Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controller is presented to block the aberrant activity of...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10892294/ |
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| author | Gabriel da Silva Lima Vinicius Rosa Cota Wallace Moreira Bessa |
| author_facet | Gabriel da Silva Lima Vinicius Rosa Cota Wallace Moreira Bessa |
| author_sort | Gabriel da Silva Lima |
| collection | DOAJ |
| description | Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controller is presented to block the aberrant activity of a network of Izhikevich neurons of three different types, used here to model the electrical activity of the basolateral amygdala during ictogenesis, i.e. its transition from asynchronous to hypersynchronous state. A Lyapunov-based nonlinear scheme is used as the main framework for the proposed controller. To avoid the issue of accessing each neuron individually, local field potentials are used to gain insight into the overall state of the Izhikevich network. Artificial neural networks are integrated into the control scheme to manage unknown dynamics and disturbances caused by brain electrical activity that are not accounted for in the model. Four different cases of ictogenesis induction were tested. The results show the efficacy of the proposed control strategy to suppress epileptic seizures and suggest its capability to address both patient-specific and patient-to-patient variability. |
| format | Article |
| id | doaj-art-02c1f4a0c84442d89dfc20c1dd17eaec |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-02c1f4a0c84442d89dfc20c1dd17eaec2025-08-20T01:52:03ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013386888010.1109/TNSRE.2025.354375610892294Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich NeuronsGabriel da Silva Lima0https://orcid.org/0000-0001-6615-078XVinicius Rosa Cota1https://orcid.org/0000-0002-2338-5949Wallace Moreira Bessa2https://orcid.org/0000-0002-0935-7730Smart Systems Laboratory, University of Turku, Turku, FinlandRehab Technologies Laboratory, Istituto Italiano di Tecnologia, Genoa, ItalySmart Systems Laboratory, University of Turku, Turku, FinlandClosed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controller is presented to block the aberrant activity of a network of Izhikevich neurons of three different types, used here to model the electrical activity of the basolateral amygdala during ictogenesis, i.e. its transition from asynchronous to hypersynchronous state. A Lyapunov-based nonlinear scheme is used as the main framework for the proposed controller. To avoid the issue of accessing each neuron individually, local field potentials are used to gain insight into the overall state of the Izhikevich network. Artificial neural networks are integrated into the control scheme to manage unknown dynamics and disturbances caused by brain electrical activity that are not accounted for in the model. Four different cases of ictogenesis induction were tested. The results show the efficacy of the proposed control strategy to suppress epileptic seizures and suggest its capability to address both patient-specific and patient-to-patient variability.https://ieeexplore.ieee.org/document/10892294/Epilepsyamygdalaseizure suppressionintelligent controlartificial neural networks |
| spellingShingle | Gabriel da Silva Lima Vinicius Rosa Cota Wallace Moreira Bessa Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons IEEE Transactions on Neural Systems and Rehabilitation Engineering Epilepsy amygdala seizure suppression intelligent control artificial neural networks |
| title | Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons |
| title_full | Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons |
| title_fullStr | Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons |
| title_full_unstemmed | Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons |
| title_short | Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons |
| title_sort | intelligent control to suppress epileptic seizures in the amygdala in silico investigation using a network of izhikevich neurons |
| topic | Epilepsy amygdala seizure suppression intelligent control artificial neural networks |
| url | https://ieeexplore.ieee.org/document/10892294/ |
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