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

被称为“多元模式”（MVP）分析的方法可用于解码使用功能磁共振成像（fMRI）获得的大脑活动信息模式。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（以及它们所使用的表征空间）很难在一个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的代码来表示相同的事物。在美国国家科学基金会的资助下。达特茅斯学院的James V. Haxby和普林斯顿大学的Peter J. Ramadge与德国马格德堡大学的Michael Hanke合作，正在开发一种新的方法来发现一种编码方案，这种编码方案可以在不同的大脑中准确地工作。正在开发的方法通过将个体大脑数据投射到一个共同的高维空间来协调大脑活动。这种方法允许研究人员为不同的皮层区域建立大脑表征空间模型，这些模型在整个大脑和广泛的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。一个被称为“超对齐”，另一个被称为“功能连接超对齐”。超对齐将个体大脑的体素空间（即大脑图像中的最小单位）旋转成一个单一的高维空间，其中每个维度都是对刺激的不同反应的轮廓，这在大脑中是常见的。功能连接超对齐基于每个皮质位置的功能连接概况（即激活的大脑区域之间的关系）来对齐体素空间。功能连接概况允许不以一致的方式响应外部刺激的区域模型，例如，在所谓的“默认内在系统”中起核心作用的那些区域在社会认知中。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好几年。开发计算方法来建立表征空间的通用模型将增强大脑活动解码技术的能力，使研究如何更精细、更详细的信息嵌入大脑活动模式，并从功能性脑成像数据中读出这些信息成为可能。提出的方法还将允许大脑解码扩展到社会认知基础的神经编码，即关于个人特征和他人精神状态的知识的表示。这些模型还将允许研究受经验、发育和精神病理影响的大脑区域内的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）正在资助一个伙伴项目。

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).