NCS-FO: Individual variation in the fine-grained structure of distributed cortical systems for cognition

这个研究项目将研究大脑系统对人脸的感知如何在个体之间存在差异。人脸感知在人与人之间的互动中起着核心作用。 健康成年人识别面孔和解释表情的能力差异很大。 有效的面部知觉在童年和成年早期缓慢发展。 对于我们经常互动的熟悉的人来说，面部感知更有效。 我们将使用新的、最先进的方法来模拟大脑的面部感知系统。 该模型具有相互作用的处理路径。 每种途径都有不同的功能。 这些包括身份的识别，表达的解释和社会知识的激活。 我们使用一种新的方法来研究个体差异，称为超对齐。超对齐使我们能够看到信息是如何在精细的大脑模式中编码的。 这些研究可以解决有关发育，教育，文化和临床疾病对大脑组织的影响的问题。 该项目将研究人类大脑皮层功能结构中的个体差异，以利用我们以前在分布式神经系统中的多变量信息模型进行面部感知，以及我们创建超对齐的工作来构建大脑皮层中信息空间的高维通用模型。我们的方法发现共享的基础功能的信息，编码在精细尺度的皮层地形，提供可靠的测量个体差异，在此详细信息的表示。 我们将研究个体差异的认知能力，发展和学习的功能，在大脑皮层系统的面孔知觉，并建立共同的模型，使用功能磁共振成像数据收集的大脑皮层信息空间在观看自然电影和休息状态。 我们将使用响应超对齐和连接超对齐来导出面部感知系统的通用模型，该模型具有用于响应调谐和功能连接的精细变化的共享基函数。通过在精细尺度上对共享神经表征进行建模，对共享表征的偏差的测量对认知功能差异的个体间差异更敏感。 我们的方法有可能为解决有关发育、教育、文化和临床障碍对大脑组织的影响的问题提供更坚实和更细致的基础。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This research project will study how brain systems for face perception differ across individuals. Face perception plays a central role in interactions among people. The ability to recognize faces and interpret expressions can vary greatly in healthy adults. Efficient face perception develops slowly through childhood and into early adulthood. Face perception is much more efficient for familiar individuals with whom we interact frequently. We will use new, state-of-the-art methods for modeling the brain's system for face perception. The model has interacting processing pathways. Each pathway serves a different function. These include recognition of identity, interpretation of expression, and activation of social knowledge. We study individual differences using a new approach, called hyperalignment. Hyperalignment allows us to see how information is encoded in fine-scale brain patterns. These studies can make it possible to address questions about the effects of development, education, culture, and clinical disorder on brain organization. The project will investigate individual variation in the human cortical functional architecture for face perception that leverages our previous work on multivariate models of information in the distributed neural system for face perception and our work creating hyperalignment to build high-dimensional common models of information spaces in cortex. Our approach discovers shared basis functions for information that is encoded in fine-scale cortical topographies, affording reliable measurement of individual differences in the representation of this detailed information. We will investigate individual variation in cortical systems for face perception as a function of cognitive ability, development, and learning, and build the common model of cortical information spaces using fMRI data collected during viewing of naturalistic movies and in the resting state. We will use response hyperalignment and connectivity hyperalignment to derive a common model of the face perception system with shared basis functions for fine-scale variation in response tuning and functional connectivity. By modeling shared neural representation at a fine scale, measures of deviations from shared representation are more sensitive to the inter-individual variation that underlies differences in cognitive function. Our methods have the potential to provide a firmer and more nuanced basis for addressing questions about the effects of development, education, culture, and clinical disorder on brain organization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.