Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy

Zhang, Ruihua; Ren, Ye; Liu, Chunyan; Xu, Na; Li, Xiaoli; Cong, Fengyu; Ristaniemi, Tapani; Wang, Yuping

doi:10.1016/j.clinph.2017.05.020

Cited by 53 publications

(53 citation statements)

References 14 publications

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“…33 In epilepsy patients, elevated CFC had been found within SOZ during the ictal period but not the nonictal periods, 34,35 probably indicating a hyperexcitable ictal state. It has been suggested that CFC is a key mechanism in maintaining the inhibitionexcitation interaction balance and facilitating cerebral information flow.…”

Section: Discussionmentioning

confidence: 99%

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

Jiang

Cai

Worrell

et al. 2019

Annals of Neurology

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Section: Discussionmentioning

confidence: 99%

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

Jiang

Cai

Worrell

et al. 2019

Annals of Neurology

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“…In addition, we tested modulation index on data generated from (15) and (17) and found that modulation index is unable to detect the cross-frequency coupling for these relationships. This is not surprising since the modulation index like metrics are tuned to detect CFC when the underlying coupling is of the form in (16), whereas the MI-in-frequency defined in this paper overcomes this shortcoming, as evident from its performance on various simulated models.…”

Section: B Comparison With Modulation Indexmentioning

confidence: 90%

“…3. Comparing the performance of MI-in-frequency against modulation index in detecting cross-frequency coupling in data generated from (16). In Fig.…”

Section: B Comparison With Modulation Indexmentioning

confidence: 99%

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Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

Malladi

Johnson

Kalamangalam

et al. 2018

IEEE Trans. Signal Process.

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We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer crossfrequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramér's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-infrequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.

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“…In these recordings, the interactions between the underlying oscillatory dynamics is presumably translated to cross frequency couplings (CFC) Canolty et al (2006); Hyafil et al (2015) whose intensity varies in a state dependent manner. Thus, the CFC patterns observed at the signal level are hypothesized to be informative on both the underlying physiological neuronal processing Canolty and Knight (2010); Voytek et al (2010); Nagasawa et al (2012); Nakatani et al (2014); Vaz et al (2017); Bergmann and Born (2018) and the aberrant activity associated to pathological brain states Nariai et al (2011); Weiss et al (2013); Ibrahim et al (2014); Weiss et al (2015); Edakawa et al (2016); Zhang et al (2017); Motoi et al (2018). From the signal processing point of view, a CFC pattern emerges when certain characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spectral harmonicity distinguishes two types of ictal phase-amplitude cross frequency couplings in patients candidate to epilepsy surgery

Dellavale

Urdapilleta

Cámpora

et al. 2020

Preprint

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Objective: Spectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing 'spurious' phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the epileptologists. Methods: PAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. Results: (1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. Conclusions: Our results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. * Corresponding authors. Highlights 1 • Harmonic and non harmonic phase-amplitude couplings coexist in epilep-73 nusoidal waveform constituting the seizure activity as a mechanism originating 74 PAC, referred as 'harmonic PAC'. Implications of the coexistence of harmonic 75 and non harmonic PAC patterns during epileptic seizures regarding underlying 76 neural mechanisms and improved therapy are also discussed.77 3 2. METHODS 78 2.1. Patients 79 The seven patients with pharmacoresistant focal epilepsy included in this 80 retrospective study were consecutively selected from the candidates for surgi-81 cal treatment between 2012 and 2015 at the Epilepsy Center -"Ramos Mejía" 82 Hospital and El Cruce "N. Kirchner" Hospital -Universidad Nacional Arturo 83 Jauretche, Argentina. The patients underwent chronic invasive EEG procedure 84 guided by clinical criteria to help identify the epileptogenic zone (EZ) for sub-85 sequent resection. The characteristics of the patients are provided in Table 1 86 and the schematic view of the SOZ, as identified by the epileptologists (NC and 87 SK), is shown in Figure 1. 88 * No, Number of implanted unipolar electrode contacts; * * Number of seizures analyzed in this study; 5 6 2.2. Localization of electrode sites and imaging for three-dimensional reconstruc-89 tion 90 Electrode placement considered not only the final target but also the pre-91 cise trajectory. Surgical planning of electrode placement in specific anatomical 92 structures was thus dependent on the pre-established hypotheses of p...

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Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy

Abstract: Phase-amplitude coupling can provide meaningful reference for accurate resection of epileptogenic focus and provide insight into the underlying neural dynamics of the epileptic seizure in patients with temporal lobe epilepsy.

Cited by 53 publications

References 14 publications

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

Spectral harmonicity distinguishes two types of ictal phase-amplitude cross frequency couplings in patients candidate to epilepsy surgery

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