Dynamics of striatal action selection and reinforcement learning.

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

A perspective on neuroscience data standardization with Neurodata Without Borders.

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science.

Towards Preclinical Validation of Arbaclofen (R-baclofen) Treatment for 16p11.2 Deletion Syndrome.

A microdeletion on human chromosome 16p11.2 is one of the most common copy number variants associated with autism spectrum disorder and other neurodevelopmental disabilities. Arbaclofen, a GABA(B) receptor agonist, is a component of racemic baclofen, which is FDA-approved for treating spasticity, and has been shown to alleviate behavioral phenotypes, including recognition memory deficits, in animal models of 16p11.2 deletion. Given the lack of reproducibility sometimes observed in mouse behavioral studies, we brought together a consortium of four laboratories to study the effects of arbaclofen on behavior in three different mouse lines with deletions in the mouse region syntenic to human 16p11.2 to test the robustness of these findings. Arbaclofen rescued cognitive deficits seen in two 16p11.2 deletion mouse lines in traditional recognition memory paradigms. Using an unsupervised machine-learning approach to analyze behavior, one lab found that arbaclofen also rescued differences in exploratory behavior in the open field in 16p11.2 deletion mice. Arbaclofen was not sedating and had modest off-target behavioral effects at the doses tested. Our studies show that arbaclofen consistently rescues behavioral phenotypes in 16p11.2 deletion mice, providing support for clinical trials of arbaclofen in humans with this deletion.

Brain-region-specific changes in neurons and glia and dysregulation of dopamine signaling in Grin2a mutant mice.

A genetically valid animal model could transform our understanding of schizophrenia (SCZ) disease mechanisms. Rare heterozygous loss-of-function (LoF) mutations in GRIN2A, encoding a subunit of the NMDA receptor, greatly increase the risk of SCZ. By transcriptomic, proteomic, and behavioral analyses, we report that heterozygous Grin2a mutant mice show (1) large-scale gene expression changes across multiple brain regions and in neuronal (excitatory and inhibitory) and non-neuronal cells (astrocytes and oligodendrocytes), (2) evidence of hypoactivity in the prefrontal cortex (PFC) and hyperactivity in the hippocampus and striatum, (3) an elevated dopamine signaling in the striatum and hypersensitivity to amphetamine-induced hyperlocomotion (AIH), (4) altered cholesterol biosynthesis in astrocytes, (5) a reduction in glutamatergic receptor signaling proteins in the synapse, and (6) an aberrant locomotor pattern opposite of that induced by antipsychotic drugs. These findings reveal potential pathophysiologic mechanisms, provide support for both the "hypo-glutamate" and "hyper-dopamine" hypotheses of SCZ, and underscore the utility of Grin2a-deficient mice as a genetic model of SCZ.

Mechanisms of SARS-CoV-2-associated anosmia.

Anosmia, the loss of the sense of smell, is one of the main neurological manifestations of COVID-19. Although the SARS-CoV-2 virus targets the nasal olfactory epithelium, current evidence suggests that neuronal infection is extremely rare in both the olfactory periphery and the brain, prompting the need for mechanistic models that can explain the widespread anosmia in COVID-19 patients. Starting from work identifying the non-neuronal cell types that are infected by SARS-CoV-2 in the olfactory system, we review the effects of infection of these supportive cells in the olfactory epithelium and in the brain and posit the downstream mechanisms through which sense of smell is impaired in COVID-19 patients. We propose that indirect mechanisms contribute to altered olfactory system function in COVID-19-associated anosmia, as opposed to neuronal infection or neuroinvasion into the brain. Such indirect mechanisms include tissue damage, inflammatory responses through immune cell infiltration or systemic circulation of cytokines, and downregulation of odorant receptor genes in olfactory sensory neurons in response to local and systemic signals. We also highlight key unresolved questions raised by recent findings.

Mouse spontaneous behavior reflects individual variation rather than estrous state.

