Using nonlinear dynamic metric tools for characterizing brain structures

Global brain dynamics are characterized in terms of electrical activity using nonlinear dynamic metric tools. The brain's spontaneous electrical activity is not a simple noise but an active signal reflecting causal responses from hidden events and sources during sensory and cognitive processing...

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Autor principal: Blanco, S.
Otros Autores: Figliola, A., Kochen, S., Rosso, O.A
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 1997
Acceso en línea:Registro en Scopus
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024 7 |2 scopus  |a 2-s2.0-0031194082 
040 |a Scopus  |b spa  |c AR-BaUEN  |d AR-BaUEN 
030 |a IEMBD 
100 1 |a Blanco, S. 
245 1 0 |a Using nonlinear dynamic metric tools for characterizing brain structures 
260 |c 1997 
270 1 0 |m Rosso, O.A.; Instituto de Calculo, FCEyN, Universidad de Buenos Aires, Ciudad Universitaria, 1428 Buenos Aires, Argentina 
506 |2 openaire  |e Política editorial 
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504 |a Meyer-Kress, G., (1986) Dimensions and Entropies in Chaotic Systems, , Springer-Verlag, Berlin 
504 |a Abraham, N.B., Albano, A.M., Passamante, A., Rapp, P., (1989) Measures of Complexity and Chaos, , NATO, Series B208 
504 |a Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S., Geometry from a time series (1980) Phys. Rev. Lett., 45, p. 712 
504 |a Mañé, R., On the dimension of compact invariant set of certain nonlinear maps (1980) Lecture Notes in Mathematics, 898, pp. 230-242. , Dynamical Systems and Turbulence, Warwick Springer-Verlag 
504 |a Takens, F., Detecting strange attractors in turbulence (1980) Lecture Notes in Mathematics, 898, pp. 366-381. , Dynamical Systems and Turbulence, Warwick Springer-Verlag 
504 |a Babloyantz, A., Evidence of chaotic dynamics of brain activity during the sleep cycle (1986) Dimensions and Entropies in Chaotic Systems, pp. 241-245. , G. Meyer-Kress (Eds): Springer-Verlag, Berlin 
504 |a Meyer-Kress, G., Layne, S.C., Dimensionality of human electroencephalogram (1987) Perspectives in Biological Dynamics and Theoretical Medicine, Ann. N. Y. Acad. Sci., 504. , S. H. Koslow, A. J. Mandel, M. F. Shlesinger (Eds) 
504 |a Basar, E., (1990) Chaos in Brain Function, , Springer-Verlag, Berlin 
504 |a Pijn, J.P.N., Van Neerven, J., Noest, A., Lopes Da Silva, F., Chaos or noise in EEG signals; dependence on state and brain site (1991) Electroencephalography and Clinical Neurophysiology, 79, pp. 371-381 
504 |a West, B.J., Fractal Physiology and Chaos in Medicine, Nonlinear Phenomena in Life Science (1990) World Scientific, 1. , West B J (Eds), Singapore 
504 |a McEwen, J.A., Anderson, G.B., Modeling the stationarity and gaussianity of spontaneous electroencephalographic activity (1975) IEEE Trans. Biomed. Engng., BME-22, p. 361 
504 |a Gasser, T., General characteristic of the EEG as a signal (1977) EEG Informatics: A Didactic Review of Methods and Applications of EEG Data Processing, pp. 37-55. , A. Remond (Eds): Elsevir, New York 
504 |a Sugimoto, H., Ishii, N., Suzumura, N., Stationarity and normality test for biomedical data (1977) Comput. Program. Biomed., 7, p. 293 
504 |a Jenkins, G.M., Watts, D., (1968) Spectral Analysis and Its Applications, , Holden-Day, San Francisco 
504 |a Blanco, S., Garcia, H., Quian Quiroga, R., Romanelli, L., Rosso, O.A., Stationarity of the EEG series (1995) IEEE Eng. in Med. and Biol., 14, p. 395 
504 |a Rosenstein, M.T., Collins, J.J., De Luca, C.J., Reconstruction expansion as a geometry-based framework for choosing proper delay times (1994) Physica D, 73, p. 82 
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504 |a Rosenstein, M.T., Collins, J.J., De Luca, C.J., A practical method for calculating largest Lyapunov exponents from small data sets (1993) Physica D, 65, p. 117 
504 |a Eckmann, J.P., Ruelle, D., Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems (1992) Physica D, 56, pp. 185-187 
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520 3 |a Global brain dynamics are characterized in terms of electrical activity using nonlinear dynamic metric tools. The brain's spontaneous electrical activity is not a simple noise but an active signal reflecting causal responses from hidden events and sources during sensory and cognitive processing of the brain. The time evolution of the system is described by a low-dimensional deterministic dynamics. The nonlinear dynamic metric invariants are dependent on brain structure as well as brain activity.  |l eng 
536 |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas 
536 |a Detalles de la financiación: Universidad de Buenos Aires 
536 |a Detalles de la financiación: National Research Council 
536 |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas 
536 |a Detalles de la financiación: This work was partially supported by the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET). The authors wish to thank Pablo Salgado and Rodrigo Quian Quiroga for useful comments on the results of this article. 
