Mirzaeian Shiva, Faghiri Ashkan, Calhoun Vince D, Iraji Armin
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
bioRxiv. 2024 Sep 27:2024.02.19.581086. doi: 10.1101/2024.02.19.581086.
Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of DMN, VS, and RFPN. In addition, TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.
脑功能可被建模为不同空间尺度上功能源之间的动态相互作用，且每个空间尺度都可包含具有独特信息的功能源，因此仅使用单一尺度可能无法全面了解脑功能。本文介绍了一种名为“伸缩独立成分分析（TICA）”的新方法，旨在利用功能磁共振成像（fMRI）数据构建空间功能层次结构，并跨多个空间尺度估计功能源。该方法采用递归独立成分分析策略，利用来自较大网络的信息来指导关于较小网络信息的提取。我们将模型应用于默认模式网络（DMN）、视觉网络（VN）和右侧额顶叶网络（RFPN）。我们通过评估健康人与精神分裂症患者之间的差异，对DMN进行了进一步研究。我们表明TICA方法能够检测出DMN、VS和RFPN的空间层次结构。此外，TICA揭示了不同队列之间与DMN相关的组间差异，如果我们仅关注单尺度独立成分分析，这些差异可能无法被发现。总之，我们提出的方法是研究功能源的一种很有前景的新工具。
