Rationale Behavioral-economic demand curve analysis offers several useful measures of drug self-administration. Although generation of demand curves previously required multiple days, recent within-session procedures allow curve construction from a single 110-min cocaine self-administration session, making behavioral-economic analyses available to a broad range of self-administration experiments. However, a mathematical approach of curve fitting has not been reported for the within-session threshold procedure. Objectives We review demand curve analysis in drug self-administration experiments and provide a quantitative method for fitting curves to single-session data that incorporates relative stability of brain drug concentration. Methods Sprague-Dawley rats were trained to self-administer cocaine, and then tested with the threshold procedure in which the cocaine dose was sequentially decreased on a fixed ratio-1 schedule. Price points (responses/mg cocaine) outside of relatively stable brain cocaine concentrations were removed before curves were fit. Curve-fit accuracy was determined by the degree of correlation between graphical and calculated parameters for cocaine consumption at low price (Q0) and the price at which maximal responding occurred (Pmax). Results Removing price points that occurred at relatively unstable brain cocaine concentrations generated precise estimates of Q0 and resulted in Pmax values with significantly closer agreement with graphical Pmax than conventional methods. Conclusion The exponential demand equation can be fit to single-session data using the threshold procedure for cocaine self-administration. Removing data points that occur during relatively unstable brain cocaine concentrations resulted in more accurate estimates of demand curve slope than graphical methods, permitting a more comprehensive analysis of drug self-administration via a behavioral-economic framework.

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Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaineseeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments.behavioral economics | reinstatement | extinction | long access | punished responding T here are currently no approved pharmacotherapies for the treatment of cocaine addiction, and timely development of future treatments will depend on animal models that predict the efficacy of treatment in human addicts. This requires a biomarker of addiction suitable for rapid testing in animals, and applicable in humans. However, animal models of neuropsychiatric diseases generally lack predictive validity (1, 2), and prevalent approaches that use drug self-administration in animals have not yet led to a successful clinical treatment for psychostimulant dependence (3, 4).Economic models provide more promising approaches for this needed cross-species addiction biomarker (5, 6). They offer a structured quantitative method to model behavior that is mathematically identical across species (7-9), and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity (10)(11)(12)(13)(14). Notably, economic demand has been shown to correlate with lifetime years of cocaine, heroin, marijuana, and benzodiazepine use (15); severity of alcohol dependence (10,11,14) and craving (16); as well as severity of nicotine dependence (12, 13) and craving (17). However, economic demand has not yet been established as a biomarker of addictionli...

Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study of complex social behavior in freely moving rodents is relatively infrequently incorporated into preclinical models. This likely contributes to limited translational impact. A major bottleneck for the adoption of socially complex, ethology-rich, preclinical procedures are the technical limitations for consistently annotating detailed behavioral repertoires of rodent social behavior. Manual annotation is subjective, prone to observer drift, and extremely time-intensive. Commercial approaches are expensive and inferior to manual annotation. Open-source alternatives often require significant investments in specialized hardware and significant computational and programming knowledge. By combining recent computational advances in convolutional neural networks and pose-estimation with further machine learning analysis, complex rodent social behavior is primed for inclusion under the umbrella of computational neuroethology.Here we present an open-source package with graphical interface and workflow (Simple Behavioral Analysis, SimBA) that uses pose-estimation to create supervised machine learning predictive classifiers of rodent social behavior, with millisecond resolution and accuracies that can out-perform human observers. SimBA does not require specialized video acquisition hardware nor extensive computational background. Standard descriptive statistical analysis, along with graphical region of interest annotation, are provided in addition to predictive classifier generation. To increase ease-of-use for behavioural neuroscientists, we designed SimBA with accessible menus for pre-processing videos, annotating behavioural training datasets, selecting advanced machine learning options, robust classifier validation functions and flexible visualizations tools. This allows for predictive classifier transparency, explainability and tunability prior to, and during, experimental use. We demonstrate that this approach is flexible and robust in both mice and rats by classifying social behaviors that are commonly central to the study of brain function and social motivation. Finally, we provide a library of poseestimation weights and behavioral predictive classifiers for resident-intruder behaviors in mice and rats. All code and data, together with detailed tutorials and documentation, are available on the SimBA GitHub repository.Graphical abstractSimBA graphical interface (GUI) for creating supervised machine learning classifiers of rodent social behavior.(a) Pre-process videos. SimBA supports common video pre-processing functions (e.g., cropping, clipping, sampling, format conversion, etc.) that can be performed either on single videos, or as a batch.(b) Managing poseestimation data and creating classification projects. Pose-estimation tracking projects in DeepLabCut and DeepPoseKit can be either imported or created and managed within the SimBA graphical user interface, and the tracking results are imported into SimBA classification projects.SimBA also supports userdrawn region-of-interests (ROIs) for descriptive statistics of animal movements, or as features in machine learning classification projects.(c) Create classifiers, perform classifications, and analyze classification data. SimBA has graphical tools for correcting poseestimation tracking inaccuracies when multiple subjects are within a single frame, annotating behavioral events from videos, and optimizing machine learning hyperparameters and discrimination thresholds. A number of validation checkpoints and logs are included for increased classifier explainability and tunability prior to, and during, experimental use. Both detailed and summary data are provided at the end of classifier analysis. SimBA accepts behavioral annotations generated elsewhere (such as through JWatcher) that can be imported into SimBA classification projects.(d) Visualize classification results. SimBA has several options for visualizing machine learning classifications, animal movements and ROI data, and analyzing the durations and frequencies of classified behaviors.See the SimBA GitHub repository for a comprehensive documentation and user tutorials.

