Velarde, Osvaldo M. (2015) Estudio de la dinámica de núcleos neuronales con aplicaciones al tratamiento de trastornos motores. / Study of the dynamics of basal ganglia with applications to motor disorders treatment. Maestría en Ciencias Físicas, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
Este trabajo está orientado a la exploración de nuevas estrategias de modulación de la actividad neuronal en el marco de la optimización de la eficiencia de dispositivos implantables destinados al tratamiento de enfermedades neurológicas (e.g., DBS:Deep Brain Stimulation). Con la intención de entender la dinámica neuronal de la red de ganglios basales, se propusieron diferentes modelos para la representación del sistema. El análisis de los mismos permitió caracterizar los distintos estados de la dinámica de la red, los cuales fueron asociados a estados fisiológicos y patológicos observados en el mal del Parkinson. Se encontró que el esquema open-loop DBS, implementado mediante el modelo, reproduce exitosamente una importante observación experimental en relación a la frecuencia de estimulación, la cual ha sido ampliamente reportada en pacientes con Parkinson sujetos a implantes DBS. Se propuso una representación simplificada de este esquema que permite mejorar la comprensión del efecto del parámetro frecuencia de estimulación DBS. El esquema closed-loop DBS propuesto en este trabajo se basa en el entrenamiento de una red neuronal artificial capaz de adaptar los parámetros de la estimulación utilizando información sobre el estado de la red de ganglios basales. Para realizar dicho objetivo, fue necesario explorar y elegir distintos algoritmos capaces de extraer rasgos relevantes a partir de las señales producidas por el modelo, tales como el análisis de Fourier en espacio y en tiempo, la descomposición en Wavelets y el acoplamiento fase-amplitud. Finalmente, se implementó el entrenamiento de la red neuronal artificial mediante un esquema de aprendizaje por refuerzo. Se observó que la red artificial es capaz de aplicar una estimulación adecuada al sistema para llevarlo al estado fisiológico. Además, se analizó la influencia de cada rasgo en el aprendizaje de la red y su nivel de relevancia.
Resumen en inglés
This work is aimed at exploring new strategies modulation of neuronal activity in the context of optimizing the efficiency of implantable devices for the treatment of neurological diseases (e.g., DBS: Deep Brain Stimulation). With the intention to understand the dynamic of neural network, different models for the representation of the system were proposed. The analysis allowed them to characterize the different states of the network dynamics, which were associated with physiological and pathological conditions observed in Parkinson's disease. It was found that the open-loop DBS scheme (implemented by model) successfully reproduces an important experimental observation in relation to the frequency of stimulation, which has been reported in patients with Parkinson subject to DBS implants. A simplified representation of this scheme that improves the understanding of the effect of DBS stimulation frequency parameter was proposed. The proposed closed - loop DBS scheme in this thesis is based on the training of an artificial neural network capable of adapting the stimulation parameters using information on the status of the network of basal ganglia. To accomplish this objective, it was necessary to explore and choose different algorithms capable of extracting relevant features from the signals produced by the model, such as Fourier analysis in space and time, decomposition in Wavelets and the phase-amplitude coupling. Finally, learning of artificial neural network using a reinforcement learning scheme was implemented. It was observed that the artificial network is able to apply appropriate stimulation system to carry the physiological state. In addition, the influence of each feature in the learning network and their level of relevance was analyzed.
