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Title
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Adaptive control of epileptiform excitability in an in vitro model of limbic seizures
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Author
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Abstract
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| 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 hippocampusentorhinal 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. |
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Language
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English
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Source (journal)
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Experimental neurology. - New York, N.Y.
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Publication
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New York, N.Y.
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2013
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ISSN
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0014-4886
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DOI
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10.1016/J.EXPNEUROL.2013.01.002
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Volume/pages
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241
(2013)
, p. 179-183
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ISI
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000315315800022
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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