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

该项目将在公共博物馆中部署非侵入性移动的脑体成像设备（MoBI），目的是在神经活动发生时测定个体和变化（例如，“in action and context”）在包括儿童在内的大量不同人群中体验固定和互动的艺术展览。像艺术博物馆这样的自然环境吸引了成千上万的人，这些人具有丰富的人口统计学因素，如年龄，性别，教育水平，职业，以及其他因素，如健康，药物和神经系统状态，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神经和认知系统动态的理解。这项研究的更广泛的影响包括整合艺术，科学和工程，以推进脑科学;通过揭示生物识别神经数据作为客观端点来研究认知，感知和行动，推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该研究中产生的产品来推进该领域。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体性的科学理解。就像基因序列的个体差异会使某些药物对某些人或多或少有效，从而产生药物基因组学的需求一样，大脑活动的个体差异不仅会影响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器械的安全性和有效性。尽管这一点至关重要，但其他地方没有共同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，这些设备具有所有诊断优势，也有助于逆向工程大脑机制。大数据分析方法用于调查来自大量不同参与者的大脑数据中的神经变异性和个性，可以帮助推进生物医学设备的开发，同时填补脑科学的知识空白。将追求三个研究目标，以产生这门科学，同时开发新的发现工具。首先，该项目需要从来自不同的大休斯顿地区的一千名参与者中获取多模态数据。其次，该研究将开发新的算法，用于分析，检查，可视化，表示，解析和搜索来自Blaffer博物馆公共环境中获取的多模态数据集的高维模式。目标是揭示与艺术的被动和交互式感知/生产相关的神经信号，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，以研究神经活动的人口分布，准确性和稳定性。 第三，该项目将生成一个独特的大数据集，并与科学界共享算法。这项研究为解决经验问题开辟了新的科学和教育视野（例如，从公共环境中的自由行为主体获取多模态数据），以及规范性问题（例如，从大脑活动模式中解码人类意图和情感）。此外，该项目将使K-12能够对不同的学生/受训者进行博士后培训。

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.