Touchscreen-based assessment of risky-choice in mice

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.

Divergent risky decision-making and impulsivity behaviors in Lewis rat substrains with low genetic difference

Substance use disorder (SUD) is associated with a cluster of cognitive disturbances that engender vulnerability to ongoing drug seeking and relapse. Two of these endophenotypes-risky decision-making and impulsivity-are amplified in individuals with SUD and are augmented by repeated exposure to illicit drugs. Identifying genetic factors underlying variability in these behavioral patterns is critical for early identification, prevention, and treatment of SUD-vulnerable individuals. Here, we compared risky decision-making and different facets of impulsivity between two fully inbred substrains of Lewis rats-LEW/NCrl and LEW/NHsd. We performed whole genome sequencing of both substrains to identify almost all relevant variants. We observed substantial differences in risky decision-making and impulsive behaviors. Relative to LEW/NHsd, the LEW/NCrl substrain accepts higher risk options in a decision-making task and higher rates of premature responses in the differential reinforcement of low rates of responding task. These phenotypic differences were more pronounced in females than males. We defined a total of ∼9,000 polymorphisms between these substrains at 40× whole genome short-read coverage. Roughly half of variants are located within a single 1.5 Mb region of Chromosome 8, but none impact protein-coding regions. In contrast, other variants are widely distributed, and of these, 38 are predicted to cause protein-coding variants. In conclusion, Lewis rat substrains differ significantly in risk-taking and impulsivity and only a small number of easily mapped variants are likely to be causal. Sequencing combined with a reduced complexity cross should enable identification of one or more variants underlying multiple complex addiction-relevant behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Visiomode: An open-source platform for building rodent touchscreen-based behavioral assays

BACKGROUND

Touchscreen-based behavioral assays provide a robust method for assessing cognitive behavior in rodents, offering great flexibility and translational potential. The development of touchscreen assays presents a significant programming and mechanical engineering challenge, where commercial solutions can be prohibitively expensive and open-source solutions are underdeveloped, with limited adaptability.

NEW METHOD

Here, we present Visiomode (www.visiomode.org), an open-source platform for building rodent touchscreen-based behavioral tasks. Visiomode leverages the inherent flexibility of touchscreens to offer a simple yet adaptable software and hardware platform. The platform is built on the Raspberry Pi computer combining a web-based interface and powerful plug-in system with an operant chamber that can be adapted to generate a wide range of behavioral tasks.

RESULTS

As a proof of concept, we use Visiomode to build both simple stimulus-response and more complex visual discrimination tasks, showing that mice display rapid sensorimotor learning including switching between different motor responses (i.e., nose poke versus reaching).

COMPARISON WITH EXISTING METHODS

Commercial solutions are the 'go to' for rodent touchscreen behaviors, but the associated costs can be prohibitive, limiting their uptake by the wider neuroscience community. While several open-source solutions have been developed, efforts so far have focused on reducing the cost, rather than promoting ease of use and adaptability. Visiomode addresses these unmet needs providing a low-cost, extensible platform for creating touchscreen tasks.

CONCLUSIONS

Developing an open-source, rapidly scalable and low-cost platform for building touchscreen-based behavioral assays should increase uptake across the science community and accelerate the investigation of cognition, decision-making and sensorimotor behaviors both in health and disease.

Reward/Punishment-Based Decision Making in Rodents

Deficits in decision making are at the heart of many psychiatric diseases, such as substance abuse disorders and attention deficit hyperactivity disorder. Consequently, rodent models of decision making are germane to understanding the neural mechanisms underlying adaptive choice behavior and how such mechanisms can become compromised in pathological conditions. A critical factor that must be integrated with reward value to ensure optimal decision making is the occurrence of consequences, which can differ based on probability (risk of punishment) and temporal contiguity (delayed punishment). This article will focus on two models of decision making that involve explicit punishment, both of which recapitulate different aspects of consequences during human decision making. We will discuss each behavioral protocol, the parameters to consider when designing an experiment, and finally how such animal models can be utilized in studies of psychiatric disease. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Behavioral training Support Protocol: Equipment testing Alternate Protocol: Reward discrimination Basic Protocol 2: Risky decision-making task (RDT) Basic Protocol 3: Delayed punishment decision-making task (DPDT).

Advances in understanding meso-cortico-limbic-striatal systems mediating risky reward seeking

The risk of an aversive consequence occurring as the result of a reward-seeking action can have a profound effect on subsequent behavior. Such aversive events can be described as punishers, as they decrease the probability that the same action will be produced again in the future and increase the exploration of less risky alternatives. Punishment can involve the omission of an expected rewarding event ("negative" punishment) or the addition of an unpleasant event ("positive" punishment). Although many individuals adaptively navigate situations associated with the risk of negative or positive punishment, those suffering from substance use disorders or behavioral addictions tend to be less able to curtail addictive behaviors despite the aversive consequences associated with them. Here, we discuss the psychological processes underpinning reward seeking despite the risk of negative and positive punishment and consider how behavioral assays in animals have been employed to provide insights into the neural mechanisms underlying addictive disorders. We then review the critical contributions of dopamine signaling to punishment learning and risky reward seeking, and address the roles of interconnected ventral striatal, cortical, and amygdala regions to these processes. We conclude by discussing the ample opportunities for future study to clarify critical gaps in the literature, particularly as related to delineating neural contributions to distinct phases of the risky decision-making process.

New frontiers in translational research: Touchscreens, open science, and the mouse translational research accelerator platform

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).