CRCNS: Collaborative Research: A Common Model of the Functional Architecture of Human Cortex

CRCNS：协作研究：人类皮质功能架构的通用模型

• 批准号: 1607801
• 负责人: Peter Ramadge
• 金额: $ 30.73万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607801&HistoricalAwards=false
• 关键词: CRCNS; Collaborative; Research; Common; Model

The human brain is perhaps the most complex known object and is the vessel that contains our thoughts, experiences, knowledge, and, collectively, our culture. Although brains have similar anatomical components, differences in size and shape and in fine structure make it difficult to discern how different brains can contain similar thoughts and knowledge that can be shared and communicated. A major challenge for brain science is to build a common model of the functional architecture of the brain that captures these similarities that are shared across brains at a fine scale. The research in this project is aimed at developing a computational basis for such a common model. The model is based on measurement of patterns of brain activity using functional magnetic resonance imaging (fMRI) while participants engage in everyday cognitive activities like watching a movie, listening to a story, or free-ranging thought while at rest. The model aligns the functional architecture of the brain at multiple scales, from fine to coarse, and captures far more shared structure than is possible with other methods that are based on alignment of brain anatomy. This model will provide infrastructure that can be used by scientists who image the brain in order to study a wide range of brain functions, from perception to social interaction, emotion, and decision making, allowing them to describe the mechanisms underlying these functions in a format that can be communicated across laboratories with a level of detail and precision that will accelerate discovery and application.Alignment of brain imaging data has relied on anatomical features that have a variable correspondence to the underlying functional architecture. Moreover, such alignment methods do not capture the fine structure of brain activity patterns that can be decoded using modern pattern analytic methods. The research in this project is based on aligning representational spaces across brains, rather than anatomical topographies, and will identify the boundaries between patches of cortex with distinct representational spaces. This innovation affords greatly superior alignment of functional architecture across brains and the development of a common model of the human brain. The research will develop computational algorithms for transforming the idiosyncratic organization of individual brains into the common representational spaces and for fine-tuning the description of individual brains by projecting, or shrink-wrapping, the common model based on a large number of individuals onto that individual brain. The development of these computational algorithms and the model will be integral to the training of graduate students and postdoctoral fellows. They will be made available as free and open-source software, with large shared data sets, to be shared and used freely by brain imaging scientists around the world, providing essential research infrastructure to maximize the impact and benefit of this research project.

人的大脑也许是最复杂的对象，是包含我们的思想，经验，知识以及共同存在的文化的船只。 尽管大脑具有类似的解剖成分，大小和形状的差异以及精细结构的差异使得很难辨别不同的大脑如何包含可以共享和传达的类似思想和知识。 脑科学的一个主要挑战是建立大脑功能架构的共同模型，该模型捕获了这些相似之处，这些相似之处是在大脑中分布的。 该项目的研究旨在为这种常见模型开发计算基础。 该模型基于使用功能磁共振成像（fMRI）对大脑活动模式的测量，而参与者则从事日常认知活动，例如观看电影，听故事或在休息时进行自由思考。 该模型将大脑的功能结构在多个尺度（从细到粗糙到粗糙）上对齐，并且捕获的结构远远超过了基于大脑解剖结构的其他方法的共享结构。 该模型将提供基础架构，科学家可以使用这些模拟大脑的科学家，以研究从感知到社交互动，情感和决策的广泛大脑功能，使他们能够描述以格式的这些功能的机制可以在跨实验室以一定程度的细节和精度来传达这种情况，这将加速发现和应用。脑成像数据的对齐取决于与基础功能架构具有可变对应关系的解剖学特征。 此外，这种比对方法不会捕获可以使用现代模式分析方法解码的大脑活动模式的精细结构。 该项目中的研究基于跨大脑的代表空间，而不是解剖构图，并将确定具有不同代表​​空间的皮质斑块之间的边界。 这种创新可以极大地跨大脑的功能结构以及人类大脑的共同模型的发展。 该研究将开发计算算法，以将单个大脑的特质组织转换为共同的代表空间，并通过投影或收缩包裹（基于大量个体的常见模型）对单个个人的共同模型进行微调描述，以便将其描述到该单个个人中。脑。 这些计算算法和模型的开发将是研究生和博士后研究员的培训不可或缺的一部分。 它们将作为免费和开源软件提供，并提供大量共享数据集，并由世界各地的脑成像科学家自由共享和自由使用，从而提供必不可少的研究基础架构，以最大程度地提高该研究项目的影响和利益。