Rationale Contemporary animal models of cocaine addiction focus on increasing the amount of drug consumption to produce addiction-like behavior. However, another critical factor is the temporal pattern of consumption, which in humans is characterized by intermittency, both within and between bouts of use. Objective To model this we combined prolonged access to cocaine (~70 days in total) with an intermittent access self-administration procedure (IntA), and used behavioral-economic indicators to quantify changes in motivation for cocaine. Results IntA produced escalation of intake, a progressive increase in cocaine demand (incentive-sensitization), and robust drug- and cue-induced reinstatement of drug-seeking behavior. We also asked whether rats that vary in their propensity to attribute incentive salience to reward cues (sign-trackers, STs vs. goal-trackers, GTs) vary in the development of addiction-like behavior. Although STs were more motivated to take cocaine after limited drug experience, after IntA, STs and GTs no longer differed on any measure of addiction-like behavior. Conclusions Exposure to large quantities of cocaine is not necessary for escalation of intake, incentive-sensitization or other addiction-like behaviors (IntA results in far less total cocaine consumption than ‘long access’ procedures). Also, the ST phenotype may increase susceptibility to addiction, not because STs are inherently susceptible to incentive-sensitization (perhaps all individuals are at risk), but because this phenotype promotes continued drug use, subjecting them to incentive-sensitization. Thus, the pharmacokinetics associated with the IntA procedure is especially effective in producing a number of addiction-like behaviors, may be valuable for studying associated neuroadaptations, and for assessing individual variation in vulnerability.

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Ventral tegmental area (VTA) dopamine (DA) neurons perform diverse functions in motivation and cognition, but their precise roles in addiction-related behaviors are still debated. Here, we targeted VTA DA neurons for bidirectional chemogenetic modulation during specific tests of cocaine reinforcement, demand, and relapse-related behaviors in male rats, querying the roles of DA neuron inhibitory and excitatory G-protein signaling in these processes. Designer receptor stimulation of G q signaling, but not G s signaling, in DA neurons enhanced cocaine seeking via functionally distinct projections to forebrain limbic regions. In contrast, engaging inhibitory G i/o signaling in DA neurons blunted the reinforcing and priming effects of cocaine, reduced stress-potentiated reinstatement, and altered behavioral strategies for cocaine seeking and taking. Results demonstrate that DA neurons play several distinct roles in cocaine seeking, depending on behavioral context, G-protein-signaling cascades, and DA neuron efferent targets, highlighting their multifaceted roles in addiction. Significance Statement G-protein-coupled receptors are crucial modulators of ventral tegmental area (VTA) dopamine neuron activity, but how this metabotropic signaling impacts the complex roles of dopamine in reward and addiction is poorly understood.Here, we bidirectionally modulate dopamine neuron G-protein signaling with DREADDs (designer receptors exclusively activated by designer drugs) during a variety of cocaine-seeking behaviors, revealing nuanced, pathway-specific roles in cocaine reward, effortful seeking, and relapse-like behaviors. G q and G s stimulation activated dopamine neurons, but only G q stimulation robustly enhanced cocaine seeking. G i/o inhibitory signaling reduced some, but not all, types of cocaine seeking. Results show that VTA dopamine neurons modulate numerous distinct aspects of cocaine addiction-and relapse-related behaviors, and point to potential new approaches for intervening in these processes to treat addiction.

The orexin/hypocretin system is involved in multiple cocaine addiction processes that involve drug-associated environmental cues, including cue-induced reinstatement of extinguished cocaine seeking and expression of conditioned place preference. However, the orexin system does not play a role in several behaviors that are less cue-dependent, such as cocaine-primed reinstatement of extinguished cocaine seeking and low-effort cocaine self-administration. We hypothesized that cocaine-associated cues, but not cocaine alone, engage signaling at orexin-1 receptors (OX1R), and this cue-engaged OX1R signaling increases motivation for cocaine. Motivation for cocaine was measured in Sprague-Dawley rats with behavioral-economic demand curve analysis after pretreatment with the OX1R antagonist SB-334867 (SB) or vehicle with and without light+tone cues. Demand for cocaine was higher when cocaine-associated cues were present, and SB only reduced cocaine demand in the presence of these cues. We then asked if cocaine demand is linked to cued-reinstatement of cocaine seeking, as both procedures are partially driven by cocaine-associated cues in an orexin-dependent manner. SB blocked cue-induced reinstatement behavior, and baseline demand predicted SB efficacy with the largest effect in high demand animals, i.e., animals with the greatest cue-dependent behavior. We conclude that OX1R signaling increases the reinforcing efficacy of cocaine-associated cues but not for cocaine alone. This supports our view that orexin plays a prominent role in the ability of conditioned cues to activate motivational responses.