Cell-type-specific population dynamics of diverse reward computations

Comparative Perspectives on Neuropeptide Function and Social Isolation

Chronic social isolation alters behavior across animal species. Genetic model organisms such as mice and flies provide crucial insight into the molecular and physiological effects of social isolation on brain cells and circuits. Here, we comparatively review recent findings regarding the function of conserved neuropeptides in social isolation in mice and flies. Analogous functions of 3 classes of neuropeptides-tachykinins, cholecystokinins, and neuropeptide Y/F-in the two model organisms suggest that these molecules may be involved in modulating behavioral changes induced by social isolation across a wider range of species, including humans. Comparative approaches armed with tools to dissect neuropeptidergic function can lead to an integrated understanding of the impacts of social isolation on brain circuits and behavior.

Cell-type-specific manifold analysis discloses independent geometric transformations in the hippocampal spatial code

Integrating analyses of genetically defined cell types with population-level approaches remains poorly explored. We investigated this question by focusing on hippocampal spatial maps and the contribution of two genetically defined pyramidal cell types in the deep and superficial CA1 sublayers. Using single- and dual-color miniscope imaging in mice running along a linear track, we found that population activity from these cells exhibited three-dimensional ring manifolds that encoded the animal position and running direction. Despite shared topology, sublayer-specific manifolds displayed distinct geometric features. Manipulating track orientation revealed rotational and translational changes in manifolds from deep cells, contrasting with more stable representations by superficial cells. These transformations were not observed in manifolds derived from the entire CA1 population. Instead, cell-type-specific chemogenetic silencing of either sublayer revealed independent geometric codes. Our results show how genetically specified subpopulations may underpin parallel spatial maps that can be manipulated independently.

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

The neural pathway from the superior subpart of the medial habenula to the interpeduncular nucleus suppresses anxiety

The medial habenula (MHb) and its projection target, the interpeduncular nucleus (IPN), are highly conserved throughout vertebrate evolution. The MHb-IPN pathway connects the limbic system to the brainstem, consisting of subpathways that project in a topographically organized manner, and has been implicated in the regulation of fear and anxiety. Previous studies have revealed subregion-specific functions of the cholinergic ventral MHb and a substance P (SP)-positive (SP+) subpart of the dorsal MHb (dMHb). In contrast, the dMHb also contains another subpart, a SP-negative subpart known as the 'superior part of MHb (MHbS)'. Although the MHbS has been characterized from various aspects, e.g. distinct c-Fos responses to stressful events and electrophysiological properties compared to other subregions, many of its physiological functions remain to be investigated. Here we found that dopamine receptor D3 (DRD3)-Cre mice enable the labeling of the IPN subregion that receives the MHbS projection. The Cre-expressing somata within the lateral subnucleus of the IPN (LIPN) were concentrated in its most lateral area, which we refer to as the 'lateral subregion of the LIPN (lLIPN)'. This region is characterized by the absence of SP+ axons, in contrast to the medial subregion of the LIPN (mLIPN) innervated by the SP+ axons from the dorsal MHb. Chemogenetic activation and genetically induced synaptic silencing of the DRD3-Cre+ cells reduced and enhanced anxiety-like behavior, respectively. Moreover, c-Fos expression was increased in the lLIPN under an anxiogenic environment. These findings suggest that the MHbS-lLIPN pathway is activated under anxiogenic environments to counteract anxiety.

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. We investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in the prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable and consistent with the attractor dynamics. However, during the trial, the activity was associated with task-related behavior and became non-stationary, thus deviating from the line attractor. Fitting a predictive model to neural data showed that the stationary state at the trial start serves as an initial condition for launching the non-stationary activity. This suggested an extension of the line attractor model with behavior-induced non-stationary dynamics. The non-stationary trajectories were separable indicating that they can represent distinct probabilistic values. Perturbing the reversal probability activity in the recurrent neural networks biased choice outcomes demonstrating its functional significance. In sum, our results show that cortical networks encode reversal probability in stable stationary state at the start of a trial and utilize it to initiate non-stationary dynamics that accommodates task-related behavior while maintaining the reversal information.

