AUTHOR=Guo Hao , Liu Lei , Chen Junjie , Xu Yong , Jie Xiang TITLE=Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset JOURNAL=Frontiers in Neuroscience VOLUME=11 YEAR=2017 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2017.00639 DOI=10.3389/fnins.2017.00639 ISSN=1662-453X ABSTRACT=

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.