Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale.

Serotonin influences many aspects of animal behavior. But how serotonin acts on its diverse receptors across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. Comprehensive genetic analyses identify three core serotonin receptors (MOD-1, SER-4, and LGC-50) that induce slow locomotion upon serotonin release and others (SER-1, SER-5, and SER-7) that interact with them to modulate this behavior. SER-4 induces behavioral responses to sudden increases in serotonin release, whereas MOD-1 induces responses to persistent release. Whole-brain imaging reveals widespread serotonin-associated brain dynamics, spanning many behavioral networks. We map all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict which neurons show serotonin-associated activity. These results reveal how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.

Low-frequency stimulation enhances ensemble co-firing and dexterity after stroke.

Electrical stimulation is a promising tool for modulating brain networks. However, it is unclear how stimulation interacts with neural patterns underlying behavior. Specifically, how might external stimulation that is not sensitive to the state of ongoing neural dynamics reliably augment neural processing and improve function? Here, we tested how low-frequency epidural alternating current stimulation (ACS) in non-human primates recovering from stroke interacted with task-related activity in perilesional cortex and affected grasping. We found that ACS increased co-firing within task-related ensembles and improved dexterity. Using a neural network model, we found that simulated ACS drove ensemble co-firing and enhanced propagation of neural activity through parts of the network with impaired connectivity, suggesting a mechanism to link increased co-firing to enhanced dexterity. Together, our results demonstrate that ACS restores neural processing in impaired networks and improves dexterity following stroke. More broadly, these results demonstrate approaches to optimize stimulation to target neural dynamics.

The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value.

Hippocampal activity represents many behaviorally important variables, including context, an animal's location within a given environmental context, time, and reward. Using longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a single feature-the field propensity. Each cell's propensity governs how many place fields it has per unit space, predicts its reward-related activity, and is preserved across distinct environments and over months. Propensity is broadly distributed-with many low, and some very high, propensity cells-and thus strongly shapes hippocampal representations. This results in a range of spatial codes, from sparse to dense. Propensity varied ∼10-fold between adjacent cells in salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. Intracellular recordings linked propensity to cell excitability. The stability of each cell's propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.

Constant Sub-second Cycling between Representations of Possible Futures in the Hippocampus.

Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per ∼125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.

An Amygdala Circuit Mediates Experience-Dependent Momentary Arrests during Exploration.

Exploration of novel environments ensures survival and evolutionary fitness. It is expressed through exploratory bouts and arrests that change dynamically based on experience. Neural circuits mediating exploratory behavior should therefore integrate experience and use it to select the proper behavioral output. Using a spatial exploration assay, we uncovered an experience-dependent increase in momentary arrests in locations where animals arrested previously. Calcium imaging in freely exploring mice revealed a genetically and projection-defined neuronal ensemble in the basolateral amygdala that is active during self-paced behavioral arrests. This ensemble was recruited in an experience-dependent manner, and closed-loop optogenetic manipulation of these neurons revealed that they are sufficient and necessary to drive experience-dependent arrests during exploration. Projection-specific imaging and optogenetic experiments revealed that these arrests are effected by basolateral amygdala neurons projecting to the central amygdala, uncovering an amygdala circuit that mediates momentary arrests in familiar places but not avoidance or anxiety/fear-like behaviors.

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level.

Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations toward mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (>10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice.

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

Area-Specificity and Plasticity of History-Dependent Value Coding During Learning.

Decision making is often driven by the subjective value of available options, a value which is formed through experience. To support this fundamental behavior, the brain must encode and maintain the subjective value. To investigate the area specificity and plasticity of value coding, we trained mice in a value-based decision task and imaged neural activity in 6 cortical areas with cellular resolution. History- and value-related signals were widespread across areas, but their strength and temporal patterns differed. In expert mice, the retrosplenial cortex (RSC) uniquely encoded history- and value-related signals with persistent population activity patterns across trials. This unique encoding of RSC emerged during task learning with a strong increase in more distant history signals. Acute inactivation of RSC selectively impaired the reward-history-based behavioral strategy. Our results indicate that RSC flexibly changes its history coding and persistently encodes value-related signals to support adaptive behaviors.

Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states.

Sexually Dimorphic Control of Parenting Behavior by the Medial Amygdala.

Social behaviors, including behaviors directed toward young offspring, exhibit striking sex differences. Understanding how these sexually dimorphic behaviors are regulated at the level of circuits and transcriptomes will provide insights into neural mechanisms of sex-specific behaviors. Here, we uncover a sexually dimorphic role of the medial amygdala (MeA) in governing parental and infanticidal behaviors. Contrary to traditional views, activation of GABAergic neurons in the MeA promotes parental behavior in females, while activation of this population in males differentially promotes parental versus infanticidal behavior in an activity-level-dependent manner. Through single-cell transcriptomic analysis, we found that molecular sex differences in the MeA are specifically represented in GABAergic neurons. Collectively, these results establish crucial roles for the MeA as a key node in the neural circuitry underlying pup-directed behaviors and provide important insight into the connection between sex differences across transcriptomes, cells, and circuits in regulating sexually dimorphic behavior.

Distinct Dopamine Receptor Pathways Underlie the Temporal Sensitivity of Associative Learning.

