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

动物和人类行为？

• 批准号: 9543143
• 负责人: Shelly Beth Flagel
• 金额: $ 1万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9543143
• 关键词: Address; Animal Experiments; Animal Model; Animals; Area; Behavior; Behavioral; Behavioral Model; Brain; Clinical; Collaborations; Computer Simulation; Computing Methodologies; Decision Making; Development; Disease; Educational workshop; Failure; Functional disorder; Future; Goals; Human; Impulsivity; Individual Differences; Intervention; Knowledge; Lead; Learning; Measures; Mental Health; Mental disorders; Modeling; Neurosciences; Outcome; Patient-Focused Outcomes; Pharmaceutical Preparations; Pharmacology; Process; Psychiatrist; Psychiatry; Psychological reinforcement; Psychopathology; Research; Research Personnel; Risk; Role; Scientist; Time; Translational Research; Translations; Treatment outcome; addiction; animal data; base; brain behavior; computational neuroscience; drug development; drug of abuse; drug seeking behavior; experimental study; human data; human model; improved; meetings; new technology; new therapeutic target; novel; novel therapeutics; practical application; symposium; translational approach; translational model

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.

项目概要/摘要 翻译，即将动物实验的结果应用于人类，对于人类来说至关重要。 然而，转化研究的价值受到了许多人的质疑。 研究结果显示动物研究的结果在人体实验中无法正确复制。 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研究 对心理健康和疾病背后过程的机械理解，部分是通过发展 基于人类数据自动分析的实际应用。 因此，计算方法，即用术语量化行为结果。 基础计算模型的研究可能在转化研究中具有重要的实用性，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，开展协作 专注于使用计算方法进行翻译研究。 发生的一些事情使这次拟议的会议变得及时：首先，殖民者开始认识到 个体差异的重要性、大脑行为关系以及传统方法的局限性 其次，随着新技术的出现，基础研究人员正在对精神疾病进行分类。 能够更好地阐明大脑与行为之间的关系，这方面的知识正在不断增加 尽管如此，动物模型和人类行为之间仍然存在差距，直到 这一差距已被填补，我们将继续在确定成功的治疗方案方面迈出一小步 本次研讨会的总体目标是找出更好地弥合精神疾病之间差距的方法。 动物模型的适应不良行为和人类病理学，以便为动物模型提供依据。 为了帮助解决临床问题，这些模型需要具有预测有效性和解释力。 将要解决的一些关键问题是：（1）可以使用计算方法来开发 更好的“处于危险中”的动物模型？（2）动物模型中的计算方法可以用来消除歧义吗？ 不同滥用药物对强迫性吸毒和寻求毒品行为的影响 (3) 可以吗？ 动物模型中的计算方法可以提高新干预措施的预测有效性吗？ 本次研讨会将为未来的研究奠定基础，利用计算方法来弥合 “转化”差距，从而改善我们确定新治疗靶点的策略 成功治疗成瘾和相关疾病。