This paper studies the problem of classifying longitudinal structural brain networks to identify meaningful substructures and their time-varying effects. The problem is motivated by a subpopulation of healthy older adults who can maintain excellent cognitive functions across time, while others usually have cognitive decline in aging. It is of substantial scientific interest to study neurological mechanisms behind this successful aging phenomena; however, existing statistical tools for longitudinal networks are very limited. We propose a structured classification method that could identify a set of small outcome-relevant subgraphs and estimate the age effect of each signal subgraph from the longitudinal network predictors, as well as an efficient algorithm for model estimation. Application of this method to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data identifies a small set of brain regions whose connectivity strengths are predictive of successful cognitive aging, which has more appealing interpretation and better predictive performance compared with unstructured classification methods.

中文翻译：

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纵向脑网络分类及其在理解高级老化方面的应用

本文研究了对纵向结构脑网络进行分类以识别有意义的子结构及其时变效应的问题。这个问题是由健康的老年人亚群引起的，他们可以随时间保持良好的认知功能，而其他人通常在衰老过程中认知能力下降。研究这种成功衰老现象背后的神经机制具有重大的科学意义。然而，纵向网络的现有统计工具非常有限。我们提出了一种结构化分类方法，可以识别一组与结果相关的小子图，并从纵向网络预测器中估计每个信号子图的年龄效应，以及一种有效的模型估计算法。该方法在阿尔茨海默病中的应用