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

被称为多变量模式(MVP)分析的方法可以用来解码通过功能磁共振成像(FMRI)获得的大脑活动的信息模式。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使用相同的代码还是特殊的代码来代表相同的事物。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·拉马奇博士与德国马格德堡大学的迈克尔·汉克合作，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单个大脑数据投射到共同的高维空间来调整大脑中的大脑活动。这种方法允许研究人员为不同的皮质区域建立大脑表征空间的模型，这些模型既适用于大脑，也适用于广泛的刺激和认知状态。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“功能性连通性超对齐”。超对齐将个体大脑的体素空间(即大脑图像中最小的单位)旋转到一个单一的高维空间，其中每个维度都是对刺激的差异反应的轮廓，这在整个大脑中都很常见。功能连通性超对齐基于每个皮质位置的功能连通性配置文件(即，激活的大脑区域之间的关系)对准体素空间。功能连接模式允许对不以一致方式对外部刺激做出反应的区域进行建模，例如，在社会认知中起核心作用的所谓的“默认内在系统”中的那些区域。研究人员是一个跨学科的合作伙伴--认知神经科学家和信号处理工程师--他们已经成功地合作了几年。开发建立表征空间通用模型的计算方法将增强大脑活动解码技术的能力，使人们能够研究大脑活动模式中如何嵌入更精细、更详细的信息，并从功能脑成像数据中读出这些信息。建议的方法还将允许将大脑解码扩展到构成社会认知的神经代码，即关于他人的个人特征和精神状态的知识的表征。这些模型还将允许研究大脑中受经验、发展和精神病理影响的区域内神经编码是如何改变的。该项目由计算神经科学合作研究和国际科学与工程办公室联合资助。德国教育和研究部(BMBF)正在资助一个配套项目。

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).