Optogenetic modulation of long-range cortical circuits in awake nonhuman primates

Causal control of short- and long-range projections between networks is necessary to study complex cognitive processes and cortical computations. Neural circuits can be studied via optogenetic approaches, which provide excellent genetic and temporal control and electrophysiological recordings. However, in nonhuman primates (NHPs), these approaches are commonly performed at a single location, missing out on the potential to test connections between separate networks. We have recently developed an approach for optogenetic manipulation in NHPs which targets intra- and interareal cortical projections. Here we describe the combination of optogenetic stimulation with standard chamber-based electrophysiological recordings in awake NHPs to monitor and manipulate both short- and long-range feedforward and feedback circuits. We describe the injection of viral constructs, the simultaneous electrophysiological recordings with the optical stimulation of neurons at various cortical distances and the evaluation of gene expression using a focal biopsy technique. We focus on details that are specific to NHP preparations, such as the precise targeting of injection sites, choosing appropriate viral constructs and considerations for behavioral measures. When combined with laminar electrode configurations (to functionally identify cortical layers) and complex cognitive behavioral tasks, our approach can be used to investigate an array of systems neuroscience questions, such as the role of feedback circuits in attention and the role of lateral connections in contrast normalization. The procedure requires 2-3 active days and 45 waiting days to transduce selected neural circuits and several weeks to complete experiments. The procedure is appropriate for users with expertise in in vivo, awake electrophysiology with NHPs.

Coordinated representations for naturalistic memory encoding and retrieval in hippocampal neural subspaces

Our naturalistic experiences are organized into memories through multiple processes, including novelty encoding, memory formation, and retrieval. However, the neural mechanisms coordinating these processes remain elusive. Using fMRI data acquired during movie viewing and subsequent narrative recall, we examine hippocampal neural subspaces associated with distinct memory processes and characterized their relationships. We quantify novelty in character co-occurrences and the valence of relationships and estimate event memorability. Within the hippocampus, the novelty subspaces encoding each type exhibit partial overlap, and these overlapping novelty subspaces align with the subspace involved in memorability. Notably, following event boundaries, hippocampal states within these subspaces align inversely along a shared coding axis, predicting subsequent recall performance. This novelty-memorability alignment is selectively observed during encoding but not during retrieval. Finally, the identified functional subspaces reflect the intrinsic functional organization of the hippocampus. Our findings offer insights into how the hippocampus dynamically coordinates representations underlying memory encoding and retrieval at the population level to transform ongoing experiences into enduring memories.

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

The complex neural activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured PFC responses within contexts and revealed equally performing mechanisms. One model implemented context-dependent recurrent dynamics and relied on transient input amplification; the other relied on subtle contextual modulations of the inputs, providing constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both models revealed properties of inputs and recurrent dynamics that were not apparent from qualitative descriptions of PFC responses. By revealing mechanisms that are quantitatively consistent with complex cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

A line attractor encoding a persistent internal state requires neuropeptide signaling

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Causal evidence of a line attractor encoding an affective state

Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position1-4. In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles2,3. Dynamical systems modelling has revealed that neurons in the hypothalamus exhibit approximate line-attractor dynamics in male mice during aggressive encounters5. We have previously hypothesized that these dynamics may encode the variable intensity and persistence of an aggressive internal state. Here we report that these neurons also showed line-attractor dynamics in head-fixed mice observing aggression6. This allowed us to identify and manipulate line-attractor-contributing neurons using two-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations yielded integration of optogenetic stimulation pulses and persistent activity that drove the system along the line attractor, while transient off-manifold perturbations were followed by rapid relaxation back into the attractor. Furthermore, single-cell stimulation and imaging revealed selective functional connectivity among attractor-contributing neurons. Notably, individual differences among mice in line-attractor stability were correlated with the degree of functional connectivity among attractor-contributing neurons. Mechanistic recurrent neural network modelling indicated that dense subnetwork connectivity and slow neurotransmission7 best recapitulate our empirical findings. Our work bridges circuit and manifold levels3, providing causal evidence of continuous attractor dynamics encoding an affective internal state in the mammalian hypothalamus.

Interpeduncular GABAergic neuron function controls threat processing and innate defensive adaptive learning

The selection of appropriate defensive behaviors in the face of potential threat is fundamental to survival. However, after repeated exposures to threatening stimuli that did not signal real danger, an animal must learn to adjust and optimize defensive behaviors. Despite extensive research on innate threat processing, little is known how individuals change their defensive behaviors when presented with recurrent threat exposures without evidence of a real risk. Insight into this process is critical as its dysregulation may contribute to neuropsychiatric conditions, such as anxiety disorders. Here, we used the visual looming stimulus (VLS) paradigm in mice to investigate innate threat processing and adaptive defensive learning. Repeated exposure to VLS over consecutive sessions reduced immediate freezing responses and time spent inside a sheltered area upon VLS events, leading to an increase in foraging behaviors. Fiber photometry recordings and optogenetic manipulations revealed that VLS innate adaptive defensive learning is associated with reduced recruitment of the midbrain interpeduncular nucleus (IPN), a structure associated with fear and anxiety-related behaviors. Functional circuit-mapping identified a role for select IPN projections to the laterodorsal tegmental nucleus in gating defensive learning. Finally, we uncovered a subpopulation of IPN neurons that express the neuropeptide somatostatin and encode safety- and avoidance signals in response to VLS. These results identify critical behavioral signatures of innate defensive responses and a circuit that regulates the essential features of threat processing.

