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

人类的大脑可能是已知的最复杂的物体，是包含我们的思想，经验，知识，以及我们的文化的容器。 虽然大脑有相似的解剖学组成部分，但大小和形状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交流的相似思想和知识。 脑科学面临的一个主要挑战是建立一个共同的大脑功能结构模型，以精细的尺度捕捉大脑之间共享的这些相似性。 该项目的研究旨在为这种通用模型开发计算基础。 该模型基于使用功能性磁共振成像（fMRI）测量大脑活动模式，同时参与者进行日常认知活动，如看电影，听故事或休息时自由思考。 该模型在从细到粗的多个尺度上对齐大脑的功能结构，并捕获比基于大脑解剖结构对齐的其他方法更多的共享结构。 这个模型将提供基础设施，可供科学家使用，他们对大脑进行成像，以研究从感知到社会互动、情感和决策的各种大脑功能，使他们能够以一种可以在实验室间交流的格式来描述这些功能背后的机制，这种格式具有一定的细节和精度，这将加速发现和应用。依赖于与潜在功能结构具有不同对应关系的解剖特征。 此外，这样的对齐方法不能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。 该项目的研究是基于在大脑中对齐表征空间，而不是解剖学拓扑结构，并将识别具有不同表征空间的皮层区域之间的边界。 这一创新提供了跨大脑的功能架构的非常优越的上级对齐和人类大脑的共同模型的开发。 该研究将开发计算算法，用于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影或收缩包装到个体大脑上来微调个体大脑的描述。 这些计算算法和模型的开发将是研究生和博士后研究员培训的组成部分。 它们将作为免费和开源软件提供，具有大型共享数据集，供世界各地的脑成像科学家免费共享和使用，提供必要的研究基础设施，以最大限度地发挥该研究项目的影响和效益。

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.