined within the RSG and across midline cortex. This lack of persistence allows LR neuron models to rapidly compute angular head velocity (AHV), independent of cholinergic changes seen during navigation. Thus, LR neurons can consistently compute AHV across brain states, highlighting the specialized RSG neural codes supporting navigation.

Running speed alters the frequency of hippocampal gamma oscillations.

Successful spatial navigation is thought to employ a combination of at least two strategies: the following of landmark cues and path integration. Path integration requires that the brain use the speed and direction of movement in a meaningful way to continuously compute the position of the animal. Indeed, the running speed of rats modulates both the firing rate of neurons and the spectral properties of low frequency, theta oscillations seen in the local field potential (LFP) of the hippocampus, a region important for spatial memory formation. Higher frequency, gamma-band LFP oscillations are usually associated with decision-making, increased attention, and improved reaction times. Here, we show that increased running speed is accompanied by large, systematic increases in the frequency of hippocampal CA1 network oscillations spanning the entire gamma range (30-120 Hz) and beyond. These speed-dependent changes in frequency are seen on both linear tracks and two-dimensional platforms, and are thus independent of the behavioral task. Synchrony between anatomically distant CA1 regions also shifts to higher gamma frequencies as running speed increases. The changes in frequency are strongly correlated with changes in the firing rates of individual interneurons, consistent with models of gamma generation. Our results suggest that as a rat runs faster, there are faster gamma frequency transitions between sequential place cell-assemblies. This may help to preserve the spatial specificity of place cells and spatial memories at vastly different running speeds.

Thalamic control of layer 1 circuits in prefrontal cortex.

Knowledge of thalamocortical (TC) processing comes mainly from studying core thalamic systems that project to middle layers of primary sensory cortices. However, most thalamic relay neurons comprise a matrix of cells that are densest in the "nonspecific" thalamic nuclei and usually target layer 1 (L1) of multiple cortical areas. A longstanding hypothesis is that matrix TC systems are crucial for regulating neocortical excitability during changing behavioral states, yet we know almost nothing about the mechanisms of such regulation. It is also unclear whether synaptic and circuit mechanisms that are well established for core sensory TC systems apply to matrix TC systems. Here we describe studies of thalamic matrix influences on mouse prefrontal cortex using optogenetic and in vitro electrophysiology techniques. Channelrhodopsin-2 was expressed in midline and paralaminar (matrix) thalamic neurons, and their L1-projecting TC axons were activated optically. Contrary to conventional views, we found that matrix TC projections to L1 could transmit relatively strong, fast, high-fidelity synaptic signals. L1 TC projections preferentially drove inhibitory interneurons of L1, especially those of the late-spiking subtype, and often triggered feedforward inhibition in both L1 interneurons and pyramidal cells of L2/L3. Responses during repetitive stimulation were far more sustained for matrix than for core sensory TC pathways. Thus, matrix TC circuits appear to be specialized for robust transmission over relatively extended periods, consistent with the sort of persistent activation observed during working memory and potentially applicable to state-dependent regulation of excitability.

Cellular rules underlying psychedelic control of prefrontal pyramidal neurons.

Classical psychedelic drugs are thought to increase excitability of pyramidal cells in prefrontal cortex via activation of serotonin 2A receptors (5-HT2ARs). Here, we instead find that multiple classes of psychedelics dose-dependently suppress intrinsic excitability of pyramidal neurons, and that extracellular delivery of psychedelics decreases excitability significantly more than intracellular delivery. A previously unknown mechanism underlies this psychedelic drug action: enhancement of ubiquitously expressed potassium "M-current" channels that is independent of 5-HT2R activation. Using machine-learning-based data assimilation models, we show that M-current activation interacts with previously described mechanisms to dramatically reduce intrinsic excitability and shorten working memory timespan. Thus, psychedelic drugs suppress intrinsic excitability by modulating ion channels that are expressed throughout the brain, potentially triggering homeostatic adjustments that can contribute to widespread therapeutic benefits.

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication.

Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ∼140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex.

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors, but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit mapping in mice to reveal a double dissociation that allows parallel circuits in superficial RSG to process disparate inputs. The anterior thalamus and dorsal subiculum, sources of spatial information, strongly and selectively recruit small low-rheobase (LR) pyramidal cells in RSG. In contrast, neighboring regular-spiking (RS) cells are preferentially controlled by claustral and anterior cingulate inputs, sources of mostly non-spatial information. Precise sublaminar axonal and dendritic arborization within RSG layer 1, in particular, permits this parallel processing. Observed thalamocortical synaptic dynamics enable computational models of LR neurons to compute the speed of head rotation, despite receiving head direction inputs that do not explicitly encode speed. Thus, parallel input streams identify a distinct principal neuronal subtype ideally positioned to support spatial orientation computations in the RSG.

Sitting in your car, about to drive home after a long day at work, you realize you have no idea which way to go: you recognize where you are right now, and you remember the name of the street your house is on, but you cannot figure out how to get there. This spatial disorientation happens to people with damage to a brain region called the retrosplenial cortex, whose role and inner workings remain poorly understood. Recent evidence has shown that this area contains 'low-rheobase' neurons which are not seen anywhere else in the brain, but what do these neurons do? Brennan, Jedrasiak-Cape, Kailasa et al. decided to explore the role of these neurons, focusing on the brain regions they are connected to. Experiments were conducted in mice using optogenetics, a technique that activates neurons using pulses of light. This revealed that brain areas involved in processing information about direction and position preferentially communicate with low-rheobase neurons rather than with nearby, more standard neurons in the retrosplenial cortex. The way these spatial signals are sent to the low-rheobase neurons allows these cells to 'calculate' how fast a mouse is turning its head using only information about which direction the mouse is facing. Essentially, this neuron can turn directional compass-like signals into a gyroscope signal that can track both direction and speed of head movement. These unique neurons may therefore be ideally suited to combine information about direction and space, suggesting that they may have evolved specifically to support spatial navigation. Individuals with Alzheimer's disease show exactly the same type of spatial disorientation as individuals with direct damage to the retrosplenial cortex. This region is also one of the first to show altered activity in Alzheimer's disease. Exploring whether these unique retrosplenial neurons and their communication patterns are altered in Alzheimer's disease models could help to understand and potentially treat this debilitating condition.

