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

TL;DR

This paper introduces graph-based permutation patterns to analyze task-related fMRI signals on DTI networks, enabling vertex-level insights into brain dynamics in mild cognitive impairment.

Contribution

It presents a novel vertex-level permutation pattern method for graph signals, overcoming limitations of existing graph entropy measures.

Findings

Graph-based patterns detect dynamics undetectable by PEG

Patterns change with disease progression in MCI patients

Method validated on synthetic and real brain data

Abstract

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Recently, Permutation Entropy for Graph signals ($PEG$) has been proposed to extend PE to data residing on irregular domains. However, $PEG$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals \emph{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 $PEG$, can be discerned using our graph-based patterns. These are then validated in 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 in individual brain regions change as the disease progresses, demonstrating potential as a method of analyzing graph-signals at a granular scale.