NCS-FO: Assaying neural individuality and variation in freely behaving people based on qEEG

NCS-FO：基于 qEEG 分析自由行为的人的神经个性和变异

• 批准号: 1533691
• 负责人: Jose Contreras-Vidal
• 金额: $ 30万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533691&HistoricalAwards=false
• 关键词: NCS; FO; Assaying; neural; individuality

This project will deploy noninvasive Mobile Brain-body Imaging devices (MoBI) in a public museum with the goal of assaying individuality and variation in neural activity as it occurs (e.g., "in action and context") in a large and diverse group of people, including children, experiencing fixed and interactive art exhibits. A natural setting such as an art museum attracts thousands of people with rich demographic factors such as age, sex, education level, occupation, and other factors such as health, medication and neurological status, thereby providing a unique opportunity to study the population distribution, accuracy and stability of neural activity and advance understanding of the dynamics of complex neural and cognitive systems in natural environments. The broader impacts of this research include integrating the arts, science and engineering to advance brain science; advancing the regulatory science of biomedical devices by uncovering biometric neural data as objective endpoints to investigate cognition, perception and action; supporting and promoting STEM education, and advancing the field through dissemination and data sharing of products generated in this research. Importantly, the efficacy and related safety of MoBI-based diagnostics and therapeutics depend on scientific understanding of neural variability and individuality. In the same way that individual variation in gene sequences makes certain drugs more or less effective for certain people, giving rise to the need for pharmacogenomics, individual variation in brain activity will not only affect the assessment of drugs which use these endpoints, but will also strongly affect the safety and efficacy of therapeutic medical devices. Despite this critical importance, there is no concerted effort elsewhere to address these basic questions that are holding back the research and development of novel noninvasive biomedical devices with all of its diagnostic benefits that could also contribute to reverse engineer brain mechanisms. A big-data analytics approach for investigating neural variability and individuality in brain data from a large number of diverse participants could help advance development of biomedical devices while filling knowledge gaps in brain science. Three research objectives will be pursued to produce this science while developing novel tools for discovery. First, this project entails the acquisition of multi-modal data from a thousand participants from the diverse Greater Houston area. Second, the research will develop novel algorithms for analyzing, inspecting, visualizing, representing, parsing, and searching high-dimensional patterns from the multi-modal datasets acquired in a public setting at the Blaffer museum. The goals are to uncover neural signals associated with the passive and interactive perception/production of art and to assess the long-term stability of neural activity acquired via quantitative electroencephalography (or qEEG). The proposed project will lead to innovative time-resolved methods and tools to study the population distribution, accuracy and stability of neural activity. Third, the project will generate a unique big dataset, and algorithms that will be shared with the scientific community. This research opens new scientific and educational horizons for addressing empirical problems (e.g., the acquisition of multimodal data from freely behaving subjects in public settings), and normative problems (e.g., decoding human intent and emotion from patterns of brain activity) in science. Moreover, the project will enable K-12 to postdoctoral training of a diverse population of students/trainees.

该项目将在公共博物馆中部署非侵入性移动脑体成像设备（MOBI），其目的是在包括儿童，包括固定和互动艺术展览的儿童，包括儿童，包括儿童，包括儿童，包括儿童，包括“行动和环境中”的神经活动变化（例如，“在行动和背景中”）。诸如艺术博物馆之类的自然环境吸引了数千名具有丰富人口统计学因素的人，例如年龄，性别，教育水平，职业以及其他因素，例如健康，药物和神经系统地位，从而提供了一个独特的机会来研究自然环境中复杂神经和认知系统动态的人群分布，神经活动的准确性和稳定性。这项研究的更广泛的影响包括将艺术，科学和工程学整合到进步脑科学；通过发现生物识别神经数据作为研究认知，感知和行动的客观终点，推进生物医学设备的监管科学；通过本研究中产生的产品的传播和数据共享来支持和促进STEM教育，并通过传播和数据共享来推进该领域。重要的是，基于MOBI的诊断和治疗剂的功效和相关安全性取决于对神经变异性和个性的科学理解。就像基因序列的个体变异使某些药物对某些人的有效性或多或少一样，引起了对药物基因组学的需求，脑活动的个体变异不仅会影响使用这些终点的药物的评估，而且还将强烈影响治疗医学设备的安全性和功效。尽管至关重要，但在其他地方没有努力解决这些基本问题，这些问题阻碍了新型非侵入性生物医学设备的研究和开发，其所有诊断益处也可能有助于反向工程师的大脑机制。一种大量分析方法，用于研究来自大量不同参与者的大脑数据中神经变异性和个性的方法，可以帮助推进生物医学设备的开发，同时填补脑科学的知识差距。在开发新颖的发现工具时，将追求三个研究目标来产生这项科学。首先，该项目需要从多元化休斯顿地区的一千名参与者中获取多模式数据。其次，这项研究将开发出用于分析，检查，可视化，代表，解析和搜索从Blaffer博物馆公共环境中获得的多模式数据集的高维模式的新颖算法。目标是揭示与被动和互动性感知/艺术的产生相关的神经信号，并评估通过定量脑电图（或QEEG）获得的神经活动的长期稳定性。拟议的项目将导致创新的时间分辨方法和工具，以研究神经活动的种群分布，准确性和稳定性。 第三，该项目将生成一个独特的大数据集和将与科学界共享的算法。这项研究为解决经验问题的新科学和教育视野开辟了新的（例如，从公共环境中自由行为受试者获取多模式数据），以及在科学中的规范性问题（例如，从大脑活动的模式中解码人类的意图和情感）。此外，该项目将使K-12能够对各种学生/受训者的多样化培训进行博士后培训。

项目成果

Assaying neural activity of children during video game play in public spaces: a deep learning approach

• DOI: 10.1088/1741-2552/ab1876
• 发表时间: 2019-05
• 期刊: Journal of Neural Engineering
• 影响因子: 4
• 作者: Akshay Sujatha Ravindran;Aryan Mobiny;Jesús G. Cruz-Garza;A. Paek;Anastasiya E. Kopteva;José L Contreras Vidal