Evaluation of Node-Inhomogeneity Effects on the Functional Brain Network Properties Using an Anatomy-Constrained Hierarchical Brain Parcellation

To investigate functional brain networks, many graph-theoretical studies have defined nodes in a graph using an anatomical atlas with about a hundred partitions. Although use of anatomical node definition is popular due to its convenience, functional inhomogeneity within each node may lead to bias or systematic errors in the graph analysis. The current study was aimed to show functional inhomogeneity of a node defined by an anatomical atlas and to show its effects on the graph topology. For this purpose, we compared functional connectivity defined using 138 resting state fMRI data among 90 cerebral nodes from the automated anatomical labeling (AAL), which is an anatomical atlas, and among 372 cerebral nodes defined using a functional connectivity-based atlas as a ground truth, which was obtained using anatomy-constrained hierarchical modularity optimization algorithm (AHMO) that we proposed to evaluate the graph properties for anatomically defined nodes. We found that functional inhomogeneity in the anatomical parcellation induced significant biases in estimating both functional connectivity and graph-theoretical network properties. We also found very high linearity in major global network properties and nodal strength at all brain regions between anatomical atlas and functional atlas with reasonable network-forming thresholds for graph construction. However, some nodal properties such as betweenness centrality did not show significant linearity in some regions. The current study suggests that the use of anatomical atlas may be biased due to its inhomogeneity, but may generally be used in most neuroimaging studies when a single atlas is used for analysis.为探究脑功能网络，诸多图论相关研究采用包含约百个分区的解剖图谱来定义图中的节点。尽管解剖学节点定义因便捷性被广泛应用，但每个节点内部存在的功能不均匀性可能会为图分析带来偏倚或系统误差。本研究旨在揭示解剖图谱定义的节点所存在的功能不均匀性，并探讨其对图拓扑结构的影响。为此，我们基于138份静息态功能磁共振成像（resting-state fMRI）数据，对比了两类节点的功能连接：一类来自解剖图谱自动解剖标记（Automated Anatomical Labeling, AAL）的90个大脑节点，另一类则是以我们提出的、用于评估解剖定义节点图属性的解剖约束层级模块化优化算法（Anatomy-Constrained Hierarchical Modularity Optimization, AHMO）所构建的功能连接图谱为金标准得到的372个大脑节点。研究发现，解剖式脑区分割中的功能不均匀性，会对功能连接及图论网络属性的估计造成显著偏倚。同时，在合理的图构建阈值下，解剖图谱与功能图谱在主要全局网络属性以及所有脑区的节点强度上，均呈现极高的线性相关性。但部分节点属性（如介数中心性）在部分脑区中未表现出显著线性相关。本研究表明，解剖图谱的应用可能因节点功能不均匀性而存在偏倚，但在仅使用单一图谱开展分析的多数神经影像学研究中，该方法仍具备普遍适用性。