Dynamic synchronization between hippocampal representations and stepping.

The hippocampus is a mammalian brain structure that expresses spatial representations1 and is crucial 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 represent mainly higher-order cognitive and locomotor variables such as position, speed and direction of movement4-7, whereas the limb movements that propel the animal can 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, here 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 around 8 Hz during movement, matching the approximately 8 Hz modulation of hippocampal activity and spatial representations 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 that are closest to the actual position of the nose of the rat, whereas between these plant times, the hippocampal representation progresses towards possible future locations. This synchronization was specifically detectable when rats approached spatial decisions. Together, our results reveal a 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.

A Systematic Review Comparing Radiofrequency versus Focused Ultrasound Pallidotomy in the Treatment of Parkinson's Disease.

INTRODUCTION: Focused ultrasound (FUS) pallidotomy is a promising new therapy for Parkinson's disease (PD). The efficacy, motor outcomes, and side effects of FUS pallidotomy compared to radiofrequency (RF) pallidotomy are unknown. METHODS: We performed a systematic review of the outcomes and side effect profiles of FUS versus RF pallidotomy in patients with PD. RESULTS: Across four RF reports and one FUS report, putative contralateral UPDRS III scores were not significantly different following RF versus FUS pallidotomy. Across 18 RF and 2 FUS reports, the mean failure rate was 14% following RF pallidotomy versus 24% following FUS pallidotomy. Across 25 RF and 3 FUS reports, cognitive deficit was significantly more prevalent following RF pallidotomy (p = 0.004). CONCLUSION: At present, limited data and heterogeneity in outcome reporting challenges comparisons of FUS and RF pallidotomy efficacy and safety. Available evidence suggests FUS pallidotomy may have broadly similar efficacy and a lower risk of cognitive impairment relative to RF pallidotomy. Standardized reporting of post-lesion outcomes in future studies would improve power and rule out potential confounders of these results.

Activation of GABAergic Neurons in the Rostromedial Tegmental Nucleus and Other Brainstem Regions Promotes Sedation and Facilitates Sevoflurane Anesthesia in Mice.

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.

Optogenetic activation of dopamine neurons in the ventral tegmental area induces reanimation from general anesthesia.

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.

Regional specialization manifests in the reliability of neural population codes.

The brain has the remarkable ability to learn and guide the performance of complex tasks. Decades of lesion studies suggest that different brain regions perform specialized functions in support of complex behaviors1-3. Yet recent large-scale studies of neural activity reveal similar patterns of activity and encoding distributed widely throughout the brain4-6. How these distributed patterns of activity and encoding are compatible with regional specialization of brain function remains unclear. Two frontal brain regions, the dorsal medial prefrontal cortex (dmPFC) and orbitofrontal cortex (OFC), are a paradigm of this conundrum. In the setting complex behaviors, the dmPFC is necessary for choosing optimal actions2,7,8, whereas the OFC is necessary for waiting for3,9 and learning from2,7,9-12 the outcomes of those actions. Yet both dmPFC and OFC encode both choice- and outcome-related quantities13-20. Here we show that while ensembles of neurons in the dmPFC and OFC of rats encode similar elements of a cognitive task with similar patterns of activity, the two regions differ in when that coding is consistent across trials ("reliable"). In line with the known critical functions of each region, dmPFC activity is more reliable when animals are making choices and less reliable preceding outcomes, whereas OFC activity shows the opposite pattern. Our findings identify the dynamic reliability of neural population codes as a mechanism whereby different brain regions may support distinct cognitive functions despite exhibiting similar patterns of activity and encoding similar quantities.

Spyglass: a framework for reproducible and shareable neuroscience research.

Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible. To address these challenges, we created Spyglass, an open-source software framework that enables reproducible analyses and sharing of data and both intermediate and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for several core analyses in neuroscience, including spectral filtering, spike sorting, pose tracking, and neural decoding. It can be easily extended to apply both existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Efficient Decoding of Multi-Dimensional Signals From Population Spiking Activity Using a Gaussian Mixture Particle Filter.

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.

Developing a personalized closed-loop controller of medically-induced coma in a rodent model.

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.

A Comparison Study of Point-Process Filter and Deep Learning Performance in Estimating Rat Position Using an Ensemble of Place Cells.

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.

Sevoflurane Induces Coherent Slow-Delta Oscillations in Rats.

Although general anesthetics are routinely administered to surgical patients to induce loss of consciousness, the mechanisms underlying anesthetic-induced unconsciousness are not fully understood. In rats, we characterized changes in the extradural EEG and intracranial local field potentials (LFPs) within the prefrontal cortex (PFC), parietal cortex (PC), and central thalamus (CT) in response to progressively higher doses of the inhaled anesthetic sevoflurane. During induction with a low dose of sevoflurane, beta/low gamma (12-40 Hz) power increased in the frontal EEG and PFC, PC and CT LFPs, and PFC-CT and PFC-PFC LFP beta/low gamma coherence increased. Loss of movement (LOM) coincided with an abrupt decrease in beta/low gamma PFC-CT LFP coherence. Following LOM, cortically coherent slow-delta (0.1-4 Hz) oscillations were observed in the frontal EEG and PFC, PC and CT LFPs. At higher doses of sevoflurane sufficient to induce loss of the righting reflex, coherent slow-delta oscillations were dominant in the frontal EEG and PFC, PC and CT LFPs. Dynamics similar to those observed during induction were observed as animals emerged from sevoflurane anesthesia. We conclude that the rat is a useful animal model for sevoflurane-induced EEG oscillations in humans, and that coherent slow-delta oscillations are a correlate of sevoflurane-induced behavioral arrest and loss of righting in rats.

Synergistic Ion-Binding Catalysis Demonstrated via an Enantioselective, Catalytic [2,3]-Wittig Rearrangement.

Sigmatropic rearrangements number among the most powerful complexity-building transformations in organic synthesis but have remained largely insensitive to enantioselective catalysis due to the diffuse nature of their transition structures. Here, we describe a synergistic ion-binding strategy for asymmetric catalysis of anionic sigmatropic rearrangements. This approach is demonstrated with the enantioselective [2,3]-Wittig rearrangement of α-allyloxy carbonyl compounds to afford highly enantioenriched homoallylic alcohol products. Chiral thiourea catalysts are shown to engage reactive anions and their countercations through a cooperative set of attractive, noncovalent interactions. Catalyst structure-reactivity-selectivity relationship studies and computational analyses provide insight into catalyst-substrate interactions responsible for enantioinduction and allude to the potential generality of this catalytic strategy.