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

独立分量分析（ICA）已成为一种发现观测数据中隐藏因素的有吸引力的分析工具，并已成功地应用于生物医学、通信、金融和遥感等广泛应用的数据分析。在许多这样的应用程序域中，数据通常是复杂值。在生物医学图像分析中也是如此，ICA已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只使用幅度信息而放弃相位，导致不必要的信息损失。此外，大多数脑成像研究收集了多种数据类型，其中每种现有的脑成像模式都报告了一个有限的领域，并提供了补充信息。因此，以原始的、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（即利用幅度和相位信息）进行分析的能力。我们的重点是三种图像类型，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关于大脑连接的补充信息，并且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它有可能对科学和信息技术产生重大影响，也在于它的教育特点。人类大脑连通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的融合方法以及使用原始、复杂形式的成像数据是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和技术领域，这些领域需要从多个来源和/或复杂形式的数据进行联合分析以进行推断。我们的建议的一个重要的更广泛的影响是，通过一个工具箱和一个医学成像数据库，使研究大脑连接的工具广泛可用，从而刺激对医学成像和信息处理之间接口的研究。

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.