Behavior is shaped by both the internal state of an animal and its individual behavioral biases. Rhythmic variation in gonadal hormones during the estrous cycle is a defining feature of the female internal state, one that regulates many aspects of sociosexual behavior. However, it remains unclear whether estrous state influences spontaneous behavior and, if so, how these effects might relate to individual behavioral variation. Here, we address this question by longitudinally characterizing the open-field behavior of female mice across different phases of the estrous cycle, using unsupervised machine learning to decompose spontaneous behavior into its constituent elements.1,2,3,4 We find that each female mouse exhibits a characteristic pattern of exploration that uniquely identifies it as an individual across many experimental sessions; by contrast, estrous state only negligibly impacts behavior, despite its known effects on neural circuits that regulate action selection and movement. Like female mice, male mice exhibit individual-specific patterns of behavior in the open field; however, the exploratory behavior of males is significantly more variable than that expressed by females both within and across individuals. These findings suggest underlying functional stability to the circuits that support exploration in female mice, reveal a surprising degree of specificity in individual behavior, and provide empirical support for the inclusion of both sexes in experiments querying spontaneous behaviors.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

ArMo: An Articulated Mesh Approach for Mouse 3D Reconstruction.

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animal’s surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

Hidden behavioral fingerprints in epilepsy.

Epilepsy is a major disorder affecting millions of people. Although modern electrophysiological and imaging approaches provide high-resolution access to the multi-scale brain circuit malfunctions in epilepsy, our understanding of how behavior changes with epilepsy has remained rudimentary. As a result, screening for new therapies for children and adults with devastating epilepsies still relies on the inherently subjective, semi-quantitative assessment of a handful of pre-selected behavioral signs of epilepsy in animal models. Here, we use machine learning-assisted 3D video analysis to reveal hidden behavioral phenotypes in mice with acquired and genetic epilepsies and track their alterations during post-insult epileptogenesis and in response to anti-epileptic drugs. These results show the persistent reconfiguration of behavioral fingerprints in epilepsy and indicate that they can be employed for rapid, automated anti-epileptic drug testing at scale.

Spontaneous behaviour is structured by reinforcement without explicit reward.

Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences1,2. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.

Persistent post-COVID-19 smell loss is associated with immune cell infiltration and altered gene expression in olfactory epithelium.

SARS-CoV-2 causes profound changes in the sense of smell, including total smell loss. Although these alterations are often transient, many patients with COVID-19 exhibit olfactory dysfunction that lasts months to years. Although animal and human autopsy studies have suggested mechanisms driving acute anosmia, it remains unclear how SARS-CoV-2 causes persistent smell loss in a subset of patients. To address this question, we analyzed olfactory epithelial samples collected from 24 biopsies, including from nine patients with objectively quantified long-term smell loss after COVID-19. This biopsy-based approach revealed a diffuse infiltrate of T cells expressing interferon-γ and a shift in myeloid cell population composition, including enrichment of CD207+ dendritic cells and depletion of anti-inflammatory M2 macrophages. Despite the absence of detectable SARS-CoV-2 RNA or protein, gene expression in the barrier supporting cells of the olfactory epithelium, termed sustentacular cells, appeared to reflect a response to ongoing inflammatory signaling, which was accompanied by a reduction in the number of olfactory sensory neurons relative to olfactory epithelial sustentacular cells. These findings indicate that T cell-mediated inflammation persists in the olfactory epithelium long after SARS-CoV-2 has been eliminated from the tissue, suggesting a mechanism for long-term post-COVID-19 smell loss.

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability are explained by a reinforcement-learning (RL) model of threat prediction in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus") and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.

Automated preclinical detection of mechanical pain hypersensitivity and analgesia.

ABSTRACT: The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high-throughput preclinical analgesic efficacy assessment.

Persistent post-COVID-19 smell loss is associated with inflammatory infiltration and altered olfactory epithelial gene expression.

Most human subjects infected by SARS-CoV-2 report an acute alteration in their sense of smell, and more than 25% of COVID patients report lasting olfactory dysfunction. While animal studies and human autopsy tissues have suggested mechanisms underlying acute loss of smell, the pathophysiology that underlies persistent smell loss remains unclear. Here we combine objective measurements of smell loss in patients suffering from post-acute sequelae of SARS-CoV-2 infection (PASC) with single cell sequencing and histology of the olfactory epithelium (OE). This approach reveals that the OE of patients with persistent smell loss harbors a diffuse infiltrate of T cells expressing interferon-gamma; gene expression in sustentacular cells appears to reflect a response to inflammatory signaling, which is accompanied by a reduction in the number of olfactory sensory neurons relative to support cells. These data identify a persistent epithelial inflammatory process associated with PASC, and suggests mechanisms through which this T cell-mediated inflammation alters the sense of smell.

A transcriptional rheostat couples past activity to future sensory responses.

