III: Small: Collaborative Research: Canonical Dependence Analysis for Multi-modal Data Fusion and Source Separation

III：小：协作研究：多模态数据融合和源分离的典型依赖分析

• 批准号: 1017718
• 负责人: Tulay Adali
• 金额: $ 24.99万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017718&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Canonical

Analysis of multiple sets of data, either of the same type as in multi-subject data, or of different type as in multi-modality data, is inherent to many problems in computer science and engineering. Biomedical image analysis figures prominently among these and is particularly challenging because of the rich nature of the data made available by different imaging modalities. Data-driven methods are particularly attractive for the analysis and fusion of such data as they can achieve useful decompositions while minimizing assumptions on the model and underlying processes, and can also incorporate reliable prior information when available. One such approach recently introduced for medical image analysis and fusion is multi-dataset canonical correlation analysis (MCCA) that has proven especially useful for the analysis and fusion of rather disparate data, owing to its high flexibility and extendibility to a wide array of problem settings.Intellectual Merit: In this proposal, the main aim is twofold. First, a number of powerful methods are developed for multi-subject (multi-set) data analysis and multi-modal data fusion based on canonical dependence analysis by significantly extending the power and flexibility of MCCA. Then, the successful application of the methods are demonstrated on a unique problem that demands these properties, namely the study of brain function and functional associations during simulated driving, a naturalistic task where data-driven methods have proven very useful. The data used in the project are complementary in nature but of very different nature: functional magnetic resonance imaging (fMRI), electroencephalography (EEG), structural MRI (sMRI), genetic array data--single nucleotide polymorphism (SNP)--and behavioral variables. The rich characteristics of the data and the problem at hand thus provide a special challenge for the methods developed and a unique testbed for the evaluation of their performance.Broader Impacts: The broad impact of the proposed work lies in its potential to substantially impact science and information technology as well as in its educational features. Analysis of multiple datasets of the same type as well as fusion of data from different modalities/sensors is a key problem in many science and engineering disciplines. The new set of methods proposed thus form attractive solutions for many other problems beyond brain function analysis. The fully integrative nature of the proposed work is also an invaluable asset in the ongoing efforts in cross-training of students and researchers as well as increasing the participation of underrepresented groups in science and technology careers.For further information, see the project web site at the URL: http://mlsp.umbc.edu/research_projects.html

在计算机科学和工程中，对多组数据进行分析是许多问题所固有的，这些数据要么是同类型的（如多主题数据），要么是不同类型的（如多模态数据）。生物医学图像分析在其中占有突出地位，由于不同成像方式提供的数据丰富，因此尤其具有挑战性。数据驱动的方法对于这些数据的分析和融合特别有吸引力，因为它们可以实现有用的分解，同时最小化对模型和底层过程的假设，并且还可以在可用时合并可靠的先验信息。最近为医学图像分析和融合引入的一种这样的方法是多数据集典型相关分析（MCCA），由于其高度灵活性和可扩展性，它已被证明对相当不同的数据的分析和融合特别有用。智力价值：在这个提议中，主要目的是双重的。首先，通过显著扩展MCCA的能力和灵活性，开发了一系列强大的基于规范依赖分析的多主题（多集）数据分析和多模态数据融合方法。然后，在一个需要这些特性的独特问题上展示了这些方法的成功应用，即在模拟驾驶过程中对大脑功能和功能关联的研究，这是一个自然的任务，数据驱动的方法已被证明非常有用。项目中使用的数据本质上是互补的，但性质非常不同：功能磁共振成像（fMRI）、脑电图（EEG）、结构磁共振成像（sMRI）、基因阵列数据——单核苷酸多态性（SNP）和行为变量。因此，数据的丰富特征和手头的问题对所开发的方法提出了特殊的挑战，并为评估其性能提供了独特的测试平台。更广泛的影响：拟议工作的广泛影响在于它有可能实质性地影响科学和信息技术，以及它的教育特点。同一类型的多个数据集的分析以及来自不同模式/传感器的数据的融合是许多科学和工程学科的关键问题。因此，提出的一套新方法为大脑功能分析之外的许多其他问题提供了有吸引力的解决方案。拟议工作的完全综合性质也是目前交叉培训学生和研究人员以及增加代表性不足群体参与科学和技术职业的努力的宝贵资产。欲了解更多信息，请参阅项目网站的URL: http://mlsp.umbc.edu/research_projects.html