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实验产生的大型，数值富，但嘈杂的数据集构成了提取信号方差并在该信号和认知变量之间建立对应关系的挑战。 常规分析通过搜索对实验操作的响应群的簇来降低fMRI数据的维度，并在这些簇中平均信号。 我们引入了一种新的fMRI数据分析方法，即“多素模式分析”，该方法研究了较高的活动活动的空间频率模式 - 区域内反应的质量变化 - 并表明该方法大大提高了FMRI的敏感性（Haxby等人（Haxby etal。2001; Hanson et and.2004 al an al al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al an al a an al a an al a an an a an 2004）; 在拟议的研究中，我们将开发新的方法来分析fMRI数据分析中两个特定问题的神经活动的空间分布模式：1。考虑功能性神经解剖学的个体间变化，以及2。空间分布的神经群体反应和认知能力的关系。 这项工作将涉及由认知神经科学家，应用数学家和信号处理工程师组成的多学科团队的努力。 我们提出了基于广泛的认知活性引起的神经活动模式来对齐单个大脑功能性神经解剖学的分析方法的发展。 我们预测，这些方法将增强fMRI数据的组统计检验的敏感性，将允许研究更高空间频率地形表示的个体间一致性，并将提供明确的衡量位置，组织，组织和空间功能范围的官能范围，并具有用于临床条件的潜在应用。 我们进一步建议开发用于检测和分析神经活动的分布模式的方法，这些方法利用了有关与这些神经活动相关的认知表示结构的先验知识。 我们预测，这些方法将增加多素模式分析的敏感性，并允许研究如何在形状组织的，空间分布的神经活动模式中表示认知信息。