Mostrar el registro sencillo del documento
Supervised group connectivity analysis for enhancing the interpretability of brain activity
dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional |
dc.contributor.advisor | Castellanos Domínguez, César Germán |
dc.contributor.advisor | Ferrández Vicente, José Manuel |
dc.contributor.author | Padilla Buriticá, Jorge Iván |
dc.date.accessioned | 2021-07-02T17:54:48Z |
dc.date.available | 2021-07-02T17:54:48Z |
dc.date.issued | 2021 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/79758 |
dc.description | Figuras, tablas |
dc.description | This document presents a supervised group connectivity analysis methodology, in which three main problems must be addressed, the first problem to overcome is the non-stationary behavior of brain activity, the second problem is the high dimension of the connectivity matrices, and finally, the grouping to select the subjects of each set of analyzes. To carry out this methodology, three databases were used, the first related to auditory and visual stimuli under the oddball paradigm, the second and the third a database with motor imagery with a different number of subjects. The results obtained show that the segmentation of the recordings in time favors the estimation of connectivity, in addition, the proposal of a supervised rule to reduce dimension, guarantees the physiological interpretability of the results obtained. Finally, it was verified that the brain activity obtained depends on the groups of subjects that conform. The methodology was verified taking into account criteria of computational cost, numerical stability, probability of error, as well as the interpretability of the results obtained. |
dc.description.abstract | En este documento se presenta una metodología de análisis de conectividad cerebral, en la cual deben abordarse tres problemas principales, el primer problema para superar es el comportamiento no estacionario de la actividad cerebral, el segundo problema es la alta dimensión de las matrices de conectividad y finalmente el agrupamiento para seleccionar los sujetos de cada conjunto de análisis. Para llevar a cabo esta metodología, fueron empleadas 3 bases de datos, la primera relacionada con estímulos auditivos y visuales bajo el paradigma oddball, la segunda y la tercera una base de datos son motor imagery con diferente número de sujetos. Los resultados obtenidos demuestran que la segmentación de los registros en el tiempo, favorece la estimación de conectividad, además, la propuesta de una regla supervisada para reducir dimensión, garantiza la interpretabilidad fisiológica de los resultados que se obtienen. Finalmente se verificó que la actividad cerebral obtenida depende de los grupos de sujetos que se conformen. Se verificó la metodología teniendo en cuenta criterios de costo computacional, estabilidad numérica, probabilidad de error, así como interpretabilidad de los resultados obtenidos. |
dc.format.extent | 114 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.subject.lcsh | Computational neuroscience |
dc.title | Supervised group connectivity analysis for enhancing the interpretability of brain activity |
dc.type | Trabajo de grado - Doctorado |
dc.type.driver | info:eu-repo/semantics/doctoralThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática |
dc.contributor.researchgroup | Procesamiento Digital de Señales |
dc.description.degreelevel | Doctorado |
dc.description.degreename | Doctor en Ingeniería - Ingeniería Automática |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Departamento de Ingeniería Eléctrica y Electrónica |
dc.publisher.faculty | Facultad de Ingeniería y Arquitectura |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Manizales |
