MRI: Development of an Instrument that Monitors Behaviors Associated with Obsessive-Compulsive Behaviors and Schizophrenia

提案编号：13- 38042 PI（s）：Papanikolopoulos，Nikolaos Banerjee，Arindam;伯恩斯坦，Gail; Hadjiyanni，Tassoulla; Lim，Kelvin，K.机构：明尼苏达大学双城分校职称： MRI/器械：研究强迫症和精神分裂症相关行为的工具项目建议：该项目开发一种新的工具，以促进与强迫行为（称为OCD）等复杂精神健康障碍的临床评估相关的数据收集，旨在实现计算机视觉，活动识别和三维重建算法的长期研究进展，以自动识别OCD受试者的典型行为。在虚拟现实室（CAVE）中沉浸选定的主题用于触发要捕获和分析的特定行为。正在开发的先进传感器系统将用于收集这些数据，并提供智能数据处理能力，使未来能够探索和测试新的诊断和治疗方案，从而为寻找儿童和青少年早期风险标志物奠定基础并发挥作用。这一仪器计划的前提是，专业知识可以准确地识别有用的诊断标志物，并相信现在可以开发技术，以前所未有的方式收集大量行为数据，并发现行为模式。该仪器预计将-提供与被诊断患有相应疾病的受试者相关的广泛数据收集（数据不仅对临床医生有用，而且对计算机视觉和机器学习研究人员等也有用），-捕获对真实的和/或合成多模态刺激（光学、声学等）的交互、行为和生理反应，-允许计算机和信息科学家开发计算工具和算法，以生成用于筛查广泛人群的定量、充分和具有成本效益的规范，-通过整合可以激发或抑制精神分裂症或强迫症发作触发因素的技术来增强认知行为治疗（CBT）程序和诊断协议，- 评估特定的增强和虚拟环境（AE/VE）和社交媒体设备（智能手机和平板电脑）及其对正常受试者与受影响受试者的认知存在的影响，以及-评估通过AE/VE增强的认知存在是否可以增加或抑制（习惯化）传感器可检测的行为症状的强度。更广泛的影响：其中包括：- 创建大型和复杂的数据集，使计算机科学家能够在其上应用最新的计算工具，-开发一种潜在的变革性技术驱动工具，用于检测强迫症的早期风险标志，-探索一种非常适合新方向的平台，以更好地表征精神障碍，-系统地开发量化的多模态数据库，并为早期检测提供更健全和更精确的基础，-减少总体成本，并同时减少由于先前延迟或错误诊断而造成的长期成本，-减少焦虑，中断，压力，有时是病人及其家人的真实的悲剧，- 通过易于检测和相关的社会效益实现早期检测和减少对基于药物的后期干预的需要，以及-学生使用仪器的教育和培训。

Proposal #: 13-38042PI(s): Papanikolopoulos, Nikolaos Banerjee, Arindam; Bernstein, Gail; Hadjiyanni, Tassoulla; Lim, Kelvin, K.Institution: University of Minnesota-Twin CitiesTitle: MRI/Dev.: Instrument that Monitors Behaviors Associated with OCD and Schizophrenia Project Proposed:This project, developing a new instrument to facilitate data collection associated with clinical assessment of complex mental health disorders such as obsessive-compulsive behaviors (known as OCD), aims to enable long-term research advances in computer vision, activity recognition and tridimensional reconstruction algorithms to automate the identification of behaviors typical of OCD subjects. The immersion of selected subjects in a virtual reality room (CAVE) is used to trigger specific behaviors to be captured and analyzed. The sophisticated sensor system under development will serve to collect these data and provide intelligent data processing capabilities that would enable future exploration and testing of new diagnostic and therapeutic protocols, leading to the establishment of the basis for, and utility in, seeking early at-risk markers in children and adolescents. This instrument initiative is based on the premise that expertise can accurately identify useful diagnostic markers and on the belief that technologies can now be developed to collect massive behavioral data in ways not previously done and discover behavioral patterns.The instrument is expected to- Provide extensive data collection associated with subjects diagnosed with the respective disorders (data useful not only to clinicians but also to computer vision and machine learning researchers among others),- Capture interactions, behaviors, and physiological reactions to real and/or synthetic multimodal stimuli (optical, acoustical, etc.),- Allow computer and information scientists to develop computational tools and algorithms to generate quantitative, adequate, and cost-effective norms for screening a broad population, - Enhance Cognitive Behavior Therapy (CBT) procedures and diagnostic protocols by integration of technologies that can excite or inhibit triggers for schizophrenic or OCD episodes,- Assess particular Augmented and Virtual Environments (AE/VEs) and social media devices (smart phones and tablets) and their impacts on the cognitive presence of normal versus afflicted subjects, and- Evaluate whether an enhanced cognitive presence via an AE/VE can increase or suppress (habituate) the intensity of behavioral symptoms detectable by sensors. Broader Impacts: Among these we have:- Creation of large and complex datasets that will enable computer scientists to apply the newest computational tools on them,Development of a potentially transformative technology-driven instrument for detecting early risk markers of OCD,- Exploration of a platform well-suited to new directions for a better characterization of mental disorders,- Systematic database development of quantified, multimodal data and a sounder and more precise basis for earlier detection,- Reduction of overall costs and a parallelizing reduction in the long-term costs due to previously delayed or incorrect diagnoses,- Reduction of anxiety, disruption, stress, and sometimes real tragedy on patients and their families, - Earlier detection and reduced need for drug-based, later stage interventions enabled by the ease of testing, and associated societal benefits, and- Student education and training in the use of the instrument.