U.S.-German Collaboration: Building common high-dimensional models of neural representational spaces

美德合作：构建神经表征空间的通用高维模型

• 批准号: 1129855
• 负责人: Peter Ramadge
• 金额: $ 34.87万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1129855&HistoricalAwards=false
• 关键词: U.S.; German; Collaboration; Building; common

Methods known as 'multivariate pattern' (MVP) analysis can be used to decode the information patterns in brain activity obtained using functional magnetic resonance imaging (fMRI). However, a new decoding model has to be built for each brain, because two brains (and the representational spaces they employ) are difficult to align at a fine spatial scale. As a consequence, we do not yet know if different brains use the same codes or idiosyncratic codes to represent the same things. With funding from the National Science Foundation, Drs. James V. Haxby of Dartmouth College, and Peter J. Ramadge of Princeton University, in collaboration with Michael Hanke of the University of Magdeburg (Germany), are developing new methods to discover a coding scheme that works accurately across different brains. The methods being developed align brain activity across brains by projecting individual brain data into a common, high-dimensional space. This approach allows the researchers to build models of brain representational spaces for different cortical areas that are valid both across brains and across a wide range of stimuli and cognitive states. The researchers are developing two algorithms. One is referred to as 'hyperalignment' and the other as 'functional connectivity hyperalignment.' Hyperalignment rotates the voxel spaces (i.e., the smallest units in a brain image) of individual brains into a single high-dimensional space, in which each dimension is a profile of differential responses to stimuli, that is common across brains. Functional connectivity hyperalignment aligns voxel spaces based on the functional connectivity profile (i.e., relationships among activated brain areas) for each cortical location. Functional connectivity profiles allow for models of areas that do not respond to external stimuli in a consistent manner, for example, those areas in the so-called 'default-intrinsic system' that plays a central role in social cognition. The investigators are an interdisciplinary partnership - cognitive neuroscientists and signal-processing engineers - who have been working together successfully for several years. Developing the computational methods to build common models of representational spaces will augment the power of brain activity decoding techniques, making it possible to investigate how finer, more detailed information is embedded in brain activity patterns, and to read out that information from functional brain imaging data. The proposed methods also will allow extension of brain decoding to the neural codes that underlie social cognition, that is, the representation of knowledge about the personal traits and mental states of others. These models also will allow investigation of how neural coding is altered within brain regions that are affected by experience, by development, and by psychopathology.This project is jointly funded by Collaborative Research in Computational Neuroscience and the Office of International Science and Engineering. A companion project is being funded by the German Ministry of Education and Research (BMBF).

被称为“多变量模式”（MVP）分析的方法可以用于解码使用功能性磁共振成像（fMRI）获得的大脑活动中的信息模式。 然而，必须为每个大脑建立一个新的解码模型，因为两个大脑（以及它们使用的表征空间）很难在精细的空间尺度上对齐。因此，我们还不知道不同的大脑是否使用相同的代码或特殊代码来表示相同的事物。在国家科学基金会的资助下，达特茅斯学院的詹姆斯·V·哈克斯比博士和普林斯顿大学的彼得·J·拉马奇博士与马格德堡大学（德国）的迈克尔·汉克博士合作，正在开发新的方法来发现一种在不同大脑中准确工作的编码方案。正在开发的方法通过将个体大脑数据投影到一个共同的高维空间来调整大脑活动。这种方法使研究人员能够为不同的皮层区域建立大脑表征空间模型，这些模型在大脑和各种刺激和认知状态下都是有效的。研究人员正在开发两种算法。一种被称为“超对齐”，另一种被称为“功能连接性超对齐”。超对齐旋转体素空间（即，大脑图像中的最小单元）到单个高维空间中，其中每个维度是对刺激的不同反应的轮廓，这在大脑中是共同的。 功能连接性超对准基于功能连接性简档来对准体素空间（即，激活的大脑区域之间的关系）。功能连接配置文件允许的模型，不对外部刺激作出反应，以一致的方式，例如，在所谓的“默认内在系统”，在社会认知中发挥核心作用的那些领域。研究人员是一个跨学科的伙伴关系-认知神经科学家和信号处理工程师-谁已经成功地合作了几年。 开发计算方法来构建表征空间的通用模型，将增强大脑活动解码技术的能力，使研究大脑活动模式中嵌入的更精细、更详细的信息成为可能，并从功能性大脑成像数据中读出这些信息。 所提出的方法还将允许将大脑解码扩展到作为社会认知基础的神经代码，即关于他人的个人特征和精神状态的知识的表示。这些模型也将允许调查神经编码是如何被改变的大脑区域内的经验，由发展，并由psychopathology.This项目是由计算神经科学合作研究和国际科学与工程办公室共同资助。 德国教育和研究部（BMBF）正在资助一个配套项目。