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M. A. F. Rodrigues, S., & R. Cota, V. (2024). SynchroLINNce: toolbox para la evaluación de la sincronización y desincronización neural en modelos animales de epilepsia. International Journal of Psychological Research, 17(2), 14–24. https://doi.org/10.21500/20112084.7329
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The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Resumen
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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Referencias
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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
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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