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

项目摘要/摘要 翻译，即将动物实验的结果应用到人类身上，对于 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，在动物实验中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动严格的 对心理健康和疾病基础过程的机械性理解，部分是通过发展 基于人类数据自动分析的实际应用。计算神经科学使用了 动物数据也采用了类似的方法。因此，计算方法，即将行为结果量化为 潜在的计算模型，可能在翻译研究中具有重要的实用价值。因此，我们的目标是 将计算研究人员和行为神经科学研究人员聚集在一起，开发协作性 工作的重点是使用计算方法进行翻译研究。几个重要的发展 已经发生的事情使这次拟议的会议适时：首先，临床医生开始认识到 个体差异、大脑-行为关系的重要性以及传统方法的局限性 对精神障碍进行分类(例如DSM)。其次，随着新技术的出现，基础研究人员正在 能够更好地阐明大脑-行为关系和这方面的知识正在以 指数率。尽管如此，动物模型和人类行为之间仍然存在差距，直到 这一空白已经填补，我们将继续在确定成功的治疗方案方面只取得很小的进展。 精神疾病。本次研讨会的总体目标是确定如何更好地弥合 适应不良行为的动物模型和人类精神病理学。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力。 将解决的一些关键问题是：(1)是否可以使用计算方法来开发 更好的“危险”动物模型？(2)动物模型中的计算方法能用来消除歧义吗？ 不同滥用药物对强迫吸毒和寻求毒品行为的贡献？(3) 动物模型中的计算方法提高了新干预措施的预测有效性？希望是 这一研讨会将为未来的研究奠定基础，以利用计算方法在 “翻译”差距，从而改进我们的战略，以确定新的治疗靶点为 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.