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

多个数据集的分析，无论是相同类型的多学科数据，或不同类型的多模态数据，是固有的许多问题，在计算机科学和工程。 生物医学图像分析在其中占有突出地位，并且由于不同成像模式提供的数据的丰富性而特别具有挑战性。数据驱动的方法对于分析和融合这些数据特别有吸引力，因为它们可以实现有用的分解，同时最大限度地减少对模型和基本过程的假设，并且还可以在可用时纳入可靠的先验信息。 一个这样的方法最近推出的医学图像分析和融合是多数据集典型相关分析（MCCA），已被证明是特别有用的分析和融合，而不同的数据，由于其高度的灵活性和可扩展性，以广泛的问题settings.Intellectual优点：在这个建议，主要目的是双重的。首先，通过显着扩展MCCA的功能和灵活性，开发了一些强大的方法用于多主题（多集）数据分析和多模态数据融合的典型依赖分析的基础上。然后，成功应用的方法证明了一个独特的问题，需要这些属性，即在模拟驾驶，一个自然的任务，其中数据驱动的方法已经证明是非常有用的大脑功能和功能协会的研究。该项目中使用的数据在性质上是互补的，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），遗传阵列数据-单核苷酸多态性（SNP）-和行为变量。丰富的特点的数据和手头的问题，从而提供了一个特殊的挑战，开发的方法和一个独特的测试平台，以评估其performance.Broader影响：广泛的影响，拟议的工作在于其潜在的重大影响科学和信息技术，以及在其教育功能。同一类型的多个数据集的分析以及来自不同模态/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，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。拟议工作的全面综合性质也是一项宝贵的资产，有助于不断努力对学生和研究人员进行交叉培训，并增加代表性不足的群体对科学和技术职业的参与。 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