Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

An implantable device for wireless monitoring of diverse physio-behavioral characteristics in freely behaving small animals and interacting groups.

Comprehensive, continuous quantitative monitoring of intricately orchestrated physiological processes and behavioral states in living organisms can yield essential data for elucidating the function of neural circuits under healthy and diseased conditions, for defining the effects of potential drugs and treatments, and for tracking disease progression and recovery. Here, we report a wireless, battery-free implantable device and a set of associated algorithms that enable continuous, multiparametric physio-behavioral monitoring in freely behaving small animals and interacting groups. Through advanced analytics approaches applied to mechano-acoustic signals of diverse body processes, the device yields heart rate, respiratory rate, physical activity, temperature, and behavioral states. Demonstrations in pharmacological, locomotor, and acute and social stress tests and in optogenetic studies offer unique insights into the coordination of physio-behavioral characteristics associated with healthy and perturbed states. This technology has broad utility in neuroscience, physiology, behavior, and other areas that rely on studies of freely moving, small animal models.

Opioid-driven disruption of the septal complex reveals a role for neurotensin-expressing neurons in withdrawal.

Because opioid withdrawal is an intensely aversive experience, persons with opioid use disorder (OUD) often relapse to avoid it. The lateral septum (LS) is a forebrain structure that is important in aversion processing, and previous studies have linked the lateral septum (LS) to substance use disorders. It is unclear, however, which precise LS cell types might contribute to the maladaptive state of withdrawal. To address this, we used single-nucleus RNA-sequencing to interrogate cell type specific gene expression changes induced by chronic morphine and withdrawal. We discovered that morphine globally disrupted the transcriptional profile of LS cell types, but Neurotensin-expressing neurons (Nts; LS-Nts neurons) were selectively activated by naloxone. Using two-photon calcium imaging and ex vivo electrophysiology, we next demonstrate that LS-Nts neurons receive enhanced glutamatergic drive in morphine-dependent mice and remain hyperactivated during opioid withdrawal. Finally, we showed that activating and silencing LS-Nts neurons during opioid withdrawal regulates pain coping behaviors and sociability. Together, these results suggest that LS-Nts neurons are a key neural substrate involved in opioid withdrawal and establish the LS as a crucial regulator of adaptive behaviors, specifically pertaining to OUD.

Individual Differences in Volitional Social Self-Administration and Motivation in Male and Female Mice Following Social Stress.

BACKGROUND: A key challenge in developing treatments for neuropsychiatric illness is the disconnect between preclinical models and the complexity of human social behavior. We integrate voluntary social self-administration into a rodent model of social stress as a platform for the identification of fundamental brain and behavior mechanisms underlying stress-induced individual differences in social motivation. METHODS: Here, we introduced an operant social stress procedure in male and female mice composed of 3 phases: 1) social self-administration training, 2) social stress exposure concurrent with reinforced self-administration testing, and 3) poststress operant testing under nonreinforced and reinforced conditions. We used social-defeat and witness-defeat stress in male and female mice. RESULTS: Social defeat attenuated social reward seeking in males but not females, whereas witness defeat had no effect in males but promoted seeking behavior in females. We resolved social stress-induced changes to social motivation by aggregating z-scored operant metrics into a cumulative social index score to describe the spectrum of individual differences exhibited during operant social stress. Clustering does not adequately describe the relative distributions of social motivation following stress and is better described as a nonbinary behavioral distribution defined by the social index score, capturing a dynamic range of stress-related alterations in social motivation inclusive of sex as a biological variable. CONCLUSIONS: We demonstrated that operant social stress can detect stable individual differences in stress-induced changes to social motivation. The inclusion of volitional behavior in social procedures may enhance the understanding of behavioral adaptations that promote stress resiliency and their mechanisms under more naturalistic conditions.

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells.

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single-cells. However, conventional fluorescent protein (FP) modifications used to discriminate single-cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and non-deleterious nuclear localization signal (NLS) tag strategy, called 'Arginine-rich NLS' (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single-cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes, and in response to both local and systemic brain wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances ML-automated segmentation of single-cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single-cells at scale and paired with behavioral procedures.

ASCL1 induces neurogenesis in human Müller glia.

In mammals, loss of retinal cells due to disease or trauma is an irreversible process that can lead to blindness. Interestingly, regeneration of retinal neurons is a well established process in some non-mammalian vertebrates and is driven by the Müller glia (MG), which are able to re-enter the cell cycle and reprogram into neurogenic progenitors upon retinal injury or disease. Progress has been made to restore this mechanism in mammals to promote retinal regeneration: MG can be stimulated to generate new neurons in vivo in the adult mouse retina after the over-expression of the pro-neural transcription factor Ascl1. In this study, we applied the same strategy to reprogram human MG derived from fetal retina and retinal organoids into neurons. Combining single cell RNA sequencing, single cell ATAC sequencing, immunofluorescence, and electrophysiology we demonstrate that human MG can be reprogrammed into neurogenic cells in vitro.

