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Many general anesthetics potentiate gamma-aminobutyric acid (GABA) A receptors but their neuroanatomic sites of action are less clear. GABAergic neurons in the rostromedial tegmental nucleus (RMTg) send inhibitory projections to multiple arousal-promoting nuclei, but the role of these neurons in modulating consciousness is unknown. In this study, designer receptors exclusively activated by designer drugs (DREADDs) were targeted to RMTg GABAergic neurons of Vgat-ires-Cre mice. DREADDs expression was found in the RMTg and other brainstem regions. Activation of these neurons decreased movement and exploratory behavior, impaired motor coordination, induced electroencephalogram (EEG) oscillations resembling nonrapid eye movement (NREM) sleep without loss of righting and reduced the dose requirement for sevoflurane-induced unconsciousness. These results suggest that GABAergic neurons in the RMTg and other brainstem regions promote sedation and facilitate sevoflurane-induced unconsciousness.

Summary paragraphThe hippocampus is a vertebrate brain structure that expresses spatial representations1 and is critical for navigation2,3. Navigation in turn intricately depends on locomotion; however, current accounts suggest a dissociation between hippocampal spatial representations and the details of locomotor processes. Specifically, the hippocampus is thought to primarily represent higher-order cognitive and locomotor variables like position, speed, and direction of movement4–7, while the limb movements that propel the animal are thought to be computed and represented primarily in subcortical circuits, including the spinal cord, brainstem, and cerebellum8–11. Whether hippocampal representations are actually decoupled from the detailed structure of locomotor processes remains unknown. To address this question, we simultaneously monitored hippocampal spatial representations and ongoing limb movements underlying locomotion at fast timescales. We found that the forelimb stepping cycle in freely behaving rats is rhythmic and peaks at ~8 Hz during movement, matching the ~8 Hz organization of information processing in the hippocampus during locomotion12. We also discovered precisely timed coordination between the time at which the forelimbs touch the ground (‘plant’ times of the stepping cycle) and the hippocampal representation of space. Notably, plant times coincide with hippocampal representations closest to the actual position of the animal, while in-between these plant times, the hippocampal representation progresses towards possible future locations. This synchronization was specifically detectable when animals approached upcoming spatial decisions. Taken together, our results reveal profound and dynamic coordination on a timescale of tens of milliseconds between central cognitive representations and peripheral motor processes. This coordination engages and disengages rapidly in association with cognitive demands and is well suited to support rapid information exchange between cognitive and sensory-motor circuits.

New recording technologies and the potential for closed-loop experiments have led to an increasing demand for computationally efficient and accurate algorithms to decode population spiking activity in multi-dimensional spaces. Exact point process filters can accurately decode low-dimensional signals, but are computationally intractable for high-dimensional signals. Approximate Gaussian filters are computationally efficient, but are inaccurate when the signals have complex distributions and nonlinear dynamics. Even particle filter methods tend to become inefficient and inaccurate when the filter distribution has multiple peaks. Here, we develop a new point process filter algorithm that combines the computational efficiency of approximate Gaussian methods with a numerical accuracy that exceeds standard particle filters. We use a mixture of Gaussian model for the posterior at each time step, allowing for an analytic solution to the computationally expensive filter integration step. During non-spike intervals, the filter needs only to update the mean, covariance, and mixture weight of each component. At spike times, a sampling procedure is used to update the filtering distribution and find the number of Gaussian mixture components necessary to maintain an accurate approximation. We illustrate the application of this algorithm to the problem of decoding a rat's position and velocity in a maze from hippocampal place cell data using both 2-D and 4-D decoders.

