A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

Nadia Mammone,Simona De Salvo,Angela Marra,Francesco Morabito,Francesco Corallo,Cosimo Ieracitano,Silvia Marino

doi:10.3390/e19100548

Nadia Mammone, Simona De Salvo + Show 5 more

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https://doi.org/10.3390/e19100548

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Abstract

In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length ( λ ) variation ( r = - 0 . 56 , p = 0 . 0006 ), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0 . 58 , p = 0 . 0004 ), global efficiency (GE, r = 0 . 57 , p = 0 . 0005 ) and small worldness (SW, r = 0 . 57 , p = 0 . 0005 ). Cognitive decline thus seems to reflect an underlying cortical “disconnection” phenomenon: worsened subjects indeed showed an increased λ and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects.

Highlights

Electroencephalography (EEG) is the main tool for monitoring brain electrical activity [1]
This paper introduces a complex network approach based on Permutation Disalignment Index (PDI), a symbolic descriptor of the coupling strength between time series, recently proposed by the authors [25] to track changes in the coupling strength between EEG signals due to cognitive decline in Mild Cognitive Impairment (MCI) subjects
The p-values associated with an increased coupling strength are highlighted in grey in Table 2 as they are indicative of improved connectivity, not indicative of connectivity degeneration

Summary

Introduction

Electroencephalography (EEG) is the main tool for monitoring brain electrical activity [1]. In the study of neurological pathologies, EEG analysis can provide valuable information as abnormalities in interaction between neurons may reflect on macroscopic abnormalities in electrical potentials that can be detected on the scalp [2]. Mild Cognitive Impairment (MCI) is a condition that may be transient, stable or progressive (if it is the prodromal symptom of a degenerative pathology that leads to dementia [3,4]). MCI can be amnestic (aMCI) and non-amnestic [5], aMCI subjects are more likely to develop dementia due to Alzheimer’s Disease (AD) [6]. MCI subjects should be monitored periodically, through specific follow-up programs, so that any necessary therapy can be undertaken

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