Neural circuits linking sleep and addiction: Animal models to understand why select individuals are more vulnerable to substance use disorders after sleep deprivation.

Individuals differ widely in their drug-craving behaviors. One reason for these differences involves sleep. Sleep disturbances lead to an increased risk of substance use disorders and relapse in only some individuals. While animal studies have examined the impact of sleep on reward circuitry, few have addressed the role of individual differences in the effects of altered sleep. There does, however, exist a rodent model of individual differences in reward-seeking behavior: the sign/goal-tracker model of Pavlovian conditioned approach. In this model, only some rats show the key behavioral traits associated with addiction, including impulsivity and poor attentional control, making this an ideal model system to examine individually distinct sleep-reward interactions. Here, we describe how the limbic neural circuits responsible for individual differences in incentive motivation overlap with those involved in sleep-wake regulation, and how this model can elucidate the common underlying mechanisms. Consideration of individual differences in preclinical models would improve our understanding of how sleep interacts with motivational systems, and why sleep deprivation contributes to addiction in only select individuals.

The neural circuitry supporting successful spatial navigation despite variable movement speeds.

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

Instantaneous amplitude and shape of postrhinal theta oscillations differentially encode running speed.

Hippocampal theta oscillations have a temporally asymmetric waveform shape, but it is not known if this theta asymmetry extends to all other cortical regions involved in spatial navigation and memory. Here, using both established and improved cycle-by-cycle analysis methods, we show that theta waveforms in the postrhinal cortex are also temporally asymmetric. On average, the falling phase of postrhinal theta cycles lasts longer than the subsequent rising phase. There are, however, rapid changes in both the instantaneous amplitude and instantaneous temporal asymmetry of postrhinal theta cycles. These rapid changes in amplitude and asymmetry are very poorly correlated, indicative of a mechanistic disconnect between these theta cycle features. We show that the instantaneous amplitude and asymmetry of postrhinal theta cycles differentially encode running speed. Although theta amplitude continues to increase at the fastest running speeds, temporal asymmetry of the theta waveform shape plateaus after medium speeds. Our results suggest that the amplitude and waveform shape of individual postrhinal theta cycles may be governed by partially independent mechanisms and emphasize the importance of employing a single cycle approach to understanding the genesis and behavioral correlates of cortical theta rhythms. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Hyperexcitable Neurons Enable Precise and Persistent Information Encoding in the Superficial Retrosplenial Cortex.

The retrosplenial cortex (RSC) is essential for memory and navigation, but the neural codes underlying these functions remain largely unknown. Here, we show that the most prominent cell type in layers 2/3 (L2/3) of the mouse granular RSC is a hyperexcitable, small pyramidal cell. These cells have a low rheobase (LR), high input resistance, lack of spike frequency adaptation, and spike widths intermediate to those of neighboring fast-spiking (FS) inhibitory neurons and regular-spiking (RS) excitatory neurons. LR cells are excitatory but rarely synapse onto neighboring neurons. Instead, L2/3 is a feedforward, not feedback, inhibition-dominated network with dense connectivity between FS cells and from FS to LR neurons. Biophysical models of LR but not RS cells precisely and continuously encode sustained input from afferent postsubicular head-direction cells. Thus, the distinct intrinsic properties of LR neurons can support both the precision and persistence necessary to encode information over multiple timescales in the RSC.

More Is More: Potential Benefits of Characterizing High-Frequency Activity Over Long Durations.

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A Straw Can Break a Neural Network's Back and Lead to Seizures-But Only When Delivered at the Right Time.

Loss of Neuronal Network Resilience Precedes Seizures and Determines the Ictogenic Nature of Interictal Synaptic Perturbations Chang WC, Kudlacek J, Hlinka J, et al. Nat Neurosci. 2018; 21(12):1742-1752. doi:10.1038/s41593-018-0278-y. PMID: 30482946. The mechanism of seizure emergence and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are 2 of the most important unresolved issues in modern epilepsy research. We found that the transition to seizure is not a sudden phenomenon, but is instead a slow process that is characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon that is observable in systems characterized by transitions between dynamical regimes. In epilepsy, this process is modulated by synchronous synaptic input from IEDs. The IEDs are external perturbations that produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either antiseizure or proseizure effects. We found that the multifaceted nature of IEDs is defined by the dynamical state of the network at the moment of the discharge occurrence.

High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone.

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy. Liu S, Gurses C, Sha Z, Quach MM, Sencer A, Bebek N, et al. Brain. 2018;141(3):713-730. doi:10.1093/brain/awx374. PMID: 29394328 . High-frequency oscillations in local field potentials recorded with intracranial electroencephalogram are putative biomarkers of seizure-onset zones in epileptic brain. However, localized 80- to 500-Hz oscillations can also be recorded from normal and nonepileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones that limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 patients with epilepsy and 5 control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent of the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step toward the translation of these signature events into valid clinical biomarkers.