Permutation Disalignment Index as an Indirect, EEG-Based, Measure of Brain Connectivity in MCI and AD Patients

Mammone, Nadia; Bonanno, Lilla; Salvo, Simona De; Marıno, Silvia; Bramantı, Placido; Bramanti, Alessia; Morabito, Francesco Carlo

doi:10.1142/s0129065717500204

Cited by 67 publications

(36 citation statements)

References 31 publications

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“…The EEG is then analysed offline. It is partitioned into M non-overlapping epochs (the width of each epoch is 5 s) and then analysed epoch by epoch [25]. For every epoch e, the corresponding EEG segment EEG e (with e = 1, .…”

Section: Methodology: Pdi-based Eeg Complex Network Analysismentioning

confidence: 99%

“…This paper introduces a complex network approach based on 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 MCI subjects. PDI is a novel measure that comes from the definition of Permutation Entropy (PE) [32].…”

Section: Projecting the Eeg Signals Into Symbolsmentioning

confidence: 99%

“…At every iteration t, the algorithm projects the two time series x i and x j into the two corresponding motifs and checks if the same motif occurred in both of them. In the present paper, the adopted parameter setting is m = 3 and L = 1, as discussed in [25].…”

Section: Projecting the Eeg Signals Into Symbolsmentioning

confidence: 99%

“…The PDI between x i and x j was shown to decrease as the coupling strength between x i and x j increases because x i and x j are expected to exhibit the same motifs with increased probability [25]. PDI was defined in [25] according to Renyi's theory [33] and the parameter α tunes the sensitivity to the Gaussianity of the pdf distribution of the time series.…”

Section: Motif Occurrence Rate Estimationmentioning

confidence: 99%

“…Motivated by the encouraging results provided by the Permutation Disalignment Index (PDI), a novel descriptor, recently proposed by the authors for the longitudinal evaluation of changes in the coupling strength between the EEG signals, was successfully applied to MCI and AD patients [25], and the present paper illustrates the construction of a complex network model, based on the PDI estimated between pairs of EEG signals, for quantifying longitudinal brain-electrical connectivity variations associated with cognitive decline. The aim is to test the hypothesis that longitudinal cognitive decline in MCI is associated with reduced efficiency of brain-electrical network organization.…”

Section: Introductionmentioning

confidence: 99%

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

Mammone

Salvo

Ieracitano

et al. 2017

Entropy

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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.

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Section: Methodology: Pdi-based Eeg Complex Network Analysismentioning

confidence: 99%

Section: Projecting the Eeg Signals Into Symbolsmentioning

confidence: 99%

Section: Projecting the Eeg Signals Into Symbolsmentioning

confidence: 99%

Section: Motif Occurrence Rate Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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