Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set.

中文翻译：

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三聚类动态功能网络连接可识别不同个体亚群中跨多个州的显着精神分裂症影响

背景：从个体收集的脑成像数据非常复杂，具有独特的变异性；然而，在关注组平均值甚至监督预测的方法中，这种变化通常会被忽略。用于分析动态功能网络连接 (dFNC) 的最先进的方法将整个时间过程细分为几个（可能重叠的）连接状态（即，滑动窗口簇）。但是，这种方法不会考虑基础数据的同质性​​，并且可能会导致数据集的子分组意义不大。