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

该项目将在一家公共博物馆部署非侵入性移动脑体成像设备(MOBI)，目的是在包括儿童在内的一大群不同的人群中，在体验固定和互动的艺术展览时，分析神经活动发生时(例如，在行动和背景中)的个性和变化。像美术馆这样的自然环境吸引了数以千计的人，他们具有丰富的人口统计因素，如年龄、性别、教育水平、职业，以及其他因素，如健康、药物和神经状况，从而提供了一个独特的机会来研究人口分布、神经活动的准确性和稳定性，并促进对自然环境中复杂的神经和认知系统动态的了解。这项研究的更广泛影响包括整合艺术、科学和工程以促进脑科学；通过揭示生物识别神经数据作为客观终点来研究认知、感知和行动，促进生物医学设备的监管科学；支持和促进STEM教育，并通过传播和数据共享本研究产生的产品来促进该领域的发展。重要的是，基于MOBI的诊断和治疗的有效性和相关安全性取决于对神经变异性和个性的科学理解。正如基因序列的个体差异使某些药物对某些人或多或少有效一样，导致需要药物基因组学一样，大脑活动的个体差异不仅会影响对使用这些终点的药物的评估，而且还会强烈影响治疗性医疗设备的安全性和有效性。尽管这一点至关重要，但其他地方并没有协调一致的努力来解决这些基本问题，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，这些设备具有所有可能有助于反向工程大脑机制的诊断好处。一种大数据分析方法，用于研究来自大量不同参与者的大脑数据中的神经变异性和个性，可能有助于推动生物医学设备的开发，同时填补脑科学中的知识空白。在开发新的发现工具的同时，将追求三个研究目标来产生这种科学。首先，该项目需要从来自不同的大休斯顿地区的1000名参与者那里获取多模式数据。其次，这项研究将开发新的算法，用于分析、检查、可视化、表示、解析和搜索在Blaffer博物馆公共环境中获得的多模式数据集中的高维模式。其目标是发现与ART的被动和交互感知/产生相关的神经信号，并评估通过定量脑电(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算法，并将与科学界共享。这项研究为解决科学中的经验性问题(例如，从公共环境中自由行为的受试者获取多模式数据)和规范性问题(例如，从大脑活动模式中解码人类意图和情感)开辟了新的科学和教育视野。此外，该项目将使K-12能够对不同群体的学生/受训人员进行博士后培训。

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.