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Behavioral testing constitutes the primary method to measure the emotional states of nonhuman animals in preclinical research. Emerging as the characteristic tool of the behaviorist school of psychology, behavioral testing of animals, particularly rodents, is employed to understand the complex cognitive and affective symptoms of neuropsychiatric disorders. Following the symptom-based diagnosis model of the DSM, rodent models and tests of depression and anxiety focus on behavioral patterns that resemble the superficial symptoms of these disorders. While these practices provided researchers with a platform to screen novel antidepressant and anxiolytic drug candidates, their construct validity—involving relevant underlying mechanisms—has been questioned. In this review, we present the laboratory procedures used to assess depressive- and anxiety-like behaviors in rats and mice. These include constructs that rely on stress-triggered responses, such as behavioral despair, and those that emerge with nonaversive training, such as cognitive bias. We describe the specific behavioral tests that are used to assess these constructs and discuss the criticisms on their theoretical background. We review specific concerns about the construct validity and translational relevance of individual behavioral tests, outline the limitations of the traditional, symptom-based interpretation, and introduce novel, ethologically relevant frameworks that emphasize simple behavioral patterns. Finally, we explore behavioral monitoring and morphological analysis methods that can be integrated into behavioral testing and discuss how they can enhance the construct validity of these tests.

Obsessive-compulsive disorder (OCD), a highly prevalent and debilitating disorder, is incompletely understood in terms of underpinning behavioural, psychological, and neural mechanisms. This is attributable to high symptomatic heterogeneity; cardinal features comprise obsessions and compulsions, including clinical subcategories. While obsessive and intrusive thoughts are arguably unique to humans, dysfunctional behaviours analogous to those seen in clinical OCD have been examined in nonhuman animals. Genetic, ethological, pharmacological, and neurobehavioural approaches all contribute to understanding the emergence and persistence of compulsive behaviour. One behaviour of particular interest is maladaptive checking, whereby human patients excessively perform checking rituals despite these serving no purpose. Dysfunctional and excessive checking is the most common symptom associated with OCD and can be readily operationalised in rodents. This review considers animal models of OCD, the neural circuitries associated with impairments in habit-based and goal-directed behaviour, and how these may link to the compulsions observed in OCD. We further review the Observing Response Task (ORT), an appetitive instrumental learning procedure that distinguishes between functional and dysfunctional checking, with translational application in humans and rodents. By shedding light on the psychological and neural bases of compulsive-like checking, the ORT has potential to offer translational insights into the underlying mechanisms of OCD, in addition to being a platform for testing psychological and neurochemical treatment approaches.

The Probabilistic Reward Task (PRT) is a laboratory-based technique used to objectively quantify responsivity to reward. The PRT was initially designed to identify reinforcement learning deficits in clinical populations and subsequently was reverse-translated for use in preclinical studies with rats and monkeys. In this task, subjects make visual discriminations and asymmetric probabilistic contingencies are arranged such that correct responses to one stimulus (rich) are reinforced more often than correct responses to the other (lean). Numerous studies have demonstrated that healthy subjects reliably develop a response bias toward the richly rewarded stimulus, whereas humans with anhedonia and laboratory animals with a history of chronic stress exhibit a blunted response bias. This is important because anhedonia, the loss of responsivity to previously rewarding stimuli, is a behavioral phenotype that is a cardinal feature of multiple neuropsychiatric conditions and is without approved pharmacotherapeutic options. To aid in addressing this critical treatment gap, this report describes validation of the first PRT designed for mice, which are a commonly utilized species in preclinical research toward neuropsychiatric medications development. Results reveal orderly psychophysical functions in response to asymmetric probabilistic contingencies in mice, with signal detection outcomes comparable to previous PRT findings in humans, rats, and monkeys. Taken together, such robust cross-species continuity in task performance confirms that the mouse is well-positioned to serve in bidirectional research efforts between human and animal laboratories. These efforts may accelerate the development of treatment options for anhedonia in the different neuropsychiatric conditions in which it is prominent.

Anxiety disorders affect millions of people worldwide and present a challenge in neuroscience research because of their substantial heterogeneity in clinical presentation. While a great deal of progress has been made in understanding the neurobiology of fear and anxiety, these insights have not led to effective treatments. Understanding the relationship between phenotypic heterogeneity and the underlying biology is a critical first step in solving this problem. We show translation, reverse translation, and computational modeling can contribute to a refined, cross-species understanding of fear and anxiety as well as anxiety disorders. More specifically, we outline how animal models can be leveraged to develop testable hypotheses in humans by using targeted, cross-species approaches and ethologically informed behavioral paradigms. We discuss reverse translational approaches that can guide and prioritize animal research in nontraditional research species. Finally, we advocate for the use of computational models to harmonize cross-species and cross-methodology research into anxiety. Together, this translational neuroscience approach will help to bridge the widening gap between how we currently conceptualize and diagnose anxiety disorders, as well as aid in the discovery of better treatments for these conditions.

Concerns about poor animal to human translation have come increasingly to the fore, in particular with regards to cognitive improvements in rodent models, which have failed to translate to meaningful clinical benefit in humans. This problem has been widely acknowledged, most recently in the field of Alzheimer’s disease, although this issue pervades the spectrum of central nervous system (CNS) disorders, including neurodevelopmental, neuropsychiatric, and neurodegenerative diseases. Consequently, recent efforts have focused on improving preclinical to clinical translation by incorporating more clinically analogous outcome measures of cognition, such as touchscreen-based assays, which can be employed across species, and have great potential to minimize the translational gap. For aging-related research, it also is important to incorporate model systems that facilitate the study of the long prodromal phase in which cognitive decline begins to emerge and which is a major limitation of short-lived species, such as laboratory rodents. We posit that to improve translation of cognitive function and dysfunction, nonhuman primate models, which have conserved anatomical and functional organization of the primate brain, are necessary to move the field of translational research forward and to bridge the translational gaps. The present studies describe the establishment of a comprehensive battery of touchscreen-based tasks that capture a spectrum of domains sensitive to detecting aging-related cognitive decline, which will provide the greatest benefit through longitudinal evaluation throughout the prolonged lifespan of the marmoset.

We recently advanced a rodent homologue for the reward-specific, event-related potential component observed in humans known as the Reward Positivity. We sought to determine the cortical source of this signal in mice to further test the nature of this homology. While similar reward-related cortical signals have been identified in rats, these recordings were all performed in cingulate gyrus. Given the value-dependent nature of this event, we hypothesized that more ventral prelimbic and infralimbic areas also contribute important variance to this signal. Depth probes assessed local field activity in 29 mice (15 males) while they completed multiple sessions of a probabilistic reinforcement learning task. Using a priori regions of interest, we demonstrated that the depth of recording in the cortical midline significantly correlated with the size of reward-evoked delta band spectral activity as well as the single trial correlation between delta power and reward prediction error. These findings provide important verification of the validity of this translational biomarker of reward responsiveness, learning, and valuation.

Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.