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: 1607845
• 负责人: James Haxby
• 金额: $ 50.23万
• 依托单位: Dartmouth College
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607845&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.

人类的大脑可能是已知的最复杂的物体，它是容纳我们的思想、经验、知识和文化的容器。尽管大脑具有相似的解剖结构，但由于大小形状和精细结构的差异，很难辨别不同的大脑是如何包含可以共享和交流的相似思想和知识的。脑科学面临的一个主要挑战是建立一个大脑功能结构的通用模型，在一个精细的尺度上捕捉大脑之间共享的这些相似性。本项目的研究旨在为这种通用模型开发一个计算基础。当参与者在休息时进行日常认知活动，如看电影、听故事或自由思考时，该模型基于使用功能性磁共振成像（fMRI）对大脑活动模式的测量。该模型在多个尺度上对大脑的功能结构进行排列，从精细到粗糙，与其他基于大脑解剖学排列的方法相比，它可以捕获更多的共享结构。该模型将为科学家提供基础设施，他们可以使用大脑成像来研究广泛的大脑功能，从感知到社会互动，情感和决策，使他们能够以一种格式描述这些功能背后的机制，这种格式可以跨实验室交流，具有一定程度的细节和精度，这将加速发现和应用。脑成像数据的对齐依赖于与潜在功能结构具有可变对应关系的解剖特征。此外，这种校准方法并不能捕捉到可以用现代模式分析方法解码的大脑活动模式的精细结构。这个项目的研究是基于对齐大脑中的表征空间，而不是解剖地形，并将识别具有不同表征空间的皮层斑块之间的边界。这一创新为跨大脑的功能结构提供了非常优越的一致性，并开发了人类大脑的通用模型。这项研究将开发计算算法，将个体大脑的特殊组织转化为共同的表征空间，并通过将基于大量个体的共同模型投射或收缩包装到个体大脑上，对个体大脑的描述进行微调。这些计算算法和模型的发展将成为研究生和博士后培训的一部分。它们将作为免费的开源软件提供，其中包含大量共享数据集，供世界各地的脑成像科学家免费共享和使用，为最大限度地发挥该研究项目的影响和效益提供必要的研究基础设施。