Automated epileptic seizure detection based on break of excitation/inhibition balance

Fan, Xiaoya; Gaspard, Nicolas; Legros, Benjamin; Lucchetti, Federico; Ercek, Rudy; Nonclercq, Antoine

doi:10.1016/j.compbiomed.2019.02.005

Cited by 13 publications

(5 citation statements)

References 58 publications

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“…Previous studies have demonstrated that the excitatory and inhibitory gain (A and B) paramters of NMM play a central role in transitioning to seizurelike activity [45,46]. Building on these observations, majority of the studies have mostly considered estimating the combination of A and B from epileptic EEG data [6,12,16,28] using NMMs. In [12] the combination of parameters a and b was also additionally explored.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity analysis guided Bayesian parameter estimation for neural mass models : Applications in Epilepsy

Subramaniyam,

Hyttinen

2024

Preprint

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It is well established that neural mass models (NMMs) can effectively simulate the mesoscopic and macroscopic dynamics of electroencephalography (EEG), including the epileptic EEG. However the use of NMMs to gain insight on the neuronal system by parameter estimation is hampered by their high dimensionality and the lack of knowledge on what NMM parameters can be reliably estimated. In this article, we analyze the parameter sensitivity of Jansen and Rit NMM (JR-NMM) in order to identify the most sensitive JR-NMM parameters for reliable parameter estimation from EEG data. We then propose a Bayesian approach for estimating the JR-NMM states and parameters based on expectation--maximization combined with unscented Kalman smoother (UKS-EM). Global sensitivity analysis methods including Morris method and Sobol method are used to perform sensitivity analysis. Results from both the Morris and Sobol method show that the average inhibitory synaptic gain, $B$ and the reciprocal of the time constant of the average inhibitory post-synaptic potentials (PSPs), $b$ have significant impact on the JR NMM output along with having the least interaction with other model parameters. The UKS-EM method for estimating the parameters $B$ and $b$ is validated using simulations under varying levels of measurement noise. Finally we apply the UKS-EM algorithm to intracranial EEG data from $16$ epileptic patients. Our results, both at individual and group-level show that the parameters $B$ and $b$ change significantly between the pre-seizure and seizure period, and between the seizure and post-seizure period, with transition to seizure characterized by decrease in average $B$ and high frequency activity in seizure characterized by an increase in $b$. These results establish sensitivity analysis guided Bayesian parameter estimation as a powerful tool for reducing the parameter space of high dimensional NMMs enabling reliable and efficient estimation of the most sensitive NMM parameters, with the potential for online and fast tracking of NMM parameters in applications such as seizure tracking and control.

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Section: Discussionmentioning

confidence: 99%

Sensitivity analysis guided Bayesian parameter estimation for neural mass models : Applications in Epilepsy

Subramaniyam,

Hyttinen

2024

Preprint

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“…Persistent phenomena must indeed emerge from simple explanations, and for neuronal population activity, no phenomenon could be more persistent than those suggesting an E/I balance operating in a neuronal system. Several attempts have indeed been proposed to elucidate the notion of E/I balance ( Deschle et al, 2021 ), even recently finding its way to applications for the clinical detection of epileptic seizure or epileptogenesis ( Fan et al, 2019 ; Lignani et al, 2020 ). This attempt to clarify the meaning of E/I balance is even less apparent in previously proposed representation of the phenomena, e.g., critical branching process, Boltzmann’s chaos ( Touboul and Destexhe, 2017 ), neutral theory ( Martinello et al, 2017 ), directed random network of integrate-and-fire neurons ( Millman et al, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

“…Local inhibition influences the alternation of periods of activity and silence, proposed as an essential inhibitory control on neural encoding ( di Santo et al, 2018 ). Globally, a diversity-based approach seems reasonable, especially at the level of populations, i.e., neuronal networks or neural mass ( Fan et al, 2019 ; Deschle et al, 2021 ). Global inhibition such as endocrine signaling has not been explicitly considered in previous descriptions of neuronal network activity.…”

Section: Differential Inclusion Modelmentioning

confidence: 99%

Neuronal Population Transitions Across a Quiescent-to-Active Frontier and Bifurcation

Juanico

2022

Front. Physiol.

