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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.

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.