| Tipo de objeto: | Tesis (Maestría en Ciencias Físicas) |
|---|---|
| Palabras Clave: | Neurology; Neurología; Algorithms; Algoritmos; Fourier analysis; Análisis de Fourier; Parkinsonism; Enfermedad de Parkinson; Neural netwoks; Redes neuronales [ Neuronal dynamic; Dinámica neuronal; Motor system; Sistema motor; Basal ganglia; Ganglios basales; Midfield model; Modelo de campo medio; One dimensional model; Modelo unidimensional; Feature extraction; Extracción de rasgos] |
| Referencias: | [1] R. Carron, A. Chaillet, A. Filipchuk, W. Pasillas-Lépine, and C. Hammond. Closing the Loop of Deep Brain Stimulation., Frontiers in Systems Neuroscience, 7 (2013). 1 [2] Heinz Steiner; Kuei Y. Tseng. Handbook of Basal Ganglia Structure and Function., Academic Press. p. 663. (2010). 2 [3] J. Lanciego, N. Luquin, J. Obeso. Functional neuroanatomy of the basal ganglia., CSH Perspectives in Medicine (2012). 3 [4] Centro de Neurociencias Clínica y Experimental depende del Instituto de Biología Celular y Neurociencias \Prof. E. De Robertis" (IBCN) - Facultad de Medicina de la Universidad de Buenos Aires (UBA) { Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) y del Hospital \Ramos Mejía" de la Ciudad de Buenos Aires. 3 [5] A. L. Hodgkin and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve,J Physiol. 117(4): 500{544 (1952). [6] Fundamentos de la fisiología, E. Cuenca, Edt. Thomson, 2006. 4 [7] A. Leblois, T. Boraud, W. Meissner, H. Bergman, and D. Hansel, Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia., J. Neurosci. 63, 733-745 (2006). 13, 32, 36, 37, 80 [8] Numerical Recipes. The Art of Scientific Computing, 3rd Edition, 2007. 17, 24 [9] D. Golomb, D. Hansel, The number of synaptic inputs and the synchronyof large, sparse neuronal networks., Neural Computation 12:1095{1139.,(2000). 34 [10] Mitzdorf U, Current source-density method and application in cat cerebral cortex: Investigation of evoked potentials and EEG phenomena., Physiol Rev 65:37{100 (1985). 41 [11] Berens P, Logothetis NK, Tolias AS, Local field potentials, BOLD and spiking activity: Relationships and physiological mechanisms., Nature Precedings 2010:1- 27, (2010). 41 [12] Rall W, Electrophysiology of a dendritic neuron model., Biophys J 2:145{167 (1962). 41 [13] Nicholson C, Freeman J, Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum., J Neurophysiol 38:356{368 (1975). 41 [14] Hamalainen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa O. Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain., Rev Mod Phys 65:413{497 (1993). 42 [15] Logothetis NK, Kayser C, Oeltermann A, In vivo measurement of cortical impedance spectrum in monkeys: Implications for signal propagation., Neuron 55:809{823 (2007). 42 [16] Goto T, Hatanaka R, Ogawa T, Sumiyoshi A, Riera J, Kawashima R, An evaluation of the conductivity profile in the somatosensory barrel cortex of Wistar rats., J Neurophysiol 104:3388{3412(2010). 42 [17] Linden H, Tetzlaff T, Potjans T, Pettersen KH, Gruen S, Diesmann M, Einevoll GT, Modeling the spatial reach of the LFP. Neuron 72:859{872 (2011a). 42 [18] Schmidt C, Grant P, Lowery M, U. van Rienen, In uence of Uncertainties in the Material Properties of Brain Tissue on the Probabilistic Volume of Tissue Activated., IEEE Biomedical Engineering Transactions, 60 (5) (2013). 47 [19] Logothetis NK,The Underpinnings of the BOLD FMRI Signal., J Neurosci 23(10):3963-3971 (2003). 47 [20] Andreas Wichert, Intelligent Big Multimedia Databases., World Scientific (2015). 53 [21] Mahalanobis P C, On the generalised distance in statistics., Proceedings of the National Institute of Sciences of India Vol 2: 49{55. (1936). 55 [22] Pearson K, On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2 (11): 559{572 (1901). 55, 56 [23] Stoica P, Moses R, Introduction to Spectral Analysis., Prentice-Hall (1997). 59 [24] Welch P, The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms., IEEE Transactions on Audio Electroacoustics, AU-15, 70{73 (1967). 60 [25] Oppenheim A, Schafer R, Discrete-Time Signal Processing., Prentice-Hall (1989). 60 [26] Miller K, Sorensen LB, Ojemann JG, et al. Power-law scaling in the brain surface electric potential., PLoS Comput Biol 5:e1000609 (2009). 