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

独立成分分析（伊卡）已成为一个有吸引力的分析工具，发现隐藏在观测数据中的因素，并已成功地应用于数据分析在广泛的应用，如生物医学，通信，金融和遥感。 在许多这样的应用领域中，数据通常是复值的。 在生物医学图像分析中，伊卡也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而丢弃相位，导致不必要的信息损失。 此外，大多数脑成像研究收集多种数据类型，其中用于对脑成像的每个现有模态报告有限的域并提供补充信息。因此，以其原生的、复杂的形式并通过利用多模态图像来处理成像数据，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伊卡算法，特别是用于生物医学成像数据的分析，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，通过利用幅度和相位信息。我们集中在三种图像类型，功能磁共振成像（fMRI），结构磁共振成像（sMRI）和扩散张量成像（DTI）。 这三个成像数据提供了关于大脑连接的互补信息，并且都可以从合并复值数据处理方法中受益，所提出的工作的广泛影响在于其对科学和信息技术以及其教育功能产生实质性影响的潜力。人脑连通性的研究是一个极具挑战性和丰富性的课题。基于ICA的融合方法以及以其原生的复杂形式使用成像数据，我们认为是该领域取得重大进展的关键。 成功演示我们的方法用于医学成像数据也将有利于其他科学和技术领域，其中来自多个来源的数据和/或复杂形式的数据需要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，通过工具箱和医学成像数据库，使大脑连接的研究工具广泛使用，从而刺激医学成像和信息处理之间的接口研究。

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.