A computational mechanism of cue-stimulus integration for pain in the brain

The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.

Enhanced osteogenic differentiation in 3D hydrogel scaffold via macrophage mitochondrial transfer

To assess the efficacy of a novel 3D biomimetic hydrogel scaffold with immunomodulatory properties in promoting fracture healing. Immunomodulatory scaffolds were used in cell experiments, osteotomy mice treatment, and single-cell transcriptomic sequencing. In vitro, fluorescence tracing examined macrophage mitochondrial transfer and osteogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs). Scaffold efficacy was assessed through alkaline phosphatase (ALP), Alizarin Red S (ARS) staining, and in vivo experiments. The scaffold demonstrated excellent biocompatibility and antioxidant-immune regulation. Single-cell sequencing revealed a shift in macrophage distribution towards the M2 phenotype. In vitro experiments showed that macrophage mitochondria promoted BMSCs' osteogenic differentiation. In vivo experiments confirmed accelerated fracture healing. The GAD/Ag-pIO scaffold enhances osteogenic differentiation and fracture healing through immunomodulation and promotion of macrophage mitochondrial transfer.

The behavioral relevance of a modular organization in the lateral habenula

Behavioral strategies for survival rely on the updates the brain continuously makes based on the surrounding environment. External stimuli-neutral, positive, and negative-relay core information to the brain, where a complex anatomical network rapidly organizes actions, including approach or escape, and regulates emotions. Human neuroimaging and physiology in nonhuman primates, rodents, and teleosts suggest a pivotal role of the lateral habenula in translating external information into survival behaviors. Here, we review the literature describing how discrete habenular modules-reflecting the molecular signatures, anatomical connectivity, and functional components-are recruited by environmental stimuli and cooperate to prompt specific behavioral outcomes. We argue that integration of these findings in the context of valence processing for reinforcing or discouraging behaviors is necessary, offering a compelling model to guide future work.

A potentiation of REM sleep-active neurons in the lateral habenula may be responsible for the sleep disturbance in depression

Psychiatric disorders with dysfunction of the lateral habenula (LHb) show sleep disturbance, especially a disinhibition of rapid eye movement (REM) sleep in major depression. However, the role of LHb in physiological sleep control and how LHb contributes to sleep disturbance in major depression remain elusive. Here, we found that functional manipulations of LHb glutamatergic neurons bidirectionally modulated both non-REM (NREM) sleep and REM sleep. Activity recording revealed heterogeneous activity patterns of LHb neurons across sleep/wakefulness cycles, but LHb neurons were preferentially active during REM sleep. Using an activity-dependent tagging method, we selectively labeled a population of REM sleep-active LHb neurons and demonstrated that these neurons specifically promoted REM sleep. Neural circuit studies showed that LHb neurons regulated REM sleep via projections to the ventral tegmental area but not to the rostromedial tegmental nucleus. Furthermore, we found that the increased REM sleep in a depression mouse model was associated with a potentiation of REM sleep-active LHb neurons, including an increased proportion, elevated spike firing, and altered activity mode. Importantly, inhibition of REM sleep-active LHb neurons not only attenuated the increased REM sleep but also alleviated depressive-like behaviors in a depression mouse model. Thus, our results demonstrated that REM sleep-active LHb neurons selectively promoted REM sleep, and a potentiation of these neurons contributed to depression-associated sleep disturbance.

