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

分析与多模式数据中的多组数据相同类型的多组数据，或与多模式数据中的不同类型的分析是计算机科学和工程中许多问题所固有的。 生物医学图像分析在其中突出，并且特别具有挑战性，因为不同成像方式提供的数据的丰富性质。数据驱动的方法对于分析和融合的数据特别有吸引力，因为它们可以实现有用的分解，同时最大程度地减少对模型和基础过程的假设，并且在可用时也可以合并可靠的先验信息。 最近引入了用于医学图像分析和融合的一种方法是多数据集的规范相关分析（MCCA），事实证明，该方法对于对较大的问题的较高灵活性和扩展性，对广泛的问题设置的高度灵活性和扩展性特别有用。Intellectual值得情况。首先，开发了许多强大的方法，用于基于规范依赖性分析的多模型（多设置）数据分析和多模式数据融合，通过显着扩展MCCA的功率和灵活性。然后，这些方法的成功应用在需要这些属性的独特问题上证明，即对模拟驾驶过程中大脑功能和功能关联的研究，这是一个自然主义的任务，在该任务中，数据驱动的方法已被证明非常有用。该项目中使用的数据本质上是互补的，但性质却非常不同：功能磁共振成像（fMRI），脑电图（EEG），结构MRI（SMRI），遗传阵列数据 - 单核核苷酸多态性（SNP） - 和行为变量。因此，数据的丰富特征和手头问题为开发的方法和评估其性能的独特测试提供了特别的挑战。BRODER的影响：拟议工作的广泛影响在于其具有实质上影响科学和信息技术以及其教育特征的潜力。在许多科学和工程学科中，对相同类型的多个数据集以及来自不同模式/传感器的数据融合的分析是一个关键问题。因此，提出的新方法构成了除了大脑功能分析之外的许多其他问题的有吸引力的解决方案。拟议的工作的完全整合性质也是在不断努力的学生和研究人员进行交叉培训以及增加代表性不足的小组参与科学和技术职业的参与方面的宝贵资产。有关更多信息，请参阅URL项目网站：http:/http://mlsp.umbc.umbc.edu/researchsearchs.hresearchs.htmlss.htmlss.htmlss.htmlss.htmls.http：/