Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment

Abstract

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Very recently, Permutation Entropy for Graph signals ($PE_G$) has been proposed to extend $PE$ to data residing on irregular domains. However, $PE_G$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals at the vertex level: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PE_G$, can be discerned using our graph-based permutation patterns. These are then validated in the analysis of DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns change in individual brain regions as the disease progresses. Thus, graph-based permutation patterns offer promise by enabling the granular scale analysis of graph signals.Comment: 5 pages, 5 figures, 1 tabl