Desynchronization Index: a New Connectivity Approach for Exploring Epileptogenic Networks
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arXiv
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| Format: | Preprint |
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2024
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| author | Mason, Federico Ferri, Lorenzo Di Vito, Lidia Alvisi, Lara Zanuttini, Luca Martinoni, Matteo Mai, Roberto Cardinale, Francesco Tinuper, Paolo Michelucci, Roberto Pasini, Elena Bisulli, Francesca |
| author_facet | Mason, Federico Ferri, Lorenzo Di Vito, Lidia Alvisi, Lara Zanuttini, Luca Martinoni, Matteo Mai, Roberto Cardinale, Francesco Tinuper, Paolo Michelucci, Roberto Pasini, Elena Bisulli, Francesca |
| contents | Objective: This work presents a new computational framework to assist neurophysiologists in Stereoelectroencephalography (SEEG) analysis, with the goal of improving the definition of the Epileptogenic Zone (EZ) in patients with drug-resistant epilepsy. Methods and procedures: We consider the Phase Transfer Entropy (PTE) to estimate the effective connectivity between SEEG channels, and design a novel algorithm, named the Desynchronization Index (DI), that identifies the EZ as the group of channels showing independent behavior with respect to the rest of the network during the seconds preceding the seizure propagation. Results: We test the proposed DI algorithm against the Epileptogenicity Index (EI) on a clinical dataset of 20 patients, considering the channels that were thermocoagulated at the end of SEEG monitoring as the detection target. Our results indicate that DI overcomes EI in terms of area under the ROC curve (AUC=0.85 vs. AUC=0.83), while combining the two algorithms as a unique tool leads to the best performance (AUC=0.87). Conclusion: The DI algorithm underscores connectivity dynamics that can hardly be identified with a pure visual analysis, increasing the accuracy in the EZ definition compared to traditional methods. Clinical impact: The integration of connectivity- and energy-based features can lead to the definition of a new biomarker of epileptogenic channels, reducing the burden required by the SEEG review in the case of extensive implants and improving the understanding of the dynamics behind the generation of seizures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Desynchronization Index: a New Connectivity Approach for Exploring Epileptogenic Networks Mason, Federico Ferri, Lorenzo Di Vito, Lidia Alvisi, Lara Zanuttini, Luca Martinoni, Matteo Mai, Roberto Cardinale, Francesco Tinuper, Paolo Michelucci, Roberto Pasini, Elena Bisulli, Francesca Signal Processing Neurons and Cognition Objective: This work presents a new computational framework to assist neurophysiologists in Stereoelectroencephalography (SEEG) analysis, with the goal of improving the definition of the Epileptogenic Zone (EZ) in patients with drug-resistant epilepsy. Methods and procedures: We consider the Phase Transfer Entropy (PTE) to estimate the effective connectivity between SEEG channels, and design a novel algorithm, named the Desynchronization Index (DI), that identifies the EZ as the group of channels showing independent behavior with respect to the rest of the network during the seconds preceding the seizure propagation. Results: We test the proposed DI algorithm against the Epileptogenicity Index (EI) on a clinical dataset of 20 patients, considering the channels that were thermocoagulated at the end of SEEG monitoring as the detection target. Our results indicate that DI overcomes EI in terms of area under the ROC curve (AUC=0.85 vs. AUC=0.83), while combining the two algorithms as a unique tool leads to the best performance (AUC=0.87). Conclusion: The DI algorithm underscores connectivity dynamics that can hardly be identified with a pure visual analysis, increasing the accuracy in the EZ definition compared to traditional methods. Clinical impact: The integration of connectivity- and energy-based features can lead to the definition of a new biomarker of epileptogenic channels, reducing the burden required by the SEEG review in the case of extensive implants and improving the understanding of the dynamics behind the generation of seizures. |
| title | Desynchronization Index: a New Connectivity Approach for Exploring Epileptogenic Networks |
| topic | Signal Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2408.16347 |