Collaborative Research: SEI: Independent Component Analysis of Complex-Valued Brain Imaging Data

合作研究：SEI：复值脑成像数据的独立成分分析

• 批准号: 0612104
• 负责人: Vince Calhoun
• 金额: $ 29.99万
• 依托单位: Hartford Hospital
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0612104&HistoricalAwards=false
• 关键词: Collaborative; Research; SEI; Independent; Component

Independent component analysis (ICA) has emerged as an attractive analysis tool for discovering hidden factors in observed data and has been successfully applied for data analysis in a wide array of applications such as biomedicine, communications, finance, and remote sensing. In a good number of these application domains, the data are typically complex valued. This is also the case in biomedical image analysis where ICA has been recognized as a promising tool for studying the brain function. Most biomedical image analysis techniques, however, use only the magnitude information and discard the phase, resulting in an unnecessary loss of information. Moreover, most brain imaging studies collect multiple data types where each existing modality for imaging the brain reports upon a limited domain and provides complementary information. Thus processing of imaging data in its native, complex form and by utilizing multiple modality images promises significant advances in our understanding of the brain function. We propose to develop a class of complex ICA algorithms, in particular for analysis of biomedical imaging data and demonstrate the power of joint data analysis as well as performing the analysis on the complete set of data, i.e., by utilizing both the magnitude and the phase information. We focus upon three image types, functional magnetic resonance imaging (fMRI), structural MRI (sMRI) and diffusion tensor imaging (DTI). These three imaging data provide complementary information about brain connectivity, and all can benefit from the incorporation of a complex-valued data processing approach.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. Study of human brain connectivity is a very challenging and rich problem. The ICA-based fusion approach as well as the use of imaging data in its native, complex form, we believe is the key for achieving significant advances in the field. Successful demonstration of our approach for medical imaging data will also benefit other areas of science and technology where data from multiple sources and/or data in complex form need to be jointly analyzed for inferences. A significant broader impact of our proposal is to stimulate research at the interface between medical imaging and information processing by making the tools for the study of brain connectivity widely available through a toolbox and a medical imaging database.

独立成分分析（ICA）已成为一种有吸引力的分析工具，用于发现观测数据中的隐藏因素，并已成功应用于生物医学、通信、金融和遥感等广泛应用中的数据分析。 在许多此类应用程序领域中，数据通常具有复值。 生物医学图像分析也是如此，ICA 被认为是研究大脑功能的有前途的工具。 然而，大多数生物医学图像分析技术仅使用幅度信息并丢弃相位，导致不必要的信息丢失。 此外，大多数大脑成像研究收集多种数据类型，其中每种现有的大脑成像模式报告有限的领域并提供补充信息。因此，以原始、复杂的形式处理成像数据并利用多模态图像，有望在我们对大脑功能的理解方面取得重大进展。 我们建议开发一类复杂的 ICA 算法，特别是用于分析生物医学成像数据，并展示联合数据分析以及对完整数据集进行分析的能力，即同时利用幅度和相位信息。我们专注于三种图像类型：功能磁共振成像 (fMRI)、结构磁共振成像 (sMRI) 和扩散张量成像 (DTI)。 这三种成像数据提供了有关大脑连接的补充信息，并且所有这些数据都可以从复杂值数据处理方法的结合中受益。拟议工作的广泛影响在于其对科学和信息技术产生重大影响的潜力以及其教育特色。人脑连接性的研究是一个非常具有挑战性和丰富的问题​​。我们相信，基于 ICA 的融合方法以及以原始、复杂的形式使用成像数据是在该领域取得重大进展的关键。 我们的医学成像数据方法的成功演示也将有利于其他科学技术领域，在这些领域需要联合分析来自多个来源的数据和/或复杂形式的数据以进行推论。我们提案的更广泛的显着影响是通过工具箱和医学成像数据库广泛提供用于研究大脑连接的工具，从而刺激医学成像和信息处理之间接口的研究。