Lateral line ablation by ototoxic compounds results in distinct rheotaxis profiles in larval zebrafish.

The zebrafish lateral line is an established model for hair cell organ damage, yet few studies link mechanistic disruptions to changes in biologically relevant behavior. We used larval zebrafish to determine how damage via ototoxic compounds impact rheotaxis. Larvae were treated with CuSO4 or neomycin to disrupt lateral line function then exposed to water flow stimuli. Their swimming behavior was recorded on video then DeepLabCut and SimBA software were used to track movements and classify rheotaxis behavior, respectively. Lateral line-disrupted fish performed rheotaxis, but they swam greater distances, for shorter durations, and with greater angular variance than controls. Furthermore, spectral decomposition analyses confirmed that lesioned fish exhibited ototoxic compound-specific behavioral profiles with distinct changes in the magnitude, frequency, and cross-correlation between fluctuations in linear and angular movements. Our observations demonstrate that lateral line input is needed for fish to hold their station in flow efficiently and reveals that commonly used lesion methods have unique effects on rheotaxis behavior.

Sex differences in appetitive and reactive aggression.

Aggression is an evolutionarily conserved, adaptive component of social behavior. Studies in male mice illustrate that aggression is influenced by numerous factors including the degree to which an individual finds aggression rewarding and will work for access to attack and subordinate mice. While such studies have expanded our understanding of the molecular and circuit mechanisms of male aggression very little is known about female aggression, within these established contexts. Here we use an ethologically relevant model of male vs. female aggression by pair housing adult male and female outbred CFW mice with opposite sex cage mates. We assess reactive (defensive) aggression in the resident intruder (RI) test and appetitive (rewarding) aggression in the aggression conditioned place preference (CPP) and operant self-administration (SA) tests. Our results show dramatic sex differences in both qualitative and quantitative aspects of reactive vs. appetitive aggression. Males exhibit more wrestling and less investigative behavior during RI, find aggression rewarding, and will work for access to a subordinate to attack. Females exhibit more bites, alternate between aggressive behaviors and investigative behaviors more readily during RI, however, they do not find aggression to be rewarding or reinforcing. These results establish sex differences in aggression in mice, providing an important resource for the field to better understand the circuit and molecular mechanisms of aggression in both sexes.

Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience.

The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field toward a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive toward behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a diagnostic resource toward explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.

SMART: An Open-Source Extension of WholeBrain for Intact Mouse Brain Registration and Segmentation.

Mapping immediate early gene (IEG) expression across intact mouse brains allows for unbiased identification of brain-wide activity patterns underlying complex behaviors. Accurate registration of sample brains to a common anatomic reference is critical for precise assignment of IEG-positive ("active") neurons to known brain regions of interest (ROIs). While existing automated voxel-based registration methods provide a high-throughput solution, they require substantial computing power, can be difficult to implement and fail when brains are damaged or only partially imaged. Additionally, it is challenging to cross-validate these approaches or compare them to any preexisting literature based on serial coronal sectioning. Here, we present the open-source R package SMART (Semi-Manual Alignment to Reference Templates) that extends the WholeBrain R package framework to automated segmentation and semi-automated registration of intact mouse brain light-sheet fluorescence microscopy (LSFM) datasets. The SMART package was created for novice programmers and introduces a streamlined pipeline for aligning, registering, and segmenting LSFM volumetric datasets across the anterior-posterior (AP) axis, using a simple "choice game" and interactive menus. SMART provides the flexibility to register whole brains, partial brains or discrete user-chosen images, and is fully compatible with traditional sectioned coronal slice-based analyses. We demonstrate SMART's core functions using example datasets and provide step-by-step video tutorials for installation and implementation of the package. We also present a modified iDISCO+ tissue clearing procedure for uniform immunohistochemical labeling of the activity marker Fos across intact mouse brains. The SMART pipeline, in conjunction with the modified iDISCO+ Fos procedure, is ideally suited for examination and orthogonal cross-validation of brain-wide neuronal activation datasets.

Incubation of palatable food craving is associated with brain-wide neuronal activation in mice.

