Colombia | Otro
dc.contributorAlonso Malaver, Carlos Eduardo
dc.contributorMartínez H, Johann
dc.creatorMendoza Ruiz, Jorge
dc.date.accessioned2020-07-08T15:45:16Z
dc.date.available2020-07-08T15:45:16Z
dc.date.created2020-07-08T15:45:16Z
dc.date.issued2020-01-20
dc.identifierMendoza-Ruiz, Jorge (2020) Complejidad de patrones en oscilaciones estocásticas corticales. Maestría thesis, Universidad Nacional de Colombia - Sede Bogotá.
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/77753
dc.description.abstractThis work presents the results of the study of two dynamical parameters from cortical stochastic oscillations in time series of a 40-subjects group of healthy adults in resting state (RS): normalized permutation entropy (H) and statistical complexty (C). 190 signals from different cortical regions of interest (ROI) were analyzed in four frequency bands broadly used in neuroscience, especially alpha band due to its high association with RS. Through symbolic representation, ordinal patterns distribution is obtained for each signal, and it is used to compute the statistics H and C in different frequency bands. A relationship between these parameters, frequency bands and observation dimension (D) was observed, as well as an spatial clustering is revealed for the ROI with greater dynamic parameter values in different lobes. Furthermore to the objectives of this work, the clustering coeficient was computed as a measure of centrality of functional networks obtained for each frequency band. A high dynamic and topological parameter values are observed in occipital and parietal lobes for alpha frequency band revealing relevant cortical activity in such regions of high cognitive processing during ER. A potential linear relationship between dynamics and structure through H and clustering coeficient was explored. It was concluded the absense of such relationship between these parameters globally and discriminated by cortical lobe.
dc.description.abstractEn el presente trabajo se exponen los resultados del estudio de dos parámetros dinámicos de oscilaciones estocásticas corticales en mediciones realizadas a 40 sujetos sanos en estado de reposo (ER): la entropía de permutación normalizada (H) y la complejidad estadística (C). Se estudian 190 series de tiempo de diferentes regiones de interés (ROI) de la corteza cerebral para cuatro bandas de frecuencia ampliamente trabajadas en neurociencia, especialmente la banda alpha, debido a su alta asociación al ER. Por medio de la representacion simbólica se obtiene la distribución de los patrones de orden de cada serie, a partir de las cuales se computan los estadísticos H y C en las bandas de frecuencia. Se observa una relación entre los parámetros dinámicos respecto a la banda de frecuencia, la dimensión de observacion (D) y la concentración espacial de las ROI que tienen mayores valores en los parámetros dinámicos asociando lobulos corticales específi cos en cada banda de frecuencia. Adicionalmente a los objetivos de esta tesis, se explora el parámetro estructural del coe ficiente de agrupamiento como medida de centralidad de las redes funcionales obtenidas para cada banda de frecuencia. La banda de frecuencia alpha exhibe altos valores de parámetros dinámicos y topólogicos en los lóbulos occipital y parietal, revelando la existencia de actividad cortical relevante en dichas regiones durante ER. Además, se estudia la posible existencia de una relación lineal entre la dinámica y estructura, representadas en el parámetro H y el coe ficiente de agrupamiento. Se determina así, una ausencia de relaciones lineales entre ambos parámetros a nivel general al discriminar por lóbulo cortical.
dc.languagespa
dc.publisherBogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisherDepartamento de Estadística
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relationJackson A. and Bolger D., The neurophysiological bases of EEG and EEG measurement: A review for the rest of us, Psychophysiology 51 (2014), no. 11, 1061-1071.
dc.relationPlastino A. and Rosso OA., Entropy and statistical complexity in brain activity, Europhysics News 36 (2005), no. 6, 224-228.
dc.relationWong AKC and You M., Entropy and distance of random graphs with application to structural pattern recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence (1985), no. 5, 599-609.
dc.relationBandt C. and Pompe B., Permutation entropy: a natural complexity measure for time series, Physical review letters 88 (2002), no. 17, 174102.
dc.relationStam C., Nonlinear dynamical analysis of EEG and MEG: review of an emerging field, Clinical neurophysiology 116 (2005), no. 10, 2266-2301.
dc.relationHerrmann CS., Str uber D., Helfrich RF., and Engel AK., EEG oscillations: from correlation to causality, International Journal of Psychophysiology 103 (2016), 12-21.
dc.relationHolling CS., The strategy of building models of complex ecological systems, Systems analysis in ecology (1966), 195-214.
