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The specific circuit mechanisms through which anesthetics induce unconsciousness have not been completely characterized. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with the anesthetic propofol. Unconsciousness was marked by slow frequency (~1 Hz) oscillations in local field potentials, entrainment of local spiking to Up states alternating with Down states of little spiking, and decreased coherence in frequencies above 4 Hz. Thalamic stimulation “awakened” anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness is linked to cortical and thalamic slow frequency synchrony coupled with decreased spiking, and loss of higher-frequency dynamics. This may disrupt cortical communication/integration.

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal., 1 Author summary Monitoring brain activity during anesthesia can provide insights into the underlying mechanisms of how anesthetics elicit altered states of consciousness. Ketamine, a commonly used anesthetic, is known to cause short duration bursts of high frequency electrophysiological activity in the brain, but the neural mechanisms underlying this activity are not well understood. A key limitation in developing accurate models of the underlying mechanism is a lack of detailed knowledge of the dynamic structure and spectral properties of ketamine-induced oscillations. In this work, we address this limitation by developing a statistical framework to quantify ketamine-induced neural activity. Our framework is based on a hidden Markov model, which assumes that the neural activity switches among discrete states, each of which has its own distinctive probabilistic spectral representation. By estimating this versatile statistical model from electrophysiology data, we generated detailed descriptions of the dynamic properties and oscillatory signatures associated with ketamine-induced neurophysiological states in non-human primates and in human patients. Furthermore, we identified additional ketamine-induced states that have not yet been reported. In summary, our detailed quantitative descriptions of ketamine-induced spectra can aid further developments of neurophysiological mechanistic models of ketamine as well as biomarker discovery for clinical anesthesia monitoring.

We know that general anesthesia produces unconsciousness but not quite how. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with propofol. Unconsciousness was marked by slow frequency (∼1 Hz) oscillations in local field potentials, entraining local spiking to Up states alternating with Down states of little spiking, and decreased higher frequency (>4 Hz) coherence. The thalamus contributed to cortical rhythms. Its stimulation “awakened” anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness thus resulted from slow frequency hypersynchrony and loss of high-frequency dynamics, partly mediated by the thalamus, that disrupts cortical communication/integration.

Visual working memory (WM) storage is largely independent between the left and right visual hemifields/cerebral hemispheres, yet somehow WM feels seamless. We studied how WM is integrated across hemifields by recording neural activity bilaterally from lateral prefrontal cortex. An instructed saccade during the WM delay shifted the remembered location from one hemifield to the other. Before the shift, spike rates and oscillatory power showed clear signatures of memory laterality. After the shift, the lateralization inverted, consistent with transfer of the memory trace from one hemisphere to the other. Transferred traces initially used different neural ensembles from feedforward-induced ones, but they converged at the end of the delay. Around the time of transfer, synchrony between the two prefrontal hemispheres peaked in theta and beta frequencies, with a directionality consistent with memory trace transfer. This illustrates how dynamics between the two cortical hemispheres can stitch together WM traces across visual hemifields.Brincat et al. use bilateral recording to show working memories transferring between the right and left prefrontal cortex. Transferred memories engage different ensembles than feedforward-induced memory traces. Trace transfer is accompanied by directed interhemispheric theta/beta synchrony., NIMH (Grant R37MH087027), ONR (Grant MURI N00014-16-1-2832), NIGMS (Grant T32GM007753)

© 2018 Elsevier Inc. Categories can be grouped by shared sensory attributes (i.e., cats) or a more abstract rule (i.e., animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in the ventral PFC processed more low-level abstractions, whereas top-down dynamics (beta power and beta spike-LFP coherence) in the dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories. Wutz et al. show that different levels of category abstraction engage different oscillatory dynamics in different prefrontal cortex (PFC) areas. This suggests a functional specialization within PFC for low-level, stimulus-based categories (e.g., cats) and high-level, rule-based categories (e.g., animals).

International audience; Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.