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

Abstract

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.

Computational models of epileptiform activity

“…Along the same line, novel perspectives are foreseen in clinical approaches for testing neurostimulations (Kahane and Depaulis, 2010). There is indeed a growing interest in epileptology for alternative therapeutic strategies aimed at modulating the electrical brain activity via direct or indirect stimulation of the epileptic regions (Fisher, 2013;Laxpati et al, 2014;Mina et al, 2013). Computational models may help us to determine the key structures to stimulate and to find the optimal stimulation parameters.…”

“…Neurostimulations are also a topic of growing interest in computational modeling. A number of NMMs have been proposed to account for the effect of electrical stimulations (i) on rhythmic self-terminating responses as observed in ECoG (Goodfellow et al, 2012a), (ii) on epileptic activity observed in focal cortical dysplasia (Mina et al, 2013) or (iii) on evoked responses as a way to link between EEG data and neuronal excitability in the perspective of anticipating seizures (Suffczynski et al, 2008). This latter field of research has also benefitted from the development of computational models.…”

“…For example, deep brain stimulation of the centro-median nucleus was found to inhibit focal cortical epileptic activity at low or high frequency stimulation but not at intermediate frequency . In Mina et al (2013), the knowledge about the effect of electrical stimulations on the neuron membrane potential, on synaptic activity and on plasticity were included in a thalamo-cortical model. This model was tuned to reproduce clinical data and insights about frequency-dependent effects of deep brain stimulation were gained.…”

Modeling sequence and quasi-uniform assumption in computational neurostimulation

“…Developing computational neurostimulation models requires the right balance of detailed multiscale model with appropriate reductionism (Douglas et al, 2015;Frohlich et al, 2015;Holt and Netoff, 2014;Karamintziou et al, 2014;Mina et al, 2013;Modolo et al, 2011;Shukla et al, 2014). This review attempts to present the modeling process as tractable, even when dealing with unknowns, including serializing modeling steps and applying the quasi-uniform assumption where relevant.…”

Computational modeling of neurostimulation in brain diseases

“…In the second category, specific models of seizure activity are first developed, and subsequently control parameters (such as inputs) are used to inform stimulation protocols to suppress the seizure activity. Parameters such as stimulation frequency can be explored in the model (Mina et al, 2013). Often, tools from control theory (the branch of engineering which investigates the effects of inputs and feedback on dynamical systems) are used, such as linear and nonlinear feedback controllers (Kramer et al, 2006).…”