536 |a Detalles de la financiación: Susana A. Blanco was born in Buenos Aires, Argentina, on January 17, 1952. She received the M.S. and Ph.D. degree in Physics from University of Buenos Aires, Argentina, in 1981 and 1989 respectively. Her research interests are in the field of nonlinear dynamical systems and their applications to biological and medical sciences. Dr. Blanco is a researcher of the National Research Council (CONICET) of Argentina and co-head of the Master on Medical Physics of the University of Buenos Aires, Argentina. E-mail: blanco @ulises.ic.fcen.uba.ar Alejandra Figliola was born in Buenos Aires, Argentina, on November 26, 1957. She received the M.S. and Ph.D. degree in Physics from University of Buenos Aires, Argentina, in 1983 and 1990 respectively. Her research interests are in the field of nonlinear dynamical systems and wavelet transform and its applications to biological, meteorological, and medical sciences. Dr. Figliola is a researcher of the National Research Council (CONICET) of Argentina and Senior Assistant, Department of Physics, of the University of Buenos Aires, Argentina. E-mail: fi-gliola@ulises.ic.fcen.uba.ar Silvia Kochen was born in Buenos Ai-res, Argentina, on February 2, 1954. She received the M.D. in Medicine and M.D in Neurology from the University of Buenos Aires, Argentinain 1977 and 1980 respectively. She held a fellowship at the College des Hopitaux de Paris, INSERM Unite 97 de recherches en Epilepsie, Paris, France, for training in epilepsy from 1981-1982. Her research interests include clinical neurophysiology, epilepsy, and quantified EEG signals in epilepsy. Currently, Dr. Kochen is head of the Center of Epilepsy, Hospital Ramos Mejia, Buenos Aires, Argentina, and a researcher of the National Research Council (CONICET) of Argentina. 
593 |a Instituto de Calculo, FCEyN, Universidad de Buenos Aires, Argentina 
593 |a Centro Municipal de Epilepsia, Division Neurologia, Universidad de Buenos Aires, Argentina 
593 |a National Research Council, Argentina 
593 |a Sci. Adviser Master on Med. Phys., University of Buenos Aires, Argentina 
593 |a Instituto de Calculo, FCEyN, Ciuidad Universitaria, 1428 Buenos Aires, Argentina 
690 1 0 |a NONLINEAR DYNAMIC METRIC TOOLS 
690 1 0 |a ALGORITHMS 
690 1 0 |a CORRELATION METHODS 
690 1 0 |a ELECTROENCEPHALOGRAPHY 
690 1 0 |a LYAPUNOV METHODS 
690 1 0 |a SIGNAL PROCESSING 
690 1 0 |a BRAIN 
690 1 0 |a ALGORITHM 
690 1 0 |a BRAIN 
690 1 0 |a ELECTROENCEPHALOGRAM 
690 1 0 |a NONLINEAR SYSTEM 
690 1 0 |a REVIEW 
690 1 0 |a ALGORITHMS 
690 1 0 |a BRAIN 
690 1 0 |a ELECTROENCEPHALOGRAPHY 
690 1 0 |a EPILEPSY 
690 1 0 |a HUMANS 
690 1 0 |a MODELS, NEUROLOGICAL 
690 1 0 |a NONLINEAR DYNAMICS 
700 1 |a Figliola, A. 
700 1 |a Kochen, S. 
700 1 |a Rosso, O.A. 
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