Analysis of Multi-Voxel Patterns of Activity in fMRI data

fMRI 数据中多体素活动模式的分析

• 批准号: 7146469
• 负责人: JAMES V HAXBY
• 金额: $ 32.95万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-18 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7146469
• 关键词: artificial intelligence; bioimaging /biomedical imaging; brain mapping; cerebral cortex; clinical research; cognition; computational neuroscience; computer program /software; face expression; functional magnetic resonance imaging; human subject; neural information processing; neuroanatomy; neuroimaging; statistics /biometry; three dimensional imaging /topography; videotape /videodisc; visual perception

DESCRIPTION (provided by applicant): fMRI experiments produce large, numerically rich, but noisy data sets that pose a challenge for extracting the signal variance and establishing the correspondence between that signal and cognitive variables. Conventional analysis has reduced the dimensionality of fMRI data by searching for clusters of voxels that show similar responses to experimental manipulations and averaging the signal within those clusters. We have introduced a new approach to fMRI data analysis, "multi-voxel pattern analysis", that examines higher spatial frequency patterns of activity - the voxel-by-voxel variation of response within a region - and have shown that this method greatly increases the sensitivity of fMRI (Haxby et al. 2001; Hanson et al. 2004; OToole et al. 2004; Polyn et al. 2004). In the proposed investigations, we will develop new methods for analysis of spatially-distributed patterns of neural activity in relation to two specific problems in fMRI data analysis: 1. accounting for inter-individual variation in functional neuroanatomy, and 2. the relation between spatially-distributed neural population responses and cognitive representations. This work will involve the efforts of a multidisciplinary team consisting of cognitive neuroscientists, applied mathematicians, and signal- processing engineers. We propose the development of analytic methods for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by a broad spectrum of cognitive activities. We predict that these methods will enhance the sensitivity of group statistical tests of fMRI data, will allow the investigation of the inter-individual consistency of higher spatial frequency topographic representations, and will provide explicit measures of inter-individual variation in the location, organization, and spatial extent of functional maps, with potential applications for studies of clinical conditions. We propose, further, to develop methods for detecting and analyzing distributed patterns of neural activity that make use of prior knowledge about the structure of the cognitive representations that are associated with those neural activities. We predict that these methods will increase the sensitivity of multi-voxel pattern analysis and will allow the investigation of how cognitive information is represented in topographically-organized, spatially-distributed patterns of neural activity.

描述（由申请人提供）：fMRI实验产生大量，数字丰富，但有噪声的数据集，这对提取信号方差和建立信号与认知变量之间的对应关系提出了挑战。传统的分析方法是通过寻找对实验操作表现出相似反应的体素簇，并在这些簇中平均信号，从而降低fMRI数据的维数。我们引入了一种新的功能磁共振成像数据分析方法，“多体素模式分析”，它检查活动的高空间频率模式——一个区域内响应的体素-体素变化——并表明这种方法大大提高了功能磁共振成像的灵敏度（Haxby等人，2001；Hanson等人，2004；OToole等人，2004；Polyn等人，2004）。在本研究中，我们将针对fMRI数据分析中的两个具体问题，开发分析神经活动空间分布模式的新方法。在功能性神经解剖学中考虑个体间的差异；空间分布神经群体反应与认知表征的关系。这项工作将涉及一个由认知神经科学家、应用数学家和信号处理工程师组成的多学科团队的努力。我们建议发展分析方法，以根据广泛的认知活动引发的神经活动模式来调整个体大脑的功能神经解剖学。我们预测，这些方法将提高fMRI数据组统计测试的灵敏度，将允许研究高空间频率地形表征的个体间一致性，并将提供功能图的位置、组织和空间范围的个体间差异的明确测量，具有临床条件研究的潜在应用。我们进一步提出，利用与这些神经活动相关的认知表征结构的先验知识，开发检测和分析神经活动分布模式的方法。我们预测这些方法将增加多体素模式分析的敏感性，并将允许研究认知信息如何在地形组织，空间分布的神经活动模式中表示。