A subset of dopamine receptor-expressing neurons in the nucleus accumbens controls feeding and energy homeostasis

Orchestrating complex behaviors, such as approaching and consuming food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also controls appetite and satiety. However, specific neuronal subtypes of the NAc that are involved and how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here we decipher the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and define a dopamine receptor D1-expressing and Serpinb2-expressing subtype controlling food consumption in male mice. Chemogenetics and optogenetics-mediated regulation of Serpinb2+ neurons bidirectionally regulate food seeking and consumption specifically. Circuitry stimulation reveals that the NAcShSerpinb2→LHLepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2+ neuron ablation reduces food intake and upregulates energy expenditure, resulting in reduced bodyweight gain. Our study reveals a neural circuit consisting of a molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, providing a potential therapeutic target for eating disorders.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex

In complex environments, animals can adopt diverse strategies to find rewards. How distinct strategies differentially engage brain circuits is not well understood. Here, we investigate this question, focusing on the cortical Vip-Sst disinhibitory circuit between vasoactive intestinal peptide-postive (Vip) interneurons and somatostatin-positive (Sst) interneurons. We characterize the behavioral strategies used by mice during a visual change detection task. Using a dynamic logistic regression model, we find that individual mice use mixtures of a visual comparison strategy and a statistical timing strategy. Separately, mice also have periods of task engagement and disengagement. Two-photon calcium imaging shows large strategy-dependent differences in neural activity in excitatory, Sst inhibitory, and Vip inhibitory cells in response to both image changes and image omissions. In contrast, task engagement has limited effects on neural population activity. We find that the diversity of neural correlates of strategy can be understood parsimoniously as the increased activation of the Vip-Sst disinhibitory circuit during the visual comparison strategy, which facilitates task-appropriate responses.

Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State

Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.

The lateral habenula: A hub for value-guided behavior

The habenula is an evolutionarily highly conserved diencephalic brain region divided into two major parts, medial and lateral. Over the past two decades, studies of the lateral habenula (LHb), in particular, have identified key functions in value-guided behavior in health and disease. In this review, we focus on recent insights into LHb connectivity and its functional relevance for different types of aversive and appetitive value-guided behavior. First, we give an overview of the anatomical organization of the LHb and its main cellular composition. Next, we elaborate on how distinct LHb neuronal subpopulations encode aversive and appetitive stimuli and on their involvement in more complex decision-making processes. Finally, we scrutinize the afferent and efferent connections of the LHb and discuss their functional implications for LHb-dependent behavior. A deepened understanding of distinct LHb circuit components will substantially contribute to our knowledge of value-guided behavior.

Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics

Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.

A molecularly defined NAcSh D1 subtype controls feeding and energy homeostasis

Orchestrating complex behavioral states, such as approach and consumption of food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also plays an important role in controlling appetite and satiety in responses to changing external stimuli. However, the specific neuronal subtypes of NAc involved as well as how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here, we deciphered the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and defined a dopamine receptor D1(Drd1)- and Serpinb2-expressing subtype located in NAcSh encoding food consumption. Chemogenetics- and optogenetics-mediated regulation of Serpinb2 + neurons bidirectionally regulates food seeking and consumption specifically. Circuitry stimulation revealed the NAcSh Serpinb2 →LH LepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2 + neuron ablation reduces food intake and upregulates energy expenditure resulting in body weight loss. Together, our study reveals a neural circuit consisted of molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, which can serve as a potential therapeutic target for eating disorders.

GABAB receptors induce phasic release from medial habenula terminals through activity-dependent recruitment of release-ready vesicles

GABAB receptor (GBR) activation inhibits neurotransmitter release in axon terminals in the brain, except in medial habenula (MHb) terminals, which show robust potentiation. However, mechanisms underlying this enigmatic potentiation remain elusive. Here, we report that GBR activation on MHb terminals induces an activity-dependent transition from a facilitating, tonic to a depressing, phasic neurotransmitter release mode. This transition is accompanied by a 4.1-fold increase in readily releasable vesicle pool (RRP) size and a 3.5-fold increase of docked synaptic vesicles (SVs) at the presynaptic active zone (AZ). Strikingly, the depressing phasic release exhibits looser coupling distance than the tonic release. Furthermore, the tonic and phasic release are selectively affected by deletion of synaptoporin (SPO) and Ca2+-dependent activator protein for secretion 2 (CAPS2), respectively. SPO modulates augmentation, the short-term plasticity associated with tonic release, and CAPS2 retains the increased RRP for initial responses in phasic response trains. The cytosolic protein CAPS2 showed a SV-associated distribution similar to the vesicular transmembrane protein SPO, and they were colocalized in the same terminals. We developed the "Flash and Freeze-fracture" method, and revealed the release of SPO-associated vesicles in both tonic and phasic modes and activity-dependent recruitment of CAPS2 to the AZ during phasic release, which lasted several minutes. Overall, these results indicate that GBR activation translocates CAPS2 to the AZ along with the fusion of CAPS2-associated SVs, contributing to persistency of the RRP increase. Thus, we identified structural and molecular mechanisms underlying tonic and phasic neurotransmitter release and their transition by GBR activation in MHb terminals.

