Faten Mina

Interictal spikes, fast ripples and seizures in partial epilepsies - combining multi-level computational models with experimental data.

Epileptic seizures, epileptic spikes and high‐frequency oscillations (HFOs) are recognized as three electrophysiological markers of epileptogenic neuronal systems. It can be reasonably hypothesized that distinct (hyper)excitability mechanisms underlie these electrophysiological signatures. The question is ‘What are these mechanisms?’. Solving this difficult question would considerably help our understanding of epileptogenic processes and would also advance our interpretation of electrophysiological signals. In this paper, we show how computational models of brain epileptic activity can be used to address this issue. With a special emphasis on the hippocampal activity recorded in various experimental models (in vivo and in vitro) as well as in epileptic patients, we confront results and insights we can get from computational models lying at two different levels of description, namely macroscopic (neural mass) and microscopic (detailed network of neurons). At each level, we show how spikes, seizures and HFOs can (or cannot) be generated depending on the model features. The replication of observed signals, the prediction of possible mechanisms as well as their experimental validation are described and discussed; as are the advantages and limitations of the two modelling approaches.

Modulation of epileptic activity by deep brain stimulation: a model-based study of frequency-dependent effects

A number of studies showed that deep brain stimulation (DBS) can modulate the activity in the epileptic brain and that a decrease of seizures can be achieved in “responding” patients. In most of these studies, the choice of stimulation parameters is critical to obtain desired clinical effects. In particular, the stimulation frequency is a key parameter that is difficult to tune. A reason is that our knowledge about the frequency-dependant mechanisms according to which DBS indirectly impacts the dynamics of pathological neuronal systems located in the neocortex is still limited. We address this issue using both computational modeling and intracerebral EEG (iEEG) data. We developed a macroscopic (neural mass) model of the thalamocortical network. In line with already-existing models, it includes interconnected neocortical pyramidal cells and interneurons, thalamocortical cells and reticular neurons. The novelty was to introduce, in the thalamic compartment, the biophysical effects of direct stimulation. Regarding clinical data, we used a quite unique data set recorded in a patient (drug-resistant epilepsy) with a focal cortical dysplasia (FCD). In this patient, DBS strongly reduced the sustained epileptic activity of the FCD for low-frequency (LFS, < 2 Hz) and high-frequency stimulation (HFS, > 70 Hz) while intermediate-frequency stimulation (IFS, around 50 Hz) had no effect. Signal processing, clustering, and optimization techniques allowed us to identify the necessary conditions for reproducing, in the model, the observed frequency-dependent stimulation effects. Key elements which explain the suppression of epileptic activity in the FCD include: (a) feed-forward inhibition and synaptic short-term depression of thalamocortical connections at LFS, and (b) inhibition of the thalamic output at HFS. Conversely, modeling results indicate that IFS favors thalamic oscillations and entrains epileptic dynamics.

Model-guided control of hippocampal discharges by local direct current stimulation

Neurostimulation is an emerging treatment for drug-resistant epilepsies when surgery is contraindicated. Recent clinical results demonstrate significant seizure frequency reduction in epileptic patients, however the mechanisms underlying this therapeutic effect are largely unknown. This study aimed at gaining insights into local direct current stimulation (LDCS) effects on hyperexcitable tissue, by i) analyzing the impact of electrical currents locally applied on epileptogenic brain regions, and ii) characterizing currents achieving an “anti-epileptic” effect (excitability reduction). First, a neural mass model of hippocampal circuits was extended to accurately reproduce the features of hippocampal paroxysmal discharges (HPD) observed in a mouse model of epilepsy. Second, model predictions regarding current intensity and stimulation polarity were confronted to in vivo mice recordings during LDCS (n = 8). The neural mass model was able to generate realistic hippocampal discharges. Simulation of LDCS in the model pointed at a significant decrease of simulated HPD (in duration and occurrence rate, not in amplitude) for cathodal stimulation, which was successfully verified experimentally in epileptic mice. Despite the simplicity of our stimulation protocol, these results contribute to a better understanding of clinical benefits observed in epileptic patients with implanted neurostimulators. Our results also provide further support for model-guided design of neuromodulation therapy.

Low-intensity Local Direct Current modulates interictal discharges in mTLE: Computational and experimental insights

Low intensity Local Direct Current Stimulation (LDCS) is an electrical stimulation technique that has been poorly investigated in vivo in the field of epilepsy. This study addresses the computational as well as the experimental in vivo effects of low intensity DC stimulation currents on Hippocampal Paroxysmal Discharges (HPDs), a common form of interictal discharges in mesial temporal lobe epilepsy. The results highlight the significance of polarity-dependent effects in silico as well as in freely moving epileptic mice. In conclusion, this combined in silico- in vivo approach shows that cathodal LDCS can significantly reduce the occurrence of HPDs.

Objective measures in cochlear implanted patients: A computational framework to evaluate artifact rejection methodologies

Auditory steady-state responses (ASSRs) constitute a reliable measure of auditory perception in normal hearing subjects. The use of these measures in cochlear implanted patients is hindered by the vast diffusion of the electrical cochlear stimulation artifact that highly contaminates EEG scalp recordings. Therefore, attenuating or moreover suppressing this artifact ahead of response detection is crucial. Yet, the currently used denoising algorithms may have unpredictable effects on these responses (ASSRs). In this paper, we propose a computational framework that allows the simulation of the mixture of the stimulation artifact and its corresponding evoked ASSRs on EEG scalp electrodes. The utility of this relatively basic model resides in its usefulness in quantifying the effects of any applied denoising method on the information contained in the signal of interest (responses known a priori). Here, an application to two independent component analysis algorithms (infomax and infomax extended) is presented. The model predicts a better performance for infomax compared to infomax extended.

How deep brain stimulation modulates cortical epileptic dynamics — Insights from computational modeling

Despite the growing scientific evidence that supports the efficacy of deep brain stimulation (DBS) for controlling epileptic seizure dynamics, further research remains mandatory to optimize DBS parameters for an efficient clinical use. In particular, progress can be expected from detailed study of the still poorly understood mechanisms that are responsible for the modulation of neural activity by DBS. In this work, a computational model of the thalamocortical loop was developed to explore the mechanisms of thalamic DBS and its effects on cortical dynamics. The model was tuned using real intracerebral EEG (iEEG) signals recorded in an epileptic patient. Results confirmed the dependence of DBS activated mechanisms on the choice of stimulation frequency. They revealed that short-term depression, feed-forward inhibition, and stimulation-induced depolarization of inhibitory reticular neurons seem to be key factors of frequency-dependent effects.