Schwarz Adam J, Gozzi Alessandro, Bifone Angelo
Biology, Psychiatry Centre of Excellence for Drug Discovery, GlaxoSmithKline Medicines Research Centre, Verona, Italy.
Magn Reson Imaging. 2008 Sep;26(7):914-20. doi: 10.1016/j.mri.2008.01.048. Epub 2008 May 13.
Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into 'sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.
从神经影像数据中得出的功能连接模式可以表示为图形或网络，其中单个图像体素或解剖学定义的结构代表节点，每对节点响应之间的相关性度量决定边。这种明确的网络表示允许将网络分析方法应用于大脑内功能连接的特征描述。复杂网络领域最近的许多研究都集中在识别社区结构的方法上，即强相互连接节点的凝聚簇。一类这样的算法基于模块化参数的优化确定网络划分为“子网络”，从而也提供了整个网络中隔离与整合程度的度量。在这里，我们证明，将基于模块化最大化的社区结构算法应用于从大鼠对急性氟西汀挑战的反应计算出的功能连接网络，可以识别出其分布对应于解剖学上有意义的结构且包括大脑中引人注目的功能细分的社区。我们还根据脑功能的隔离和整合来讨论模块化参数的生物学解释。