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The mechanistic understanding of why neuronal population activity hovers on criticality remains unresolved despite the availability of experimental results. Without a coherent mathematical framework, the presence of power-law scaling is not straightforward to reconcile with findings implying epileptiform activity. Although multiple pictures have been proposed to relate the power-law scaling of avalanche statistics to phase transitions, the existence of a phase boundary in parameter space is until now an assumption. Herein, a framework based on differential inclusions, which departs from approaches constructed from differential equations, is shown to offer an adequate consolidation of evidences apparently connected to criticality and those linked to hyperexcitability. Through this framework, the phase boundary is elucidated in a parameter space spanned by variables representing levels of excitation and inhibition in a neuronal network. The interpretation of neuronal populations based on this approach offers insights on the role of pharmacological and endocrinal signaling in the homeostatic regulation of neuronal population activity.

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“…These evidences were successfully implemented in a NMM of epilepsy in humans 46 , 63 based on four neuronal population subgroups (main population of pyramidal cells, excitatory interneurons, slow inhibitory and fast inhibitory interneurons). The NMM was automatically fitted to EEG to analyze the temporal evolution (between 30 s before to 60 s after seizure onset) of three meaningful parameters—the average excitatory (Ae), the slow and fast inhibitory synaptic gains (B and G) – as previously proposed 46 , 63 , 73 , 74 . We calculated the AUC and the lag from the beginning of the seizure to the peak of the variation for each parameter.…”

Section: Methodsmentioning

confidence: 99%

Sepsis modulates cortical excitability and alters the local and systemic hemodynamic response to seizures

Ferlini

Nonclercq

et al. 2022

Sci Rep

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Non-convulsive seizures and status epilepticus are frequent and associated with increased mortality in septic patients. However, the mechanism through which seizures impact outcome in these patients is unclear. As previous studies yielded an alteration of neurovascular coupling (NVC) during sepsis, we hypothesized that non-convulsive seizures, might further impair NVC, leading to brain tissue hypoxia. We used a previously developed ovine model of sepsis. Animals were allocated to sham procedure or sepsis; septic animals were studied either during the hyperdynamic phase (sepsis group) or after septic shock occurrence (septic shock group). After allocation, seizures were induced by cortical application of penicillin. We recorded a greater seizure-induced increase in the EEG gamma power in the sepsis group than in sham. Using a neural mass model, we also found that the theoretical activity of the modeled inhibitory interneurons, thought to be important to reproduce gamma oscillations, were relatively greater in the sepsis group. However, the NVC was impaired in sepsis animals, despite a normal brain tissue oxygenation. In septic shock animals, it was not possible to induce seizures. Cortical activity declined in case of septic shock, but it did not differ between sham or sepsis animals. As the alteration in NVC preceded cortical activity reduction, we suggest that, during sepsis progression, the NVC inefficiency could be partially responsible for the alteration of brain function, which might prevent seizure occurrence during septic shock. Moreover, we showed that cardiac output decreased during seizures in sepsis animals instead of increasing as in shams. The alteration of the seizure-induced systemic hemodynamic variations in sepsis might further affect cerebrovascular response to neuronal activation. Our findings support the hypothesis that anomalies in the cerebral blood flow regulation may contribute to the sepsis-associated encephalopathy and that seizures might be dangerous in such a vulnerable setting.

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Automated epileptic seizure detection based on break of excitation/inhibition balance

Cited by 13 publications

References 58 publications

Sensitivity analysis guided Bayesian parameter estimation for neural mass models : Applications in Epilepsy

Sensitivity analysis guided Bayesian parameter estimation for neural mass models : Applications in Epilepsy

Neuronal Population Transitions Across a Quiescent-to-Active Frontier and Bifurcation

Sepsis modulates cortical excitability and alters the local and systemic hemodynamic response to seizures

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