Animals traversing different environments encounter both stable background stimuli and novel cues, which are thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Here, we show that each of the ∼1,000 olfactory sensory neuron (OSN) subtypes in the mouse harbors a distinct transcriptome whose content is precisely determined by interactions between its odorant receptor and the environment. This transcriptional variation is systematically organized to support sensory adaptation: expression levels of more than 70 genes relevant to transforming odors into spikes continuously vary across OSN subtypes, dynamically adjust to new environments over hours, and accurately predict acute OSN-specific odor responses. The sensory periphery therefore separates salient signals from predictable background via a transcriptional rheostat whose moment-to-moment state reflects the past and constrains the future; these findings suggest a general model in which structured transcriptional variation within a cell type reflects individual experience.

GABA-receptive microglia selectively sculpt developing inhibitory circuits.

Microglia, the resident immune cells of the brain, have emerged as crucial regulators of synaptic refinement and brain wiring. However, whether the remodeling of distinct synapse types during development is mediated by specialized microglia is unknown. Here, we show that GABA-receptive microglia selectively interact with inhibitory cortical synapses during a critical window of mouse postnatal development. GABA initiates a transcriptional synapse remodeling program within these specialized microglia, which in turn sculpt inhibitory connectivity without impacting excitatory synapses. Ablation of GABAB receptors within microglia impairs this process and leads to behavioral abnormalities. These findings demonstrate that brain wiring relies on the selective communication between matched neuronal and glial cell types.

Internal State: Dynamic, Interconnected Communication Loops Distributed Across Body, Brain, and Time.

Internal state profoundly alters perception and behavior. For example, a starved fly may approach and consume foods that it would otherwise find undesirable. A socially engaged newt may remain engaged in the presence of a predator, whereas a solitary newt would otherwise attempt to escape. Yet, the definition of internal state is fluid and ill-defined. As an interdisciplinary group of scholars spanning five career stages (from undergraduate to full professor) and six academic institutions, we came together in an attempt to provide an operational definition of internal state that could be useful in understanding the behavior and the function of nervous systems, at timescales relevant to the individual. In this perspective, we propose to define internal state through an integrative framework centered on dynamic and interconnected communication loops within and between the body and the brain. This framework is informed by a synthesis of historical and contemporary paradigms used by neurobiologists, ethologists, physiologists, and endocrinologists. We view internal state as composed of both spatially distributed networks (body-brain communication loops), and temporally distributed mechanisms that weave together neural circuits, physiology, and behavior. Given the wide spatial and temporal scales at which internal state operates-and therefore the broad range of scales at which it could be defined-we choose to anchor our definition in the body. Here we focus on studies that highlight body-to-brain signaling; body represented in endocrine signaling, and brain represented in sensory signaling. This integrative framework of internal state potentially unites the disparate paradigms often used by scientists grappling with body-brain interactions. We invite others to join us as we examine approaches and question assumptions to study the underlying mechanisms and temporal dynamics of internal state.

Flexible scaling and persistence of social vocal communication.

Innate vocal sounds such as laughing, screaming or crying convey one's feelings to others. In many species, including humans, scaling the amplitude and duration of vocalizations is essential for effective social communication1-3. In mice, female scent triggers male mice to emit innate courtship ultrasonic vocalizations (USVs)4,5. However, whether mice flexibly scale their vocalizations and how neural circuits are structured to generate flexibility remain largely unknown. Here we identify mouse neurons from the lateral preoptic area (LPOA) that express oestrogen receptor 1 (LPOAESR1 neurons) and, when activated, elicit the complete repertoire of USV syllables emitted during natural courtship. Neural anatomy and functional data reveal a two-step, di-synaptic circuit motif in which primary long-range inhibitory LPOAESR1 neurons relieve a clamp of local periaqueductal grey (PAG) inhibition, enabling excitatory PAG USV-gating neurons to trigger vocalizations. We find that social context shapes a wide range of USV amplitudes and bout durations. This variability is absent when PAG neurons are stimulated directly; PAG-evoked vocalizations are time-locked to neural activity and stereotypically loud. By contrast, increasing the activity of LPOAESR1 neurons scales the amplitude of vocalizations, and delaying the recovery of the inhibition clamp prolongs USV bouts. Thus, the LPOA disinhibition motif contributes to flexible loudness and the duration and persistence of bouts, which are key aspects of effective vocal social communication.