60 [27] Hebb A, Darvas F, Miller K, Transient and state modulation of beta power in human subthalamic nucleus during speech production and finger movement., Neuroscience 202:218{33 (2012). 60, 80 [28] Miller K, Zanos S, Fetz E, et al., Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans., J Neurosci 29:3132{7 (2009). 60, 80 [29] Mallat S, A theory for multiresolution signal decomposition: the wavelet representation., IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp 674{693 (1989). 61, 62 [30] Sherwood J, Derakshani R, On Classifiability of Wavelet Features for EEG-Based Brain-computer Interfaces., Proceedings of the 2009 International Joint Conference on Neural Networks (2009). 62 [31] de Hemptinne C, et al., Exaggerated phase-amplitude coupling in the primary motor cortex in Parkinson disease. Proc. Natl. Acad. Sci. USA 110, 4780{4785 (2013). 63, 80 [32] de Hemptinne C, et al., Therapeutic deep brain stimulation reduces cortical phaseamplitude coupling in Parkinson's disease. Nat. Neurosci., 18(5):779-86 (2015). 63, 64, 65, 79, 80 [33] Canolty R L, et al., High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex., Science 313 1626 (2006). 64 [34] Tort A, et al., Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task., Proc. Natl. Acad. Sci. USA 105(51) (2008). 64, 80 [35] Tort A, et al., Measuring Phase-Amplitude Coupling Between Neuronal Oscillations of Different Frequencies, J. Neurophysiol. 104:1195-1210 (2010). 64 [36] Hertz J, Krogh Anders, Palmer R, Introduction to the theory of neural computation., Vol 1 of Santa Fe Institute, Studies in the sciences of complexity. Addison- Wesley (2009). 71, 72 [37] Damián Dellavale, C. Leibold, G. Payáa-Vayá, H. Blume, M. Alam, K. Schwabe, and J. K. Krauss, Optimization of a Phase-to-Amplitude Coupling Algorithm for Real-Time processing of Brain Electrical Signals., Proc. of the Information & Communication Technology Conference, pp. 68-73, ISBN/EAN: 978-90- 73461-84- 0, Eindhoven, The Netherlands (2013). 77, 78, 81 [38] A Avestruz,WSanta, D Carlson, R Jensen, S Stanslaski, A Helfenstine, T Denison, A 5 µ W /Channel Spectral Analysis IC for Chronic Bidirectional Brain{Machine Interfaces., IEEE Journal of Solid-State Circuits, vol. 43, No 12 (2008). 78 [39] Rouse, Adam et al. A Chronic Generalized Bi-Directional Brain-Machine Interface., Journal of neural engineering 8.3 (2011). 78 [40] S Stanslaski, PAfshar, P Cong, J Giftakis, P Stypulkowski, D Carlson, D Linde, D Ullestad, A Avestruz, T Denison, Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation., IEEE Trans Neural Syst Rehabil Eng. 20(4): 410{421, (2012). 78 [41] Damián Dellavale, Germán Mato, Osvaldo Velarde, Desarrollo de un paradigma de estimulación cerebral profunda adaptativo para el tratamiento de trastornos motores, Catálogo INNOVAR 2015 p. 119 (http://www.innovar.mincyt.gob.ar/catalogos/2015.pdf). 78 [42] Little S, Pogosyan A, Neal S, et al. Adaptive Deep Brain Stimulation In Advanced Parkinson Disease., Annals of Neurology. 74(3):449-457 (2013). 79, 80 [43] Carron R, Chaillet A, Filipchuk A, Pasillas-Lépine W, Hammond C. Closing the loop of deep brain stimulation., Frontiers in Systems Neuroscience. 7:112, (2013). 79 [44] Hebb AO, Zhang JJ, Mahoor MH, et al. Creating the feedback loop: Closed Loop Neurostimulation., Neurosurgery clinics of North America. 25(1):187-204 (2014). 79 [45] Krook-Magnuson E, Gelinas JN, Soltesz I, Buzsáki G. Neuroelectronics and Biooptics: Closed-Loop Technologies in Neurological Disorders., JAMA Neurol. 72(7):823-829 (2015). 79 [46] Gerrit E Gmel et al, A new biomarker for subthalamic deep brain stimulation for patients with advanced Parkinson's disease|a pilot study., J. Neural Eng. Vol 12 No 6 (2015). 79 |
| Materias: | Medicina Medicina > Neurociencias |
| Divisiones: | Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Materia condensada > Bajas temperaturas |
| Código ID: | 520 |
| Depositado Por: | USUARIO INVÁLIDO |
| Depositado En: | 31 Mar 2016 14:06 |
| Última Modificación: | 31 Mar 2016 14:07 |
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