Supervised group connectivity analysis for enhancing the interpretability of brain activity

Figuras, tablas

Autores:: Padilla Buriticá, Jorge Iván

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

id	UNACIONAL2_6b7aa6e635beec50a5dc1010a805243b
oai_identifier_str	oai:repositorio.unal.edu.co:unal/79758
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Supervised group connectivity analysis for enhancing the interpretability of brain activity
dc.title.translated.spa.fl_str_mv	Análisis de conectividad supervisado y de grupo para mejorar la interpretación de actividad cerebral
title	Supervised group connectivity analysis for enhancing the interpretability of brain activity
spellingShingle	Supervised group connectivity analysis for enhancing the interpretability of brain activity 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Computational neuroscience Neurociencia computacional Non-stationary Change point detection Functional connectivity Supervised model Dimensionality reduction Clustering Brain connectivity Thresholding No-estacionariedad Detección de puntos de cambio Conectividad funcional Modelo supervisado Reducción de dimensión Clustering Conectividad cerebral
title_short	Supervised group connectivity analysis for enhancing the interpretability of brain activity
title_full	Supervised group connectivity analysis for enhancing the interpretability of brain activity
title_fullStr	Supervised group connectivity analysis for enhancing the interpretability of brain activity
title_full_unstemmed	Supervised group connectivity analysis for enhancing the interpretability of brain activity
title_sort	Supervised group connectivity analysis for enhancing the interpretability of brain activity
dc.creator.fl_str_mv	Padilla Buriticá, Jorge Iván
dc.contributor.advisor.none.fl_str_mv	Castellanos Domínguez, César Germán Ferrández Vicente, José Manuel
dc.contributor.author.none.fl_str_mv	Padilla Buriticá, Jorge Iván
dc.contributor.researchgroup.spa.fl_str_mv	Procesamiento Digital de Señales
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Computational neuroscience Neurociencia computacional Non-stationary Change point detection Functional connectivity Supervised model Dimensionality reduction Clustering Brain connectivity Thresholding No-estacionariedad Detección de puntos de cambio Conectividad funcional Modelo supervisado Reducción de dimensión Clustering Conectividad cerebral
dc.subject.lcsh.none.fl_str_mv	Computational neuroscience
dc.subject.lemb.none.fl_str_mv	Neurociencia computacional
dc.subject.proposal.eng.fl_str_mv	Non-stationary Change point detection Functional connectivity Supervised model Dimensionality reduction Clustering Brain connectivity Thresholding
dc.subject.proposal.spa.fl_str_mv	No-estacionariedad Detección de puntos de cambio Conectividad funcional Modelo supervisado Reducción de dimensión Clustering Conectividad cerebral
description	Figuras, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-07-02T17:54:48Z
dc.date.available.none.fl_str_mv	2021-07-02T17:54:48Z
dc.date.issued.none.fl_str_mv	2021
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79758
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79758 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[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.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	114 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.program.spa.fl_str_mv	Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Eléctrica y Electrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Manizales
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/79758/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/79758/2/1060647014.2021.pdf https://repositorio.unal.edu.co/bitstream/unal/79758/3/1060647014.2021.pdf.jpg
bitstream.checksum.fl_str_mv	cccfe52f796b7c63423298c2d3365fc6 189ea0bef40c92084d867fd74b0d59da 51080037c7df41fc1cfc0ff292917547
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089920482902016
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Domínguez, César Germánae15fbaaab595270cf72416c27b8b987Ferrández Vicente, José Manuel0dcd4c136cbd799b01c3f0af916c9ed9Padilla Buriticá, Jorge Iván3d2983fcca1bc8fa4b3963a07f17a5b5Procesamiento Digital de Señales2021-07-02T17:54:48Z2021-07-02T17:54:48Z2021https://repositorio.unal.edu.co/handle/unal/79758Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Figuras, tablasThis 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.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.DoctoradoDoctor en Ingeniería - Ingeniería Automática114 páginasapplication/pdfeng000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresComputational neuroscienceNeurociencia computacionalNon-stationaryChange point detectionFunctional connectivitySupervised modelDimensionality reductionClusteringBrain connectivityThresholdingNo-estacionariedadDetección de puntos de cambioConectividad funcionalModelo supervisadoReducción de dimensiónClusteringConectividad cerebralSupervised group connectivity analysis for enhancing the interpretability of brain activityAnálisis de conectividad supervisado y de grupo para mejorar la interpretación de actividad cerebralTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06TextManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de Ingeniería y ArquitecturaUniversidad Nacional de Colombia - Sede Manizales[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.Colciencias-Colfuturo MINCIENCIAS - convocatoria 647 de 2014 para doctorados nacionalesLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79758/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL1060647014.2021.pdf1060647014.2021.pdfTesis de Doctorado en Ingeniería - Línea de Investigación en Automáticaapplication/pdf3012528https://repositorio.unal.edu.co/bitstream/unal/79758/2/1060647014.2021.pdf189ea0bef40c92084d867fd74b0d59daMD52THUMBNAIL1060647014.2021.pdf.jpg1060647014.2021.pdf.jpgGenerated Thumbnailimage/jpeg3838https://repositorio.unal.edu.co/bitstream/unal/79758/3/1060647014.2021.pdf.jpg51080037c7df41fc1cfc0ff292917547MD53unal/79758oai:repositorio.unal.edu.co:unal/797582024-07-23 23:35:40.4Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Supervised group connectivity analysis for enhancing the interpretability of brain activity

Publicaciones similares