SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia
La epilepsia es un problema de salud pública mundial debido a sus impactos biológicos, sociales y económicos. Considerando varias preguntas abiertas sobre los mecanismos de sincronización y desincronización que subyacen a los fenómenos epilépticos, el desarrollo de algoritmos y toolboxes computacion...
- Autores:
-
M. A. F. Rodrigues, Sofia
R. Cota, Vinícius
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/28994
- Acceso en línea:
- https://hdl.handle.net/10819/28994
https://doi.org/10.21500/20112084.7329
- Palabra clave:
- Toolbox
MATLAB
epileptiform spike
epilepsy
neural synchronization
Caja de herramientas
MATLAB
espiga epileptiforme
epilepsia
sincronización neuronal
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| dc.title.translated.spa.fl_str_mv |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| title |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| spellingShingle |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia Toolbox MATLAB epileptiform spike epilepsy neural synchronization Caja de herramientas MATLAB espiga epileptiforme epilepsia sincronización neuronal |
| title_short |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| title_full |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| title_fullStr |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| title_full_unstemmed |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| title_sort |
SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia |
| dc.creator.fl_str_mv |
M. A. F. Rodrigues, Sofia R. Cota, Vinícius |
| dc.contributor.author.eng.fl_str_mv |
M. A. F. Rodrigues, Sofia R. Cota, Vinícius |
| dc.subject.eng.fl_str_mv |
Toolbox MATLAB epileptiform spike epilepsy neural synchronization |
| topic |
Toolbox MATLAB epileptiform spike epilepsy neural synchronization Caja de herramientas MATLAB espiga epileptiforme epilepsia sincronización neuronal |
| dc.subject.spa.fl_str_mv |
Caja de herramientas MATLAB espiga epileptiforme epilepsia sincronización neuronal |
| description |
La epilepsia es un problema de salud pública mundial debido a sus impactos biológicos, sociales y económicos. Considerando varias preguntas abiertas sobre los mecanismos de sincronización y desincronización que subyacen a los fenómenos epilépticos, el desarrollo de algoritmos y toolboxes computacionales para dicho análisis es altamente relevante para su investigación. Además, dado el desarrollo reciente de la neurotecnología para la epilepsia, es esencial entender que propuestas como las herramientas computacionales pueden proporcionar datos consistentes para sistemas de control en bucle cerrado, necesarios en alternativas de tratamiento de neuromodulación, y para sistemas de monitoreo en tiempo real para predecir la ocurrencia de crisis epilépticas. En el presente trabajo, se propone SynchroLINNce, una toolbox de MATLAB de distribución libre, diseñada para ser utilizada por neurocientíficos especializados en epilepsia (incluidos aquellos sin formación en software). Entre sus características, se presentan varias funcionalidades, como la visualización de grabaciones, el filtrado digital y el análisis de correlación, así como metodologías más específicas, como mecanismos para la detección automática de picos epileptiformes, el análisis de morfología de estos picos y su coincidencia entre canales. |
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2024 |
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2024-09-03T00:00:00Z 2025-08-22T16:59:33Z |
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2024-09-03T00:00:00Z 2025-08-22T16:59:33Z |
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2024-09-03 |
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10.21500/20112084.7329 |
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2011-7922 |
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2011-2084 |
