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

Abstract

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.

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

“…A similar number of electrodes was considered in related works (Wang et al, 2015 ; Kulkarni and Bairagi, 2017 ; Chen et al, 2018 ; Yu et al, 2020 ). With respect to epoch length, since EEG signals are non-stationary, we utilized an intermediate length of 4 s (Cassani et al, 2018 ), comparable to the duration selected in analogous works (Coronel et al, 2017 ; Mammone et al, 2017 ; Durongbhan et al, 2019 ). Regarding artifact rejection, works on AD detection traditionally have applied manual epoch selection for artifact removal (Simons et al, 2015 ; Azami et al, 2017 ; Mammone et al, 2017 ; Chen et al, 2018 ; Ruiz-Gomez et al, 2018 ), what may be counterproductive from the early detection standpoint.…”

“…With respect to epoch length, since EEG signals are non-stationary, we utilized an intermediate length of 4 s (Cassani et al, 2018 ), comparable to the duration selected in analogous works (Coronel et al, 2017 ; Mammone et al, 2017 ; Durongbhan et al, 2019 ). Regarding artifact rejection, works on AD detection traditionally have applied manual epoch selection for artifact removal (Simons et al, 2015 ; Azami et al, 2017 ; Mammone et al, 2017 ; Chen et al, 2018 ; Ruiz-Gomez et al, 2018 ), what may be counterproductive from the early detection standpoint. Alternatively, since we wanted to perform a preliminary evaluation of an automated classifier, we decided to perform artifact processing through Autoreject and automated ICA.…”

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An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

“…A quantitative spectral analysis on EEG signals of MCI subjects was carried out in 10 , revealing a decreased alpha activity in follow-up MCI converted to AD, especially over posterior leads; whereas, in 11 a complex network based strategy was proposed, showing increasing characteristic path length and decreasing efficiency along with AD progression. In 12 and 13 two novel coupling strength metrics between time series, namely, the Permutation Disalignment Index, (PDI) and the Permutation Jaccard Distance (PJD), respectively, were introduced. Experimental results reported an increase of PDI and PJD, namely a decrease of coupling strength in delta and theta sub-band, in the converted patients.…”