Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Farah, Firas H.; Grigorovsky, Vasily; Bardakjian, Berj L.

doi:10.1142/s0129065718500417

Cited by 7 publications

(4 citation statements)

References 101 publications

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“…Yet, despite these results an earlier study argued that there exists a ‘neuronal emergency brake’ mechanism that prevents status epilepticus and when ‘activated too strongly or persistently, PGES occurs’ ( Bauer et al , 2017 ), suggesting that the PGES is a result of a dampened network activity. In line with our results, a recent modelling study, demonstrated that seizure cessation and an overall suppressed state was achieved by increasing the coupling of oscillatory units, rather than reducing their activity ( Farah et al , 2019 ). These data provide evidence that the PGES state is not characterized by reduced activity, but rather by activity that is different from either ictal or interictal states.…”

Section: Discussionsupporting

confidence: 92%

“…This is supported by a recent modelling study which showed that the excitation/inhibition balance drove the ictal frequency and coupling dynamics ( Liu et al , 2020 ). Several modelling papers also showed that microglial effects were responsible for increasing the time between spontaneous epileptiform discharges ( Grigorovsky and Bardakjian, 2018 a ; Farah et al , 2019 ), suggesting that microglial pruning could be involved in longer PGES durations. This is supported by experimental evidence that ictal activity can enhance motility and drive alterations in microglial physiology ( Sepulveda-Rodriguez et al , 2019 ), and that seizure activity increases microglial process numbers, suggesting that under certain conditions microglia have neuroprotective effects in the epileptic brain ( Eyo et al , 2014 ).…”

Section: Discussionmentioning

confidence: 99%

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Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Grigorovsky

Jacobs

Breton

et al. 2020

Brain Communications

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Phase-amplitude coupling analysis shows that a state of postictal generalized EEG suppression has increased delta-gamma coupling. These coupling features, used with an unsupervised hidden Markov model, reliably differentiated four substates in seizure episodes. A sudden unexpected death in epilepsy case study showed coupling activity similar to a postictal state. Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier – a hidden Markov model – was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics, that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Grigorovsky

Jacobs

Breton

et al. 2020

Brain Communications

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“…Unlike the multistate bistable attractor technique, intermittence corresponds to ictal events integral in the interictal attractor (or state) and doesn’t require system noise for state transition (in these models, the critical mechanism for transitions to and from epileptic seizures is the existence of multiple attractors). A model that exploits this approach has been used to analyze different pathways leading to hyperexcitability and recommended a critical role for astrocytes and microglia in generating spontaneous epileptiform discharges (SEDs) ( Farah et al, 2019 ). This model was built on the concept of coupled Cognitive Rhythm Generators (CRGs).…”

Section: Seizure Activity and Coupled Oscillationsmentioning

confidence: 99%

Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond

Földi

Lőrincz

Berényi

2021

Front. Neural Circuits

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Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these “oscillopathies” in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.

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“…As a result, fractional-order complex networks with non-linear systems have been investigated theoretically [23,24]. However, coupled control is the most direct way to build a network [25][26][27]. Because there are few references regarding numerical analysis of a fractional-order network, especially when chimera states exist, it is interesting to investigate chimera states in the coupled fractional-order network.…”

Section: Introductionmentioning

confidence: 99%

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

2020

Front. Appl. Math. Stat.

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At present, dynamics and coupled control of fractional-order non-linear systems are arousing much interest from researchers. In this paper, the fractional-order derivative is introduced into an improved memristor neural system. The dynamics of the fractional-order memristor neural model are investigated by means of bifurcation diagrams, Lyapunov exponents, and phase diagrams. To discuss the dynamical behavior of a fractional-order memristor neuron in a network, we construct a ring network of neurons and capture the spatiotemporal patterns of the neurons in the network in the presence of different excitations. Finally, the chimera state is observed, and the complexity of the network is analyzed. The analysis shows that the complexity algorithm provides a new approach for the dynamical analysis of the network.

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Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Cited by 7 publications

References 101 publications

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

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