Studies using rodent models have shown that relapse to drug or food seeking increases progressively during abstinence, a behavioral phenomenon termed "incubation of craving." Mechanistic studies of incubation of craving have focused on specific neurobiological targets within preselected brain areas. Recent methodological advances in whole-brain immunohistochemistry, clearing, and imaging now allow unbiased brain-wide cellular resolution mapping of regions and circuits engaged during learned behaviors. However, these whole-brain imaging approaches were developed for mouse brains, while incubation of drug craving has primarily been studied in rats, and incubation of food craving has not been demonstrated in mice. Here, we established a mouse model of incubation of palatable food craving and examined food reward seeking after 1, 15, and 60 abstinence days. We then used the neuronal activity marker Fos with intact-brain mapping procedures to identify corresponding patterns of brain-wide activation. Relapse to food seeking was significantly higher after 60 abstinence days than after 1 or 15 days. Using unbiased ClearMap analysis, we identified increased activation of multiple brain regions, particularly corticostriatal structures, following 60 but not 1 or 15 abstinence days. We used orthogonal SMART2 analysis to confirm these findings within corticostriatal and thalamocortical subvolumes and applied expert-guided registration to investigate subdivision and layer-specific activation patterns. Overall, we 1) identified brain-wide activity patterns during incubation of food seeking using complementary analytical approaches and 2) provide a single-cell resolution whole-brain atlas that can be used to identify functional networks and global architecture underlying the incubation of food craving.

Quantitative standardization of resident mouse behavior for studies of aggression and social defeat.

Territorial reactive aggression in mice is used to study the biology of aggression-related behavior and is also a critical component of procedures used to study mood disorders, such as chronic social defeat stress. However, quantifying mouse aggression in a systematic, representative, and easily adoptable way that allows direct comparison between cohorts within or between studies remains a challenge. Here, we propose a structural equation modeling approach to quantify aggression observed during the resident-intruder procedure. Using data for 658 sexually experienced CD-1 male mice generated by three research groups across three institutions over a 10-year period, we developed a higher-order confirmatory factor model wherein the combined contributions of latency to the first attack, number of attack bouts, and average attack duration on each trial day (easily observable metrics that require no specialized equipment) are used to quantify individual differences in aggression. We call our final model the Mouse Aggression Detector (MAD) model. Correlation analyses between MAD model factors estimated from multiple large datasets demonstrate generalizability of this measurement approach, and we further establish the stability of aggression scores across time within cohorts and demonstrate the utility of MAD for selecting aggressors which will generate a susceptible phenotype in social defeat experiments. Thus, this novel aggression scoring technique offers a systematic, high-throughput approach for aggressor selection in chronic social defeat stress studies and a more consistent and accurate study of mouse aggression itself.

Regulation of impulsive and aggressive behaviours by a novel lncRNA.

High impulsive and aggressive traits associate with poor behavioural self-control. Despite their importance in predicting behavioural negative outcomes including suicide, the molecular mechanisms underlying the expression of impulsive and aggressive traits remain poorly understood. Here, we identified and characterized a novel long noncoding RNA (lncRNA), acting as a regulator of the monoamine oxidase A (MAOA) gene in the brain, and named it MAOA-associated lncRNA (MAALIN). Our results show that in the brain of suicide completers, MAALIN is regulated by a combination of epigenetic mechanisms including DNA methylation and chromatin modifications. Elevated MAALIN in the dentate gyrus of impulsive-aggressive suicides was associated with lower MAOA expression. Viral overexpression of MAALIN in neuroprogenitor cells decreased MAOA expression while CRISPR-mediated knock out resulted in elevated MAOA expression. Using viral-mediated gene transfer, we confirmed that MAALIN in the hippocampus significantly decreases MAOA expression and exacerbates the expression of impulsive-aggressive behavioural traits in CD1 aggressive mice. Overall, our findings suggest that variations in DNA methylation mediate the differential expression of a novel lncRNA that acts on MAOA expression to regulate impulsive-aggressive behaviours.

Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

RATIONALE: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods. OBJECTIVES: In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies. KEY RESULTS: Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species. DISCUSSION AND CONCLUSIONS: Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.

Depression and Social Defeat Stress Are Associated with Inhibitory Synaptic Changes in the Nucleus Accumbens.

Chronic stress in both humans and rodents induces a robust downregulation of neuroligin-2, a key component of the inhibitory synapse, in the NAc that modifies behavioral coping mechanisms and stress resiliency in mice. Here we extend this observation by examining the role of two other inhibitory synapse constituents, vesicular GABA transporter (vGAT) and gephyrin, in the NAc of male mice that underwent chronic social defeat stress (CSDS) and in patients with major depressive disorder (MDD). We first performed transcriptional profiling of vGAT and gephyrin in postmortem NAc samples from a cohort of healthy controls, medicated, and nonmedicated MDD patients. In parallel, we conducted whole-cell electrophysiology recordings in the NAc of stress-susceptible and stress-resilient male mice following 10 d of CSDS. Finally, we used immunohistochemistry to analyze protein levels of vGAT and gephyrin in the NAc of mice after CSDS. We found that decreased vGAT and gephyrin mRNA in the NAc of nonmedicated MDD patients is paralleled by decreased inhibitory synapse markers and decreased frequency of mini inhibitory postsynaptic currents (mIPSC) in the NAc of susceptible mice, indicating a reduction in the number of NAc inhibitory synapses that is correlated with depression-like behavior. Overall, these findings suggest a common state of reduced inhibitory tone in the NAc in depression and stress susceptibility.SIGNIFICANCE STATEMENT Existing studies focus on excitatory synaptic changes after social stress, although little is known about stress-induced inhibitory synaptic plasticity and its relevance for neuropsychiatric disease. These results extend our previous findings on the critical role of impaired inhibitory tone in the NAc following stress and provide new neuropathological evidence for reduced levels of inhibitory synaptic markers in human NAc from nonmedicated major depressive disorder patients. This finding is corroborated in stress-susceptible male mice that have undergone chronic social defeat stress, a mouse model of depression, at both the level of synaptic function and protein expression. These data support the hypothesis that reduced inhibitory synaptic transmission within the NAc plays a critical role in the stress response.

