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

项目概要/摘要 翻译，即将动物实验的结果应用于人类，对人类来说至关重要。 行为神经科学领域。然而，翻译研究的价值受到了许多人的质疑， 这些发现表明，动物研究的结果不能在人体实验中正确复制。 计算精神病学是一个年轻的领域，它使用计算方法来推进严格的 对心理健康和疾病的基础过程的机械理解，部分是通过发展 基于人类数据自动分析的实际应用。计算神经科学已经使用 类似的方法用于动物数据。因此，计算方法，即量化行为结果的条款 的基础计算模型，可能有显着的效用在翻译研究。因此，我们的目标是 将计算研究人员与行为神经科学研究人员聚集在一起， 努力集中于使用计算方法进行转化研究。几项重要发展 发生了，使这个建议的会议及时：首先，临床医生开始认识到， 个体差异的重要性，脑行为关系和传统方法的局限性， 分类精神疾病（例如DSM）。其次，随着新技术的出现，基础研究人员在 能够更好地阐明大脑行为的关系和知识在这方面正在增加， 指数速率尽管如此，动物模型和人类行为之间仍然存在差距，直到 如果这一差距得到填补，我们将继续在确定成功的治疗方案方面取得小步。 精神病本次研讨会的总体目标是确定如何更好地弥合以下方面的差距： 适应不良行为和人类精神病理学的动物模型。为了让动物模型提供 为了帮助解决临床问题，这些模型需要同时具有预测有效性和解释能力。 将解决的一些关键问题是：（1）计算方法可以用来开发 更好的"高危"动物模型(2)动物模型中的计算方法是否可以用来消除 不同药物滥用对强迫性吸毒和觅药行为的影响？(3)可以 动物模型中的计算方法提高了新干预措施的预测有效性？人们希望 这次研讨会将为未来的研究奠定基础，利用计算方法来弥合 "翻译"缺口，从而改善我们的战略，确定新的治疗目标， 成功治疗成瘾和相关疾病。

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.