dc.relationPapo D., Martínez J., Ariza P., Pineda-Pardo JA., Boccaletti S., and Buldú JM., Las redes funcionales bajo la perspectiva de la teoría de grafos, Conectividad funcional y anatómica en el cerebro humano: análisis de señales y aplicaciones en ciencias de la salud, 2015, pp. 81-90.
dc.relationPapo D., Buldú JM., Boccaletti S., and Bullmore ET., Complex network theory and the brain, 2014.
dc.relationCosta L da F., Rodrigues FA., Travieso G., and Villas Boas PR., Characterization of complex networks: A survey of measurements, Advances in physics 56 (2007), no. 1, 167-242.
dc.relationCampos-Romero DC. and Isaza-Delgado JF., Prolegómenos a los sistemas dinámicos, Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de Física, 2002.
dc.relationBullmore E. and Sporns O., Complex brain networks: graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience 10 (2009), no. 3, 186.
dc.relationDe Vico Fallani F., Latora V., and Chavez M., A topological criterion for ltering information in complex brain networks, PLoS computational biology 13 (2017), no. 1, e1005305.
dc.relationPeriago F., Teoría de campos y ecuaciones en derivadas parciales, Horacio Escarabajal Editores, 2003.
dc.relationCarter GC., Coherence and time delay estimation, Proceedings of the IEEE 75 (1987), no. 2, 236-255.
dc.relationCurtis H., Barnes S., Schneck A., and Flores G., Biología, Editorial Médica Panamericana, 2001.
dc.relationJuebin Huang, Overview of Cerebral Function, https:// www.msdmanuals.com/professional/neurologic-disorders/ function-and-dysfunction-of-the-cerebral-lobes/ overview-of-cerebral-function, 2019, [Online; accessed 10-November-2019].
dc.relationEchegoyen I., Vera-Ávila V., Sevilla-Escoboza R., Martínez JH, and Buldú JM., Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series, Chaos, Solitons & Fractals 119 (2019), 8-18.
dc.relationGrosse I., Bernaola-Galván P., Carpena P., Román-Roldán R., Oliver J., and Stanley HE., Analysis of symbolic sequences using the jensen-shannon divergence, Physical Review E 65 (2002), no. 4, 041905.
dc.relationLin J., Divergence measures based on the shannon entropy, IEEE Transactions on Information theory 37 (1991), no. 1, 145-151.
dc.relationMartínez J., López ME., Ariza P., Chavez M., Pineda-Pardo JA., López-Sanz D., Gil P., Maestú F., and Buldú JM., Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics, Scientifi c reports 8 (2018), no. 1, 10525.
dc.relationPapo D. Goñi J. and Buldú JM., On the relation of dynamics and structure in brain networks, 2017.
dc.relationTheiler J., Eubank S., Longtin A., Galdrikian B., and Farmer JD., Testing for nonlinearity in time series: the method of surrogate data, Physica D: Nonlinear Phenomena 58 (1992), no. 1-4, 77-94.
dc.relationWalleczek J., Self-organized biological dynamics and nonlinear control: toward understanding complexity, chaos and emergent function in living systems, Cambridge University Press, 2006.
dc.relationIsaza-Delgado JF. and Campos-Romero DC., Ecología: una mirada desde los sistemas dinámicos, Pontifi cia Universidad Javeriana, 2006.
dc.relationHerrera-Diestra JL., Buldú JM., Chávez M., and Martínez JH., Using symbolic networks to analyze dynamical properties of disease outbreaks, arXiv preprint ar- Xiv:1911.05646 (2019).
dc.relationNieto-Villar JM., Izquierdo-Kulich E., Betancourt-Mar JA., and Tejera E., Complejidad y auto-organización de patrones naturales, Editorial UH, La Habana, Cuba, 2013.
dc.relationLansing JS., Complex adaptive systems, Annual review of anthropology 32 (2003), no. 1, 183-204.
dc.relationDolan KT. and Spano ML., Surrogate for nonlinear time series analysis, Physical Review E 64 (2001), no. 4, 046128.
dc.relationLarson-Prior LJ., Oostenveld R., Della Penna S., Michalareas G., Prior F., Babajani- Feremi A., Schoffelen J-M., Marzetti L., de Pasquale F., Di Pompeo F., et al., Adding dynamics to the human connectome project with meg, Neuroimage 80 (2013), 190-201.
