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.

项目摘要/摘要 翻译，即从动物实验到人类的发现的应用至关重要 行为神经科学领域。但是，翻译研究的价值受到了许多人的挑战 发现在人类实验中未正确复制的动物研究中的结果。 计算精神病学是一个使用计算方法来推进严格的年轻领域 对心理健康和疾病基础的过程的机械理解，部分是通过发展 基于人类数据的自动分析的实际应用。计算神经科学已使用 动物数据的类似方法。那就是计算方法，即用量化行为结果 在基本计算模型中，可以在翻译研究中具有重要的效用。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汇总在一起，以发展协作 努力专注于使用计算方法进行翻译研究。几个重要的发展 发生了及时提议的会议的情况：首先，临床医生开始认识 个体差异的重要性，脑行为关系以及传统手段的局限性 分类精神疾病（例如DSM）。第二，随着新技术的冒险，基础研究人员是 在这方面，它可以更好地阐明脑行为的关系和知识正在增加 指数率。尽管如此，动物模型与人类行为之间仍然存在差距，直到 这个差距填补了，我们将继续仅在确定成功的治疗方案方面取得较小的进步 精神病。该研讨会的总体目标是确定手段以更好地弥合 适应不良行为和人类心理病理学的动物模型。为了使动物模型提供 帮助解决临床问题，这些模型将需要具有预测有效性和剥夺能力。 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“危险”动物模型？ （2）可以使用动物模型中的计算方法来消除歧义 不同滥用药物对强迫毒品和寻求毒品行为的贡献？ （3）可以 动物模型中的计算方法提高了新干预的预测有效性？希望是 该研讨会将为未来的研究奠定舞台，以利用计算方法桥接 “翻译”差距，从而改善了我们确定新的治疗靶标的策略 成功治疗成瘾和相关疾病。