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https://hdl.handle.net/10819/28994 |
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https://revistas.usb.edu.co/index.php/IJPR/article/download/7329/5487 |
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Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind |
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Ahmadi, N., Constandinou, T. G., & Bouganis, C. S. (2021). Inferring entire spiking activity from local field potentials. Scientific Reports, 11(1), 19045. https://doi.org/10.1038/s41598-021-98021-9 Bromfield, E. B., Cavazos, J. E., & Sirven, J. I. (2006). Clinical epilepsy. In An Introduction to Epilepsy [Internet]. American Epilepsy Society. Cai, F., Wang, K., Zhao, T., Wang, H., Zhou, W., & Hong, B. (2022). BrainQuake: an open-source python toolbox for the stereoelectroencephalography spatiotemporal analysis. Frontiers in Neuroinformatics, 15, 773890. https://doi.org/10.3389%2Ffninf.2021.773890 Carvalho, V. R., Moraes, M. F., Braga, A. P., & Mendes, E. M. (2020). Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomedical Signal Processing and Control, 62, 102073. https://doi.org/10.1016/j.bspc.2020.102073 Chiang, H. S., Chen, M. Y., & Huang, Y. J. (2019). Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access, 7, 103255-103262. https://doi.org/10.1109/ACCESS.2019.2929266 Cota, V. R., de Castro Medeiros, D., da Páscoa Vilela, M. R. S., Doretto, M. C., & Moraes, M. F. D. (2009). Distinct patterns of electrical stimulation of the basolateral amygdala influence pentylenetetrazole seizure outcome. Epilepsy & Behavior, 14(1), 26-31. https://doi.org/10.1016/j.yebeh.2008.09.006 Cota, V. R., Cançado, S. A. V., & Moraes, M. F. D. (2023). On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram. Frontiers in Neuroinformatics, 17, 1173597. https://doi.org/10.3389/fninf.2023.1173597 De Oliveira, J. C., Drabowski, B. M. B., Rodrigues, S. M. A. F., Maciel, R. M., Moraes, M. F. D., & Cota, V. R. (2019). Seizure suppression by asynchronous non-periodic electrical stimulation of the amygdala is partially mediated by indirect desynchronization from nucleus accumbens. Epilepsy Research, 154, 107-115. https://doi.org/10.1016/j.eplepsyres.2019.05.009 De Sousa, B. M., de Oliveira, E. F., da Silva Beraldo, I. J., Polanczyk, R. S., Leite, J. P., & Aguiar, C. L. (2022). An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI. Journal of Neural Engineering, 19(4), 046040. https://doi.org/10.1088/1741-2552/ac857b Delorme, A., & Makeig, S. (2004). EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Dubarry, A. S., Liégeois-Chauvel, C., Trébuchon, A., Bénar, C., & Alario, F. X. (2022). An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA). NeuroImage, 257, 119251. https://doi.org/10.1016/j.neuroimage.2022.119251 Fisher, R. A. (1970). Statistical methods for research workers. In S. Kotz & N. Johnson (Eds.), Breakthroughs in statistics: Methodology and distribution (pp. 66-70). Springer New York. Gloor, P. (1975). Contributions of electroencephalography and electrocorticography to the neurosurgical treatment of the epilepsies. Advances in neurology, 8, 59-105. Jackson, A., & Hall, T. M. (2016). Decoding local field potentials for neural interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1705-1714. https://doi.org/10.1109/tnsre.2016.2612001 Janca, R., Jezdik, P., Cmejla, R., Tomasek, M., Worrell, G. A., Stead, M., Wagenaar, J., Jefferys, J. G., Krsek, P., Komarek, V., Jiruska, P., & Marusic, P. (2015). Detection of interictal epileptiform discharges using signal envelope distribution modelling: application to epileptic and non-epileptic intracranial recordings. Brain topography, 28(1), 172-183. https://doi.org/10.1007/s10548-014-0379-1 Jiruska, P., De Curtis, M., Jefferys, J. G., Schevon, C. A., Schiff, S. J., & Schindler, K. (2013). Synchronization and desynchronization in epilepsy: controversies and hypotheses. The Journal of physiology, 591(4), 