MRI: Development of an Instrument that Performs Behavioral Analysis for Neuropsychiatric Disorders like Tourette Syndrome

MRI：开发一种对抽动秽语综合症等神经精神疾病进行行为分析的仪器

• 批准号: 1919631
• 负责人: Nikolaos Papanikolopoulos
• 金额: $ 183.13万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919631&HistoricalAwards=false
• 关键词: MRI; Development; Instrument; Performs; Behavioral

This project, developing an instrument to enable a pioneering way to canonically represent and systematically quantify behavioral manifestations for a suite of neuropsychiatric disorders, integrates a powerful computer cluster with various sensor modalities and associated immersive technology components that facilitate data analysis and perception of physical and virtual experiences. The instrument will bring together neuropsychiatrists and engineering scientists seeking the development and deployment of tools that promote discovery and new knowledge in their respective domains, and innovation with immediate societal impact. It aims to enable medical experts to explore clinical hypotheses during diagnosis and treatment. Concurrently, scientists/engineers will explore the implementation of new computational tools and algorithms that satisfy new requirements utilizing a set of clinical hypotheses.Challenging the current understanding of a spectrum of neuropsychiatric conditions and triggering the explorations of new ways to affect them, this instrument should contribute to our understanding of the presence and evolution of mental states. Its unique characteristics will help in integrating a range of informational cues (e.g., visual, acoustic, haptic) along with virtual simulated information. This interplay between physical and virtual experiences and stimuli and their perception in an immersive environment constitutes the ultimate objective which will unleash creativity in better understanding and treat mental illnesses. The tight coupling of hardware and software components will enhance the various types of interactions such as expert-patient, and patient-virtual objects. Specifically, the instrument will support the following research efforts: - Comprehensive compilation of behavioral data, - Development of visualization tools, - Delivery of haptic information feedback, - Creation and interaction of virtual objects, and - Enhanced sentiment understanding from audio/visual signals, facial expression representation and analysis.The instrument enables the creation of communication channels with clinics across the country. Moreover, it contributes in forming a strong foundation to explore and understand a broad variety of neuropsychiatric disorders manifested via verbal and non-verbal communication pathways, aims to form a foundation that promotes strong collaboration targeting real-world challenges with immediate societal and economic impact, and offers an environment for discovery via the strong coupling of data visualization, User Interfaces-User eXperience (UI-UX) design, immersive perception, data exploration, and machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目开发一种仪器，以开创性的方式来规范地表示和系统地量化一系列神经精神疾病的行为表现，将强大的计算机集群与各种传感器模式和相关的沉浸式技术组件集成在一起，以促进数据分析和物理感知和虚拟体验。该工具将汇集神经精神病学家和工程科学家，寻求开发和部署工具，促进各自领域的发现和新知识，以及具有直接社会影响的创新。它旨在使医学专家在诊断和治疗过程中探索临床假设。与此同时，科学家/工程师将探索新的计算工具和算法的实现，以满足新的要求，利用一套临床假设。巩固目前的理解谱的神经精神疾病，并引发探索新的方式来影响他们，这个工具应该有助于我们的理解的存在和精神状态的演变。其独特的特征将有助于整合一系列信息线索（例如，视觉、听觉、触觉）沿着虚拟模拟信息。物理和虚拟体验与刺激及其在沉浸式环境中的感知之间的相互作用构成了最终目标，这将释放创造力，更好地理解和治疗精神疾病。硬件和软件组件的紧密耦合将增强各种类型的交互，例如专家-患者和患者-虚拟对象。具体来说，该仪器将支持以下研究工作：-行为数据的全面汇编，-可视化工具的开发，-触觉信息反馈的传递，-虚拟对象的创建和交互，以及 - 通过音频/视频信号、面部表情表示和分析增强情感理解。该仪器能够与全国各地的诊所建立沟通渠道。此外，它有助于形成一个强大的基础，以探索和理解各种各样的神经精神疾病表现通过口头和非口头沟通途径，旨在形成一个基础，促进强有力的合作，针对现实世界的挑战，直接的社会和经济影响，并提供一个环境，通过数据可视化，用户界面，用户体验（UI-UX）设计，沉浸式感知、数据探索和机器学习。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning Log-Determinant Divergences for Positive Definite Matrices

• DOI: 10.1109/tpami.2021.3073588
• 发表时间: 2021-04
• 期刊: IEEE Transactions on Pattern Analysis and Machine Intelligence
• 影响因子: 23.6
• 作者: A. Cherian;P. Stanitsas;Jue Wang;Mehrtash Harandi;V. Morellas;N. Papanikolopoulos

Robotic Embodiment of Human-Like Motor Skills via Reinforcement Learning

通过强化学习实现类人运动技能的机器人

• DOI: 10.1109/lra.2022.3147453
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Guzman, Luis;Morellas, Vassilios;Papanikolopoulos, Nikolaos
• 通讯作者: Papanikolopoulos, Nikolaos