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

分析多组数据是计算机科学和工程中的许多问题所固有的，无论是与多主题数据相同类型的数据，还是与多模式数据不同类型的数据。生物医学图像分析在这些研究中占有重要地位，由于不同成像模式提供的数据具有丰富的性质，因此尤其具有挑战性。数据驱动的方法对于这类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，同时最大限度地减少对模型和基本过程的假设，并且还可以在可用的情况下纳入可靠的先验信息。最近引入的一种用于医学图像分析和融合的方法是多数据集典型相关分析(MCCA)，由于其高度的灵活性和对广泛问题集的可扩展性，已被证明对非常不同的数据的分析和融合特别有用。首先，通过显著扩展MCCA的能力和灵活性，提出了一系列基于典型相关性分析的多主体(多集合)数据分析和多模式数据融合的有效方法。然后，这些方法的成功应用被展示在一个需要这些特性的独特问题上，即研究模拟驾驶过程中的大脑功能和功能关联，这是一项自然任务，其中数据驱动方法被证明非常有用。该项目使用的数据在性质上是互补的，但性质却截然不同：功能磁共振成像(FMRI)、脑电(EEG)、结构磁共振(SMRI)、基因阵列数据--单核苷酸多态(SNP)--以及行为变量。数据的丰富特点和手头的问题因此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一个独特的试验台。广泛影响：拟议工作的广泛影响在于其可能对科学和信息技术及其教育特征产生重大影响。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中的关键问题。因此，新提出的一套方法形成了对大脑功能分析以外的许多其他问题的有吸引力的解决方案。拟议工作的充分综合性也是正在进行的学生和研究人员交叉培训以及增加未被充分代表的群体参与科学和技术事业的努力的宝贵财富。有关更多信息，请参阅该项目的网站：http://mlsp.umbc.edu/research_projects.html

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