Phase transfer entropy: a novel measure for effective connectivity among neuronal oscillations

Siebenhuehner, Felix; Lobier, Muriel; Palva, Satu; Palva, Matias

doi:10.1186/1471-2202-14-s1-p305

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Third, PTE is both sample-size and computationally efficient. Finally, PTE can identify frequency band limited information flow ( Lobier et al, 2014 , Siebenhuehner et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach

Wang

Zhou

et al. 2017

NeuroImage: Clinical

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The aim of this research is to apply an approach based on phase transfer entropy (PTE) and graph theory to study the interactions between the stereo-electroencephalography (SEEG) activities recorded in multilobar origin, in order to evaluate their ability to detect the epileptogenic zone (EZ) of temporal lobe epilepsies (TLE). Forty-three patients were included in this retrospective study. Five to sixteen (median = 12) multilead electrodes were implanted per patient, and, for each patient, a sub-set of between 10 and 32 (median = 22) bipolar derivations was selected for analysis. The leads were classified into the onset leads (OLs), the early propagation leads (EPLs), and the rest of the leads (RLs). The results showed that a significantly different dynamic trend of the out/in ratio (more obvious in the gamma band) distinguishes the OLs from RLs in the 23 patients who were seizure-free not only during the ictal event (significant elevation), but also during the inter-,pre-, late-ictal periods, and especially in the post-ictal (sharp decline) state. However, in the 20 patients who were not-seizure-free, the differences between the OLs and RLs during the post-ictal period were not found in any frequency band. The dynamic trend was used to predict surgical outcome, and the results showed that the sensitivity was 91% and the specificity was 70%. In brief, this study indicates that our approach may add new and valuable information, providing efficient quantitative measures useful for localizing the EZ.

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“…Third, PTE is both sample-size and computationally efficient. Finally, PTE can identify frequency band limited information flow ( Lobier et al, 2014 , Siebenhuehner et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach

Wang

Zhou

et al. 2017

NeuroImage: Clinical

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“…The information flow between ROIs was estimated using phase transfer entropy (PTE) based on the same principle as Granger Causality (Siebenhuehner et al, 2013;Hillebrand et al, 2016;Cao et al, 2018b). It is calculated as the difference between the uncertainty of the target signal Y conditioned by its past and the uncertainty of the target signal conditioned on both its past and the source signal X (Hillebrand et al, 2016;Cao et al, 2018b):…”

Section: Phase Transfer Entropy (Pte) and Directed Pte (Dpte) Data Anmentioning

confidence: 99%

Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task

et al. 2020

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“…PTE is a novel, phasebased measure of directed connectivity for biomedical signal. Previous studies have showed that PTE can detect both strength and direction of the connectivity even if there is a lot of noise and linear mixing, meanwhile it is more robust and more computationally efficient [23,24]. Another study also used such PTE method to research consistency and dynamical variations of EC successfully in different brain states based on EEG data [25].…”

Section: Introductionmentioning

confidence: 99%

Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

Wang

Chen

2020

J. Neural Eng.

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Objective. Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation. Approach. To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays. Main results. A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state. Significance. The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.

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Phase transfer entropy: a novel measure for effective connectivity among neuronal oscillations

Cited by 5 publications

References 6 publications

Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach

Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach

Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task

Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

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