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

NCS-FO：分布式皮质认知系统细粒度结构的个体差异

基本信息

批准号：
1835200
负责人：
James Haxby
金额：
$ 100万
依托单位：
Dartmouth College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835200&HistoricalAwards=false
关键词：
NCS FO Individual variation fine

项目摘要

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.

这项研究项目将研究脸部感知的大脑系统在不同个体之间的差异。面孔知觉在人与人之间的互动中起着核心作用。在健康的成年人中，识别人脸和理解表情的能力可能会有很大的差异。有效的面孔知觉从儿童时期到成年初期都是缓慢发展的。对于我们经常互动的熟悉的人来说，面孔感知要有效得多。我们将使用新的、最先进的方法来为大脑的脸部感知系统建模。该模型具有交互作用的加工路径。每条途径都有不同的功能。这些包括身份识别、表达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，这种方法被称为超对齐。超对齐让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、文化和临床障碍对大脑组织的影响的问题。该项目将研究人类大脑皮质面部感知功能结构中的个体差异，利用我们之前在用于面部感知的分布式神经系统中的多变量信息模型所做的工作，以及我们创建超配准以建立皮质信息空间的高维通用模型的工作。我们的方法发现了编码在精细皮质地形图中的信息的共享基函数，为表示这种详细信息的个体差异提供了可靠的测量。我们将研究作为认知能力、发展和学习功能的面孔感知皮层系统的个体差异，并使用观看自然主义电影时和静止状态下收集的fMRI数据建立大脑皮层信息空间的通用模型。我们将使用反应超对准和连通性超对准来推导出具有共享基函数的人脸感知系统的通用模型，以用于响应调节和功能连接的精细变化。通过在精细尺度上对共享神经表征进行建模，对共享表征偏差的测量对认知功能差异背后的个体间差异更加敏感。我们的方法有可能为解决发展、教育、文化和临床疾病对脑组织的影响提供更坚实、更细微的基础。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。