NCS-FO: Integrative Knowledge Modeling in Cognitive Neuroimaging

神经影像学研究的数量和范围都在增加，需要“大数据”方法来发现。神经影像学已经存在许多重要的资源，包括数据库、众包知识库、标准化本体和术语以及元分析库。然而，虽然数据和代码共享的努力在不断增加，但平台之间的互动很少，知识共享有限。机器学习方法正被应用于其他领域以提取锁定在文本中的知识（例如，期刊文章、患者记录、社交媒体帖子），其目的是识别实体之间的关系（例如，“药物X导致不良事件Y”）。认知神经科学家还确定关系，特别是大脑区域与认知，感知和运动过程之间的关系（例如，“mental function X activates brain network Y”），但是在对不断增长的公开文本使用高通量自动化方法时受到阻碍。要确定在给定的项目中研究了哪些认知过程，或者确定哪些大脑网络与哪些心理过程相关，这不是一个简单的过程。研究人员提出了一个综合的元数据框架，描述了实验设计的特点和结果，以及研究提供的知识。 有效的知识共享可能最好通过一个交互式数据生态系统来实现，该生态系统在描述认知神经成像实验中获得的知识时使用透明和开放的标准。开发这种用于神经成像的综合元数据框架将提高社区共享数据和评估由此产生的心灵和大脑之间关系的可靠性的能力。该项目旨在提供科学文献的大规模整合方面的改进，更快地理解大脑研究和神经认知模型的复杂性，在教育环境中培养STEM学生和加速研究生产力。神经科学研究通常需要效率，跨学科合作和跨领域灵活性。有效的知识共享最好通过依赖于综合元数据框架的互动数据生态系统来实现。这样一个框架将通过在描述认知神经成像实验得出的知识时提供透明和开放的标准来解决科学可重复性问题。此外，认知神经成像的综合元数据框架的发展将增强现有神经信息学资源之间的互动，提高社区共享数据和评估实验结果可靠性的能力。该项目将开发认知神经影像研究的知识建模工具，以及认知模型的大规模元分析评估。研究人员将在以前的工作基础上，从文本中提取实验设计特征，为全文论文创建一个分类器集合。研究人员将与外部顾问委员会合作进行评估和反馈，并将使用该框架自动提取有关执行功能，情感处理和奖励反馈的范例领域内的心灵/大脑模型的知识。研究人员将把分类器和方法与其他国际标准相结合，以实现数据和结果共享（例如，NI-DM，CEDAR和ISA-TAB，BioCaddie）和其他存储库（例如Neurosynth，BrainSpell），以便在社区中更广泛地使用。这个项目的智力价值是增强了对目前锁定在文本中的认知神经科学知识的访问。该项目的成功将使研究界能够共同解决诸如注释自己的数据和通过公共存储库，期刊或知识发现平台中的集成注释共享他们的数据/结果等障碍，并最终导致跨领域神经认知模型开发的长期战略。该项目的设计具有很高的综合价值，将与现有的神经信息学资源进行交互，协调和共享数据和算法，这些资源将受益于增强的知识建模技术。此外，为了提高透明度，可靠性和可重复性，该项目将在开放科学框架和Github上公开（例如，标签、分类器、代码）。这样一个综合的元数据框架可以被视为结缔组织，将促进新一代的认知模型的发展，提供了一个潜在的变革性的战略建模的文献，并最终导致更明智的，循证的，可重复的神经认知模型的大脑功能。

Neuroimaging research is increasing in volume and scope, needing "big data" methods for discovery. A number of important resources already exist for neuroimaging, including data repositories, crowd-sourcing knowledge bases, standardized ontologies and terminologies, and meta-analytic repositories. However, while data and code-sharing efforts are growing, there is little interaction and limited sharing of knowledge across platforms. Machine learning methods are being applied in other fields to extract knowledge locked in text (e.g., journal articles, patient records, social media posts), with the goal of recognizing relations among entities (e.g., "drug X causes adverse event Y"). Cognitive neuroscientists also determine relations, specifically between brain regions and cognitive, perceptual, and motor processes (e.g., "mental function X activates brain network Y"), but are hampered in using high-throughput automated methods on the ever-growing published text. It is not a simple process to identify which cognitive processes were studied in a given project, or what brain networks were identified as related to which mental process. The investigators propose an integrative metadata framework that describes the experimental design characteristics and results, as well as the knowledge that the research provides. Efficient knowledge sharing may best be achieved via an interactive data ecosystem that uses standards for transparency and openness when describing knowledge derived from cognitive neuroimaging experiments. Developing this integrative metadata framework for neuroimaging will increase the community's ability to share data and evaluate reliability in the resulting relationships between mind and brain. This project aims to provide improvements in large-scale integration of the scientific literature, with more rapid understanding of the complexity of brain research and neurocognitive models, within an educational setting for training STEM students and accelerated research productivity.Neuroscientific research frequently requires efficiency, transdisciplinary collaborations, and cross-domain flexibility. Efficient knowledge sharing may best be achieved via an interactive data ecosystem that relies on an integrative metadata framework. Such a framework would address scientific reproducibility by providing standards for transparency and openness when describing knowledge derived from cognitive neuroimaging experiments. Moreover, development of an integrative metadata framework for cognitive neuroimaging will enhance interaction between existing neuroinformatics resources, increasing the community's ability to share data and evaluate reliability in experimental findings. This project will develop knowledge modeling tools for cognitive neuroimaging studies, as well as large-scale meta-analytic evaluations of cognitive models. The investigators will build on previous work extracting experimental design features from the text to create an ensemble of classifiers for full text papers. The investigators will work with an External Advisory Board for evaluation and feedback, and will use the framework to automatically extract knowledge regarding mind/brain models within the exemplar domains of executive function, affective processing, and reward feedback. The investigators will integrate classifiers and methods with other international standards for data and results sharing (e.g., NI-DM, CEDAR and ISA-TAB, BioCaddie) and other repositories (e.g. Neurosynth, BrainSpell) for broader use in the community. The intellectual merit of this project is the enhanced access to cognitive neuroscience knowledge that is currently locked in text. This project's success will allow the research community to collectively address hurdles such as annotating their own data and sharing their data/results via integrated annotations in a public repository, journal, or knowledge discovery platforms, and ultimately lead to long-term strategies for cross-domain neurocognitive model development. This project has been designed to have high integrative value and will interact, harmonize, and share data and algorithms with existing neuroinformatics resources that will benefit from enhanced knowledge modeling techniques. Moreover, in an effort to promote transparency, reliability, and reproducibility, this project will be publicly available on the Open Science Framework and Github (e.g., Labels, Classifiers, Code). Such an integrative metadata framework may be viewed as the connective tissue that will facilitate a new generation of cognitive model development, providing a potentially transformative strategy for modeling the literature, and ultimately leading to more informed, evidence-based, and reproducible neurocognitive models of brain function.