EAPSI: Developing a Predictive Model for Compulsive Behavior in Individuals with Obsessive Compulsive Disorder

EAPSI：开发强迫症患者强迫行为的预测模型

• 批准号: 1713785
• 负责人: Lindsay Fields
• 金额: $ 0.54万
• 依托单位: Fields Lindsay D
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713785&HistoricalAwards=false
• 关键词: EAPSI; Developing; Predictive; Model; Compulsive

The most often used method of diagnosing Obsessive-Compulsive Disorder (OCD) is the Yale-Brown scale, however the scale only considers the quantifiable time and energy lost to compulsions, while also relying on potentially false self-reporting. Furthermore, OCD differs significantly from other anxiety disorders by the existence of compulsive behavior. Therefore, current means of diagnosing and treating OCD cannot be measured by their effectiveness in treating other anxiety disorders. This research is focused on developing a predictive model of compulsive behavior based upon Minsky's Society of Mind. The objective is to develop a model which would predict the probability of an individual performing compulsive behavior. The research has applications both in understanding pervasive anxiety, and in treating the physical consequences therein. This project will be conducted at Kyoto Prefectural University of Medicine in Kyoto, Japan under the mentorship of Dr. Takashi Nakamae. The collaboration provides access to unique data that will enable new insights into compulsive behavior.By considering each neurological agent as an automaton, which has a certain probability of reacting to an environmental stimulus and moving into an excited state, it is possible to observe the system?s behavior. By applying this concept continually, with agents given by the sectors of the worry circuit, a computer algorithm was designed which implied that the number of compulsions performed could be predicted using a threshold cellular automaton. Later revisions of the model may potentially take into consideration probabilistic determination of playing order, as well as utilizing a function of the number of compulsions already performed to determine the probability of each agent reacting to a stimulus. A successful method must be capable of empirically quantifying compulsivity, however it has the potential to improve the therapeutic treatment of OCD. Additionally, by including a variant for the subject?s average number of compulsions, the model may be customized and thus provide a more personal means of treatment. Finally, the proposed research has implications for brain mapping; by detailing the nature of compulsivity, scientists may be able to isolate the direct functions of the worry circuit which could give us clues into brain cell function.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.

诊断强迫症(OCD)最常用的方法是耶鲁-布朗(Yale-Brown)量表，但该量表只考虑强迫症造成的可量化的时间和精力损失，同时也依赖于潜在的虚假自我报告。此外，强迫症与其他焦虑症的显著不同在于强迫行为的存在。因此，目前诊断和治疗强迫症的方法不能用它们治疗其他焦虑症的有效性来衡量。本研究以明斯基的心智社会为基础，致力于发展一种强迫症行为的预测模型。我们的目标是开发一种模型，可以预测一个人实施强迫行为的可能性。这项研究在理解广泛性焦虑和治疗由此产生的身体后果方面都有应用。该项目将在日本京都的京都地方医科大学进行，由Takashi Nakamae博士指导。这项合作提供了获取独特数据的途径，这些数据将使人们能够对强迫行为有新的见解。通过将每个神经毒剂视为自动机，它有一定的概率对环境刺激做出反应并进入兴奋状态，因此观察系统-S的行为是可能的。通过不断地应用这一概念，在担忧电路的各个部分给出代理的情况下，设计了一种计算机算法，该算法意味着可以使用阈值细胞自动机来预测执行的强迫次数。该模型的以后修订可能会考虑游戏顺序的概率确定，以及利用已经执行的强迫次数的函数来确定每个代理人对刺激做出反应的概率。一种成功的方法必须能够经验性地量化强迫症，但它有可能改进强迫症的治疗。此外，通过包括受试者的变体-S平均强迫次数，该模型可能会被定制，从而提供更个人化的治疗手段。最后，这项拟议的研究对大脑图谱有影响；通过详细描述强迫症的本质，科学家们可能能够分离出担忧回路的直接功能，这可能会为我们提供脑细胞功能的线索。该奖项由东亚和太平洋暑期研究所项目设立，支持一名美国研究生的夏季研究，由NSF和日本科学促进会共同资助。