Department of Physics & Astronomy Brain & Mind Institute Western University London ON Canada.
Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium.
Brain Behav. 2017 Feb 16;7(3):e00626. doi: 10.1002/brb3.626. eCollection 2017 Mar.
Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.
Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.
First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.
Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.
This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.
独立成分分析（ICA）已被广泛应用于将无任务态 fMRI 记录数据简化为空间图谱及其相关时间序列。在空间上识别出的独立成分可以被视为非连续区域的内在连接网络（ICN）。迄今为止，这些网络的空间模式已经通过针对容积数据开发的技术进行了分析。
本文详细介绍了一种图形构建技术，该技术允许使用图论对这些 ICN 进行分析。
首先，我们在 15 名健康志愿者中使用 3T MRI 扫描仪在个体水平上进行 ICA。通过多模板匹配过程和基于网络“神经元”特性的后续成分分类来识别九个网络。其次，为每个识别出的网络定义节点为 1015 个解剖分割区域。第三，通过为每个网络构建边权重来建立节点间的功能连接。最后，对每个网络进行组水平的图形分析，并与经典网络进行比较。
经典网络和九个网络之间的网络图形比较显示，在听觉和视觉内侧网络中，平均度数和边数存在显著差异，而在视觉外侧网络中，小世界程度存在显著差异。
这种新方法使我们能够充分利用 ICA 在 BOLD 信号分解中的强大功能，同时利用成熟的图形度量标准来评估连接差异。此外，通过为每个单独的网络提供一个图形，它可以提供以特定方式为每个网络提取图形度量的可能性。这种增加的特异性可能与研究病理性脑活动或麻醉或睡眠引起的意识改变有关，在这些情况下，已知特定网络会以不同的强度发生改变。
