Animal and Human Behavior ? Using Computational Approaches to Build a Two-way Bridge

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PROJECT SUMMARY/ABSTRACT Translation, i.e. the application of findings from animal experiments to humans, is of central importance for the field of behavioral neuroscience. However, the value of translational research has been challenged by many findings, which show results in animal studies that do not properly replicate in human experiments. Computational psychiatry is a young field that uses computational approaches to advance rigorous mechanistic understanding of the processes that underlie mental health and disease, in part by developing practical applications based on the automated analysis of human data. Computational neuroscience has used a similar approach for animal data. Thus, computational approaches, i.e. quantifying behavioral results in terms of underlying computational models, may have significant utility in translational research. Therefore, we aim to bring together computational researchers with behavioral neuroscience researchers to develop collaborative efforts focused on using computational approaches for translational research. Several important developments have occurred that make this proposed meeting timely: First, clinicians are beginning to recognize the importance of individual differences, brain-behavior relationships and the limitations of traditional means of classifying psychiatric disorders (e.g. DSM). Second, with the advent of new technology, basic researchers are able to better elucidate brain-behavior relationships and knowledge in this regard is increasing at an exponential rate. Nonetheless, there remains a gap between animal models and human behavior, and until that gap is filled, we will continue to make only small strides in identifying successful treatment options for psychiatric illness. The overall goal of this workshop is to identify means to better bridge the gap between animal models of maladaptive behavior and human psychopathology. In order for animal models to provide help with clinical questions, these models will need to have both predictive validity and explanatory power. Some of the key questions that will be addressed are: (1) Can computational approaches be used to develop better “at risk” animal models? (2) Can computational approaches in animal models be used to disambiguate the contributions of different drugs of abuse to compulsive drug-taking and drug-seeking behaviors? (3) Can computational approaches in animal models improve the predictive validity of novel interventions? The hope is that this workshop will set the stage for future studies to utilize computational methods to bridge the “translational” gap and thereby improve our strategies for identifying novel therapeutic targets for the successful treatment of addiction and related disorders.