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Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity

机译：动态功能磁共振成像连通性张量分解：分析和解释动态大脑连通性的新方法

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摘要

There is a growing interest in using so-called dynamic functional connectivity, as the conventional static brain connectivity models are being questioned. Brain network analyses yield complex network data that are difficult to analyze and interpret. To deal with the complex structures, decomposition/factorization techniques that simplify the data are often used. For dynamic network analyses, data simplification is of even greater importance, as dynamic connectivity analyses result in a time series of complex networks. A new challenge that must be faced when using these decomposition/factorization techniques is how to interpret the resulting connectivity patterns. Connectivity patterns resulting from decomposition analyses are often visualized as networks in brain space, in the same way that pairwise correlation networks are visualized. This elevates the risk of conflating connections between nodes that represent correlations between nodes' time series with connections between nodes that result from decomposition analyses. Moreover, dynamic connectivity data may be represented with three-dimensional or four-dimensional (4D) tensors and decomposition results require unique interpretations. Thus, the primary goal of this article is to (1) address the issues that must be considered when interpreting the connectivity patterns from decomposition techniques and (2) show how the data structure and decomposition method interact to affect this interpretation. The outcome of our analyses is summarized as follows. (1) The edge strength in decomposition connectivity patterns represents complex relationships pairwise interactions between the nodes. (2) The structure of the data significantly alters the connectivity patterns, for example, 4D data result in connectivity patterns with higher regional connections. (3) Orthogonal decomposition methods outperform in feature reduction applications, whereas nonorthogonal decomposition methods are better for mechanistic interpretation.
机译：随着对常规静态大脑连接模型的质疑，使用所谓的动态功能连接的兴趣越来越浓厚。脑网络分析会产生难以分析和解释的复杂网络数据。为了处理复杂的结构，经常使用简化数据的分解/分解技术。对于动态网络分析，数据简化更为重要，因为动态连接分析会导致一系列复杂的网络。使用这些分解/分解技术时必须面对的新挑战是如何解释最终的连接模式。分解分析产生的连通性模式通常可视化为大脑空间中的网络，就像可视化成对相关网络一样。这增加了将表示节点时间序列之间的相关性的节点之间的连接与分解分析所导致的节点之间的连接相混淆的风险。此外，动态连接性数据可以用三维或三维（4D）张量表示，并且分解结果需要唯一的解释。因此，本文的主要目标是（1）解决从分解技术解释连接模式时必须考虑的问题，以及（2）显示数据结构和分解方法如何相互作用以影响这种解释。我们的分析结果总结如下。 （1）分解连通性模式中的边缘强度表示节点之间的复杂关系成对相互作用。 （2）数据的结构显着改变了连通性模式，例如4D数据导致具有较高区域连接性的连通性模式。 （3）正交分解方法在特征约简应用中表现出色，而非正交分解方法更适合机械解释。

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