Adaptive control of epileptiform excitability in an in vitro model of limbic seizures

By G. Panuccio, Arthur Guez, Robert D. Vincent, Massimo Avoli, Joelle Pineau. 2013

26 citations recorded by the source corpus.

Deep brain stimulation (DBS) is a promising tool for treating drug-resistant epileptic patients. Currently, the most common approach is fixed-frequency stimulation (periodic pacing) by means of stimulating devices that operate under open-loop control. However, a drawback of this DBS strategy is the impossibility of tailoring a personalized treatment, which also limits the optimization of the stimulating apparatus. Here, we propose a novel DBS methodology based on a closed-loop control strategy, developed by exploiting statistical machine learning techniques, in which stimulation parameters are adapted to the current neural activity thus allowing for seizure suppression that is fine-tuned on the individual scale (adaptive stimulation). By means of field potential recording from adult rat hippocampus–entorhinal cortex (EC) slices treated with the convulsant drug 4-aminopyridine we determined the effectiveness of this approach compared to low-frequency periodic pacing, and found that the closed-loop stimulation strategy: (i) has similar efficacy as low-frequency periodic pacing in suppressing ictal-like events but (ii) is more efficient than periodic pacing in that it requires less electrical pulses. We also provide evidence that the closed-loop stimulation strategy can alternatively be employed to tune the frequency of a periodic pacing strategy. Our findings indicate that the adaptive stimulation strategy may represent a novel, promising approach to DBS for individually-tailored epilepsy treatment.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Gabriella Panuccio
2. M. Semprini
3. S. Buccelli
4. Lorenzo Natale
5. I. Colombi
6. M. Chiappalone
7. Chao Yu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, neutral): “validation work [158] showed generally similar results that RL-based policy could prevent epilepsy with a significant reduced amount of stimulation, compared to fixed-frequency stimulation strategies.”
• Progress in Neuroengineering for brain repair: New challenges and open issues (2018, background): “, 2016) and adaptive algorithms for DBS technology to treat epileptic disorders (Panuccio et al., 2013; Pineau et al., 2009).”