787-797. https://doi.org/10.1113/jphysiol.2012.239590 Jurkiewicz, G. J., Hunt, M. J., & Żygierewicz, J. (2021). Addressing pitfalls in phase-amplitude coupling analysis with an extended modulation index toolbox. Neuroinformatics, 19, 319-345. https://doi.org/10.1007/s12021-020-09487-3 Kandratavicius, L., Balista, P. A., Lopes-Aguiar, C., Ruggiero, R. N., Umeoka, E. H., Garcia-Cairasco, N., Bueno-Junior, L. S., & Leite, J. P. (2014). Animal models of epilepsy: use and limitations. Neuropsychiatric disease and treatment, 10, 1693-1705. https://doi.org/10.2147%2FNDT.S50371 Kiloh, L. G., McComas, A. J., & Osselton, J. W. (2013). Clinical electroencephalography. Butterworth-Heinemann. Kuhlmann, L., Karoly, P., Freestone, D. R., Brinkmann, B. H., Temko, A., Barachant, A., Li, F., Titericz, G., Jr, Lang, B. W., Lavery, D., Roman, K., Broadhead, D., Dobson, S., Jones, G., Tang, Q., Ivanenko, I., Panichev, O., Proix, T., Náhlík, M., Grunberg, D. B., Rueben, C., Worrell, G., Litt, B., Liley, D. R. J., Grayden, D. B., & Cook, M. J. (2018). Epilepsyecosystem. org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain, 141(9), 2619-2630. https://doi.org/10.1093/brain/awy210 Marti Fuster, B., Esteban, O., Planes, X., Aguiar, P., Crespo, C., Falcon, C., Wollny, G., Rubí Sureda, S., Setoain, X., Frangi, A. F., Ledesma, M. J., Santos, A., Pavía, J., & Ros, D. (2013). FocusDET, a new toolbox for SISCOM analysis. Evaluation of the registration accuracy using Monte Carlo simulation. Neuroinformatics, 11, 77-89. https://doi.org/10.1007/s12021-012-9158-x Moraes, M. F. D., de Castro Medeiros, D., Mourao, F. A. G., Cancado, S. A. V., & Cota, V. R. (2021). Epilepsy as a dynamical system, a most needed paradigm shift in epileptology. Epilepsy & Behavior, 121. Navas-Olive, A., Rubio, A., Abbaspoor, S., Hoffman, K. L., & de la Prida, L. M. (2023). A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species. bioRxiv. https://doi.org/10.1101%2F2023.07.02.547382 Niedermeyer, E. (2011). Niedermeyer's electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins. Quitadamo, L. R., Foley, E., Mai, R., De Palma, L., Specchio, N., & Seri, S. (2018). EPINETLAB: a software for seizure-onset zone identification from intracranial EEG signal in epilepsy. Frontiers in neuroinformatics, 12, 45. https://doi.org/10.1101%2F2023.07.02.547382 Reus, E. E. M., Cox, F. M. E., van Dijk, J. G., & Visser, G. H. (2022). Automated spike detection: Which software package? Seizure, 95, 33-37. https://pubmed.ncbi.nlm.nih.gov/34974231/ Rodrigues, S. M., de Oliveira, J. C., & Cota, V. R. (2019). Epileptiform Spike Detection in Electroencephalographic Recordings of Epilepsy Animal Models Using Variable Threshold. In Computational Neuroscience: Second Latin American Workshop, LAWCN 2019, São João Del-Rei, Brazil, September 18–20, 2019, Proceedings 2 (pp. 142-156). Springer International Publishing. Santos, P. H., Oliveira, J. C., Cota, V. R., & Rodrigues¹, S. M. (2021). Automatic classifier for pattern recognition in epilepsy electroencephalographic recordings. Computational Neuroscience (LAWCN 2021), 3. Siegle, J. H., López, A. C., Patel, Y. A., Abramov, K., Ohayon, S., & Voigts, J. (2017). Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. Journal of neural engineering, 14(4), 045003. https://doi.org/10.1088/1741-2552/aa5eea Stirling, R. E., Cook, M. J., Grayden, D. B., & Karoly, P. J. (2021). Seizure forecasting and cyclic control of seizures. Epilepsia, 62(1), S2-S14. https://doi.org/10.1111/epi.16541 Tang, F., Hartz, A. M., & Bauer, B. (2017). Drug-resistant epilepsy: multiple hypotheses, few answers. Frontiers in neurology, 8, 301. https://doi.org/10.3389/fneur.2017.00301 West, S., Nevitt, S. J., Cotton, J., Gandhi, S., Weston, J., Sudan, A., Ramirez, R., & Newton, R. (2019). Surgery for epilepsy. Cochrane Database of Systematic Reviews, 6(6). https://doi.org/10.3389/fneur.2017.00301 Teixeira, C. A., Direito, B., Feldwisch-Drentrup, H., Valderrama, M., Costa, R. P., Alvarado-Rojas, C., Nikolopoulos, S., Le Van Quyen, M., Timmer, J., Schelter, B., & Dourado, A. (2011). EPILAB: A software package for studies on the prediction of epileptic seizures. Journal of Neuroscience Methods, 200(2), 257-271. https://doi.org/10.1016/j.jneumeth.2011.07.002 Tort, A. B., Komorowski, R., Eichenbaum, H., & Kopell, N. (2010). Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. Journal of neurophysiology, 104(2), 1195-1210. https://doi.org/10.1152/jn.00106.2010 Wong, S. M., Ibrahim, G. M., Ochi, A., Otsubo, H., Rutka, J. T., Snead III, O. C., & Doesburg, S. M. (2016). MoviEEG: an animation toolbox for visualization of intracranial electroencephalography synchronization dynamics. Clinical Neurophysiology, 127(6), 2370-2378. https://doi.org/10.1016/j.clinph.2016.03.001 Yakovleva, T. V., Kutepov, I. E., Karas, A. Y., Yakovlev, N. M., Dobriyan, V. V., Papkova, I. V., Zhigalov, M. V., Saltykova, O. A., Krysko, A. V., Yaroshenko, T. Y., Erofeev, N. P., & Krysko, V. A. (2020). EEG analysis in structural focal epilepsy using the methods of nonlinear dynamics (Lyapunov exponents, Lempel–Ziv complexity, and multiscale entropy). The Scientific World Journal, 2020(1), 8407872. https://doi.org/10.1155%2F2020%2F8407872 |
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M. A. F. Rodrigues, SofiaR. Cota, Vinícius2024-09-03T00:00:00Z2025-08-22T16:59:33Z2024-09-03T00:00:00Z2025-08-22T16:59:33Z2024-09-03La epilepsia es un problema de salud pública mundial debido a sus impactos biológicos, sociales y económicos. Considerando varias preguntas abiertas sobre los mecanismos de sincronización y desincronización que subyacen a los fenómenos epilépticos, el desarrollo de algoritmos y toolboxes computacionales para dicho análisis es altamente relevante para su investigación. Además, dado el desarrollo reciente de la neurotecnología para la epilepsia, es esencial entender que propuestas como las herramientas computacionales pueden proporcionar datos consistentes para sistemas de control en bucle cerrado, necesarios en alternativas de tratamiento de neuromodulación, y para sistemas de monitoreo en tiempo real para predecir la ocurrencia de crisis epilépticas. En el presente trabajo, se propone SynchroLINNce, una toolbox de MATLAB de distribución libre, diseñada para ser utilizada por neurocientíficos especializados en epilepsia (incluidos aquellos sin formación en software). Entre sus características, se presentan varias funcionalidades, como la visualización de grabaciones, el filtrado digital y el análisis de correlación, así como metodologías más específicas, como mecanismos para la detección automática de picos epileptiformes, el análisis de morfología de estos picos y su coincidencia entre canales.Epilepsy is a worldwide public health issue, given its biological, social, and economic impacts. Considering several open questions about synchronization and desynchronization mechanisms underlying epileptic phenomena, the development of algorithms and computational toolboxes for such analysis is highly relevant to their research. Moreover, given the recent developments of neurotechnology for epilepsy, it is essential to understand that proposals like computational tools may provide consistent data for closed-loop control systems, necessary in neuromodulation treatment alternatives, and for real-time monitoring systems to predict the occurrence of epileptic seizures. In the present work, SynchroLINNce, a freely distributable MATLAB toolbox designed to be used by epilepsy neuroscientists, including software-untrained), is proposed. Among its features, several functionalities such as recording visualization, digital filtering, and correlation analysis, as well as more specific methodologies, such as mechanisms for the automatic detection of epileptiform spikes, morphology analysis of these spikes, and their coincidence between channels are