Assessing cross-contamination in spike-sorted electrophysiology data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.

Plasticity of neuronal dynamics in the lateral habenula for cue-punishment associative learning

The brain's ability to associate threats with external stimuli is vital to execute essential behaviours including avoidance. Disruption of this process contributes instead to the emergence of pathological traits which are common in addiction and depression. However, the mechanisms and neural dynamics at the single-cell resolution underlying the encoding of associative learning remain elusive. Here, employing a Pavlovian discrimination task in mice we investigate how neuronal populations in the lateral habenula (LHb), a subcortical nucleus whose excitation underlies negative affect, encode the association between conditioned stimuli and a punishment (unconditioned stimulus). Large population single-unit recordings in the LHb reveal both excitatory and inhibitory responses to aversive stimuli. Additionally, local optical inhibition prevents the formation of cue discrimination during associative learning, demonstrating a critical role of LHb activity in this process. Accordingly, longitudinal in vivo two-photon imaging tracking LHb calcium neuronal dynamics during conditioning reveals an upward or downward shift of individual neurons' CS-evoked responses. While recordings in acute slices indicate strengthening of synaptic excitation after conditioning, support vector machine algorithms suggest that postsynaptic dynamics to punishment-predictive cues represent behavioral cue discrimination. To examine the presynaptic signaling in LHb participating in learning we monitored neurotransmitter dynamics with genetically-encoded indicators in behaving mice. While glutamate, GABA, and serotonin release in LHb remain stable across associative learning, we observe enhanced acetylcholine signaling developing throughout conditioning. In summary, converging presynaptic and postsynaptic mechanisms in the LHb underlie the transformation of neutral cues in valued signals supporting cue discrimination during learning.

Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State

Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Structural basis for ion selectivity in potassium-selective channelrhodopsins

KCR channelrhodopsins (K+-selective light-gated ion channels) have received attention as potential inhibitory optogenetic tools but more broadly pose a fundamental mystery regarding how their K+ selectivity is achieved. Here, we present 2.5-2.7 Å cryo-electron microscopy structures of HcKCR1 and HcKCR2 and of a structure-guided mutant with enhanced K+ selectivity. Structural, electrophysiological, computational, spectroscopic, and biochemical analyses reveal a distinctive mechanism for K+ selectivity; rather than forming the symmetrical filter of canonical K+ channels achieving both selectivity and dehydration, instead, three extracellular-vestibule residues within each monomer form a flexible asymmetric selectivity gate, while a distinct dehydration pathway extends intracellularly. Structural comparisons reveal a retinal-binding pocket that induces retinal rotation (accounting for HcKCR1/HcKCR2 spectral differences), and design of corresponding KCR variants with increased K+ selectivity (KALI-1/KALI-2) provides key advantages for optogenetic inhibition in vitro and in vivo. Thus, discovery of a mechanism for ion-channel K+ selectivity also provides a framework for next-generation optogenetics.

Periodic hypothalamic attractor-like dynamics during the estrus cycle

Cyclic changes in hormonal state are well-known to regulate mating behavior during the female reproductive cycle, but whether and how these changes affect the dynamics of neural activity in the female brain is largely unknown. The ventromedial hypothalamus, ventro-lateral subdivision (VMHvl) contains a subpopulation of VMHvl Esr1+,Npy2r- neurons that controls female sexual receptivity. Longitudinal single cell calcium imaging of these neurons across the estrus cycle revealed that overlapping but distinct subpopulations were active during proestrus (mating-accepting) vs. non-proestrus (rejecting) phases. Dynamical systems analysis of imaging data from proestrus females uncovered a dimension with slow ramping activity, which generated approximate line attractor-like dynamics in neural state space. During mating, the neural population vector progressed along this attractor as male mounting and intromission proceeded. Attractor-like dynamics disappeared in non-proestrus states and reappeared following re-entry into proestrus. They were also absent in ovariectomized females but were restored by hormone priming. These observations reveal that hypothalamic line attractor-like dynamics are associated with female sexual receptivity and can be reversibly regulated by sex hormones, demonstrating that attractor dynamics can be flexibly modulated by physiological state. They also suggest a potential mechanism for the neural encoding of female sexual arousal.

