Alzheimer's disease (AD) disrupts the topological architecture of whole-brain connectivity. Minimum spanning tree (MST), which captures the most important connections in a network, has been considered an unbiased method for brain network analysis. However, the alterations in the MST of functional brain networks during the progression of AD remain unclear. Here, we performed an MST analysis to examine the alterations in functional networks among normal controls (NCs), mild cognitive impairment (MCI) patients, and AD patients. We identified substantial differences in the connections among the three groups. The maximum betweenness centrality, leaf number, and tree hierarchy of the MSTs showed significant group differences, indicating a more star-like topology in the MCI patients and a more line-like topology in the NCs and AD patients. These findings may correspond to changes in the core of the functional brain networks. For nodal properties (degree and betweenness centrality), we determined that brain regions around the cingulate gyrus, occipital lobes, subcortex, and inferior temporal gyrus showed significant differences among the three groups and contributed to the global topological alterations. The leaf number and tree hierarchy, as well as the nodal properties, were significantly correlated with clinical features in the MCI and AD patients, which demonstrated that more star-to-line topology changes were associated with worse cognitive performance in these patients. These findings indicated that MST properties could capture slight alterations in network topology, particularly for the differences between NCs and MCI patients, and may be applicable as neuroimaging markers of the early stage of AD.

Abnormal Functional Brain Networks in Mild Cognitive Impairment and Alzheimer's Disease: A Minimum Spanning Tree Analysis.

In this paper, we present an efficient implementation of Kruskal's algorithm to obtain a minimum spanning tree. The proposed method utilizes the union-find data structure, reducing the depth of the tree of the node set by making the nodes in the path to root be the child node of the root of combined tree. This method can reduce the depth of the tree by shortening the path to the root and lowering the level of the node. This is an efficient method because if the tree's depth reduces, it could shorten the time of finding the root of the tree to which the node belongs. The performance of the proposed method is evaluated through the graphs generated randomly. The results showed that the proposed method outperformed the conventional method in terms of the depth of the tree. ▸

https://www.koreascience.or.kr:443/article/JAKO201424635079948.pdf

Lee Ju-Young

Papers

An Efficient Implementation of Kruskal's Algorithm for A Minimum Spanning Tree