Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction
The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epilepto...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-03-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811919310018 |
| _version_ | 1818509140527415296 |
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| author | Manel Vila-Vidal Carmen Pérez Enríquez Alessandro Principe Rodrigo Rocamora Gustavo Deco Adrià Tauste Campo |
| author_facet | Manel Vila-Vidal Carmen Pérez Enríquez Alessandro Principe Rodrigo Rocamora Gustavo Deco Adrià Tauste Campo |
| author_sort | Manel Vila-Vidal |
| collection | DOAJ |
| description | The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition. |
| first_indexed | 2024-12-10T22:41:26Z |
| format | Article |
| id | doaj.art-9d243ef64cee40d6841dfbbe01522ac3 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-10T22:41:26Z |
| publishDate | 2020-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-9d243ef64cee40d6841dfbbe01522ac32022-12-22T01:30:42ZengElsevierNeuroImage1095-95722020-03-01208116410Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus predictionManel Vila-Vidal0Carmen Pérez Enríquez1Alessandro Principe2Rodrigo Rocamora3Gustavo Deco4Adrià Tauste Campo5Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Corresponding author. Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.Hospital del Mar Medical Research Institute, 08003, Barcelona, SpainHospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, SpainHospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain; Corresponding author. Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain.Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, 08010, Barcelona, SpainCenter for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Corresponding author. Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.http://www.sciencedirect.com/science/article/pii/S1053811919310018Seizure onset zoneIntracranial EEGTime-frequency analysisAutomated detection algorithmsPost-operative outcome |
| spellingShingle | Manel Vila-Vidal Carmen Pérez Enríquez Alessandro Principe Rodrigo Rocamora Gustavo Deco Adrià Tauste Campo Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction NeuroImage Seizure onset zone Intracranial EEG Time-frequency analysis Automated detection algorithms Post-operative outcome |
| title | Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction |
| title_full | Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction |
| title_fullStr | Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction |
| title_full_unstemmed | Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction |
| title_short | Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction |
| title_sort | low entropy map of brain oscillatory activity identifies spatially localized events a new method for automated epilepsy focus prediction |
| topic | Seizure onset zone Intracranial EEG Time-frequency analysis Automated detection algorithms Post-operative outcome |
| url | http://www.sciencedirect.com/science/article/pii/S1053811919310018 |
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