presented.application/pdf10.21500/20112084.73292011-79222011-2084https://hdl.handle.net/10819/28994https://doi.org/10.21500/20112084.7329engUniversidad San Buenaventura - USB (Colombia)https://revistas.usb.edu.co/index.php/IJPR/article/download/7329/5487Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind2421417International Journal of Psychological ResearchAhmadi, N., Constandinou, T. G., & Bouganis, C. S. (2021). Inferring entire spiking activity from local field potentials. Scientific Reports, 11(1), 19045. https://doi.org/10.1038/s41598-021-98021-9 Bromfield, E. B., Cavazos, J. E., & Sirven, J. I. (2006). Clinical epilepsy. In An Introduction to Epilepsy [Internet]. American Epilepsy Society. Cai, F., Wang, K., Zhao, T., Wang, H., Zhou, W., & Hong, B. (2022). BrainQuake: an open-source python toolbox for the stereoelectroencephalography spatiotemporal analysis. Frontiers in Neuroinformatics, 15, 773890. https://doi.org/10.3389%2Ffninf.2021.773890 Carvalho, V. R., Moraes, M. F., Braga, A. P., & Mendes, E. M. (2020). Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomedical Signal Processing and Control, 62, 102073. https://doi.org/10.1016/j.bspc.2020.102073 Chiang, H. S., Chen, M. Y., & Huang, Y. J. (2019). Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access, 7, 103255-103262. https://doi.org/10.1109/ACCESS.2019.2929266 Cota, V. R., de Castro Medeiros, D., da Páscoa Vilela, M. R. S., Doretto, M. C., & Moraes, M. F. D. (2009). Distinct patterns of electrical stimulation of the basolateral amygdala influence pentylenetetrazole seizure outcome. Epilepsy & Behavior, 14(1), 26-31. https://doi.org/10.1016/j.yebeh.2008.09.006 Cota, V. R., Cançado, S. A. V., & Moraes, M. F. D. (2023). On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram. Frontiers in Neuroinformatics, 17, 1173597. https://doi.org/10.3389/fninf.2023.1173597 De Oliveira, J. C., Drabowski, B. M. B., Rodrigues, S. M. A. F., Maciel, R. M., Moraes, M. F. D., & Cota, V. R. (2019). Seizure suppression by asynchronous non-periodic electrical stimulation of the amygdala is partially mediated by indirect desynchronization from nucleus accumbens. Epilepsy Research, 154, 107-115. https://doi.org/10.1016/j.eplepsyres.2019.05.009 De Sousa, B. M., de Oliveira, E. F., da Silva Beraldo, I. J., Polanczyk, R. S., Leite, J. P., & Aguiar, C. L. (2022). An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI. Journal of Neural Engineering, 19(4), 046040. https://doi.org/10.1088/1741-2552/ac857b Delorme, A., & Makeig, S. (2004). EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Dubarry, A. S., Liégeois-Chauvel, C., Trébuchon, A., Bénar, C., & Alario, F. X. (2022). An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA). NeuroImage, 257, 119251. https://doi.org/10.1016/j.neuroimage.2022.119251 Fisher, R. A. (1970). Statistical methods for research workers. In S. Kotz & N. Johnson (Eds.), Breakthroughs in statistics: Methodology and distribution (pp. 66-70). Springer New York. Gloor, P. (1975). Contributions of electroencephalography and electrocorticography to the neurosurgical treatment of the epilepsies. 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The Scientific World Journal, 2020(1), 8407872. https://doi.org/10.1155%2F2020%2F8407872info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.http://creativecommons.org/licenses/by-nc-nd/4.0https://revistas.usb.edu.co/index.php/IJPR/article/view/7329ToolboxMATLABepileptiform spikeepilepsyneural synchronizationCaja de herramientasMATLABespiga epileptiformeepilepsiasincronización neuronalSynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsiaSynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2646https://bibliotecadigital.usb.edu.co/bitstreams/a9440eb2-359d-4d0e-a30e-6819e80ab074/downloadfbd325e49dd86629dad723e58c30b04cMD5110819/28994oai:bibliotecadigital.usb.edu.co:10819/289942025-08-22 11:59:33.488http://creativecommons.org/licenses/by-nc-nd/4.0https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.com |