Adeno-associated virus 2 infection in children with non-A-E hepatitis

An outbreak of acute hepatitis of unknown aetiology in children was reported in Scotland1 in April 2022 and has now been identified in 35 countries2. Several recent studies have suggested an association with human adenovirus with this outbreak, a virus not commonly associated with hepatitis. Here we report a detailed case-control investigation and find an association between adeno-associated virus 2 (AAV2) infection and host genetics in disease susceptibility. Using next-generation sequencing, PCR with reverse transcription, serology and in situ hybridization, we detected recent infection with AAV2 in plasma and liver samples in 26 out of 32 (81%) cases of hepatitis compared with 5 out of 74 (7%) of samples from unaffected individuals. Furthermore, AAV2 was detected within ballooned hepatocytes alongside a prominent T cell infiltrate in liver biopsy samples. In keeping with a CD4+ T-cell-mediated immune pathology, the human leukocyte antigen (HLA) class II HLA-DRB1*04:01 allele was identified in 25 out of 27 cases (93%) compared with a background frequency of 10 out of 64 (16%; P = 5.49 × 10-12). In summary, we report an outbreak of acute paediatric hepatitis associated with AAV2 infection (most likely acquired as a co-infection with human adenovirus that is usually required as a 'helper virus' to support AAV2 replication) and disease susceptibility related to HLA class II status.

Understanding the habenula: A major node in circuits regulating emotion and motivation

Over the last decade, the understanding of the habenula has rapidly advanced from being an understudied brain area with the Latin name 'habena" meaning "little rein", to being considered a "major rein" in the control of key monoaminergic brain centers. This ancient brain structure is a strategic node in the information flow from fronto-limbic brain areas to brainstem nuclei. As such, it plays a crucial role in regulating emotional, motivational, and cognitive behaviors and has been implicated in several neuropsychiatric disorders, including depression and addiction. This review will summarize recent findings on the medial (MHb) and lateral (LHb) habenula, their topographical projections, cell types, and functions. Additionally, we will discuss contemporary efforts that have uncovered novel molecular pathways and synaptic mechanisms with a focus on MHb-Interpeduncular nucleus (IPN) synapses. Finally, we will explore the potential interplay between the habenula's cholinergic and non-cholinergic components in coordinating related emotional and motivational behaviors, raising the possibility that these two pathways work together to provide balanced roles in reward prediction and aversion, rather than functioning independently.

Cardiogenic control of affective behavioural state

Emotional states influence bodily physiology, as exemplified in the top-down process by which anxiety causes faster beating of the heart1-3. However, whether an increased heart rate might itself induce anxiety or fear responses is unclear3-8. Physiological theories of emotion, proposed over a century ago, have considered that in general, there could be an important and even dominant flow of information from the body to the brain9. Here, to formally test this idea, we developed a noninvasive optogenetic pacemaker for precise, cell-type-specific control of cardiac rhythms of up to 900 beats per minute in freely moving mice, enabled by a wearable micro-LED harness and the systemic viral delivery of a potent pump-like channelrhodopsin. We found that optically evoked tachycardia potently enhanced anxiety-like behaviour, but crucially only in risky contexts, indicating that both central (brain) and peripheral (body) processes may be involved in the development of emotional states. To identify potential mechanisms, we used whole-brain activity screening and electrophysiology to find brain regions that were activated by imposed cardiac rhythms. We identified the posterior insular cortex as a potential mediator of bottom-up cardiac interoceptive processing, and found that optogenetic inhibition of this brain region attenuated the anxiety-like behaviour that was induced by optical cardiac pacing. Together, these findings reveal that cells of both the body and the brain must be considered together to understand the origins of emotional or affective states. More broadly, our results define a generalizable approach for noninvasive, temporally precise functional investigations of joint organism-wide interactions among targeted cells during behaviour.

An approximate line attractor in the hypothalamus encodes an aggressive state

The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA and VMHvl, respectively. Here, we perform dynamical systems modeling of population neuronal activity in these nuclei during social behaviors. In VMHvl, unsupervised analysis identified a dominant dimension of neural activity with a large time constant (>50 s), generating an approximate line attractor in neural state space. Progression of the neural trajectory along this attractor was correlated with an escalation of agonistic behavior, suggesting that it may encode a scalable state of aggressiveness. Consistent with this, individual differences in the magnitude of the integration dimension time constant were strongly correlated with differences in aggressiveness. In contrast, approximate line attractors were not observed in MPOA during mating; instead, neurons with fast dynamics were tuned to specific actions. Thus, different hypothalamic nuclei employ distinct neural population codes to represent similar social behaviors.