dc.relation.references | [Acharya et al., 2015] Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E., and Adeli, A. (2015). Computer-aided diagnosis of depression using EEG signals. European neurology, 73(5-6):329{336. [Allen et al., 2018] Allen, E., Damaraju, E., Eichele, T., Wu, L., and Calhoun, V. D. (2018). EEG signatures of dynamic functional network connectivity states. Brain Topography, 31(1):101{116. [Allen et al., 2014] Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., and Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral cortex, 24(3):663{676. [Astolfi et al., 2007] Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., Marciani, M., Bufalari, S., Salinari, S., Colosimo, A., Ding, L., Edgar, J., et al. (2007). Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory. Psychophysiology, 44(6):880{893. [Aviyente et al., 2017] Aviyente, S., Tootell, A., and Bernat, E. M. (2017). Time-frequency phase-synchrony approaches with ERPs. International Journal of Psychophysiology, 111:88{97. [Babiloni et al., 2016] Babiloni, C., Lizio, R., Marzano, N., Capotosto, P., Soricelli, A., Triggiani, A. I., Cordone, S., Gesualdo, L., and Del Percio, C. (2016). Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting-state EEG rhythms. International Journal of Psychophysiology, 103:88{102. [Baillet et al., 2001] Baillet, S., Mosher, J. C., and Leahy, R. M. (2001). Electromagnetic brain mapping. IEEE Signal processing magazine, 18(6):14{30. [Bakhshayesh et al., 2019] Bakhshayesh, H., Fitzgibbon, S. P., Janani, A. S., Grummett, T. S., and Pope, K. J. (2019). Detecting synchrony in EEG: A comparative study of functional connectivity measures. Computers in Biology and Medicine, 105:1{15. [Bassett and Gazzaniga, 2011] Bassett, D. S. and Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in cognitive sciences, 15(5):200{209. [Bassett and Sporns, 2017] Bassett, D. S. and Sporns, O. (2017). Network neuroscience. Nature neuroscience, 20(3):353. [Bastos and Scho elen, 2016] Bastos, A. M. and Schoffelen, J.-M. (2016). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in systems neuroscience, 9:175. [Bathelt et al., 2013] Bathelt, J., O'Reilly, H., Clayden, J. D., Cross, J. H., and de Haan, M. (2013). Functional brain network organization of children between 2 and 5 years derived from the reconstructed activity of cortical sources of high-density EEG recordings. NeuroImage, 82:595{604. [Berger, 1934] Berger, H. (1934). Uber das Elektrenkephalogramm des Menschen. Deutsche Medizinische Wochenschrift, 60(51):1947{1949. [Betzel and Bassett, 2017] Betzel, R. F. and Bassett, D. S. (2017). Multi-scale brain networks. Neuroimage, 160:73{83. [Betzel et al., 2012] Betzel, R. F., Erickson, M. A., Abell, M., O'Donnell, B. F., Hetrick, W. P., and Sporns, O. (2012). Synchronization dynamics and evidence for a repertoire of network states in resting EEG. Frontiers in computational neuroscience, 6:74. [Bielczyk et al., 2018] Bielczyk, N. Z., Walocha, F., Ebel, P. W., Haak, K. V., Llera, A., Buitelaar, J. K., Glennon, J. C., and Beckmann, C. F. (2018). Thresholding functional connectomes by means of mixture modeling. NeuroImage, 171:402{414. [Bijsterbosch et al., 2018] Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Van Essen, D. C., Harrison, S. J., and Smith, S. M. (2018). The relationship between spatial confi guration and functional connectivity of brain regions. Elife, 7:e32992. |
dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | Neurociencia computacional |
dc.subject.proposal | Non-stationary |
dc.subject.proposal | Change point detection |
dc.subject.proposal | Functional connectivity |
dc.subject.proposal | Supervised model |
dc.subject.proposal | Dimensionality reduction |
dc.subject.proposal | Clustering |
dc.subject.proposal | Brain connectivity |
dc.subject.proposal | Thresholding |
dc.subject.proposal | No-estacionariedad |
dc.subject.proposal | Detección de puntos de cambio |
dc.subject.proposal | Conectividad funcional |
dc.subject.proposal | Modelo supervisado |
dc.subject.proposal | Reducción de dimensión |
dc.subject.proposal | Clustering |
dc.subject.proposal | Conectividad cerebral |
dc.title.translated | Análisis de conectividad supervisado y de grupo para mejorar la interpretación de actividad cerebral |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
oaire.fundername | Colciencias-Colfuturo MINCIENCIAS - convocatoria 647 de 2014 para doctorados nacionales |
Archivos en el documento
Este documento aparece en la(s) siguiente(s) colección(ones)
![Atribución-NoComercial-SinDerivadas 4.0 Internacional](/themes/Mirage2//images/creativecommons/cc-generic.png)