Animals rely on the relative timing of events in their environment to form and update predictive associations, but the molecular and circuit mechanisms for this temporal sensitivity remain incompletely understood. Here, we show that olfactory associations in Drosophila can be written and reversed on a trial-by-trial basis depending on the temporal relationship between an odor cue and dopaminergic reinforcement. Through the synchronous recording of neural activity and behavior, we show that reversals in learned odor attraction correlate with bidirectional neural plasticity in the mushroom body, the associative olfactory center of the fly. Two dopamine receptors, DopR1 and DopR2, contribute to this temporal sensitivity by coupling to distinct second messengers and directing either synaptic depression or potentiation. Our results reveal how dopamine-receptor signaling pathways can detect the order of events to instruct opposing forms of synaptic and behavioral plasticity, allowing animals to flexibly update their associations in a dynamic environment.

Neuronal Dynamics Regulating Brain and Behavioral State Transitions.

Prolonged behavioral challenges can cause animals to switch from active to passive coping strategies to manage effort-expenditure during stress; such normally adaptive behavioral state transitions can become maladaptive in psychiatric disorders such as depression. The underlying neuronal dynamics and brainwide interactions important for passive coping have remained unclear. Here, we develop a paradigm to study these behavioral state transitions at cellular-resolution across the entire vertebrate brain. Using brainwide imaging in zebrafish, we observed that the transition to passive coping is manifested by progressive activation of neurons in the ventral (lateral) habenula. Activation of these ventral-habenula neurons suppressed downstream neurons in the serotonergic raphe nucleus and caused behavioral passivity, whereas inhibition of these neurons prevented passivity. Data-driven recurrent neural network modeling pointed to altered intra-habenula interactions as a contributory mechanism. These results demonstrate ongoing encoding of experience features in the habenula, which guides recruitment of downstream networks and imposes a passive coping behavioral strategy.

Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states. VIDEO ABSTRACT.

Stress-Induced Metabolic Disorder in Peripheral CD4+ T Cells Leads to Anxiety-like Behavior.

Physical or mental stress leads to neuroplasticity in the brain and increases the risk of depression and anxiety. Stress exposure causes the dysfunction of peripheral T lymphocytes. However, the pathological role and underlying regulatory mechanism of peripheral T lymphocytes in mood disorders have not been well established. Here, we show that the lack of CD4+ T cells protects mice from stress-induced anxiety-like behavior. Physical stress-induced leukotriene B4 triggers severe mitochondrial fission in CD4+ T cells, which further leads to a variety of behavioral abnormalities including anxiety, depression, and social disorders. Metabolomic profiles and single-cell transcriptome reveal that CD4+ T cell-derived xanthine acts on oligodendrocytes in the left amygdala via adenosine receptor A1. Mitochondrial fission promotes the de novo synthesis of purine via interferon regulatory factor 1 accumulation in CD4+ T cells. Our study implicates a critical link between a purine metabolic disorder in CD4+ T cells and stress-driven anxiety-like behavior.

The Fly Brain Atlas.

The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains that can be examined and manipulated by genetic means offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In Drosophila, two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord.

The Budding Neuroscience of Ant Social Behavior.

Ant physiology has been fashioned by 100 million years of social evolution. Ants perform many sophisticated social and collective behaviors yet possess nervous systems similar in schematic and scale to that of the fruit fly Drosophila melanogaster, a popular solitary model organism. Ants are thus attractive complementary subjects to investigate adaptations pertaining to complex social behaviors that are absent in flies. Despite research interest in ant behavior and the neurobiological foundations of sociality more broadly, our understanding of the ant nervous system is incomplete. Recent technical advances have enabled cutting-edge investigations of the nervous system in a fashion that is less dependent on model choice, opening the door for mechanistic social insect neuroscience. In this review, we revisit important aspects of what is known about the ant nervous system and behavior, and we look forward to how functional circuit neuroscience in ants will help us understand what distinguishes solitary animals from highly social ones.

Neural Control of Naturalistic Behavior Choices.

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

Considering Organismal Physiology in Laboratory Studies of Rodent Behavior.

Any experiment conducted in a rodent laboratory is done so against the backdrop of each animal's physiological state at the time of the experiment. This physiological state can be the product of multiple factors, both internal (e.g., animal sex, strain, hormone cycles, or circadian rhythms) and external (e.g., housing conditions, social status, and light/dark phases). Each of these factors has the potential to influence experimental outcomes, either independently or via interactions with others, and yet there is little consistency across laboratories in terms of the weight with which they are considered in experimental design. Such discrepancies-both in practice and in reporting-likely contribute to the perception of a reproducibility crisis in the field of behavioral neuroscience. In this review, we discuss how several of these sources of variability can impact outcomes within the realm of common learning and memory paradigms.

A virtual rodent predicts the structure of neural activity across behaviours.

Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. Here, to facilitate this, we built a 'virtual rodent', in which an artificial neural network actuates a biomechanically realistic model of the rat1 in a physics simulator2. We used deep reinforcement learning3-5 to train the virtual agent to imitate the behaviour of freely moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behaviour. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent's network activity than by any features of the real rat's movements, consistent with both regions implementing inverse dynamics6. Furthermore, the network's latent variability predicted the structure of neural variability across behaviours and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behaviour and relate it to theoretical principles of motor control.

Descending networks transform command signals into population motor control.

To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours1-the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2-4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.