CIF: Small: Low-Dimensional Structure Learning for Tensor Data with Applications to Neuroimaging

CIF：小：张量数据的低维结构学习及其在神经影像中的应用

• 批准号: 1615489
• 负责人: Selin Aviyente
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615489&HistoricalAwards=false
• 关键词: CIF; Small; Low; Dimensional; Structure

Advances in information technology are making it possible to collect increasingly massive amounts of multidimensional, multi-modal data across a diverse range of disciplines including bioinformatics, neuroscience, and the social sciences. One particular area where such multidimensional, multi-modal, and often nonlinear data is collected is neuroscience, in particular human brain connectomics. Connectomics aims to offer a comprehensive framework to describe neuronal connectivity by constructing networks from multi-modal and multi-subject, as well as both temporal and spatial, data. These high dimensional datasets pose a challenge to the signal processing community to develop data reduction methods that can exploit their rich structure in order to extract meaningful summarizations. Over the past several decades a tremendous amount of work has focused on the analysis and compression of high dimensional point-cloud datasets via low-dimensional manifold and subspace techniques. Although the research on low-dimensional structure learning from vector-type data is well developed, the direct application of these methods to higher order data poses significant challenges, including both increased computational complexity, and their inability to capture the couplings across different modes. This research addresses these problems through a tensor-based framework for data reduction and low-dimensional structure learning with a particular focus on reducing dynamic functional connectivity networks (dFCNs) into physiologically meaningful network components.The investigators develop two complementary approaches to address this high order data reduction problem: 1) Robust low-rank+sparse linear structure learning algorithms for tensors; 2) Multi-scale, locally linear adaptive tensor decomposition algorithms for compressing and learning structure from tensor data. Finally, this tensor based framework is applied to dFCNs constructed from electroencephalogram (EEG) data to assess well-known salience and control functional networks associated with affective regulation and cognitive control.

信息技术的进步使得可以在包括生物信息学，神经科学和社会科学在内的各种学科中收集越来越多的多维多模式数据。这种多维，多模式和通常非线性数据的特定领域是神经科学，特别是人脑连接组。 Connectomics旨在通过构建来自多模式和多模式的网络以及时间和空间，数据来描述神经元连接的综合框架。这些高维数据集对信号处理社区提出了挑战，以开发可利用其丰富结构以提取有意义的摘要的数据减少方法。在过去的几十年中，大量工作集中在通过低维歧管和子空间技术对高维点云数据集进行分析和压缩。尽管从矢量类型数据中学习的低维结构学习的研究已经很好地开发了，但这些方法在高阶数据中的直接应用带来了重大挑战，包括增加计算复杂性，以及它们无法捕获不同模式的耦合。这项研究通过基于张量的数据减少和低维结构学习来解决这些问题，特别着眼于将动态功能连通性网络（DFCN）降低到生理上有意义的网络组件中。 2）多尺度，局部线性自适应张量分解算法，用于从张量数据中压缩和学习结构。最后，该基于张量的框架应用于由脑电图（EEG）数据构建的DFCN，以评估众所周知的显着性和与情感调节和认知控制相关的控制功能网络。