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

最常用的诊断强迫症（OCD）的方法是耶鲁-布朗量表，但该量表只考虑可量化的时间和精力损失，同时还依赖于潜在的错误自我报告。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。本研究的重点是开发一个基于明斯基的社会心理的预测模型的强迫行为。目的是开发一个模型，预测个人执行强迫行为的概率。这项研究在理解普遍的焦虑和治疗其中的身体后果方面都有应用。该项目将在日本京都的京都县立医科大学进行，由Takashi Nakamae博士指导。这项合作提供了对独特数据的访问，这将使对强迫行为的新见解成为可能。通过将每个神经代理视为一个自动机，它有一定的概率对环境刺激做出反应并进入兴奋状态，有可能观察系统？的行为。通过不断应用这一概念，利用担忧回路的各个部分所给出的代理，设计了一种计算机算法，这意味着可以使用阈值元胞自动机来预测执行的强制的数量。模型的后续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制数量的函数来确定每个代理对刺激做出反应的概率。成功的方法必须能够根据经验量化强迫症，但它有可能改善强迫症的治疗。此外，通过包括受试者的变体？的平均数量的强迫，该模型可以定制，从而提供一个更个人化的治疗手段。最后，这项研究对大脑绘图也有意义。通过详细描述强迫性的本质，科学家们或许能够分离出焦虑回路的直接功能，从而为我们提供了解脑细胞功能的线索。该奖项由美国国家科学基金会（NSF）和日本科学促进会（Japan Society for the Promotion of Science）共同资助，由东亚和太平洋夏季研究所（East Asia and Pacific Summer Institutes）项目资助，旨在支持一名美国研究生的夏季研究。

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.