Dopamine (DA) promotes wakefulness, and DA transporter inhibitors such as dextroamphetamine and methylphenidate are effective for increasing arousal and inducing reanimation, or active emergence from general anesthesia. DA neurons in the ventral tegmental area (VTA) are involved in reward processing, motivation, emotion, reinforcement, and cognition, but their role in regulating wakefulness is less clear. The current study was performed to test the hypothesis that selective optogenetic activation of VTA DA neurons is sufficient to induce arousal from an unconscious, anesthetized state. Floxed-inverse (FLEX)-Channelrhodopsin2 (ChR2) expression was targeted to VTA DA neurons in DA transporter (DAT)-cre mice (ChR2+ group; n = 6). Optical VTA stimulation in ChR2+ mice during continuous, steady-state general anesthesia (CSSGA) with isoflurane produced behavioral and EEG evidence of arousal and restored the righting reflex in 6/6 mice. Pretreatment with the D1 receptor antagonist SCH-23390 before optical VTA stimulation inhibited the arousal responses and restoration of righting in 6/6 ChR2+ mice. In control DAT-cre mice, the VTA was targeted with a viral vector lacking the ChR2 gene (ChR2- group; n = 5). VTA optical stimulation in ChR2- mice did not restore righting or produce EEG changes during isoflurane CSSGA in 5/5 mice. These results provide compelling evidence that selective stimulation of VTA DA neurons is sufficient to induce the transition from an anesthetized, unconscious state to an awake state, suggesting critical involvement in behavioral arousal.

The emergence of deep learning techniques has provided new tools for the analysis of complex data in the field of neuroscience. In parallel, advanced statistical approaches like point-process modeling provide powerful tools for analyzing the spiking activity of neural populations. How statistical and machine learning techniques compare when applied to neural data remains largely unclear. In this research, we compare the performance of a point-process filter and a long short-term memory (LSTM) network in decoding the 2D movement trajectory of a rat using the neural activity recorded from an ensemble of hippocampal place cells. We compute the least absolute error (LAE), a measure of accuracy of prediction, and the coefficient of determination (R2), a measure of prediction consistency, to compare the performance of these two methods. We show that the LSTM and point-process filter provide comparable accuracy in predicting the position; however, the point-process provides further information about the prediction which is unavailable for LSTM. Though previous results report better performance using deep learning techniques, our results indicate that this is not universally the case. We also investigate how these techniques encode information carried by place cell activity and compare the computational efficiency of the two methods. While the point-process model is built using the receptive field for each place cell, we show that LSTM does not necessarily encode receptive fields, but instead decodes the movement trajectory using other features of neural activity. Although it is less robust, LSTM runs more than 7 times faster than the fastest point-process filter in this research, providing a strong advantage in computational efficiency. Together, these results suggest that the point-process filters and LSTM approaches each provide distinct advantages; the choice of model should be informed by the specific scientific question of interest.

OBJECTIVE Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Current control systems for medically-induced coma require a separate offline model fitting experiment to deal with inter-subject variabilities, which would lead to therapy interruption. Removing the need for these offline interruptions could help facilitate clinical feasbility. In addition, current systems do not track intra-subject variabilities. Tracking intra-subject variabilities is essential for studying whether or how the brain response to anesthetic infusion rate changes during therapy. Further, such tracking could enhance control precison and thus help facilitate clinical feasibility. APPROACH Here we develop a personalized closed-loop anesthetic delivery (CLAD) system in a rodent model that tracks both inter- and intra-subject variabilities in real time while simultaneously controlling the anesthetic in closed loop. We tested the CLAD in rats by administrating propofol to control the electroencephalogram (EEG) burst suppression. We first examined whether the CLAD can remove the need for offline model fitting interruption. We then used the CLAD as a tool to study whether and how the brain response to anesthetic infusion rate changes as a function of changes in the depth of medically-induced coma. Finally, we studied whether the CLAD can enhance control compared with prior systems by tracking intra-subject variabilities. MAIN RESULTS The CLAD precisely controlled the EEG burst suppression in each rat without performing offline model fitting experiments. Further, using the CLAD, we discovered that the brain response to anesthetic infusion rate varied during control, and that these variations correlated with the depth of medically-induced coma in a consistent manner across individual rats. Finally, tracking these variations reduced control bias and error by more than 70% compared with prior systems. SIGNIFICANCE This personalized CLAD provides a new tool to study the dynamics of brain response to anesthetic infusion rate and has significant implications for enabling clinically-feasible automatic control of medically-induced coma.