dc.relationCohen M., Analyzing neural time series data: theory and practice, MIT press, 2014.
dc.relationCohen M., Where does EEG come from and what does it mean?, Trends in neurosciences 40 (2017), no. 4, 208-218.
dc.relationHämäläinen M., Hari R., Ilmoniemi RJ., Knuutila J., and Lounasmaa OV., Magnetoencephalography - theory, instrumentation, and applications to noninvasive studies of the working human brain, Reviews of modern Physics 65 (1993), no. 2, 413.
dc.relationRubinov M. and Sporns O., Complex network measures of brain connectivity: uses and interpretations, Neuroimage 52 (2010), no. 3, 1059-1069.
dc.relationWiedermann M., Donges JF, Kurths J., and Donner RV., Mapping and discrimination of networks in the complexity-entropy plane, Physical Review E 96 (2017), no. 4, 042304.
dc.relationZanin M., Zunino L., Rosso OA., and Papo D., Permutation entropy and its main biomedical and econophysics applications: a review, Entropy 14 (2012), no. 8, 1553- 1577.
dc.relationKramer MA., An introduction to eld analysis techniques: The power spectrum and coherence, The Science of Large Data Sets: Spikes, Fields, and Voxels. Short Course by the Society for Neuroscience. 202 (2013), 18-25.
dc.relationRaichle ME., The restless brain, Brain connectivity 1 (2011), no. 1, 3-12.
dc.relationMartin MT., Plastino A., and Rosso OA., Generalized statistical complexity measures: Geometrical and analytical properties, Physica A: Statistical Mechanics and its Applications 369 (2006), no. 2, 439-462.
dc.relationShanks N. and Joplin KH., Redundant complexity: A critical analysis of intelligent design in biochemistry, Philosophy of Science 66 (1999), no. 2, 268-282.
dc.relationJohnson NF., Jefferies P., Hui PM., et al., Financial market complexity, Oxford University Press, 2003.
dc.relationPackard NH, Crutch eld JP., Farmer JD., and Shaw RS., Geometry from a time series, Physical review letters 45 (1980), no. 9, 712.
dc.relationRosso OA., Larrondo HA., Martin MT., Plastino A., and Fuentes MA., Distinguishing noise from chaos, Physical review letters 99 (2007), no. 15, 154102.
dc.relationRosso OA., De Micco L., Larrondo HA., Martín MT., and Plastino A., Generalized statistical complexity measure, International Journal of Bifurcation and Chaos 20 (2010), no. 03, 775-785.
dc.relationM.B. Priestley, Spectral analysis and time series, Academic Press, 1981.
dc.relationHuman Connectome Project, Wu-minn HCP MEG initial data release: Reference manual, 2014.
dc.relationLamberti PW., Martin MT., Plastino A., and Rosso OA., Intensive entropic nontriviality measure, Physica A: Statistical Mechanics and its Applications 334 (2004), no. 1-2, 119-131.
dc.relationKlages R., Introduction to dynamical systems, Lecture Notes for MAS424/MTHM021. Queen Mary University of London 24 (2008), 26.
dc.relationLopez-Ruiz R., Mancini HL., and Calbet X., A statistical measure of complexity, Physics Letters A 209 (1995), no. 5-6, 321-326.
dc.relationBaillet S., Magnetoencephalography for brain electrophysiology and imaging, Nature neuroscience 20 (2017), no. 3, 327.
dc.relationCover T. and Thomas J., Elements of information theory. second edition, John Wiley & Sons, 2006.
dc.relationGross T. and Blasius B., Adaptive coevolutionary networks: a review, Journal of the Royal Society Interface 5 (2007), no. 20, 259-271.
dc.relationMcKenna T., McMullen T., and Shlesinger M., The brain as a dynamic physical system, Neuroscience 60 (1994), no. 3, 587-605.
dc.relationR Core Team, The R project for statistical computing, 2019, [Online; accessed 10- November-2019].
dc.relationVan Drongelen W., Signal processing for neuroscientists, Academic press, 2018.
dc.relationRamón y Cajal S., Textura del sistema nervioso del hombre y de los vertebrados: estudios sobre el plan estructural y composición histológica de los centros nerviosos adicionados de consideraciones fisiológicas fundadas en los nuevos descubrimentos, Moya, 1899.
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleComplejidad de patrones dinámicos en oscilaciones estocásticas corticales
dc.typeOtro


Este ítem pertenece a la siguiente institución