Orexin signaling in GABAergic lateral habenula neurons modulates aggressive behavior in male mice.

Heightened aggression is characteristic of multiple neuropsychiatric disorders and can have various negative effects on patients, their families and the public. Recent studies in humans and animals have implicated brain reward circuits in aggression and suggest that, in subsets of aggressive individuals, domination of subordinate social targets is reinforcing. In this study, we showed that, in male mice, orexin neurons in the lateral hypothalamus activated a small population of glutamic acid decarboxylase 2 (GAD2)-expressing neurons in the lateral habenula (LHb) via orexin receptor 2 (OxR2) and that activation of these GAD2 neurons promoted male-male aggression and conditioned place preference for aggression-paired contexts. Moreover, LHb GAD2 neurons were inhibitory within the LHb and dampened the activity of the LHb as a whole. These results suggest that the orexin system is important for the regulation of inter-male aggressive behavior and provide the first functional evidence of a local inhibitory circuit within the LHb.

Molecular adaptations of the blood-brain barrier promote stress resilience vs. depression.

Preclinical and clinical studies suggest that inflammation and vascular dysfunction contribute to the pathogenesis of major depressive disorder (MDD). Chronic social stress alters blood-brain barrier (BBB) integrity through loss of tight junction protein claudin-5 (cldn5) in male mice, promoting passage of circulating proinflammatory cytokines and depression-like behaviors. This effect is prominent within the nucleus accumbens, a brain region associated with mood regulation; however, the mechanisms involved are unclear. Moreover, compensatory responses leading to proper behavioral strategies and active resilience are unknown. Here we identify active molecular changes within the BBB associated with stress resilience that might serve a protective role for the neurovasculature. We also confirm the relevance of such changes to human depression and antidepressant treatment. We show that permissive epigenetic regulation of cldn5 expression and low endothelium expression of repressive cldn5-related transcription factor foxo1 are associated with stress resilience. Region- and endothelial cell-specific whole transcriptomic analyses revealed molecular signatures associated with stress vulnerability vs. resilience. We identified proinflammatory TNFα/NFκB signaling and hdac1 as mediators of stress susceptibility. Pharmacological inhibition of stress-induced increase in hdac1 activity rescued cldn5 expression in the NAc and promoted resilience. Importantly, we confirmed changes in HDAC1 expression in the NAc of depressed patients without antidepressant treatment in line with CLDN5 loss. Conversely, many of these deleterious CLDN5-related molecular changes were reduced in postmortem NAc from antidepressant-treated subjects. These findings reinforce the importance of considering stress-induced neurovascular pathology in depression and provide therapeutic targets to treat this mood disorder and promote resilience.

Animal Models of (or for) Aggression Reward, Addiction, and Relapse: Behavior and Circuits.

Inappropriate and pathological aggression plays a leading role in the suffering and death of millions of people, and further places an untenable strain on the caregivers and families of those afflicted. In some cases, such as addictive drugs, aggression can be highly rewarding (appetitive) and continually pursued despite short- and long-term negative consequences. Similarly, recidivism (relapse) rates for repeat violent offenders are as high as relapse rates for drug addicts. Appetitive aggression and relapse to aggression seeking can be modeled in mice studies using conditioned place preference and self-administration procedures followed by a period of abstinence and subsequent tests for relapse to aggression preference and aggression seeking. These procedures allow for the study of the mechanisms that control the appetitive versus the consummatory (attack) phases of aggressive behavior. In this review, we first discuss the behavioral procedures developed to probe appetitive aggression in mouse models, spanning from Pavlovian to operant tasks, and we also describe the recently proposed phenomenon of "aggression addiction." Next, we discuss the pharmacological and circuit mechanisms of aggression conditioned place preference and aggression self-administration, seeking, and relapse, highlighting mechanistic congruence and divergence between appetitive and consummatory phases of aggression. We conclude by discussing clinical implications of the studies reviewed.