Core Project #2
Nonlinear
Modeling of Complex Biomedical Systems
Vasilis Z. Marmarelis,
Ph.D.
Project
Leader
The term “complex” in the title of this project indicates that the research goal of this project is the simultaneous analysis of multiple interconnected variables (e.g. multi-unit neuronal recordings) and the modeling of their dynamic interrelationships, including the case of possible nested-loops (e.g. blood glucose, insulin and free fatty acids in spontaneous natural operation).
The specific aims of this project can be clustered into the following two areas of research and development of advanced methodologies for data-based nonlinear modeling of the dynamic interrelationships among multiple observed variables that yield reliable and interpretable models under realistic conditions of operation (i.e. unconstrained experimental paradigms):
(1) modeling of multi-unit neuronal dynamics using data from single multi-electrode arrays (MEAs) (encoding/decoding) or multiple MEAs (inter-ensemble transformations) in the hippocampal formation of the brain;
(2) nested-loop homeostatic/homeodynamic modeling of autoregulatory systems (endocrine-metabolic or neural-cardiovascular) observed through multiple variables during stable physiological operation -- with or without external stimulation -- that may advance our scientific understanding, yield useful diagnostic/clinical information and allow desirable control action.
The common objective in all these cases is the development and testing of the appropriate modeling methodologies that render the aforementioned problems solvable and facilitate their broad adoption by the peer community in order to overcome the critical methodological/computational barrier that has impeded rapid progress in many fields of physiology and medicine.
The following specific aims are defined:
Aim 1: Optimize the kernel-based general methodology of multi-input/multi-output modeling to obtain compact, reliable and interpretable nonlinear dynamic models using data from two or more MEAs placed in the hippocampus of behaving rats and adapt it to the case of nonlinear auto-regressive modeling for single MEA recordings.
Aim 2: Develop the novel Boolean modeling methodology for point-process (spike) data of multi-unit neuronal activity and test/validate it with MEA data similar to the ones used in Aim 1, including the case of Boolean auto-regressive modeling of single MEA recordings.
Aim 3: Adapt the kernel-based (Volterra) and Boolean modeling methodologies for multi-unit recordings (developed in the previous two Aims) to the case of data obtained under dual electro-chemical stimulation and examine the utilization of such models for advanced neuromodulation and novel forms of functional neurosurgery.
Aim 4: Develop, test and validate a general homeostatic/homeodynamic modeling methodology for nested-loop systems observed through multiple variables in stable physiological operation, using data from the endocrine-metabolic and neural-cardiovascular autoregulatory systems.
Aim 5: Develop and test the requisite software for implementing the modeling methodologies developed by the Aims 1-4 in an efficient and user-friendly manner, so that it can be easily adopted by the broader peer community. This software will be distributed and supported under the Service activity of the BMSR.
The results of the proposed Aims are expected to have broad application to many physiological domains and are germane to the objectives of the other three Core Research projects in the BMSR - with which interactions have had strong synergistic effects. The developed modeling methodologies will also be tested with data from the four Collaborative Projects affiliated with this Core Project that represent show-case applications to the nervous system (neuronal interactions and sensory integration), the metabolic-endocrine system (insulin-glucose-fatty acid interactions) and the cardiovascular system (autoregulation of cerebral hemodynamics). To maximize the impact of the developed “cutting edge” methodologies on the advancement of the state of the art, we develop and distribute inter-operable, user-friendly software to the peer community free of charge.
Selected Publications
Zanos T.P., S.H. Courellis, R.E. Hampson, S.A. Deadwyler, T.W. Berger and V.Z. Marmarelis. Nonlinear modeling of causal interrelationships in neuronal ensembles. IEEE Transactions on Neural Systems & Rehabilitation Engineering 16(4): 336-352, 2008. [PDF - 1648 KB]
Dimoka, A., S.H. Courellis, V.Z. Marmarelis and T.W. Berger. Modeling the nonlinear dynamic interactions of afferent pathways in the dentate gyrus of the hippocampus. Annals of Biomedical Engineering 36(5): 852-864, 2008. [PDF – 804 KB]
Song, D., Z. Wang, V.Z. Marmarelis and T.W. Berger. Parametric versus non-parametric modeling of short-term synaptic plasticity. Part I: Computational study. Journal of Computational Neuroscience Epub ahead of print, 2008. [PDF - 617 KB]
Song, D., Z. Wang, V.Z. Marmarelis and T.W.
Berger. Parametric versus non-parametric modeling of
short-term synaptic plasticity in the hippocampus. Part II: Experimental
study. Journal of Computational Neuroscience Epub ahead of print, 2008. [PDF - 863 KB]
Dimoka, A., S.H. Courellis, G.
Gholmieh, V.Z. Marmarelis and T.W. Berger. Modeling the nonlinear properties of
the in vitro hippocampal perforant
path-dentate system using multi-electrode array technology. IEEE Transactions on Biomedical Engineering 55: 693-702, 2008. [PDF - 1256]
Mitsis, G.D., A.S. French, U. Höger, S. Courellis, and V Z. Marmarelis. Principal dynamic mode analysis of action potential firing in a spider mechanoreceptor. Cybernetics 96(1):113-127, 2007. [PDF – 601 KB]
Song D., R.H.M. Chan, V.Z. Marmarelis, R.E. Hampson, S.A. Deadwyler and T. W. Berger. Nonlinear dynamic modeling of spike train transformation for hippocampal-cortical prostheses. IEEE Transactions on Biomedical Engineering 54(6):1053-1065, 2007. [PDF – 1,008 KB]
Gholmieh, G., S. Courellis, V.Z. Marmarelis and T.W. Berger. Nonlinear dynamic model of CA1 short-term plasticity using random impulse train stimulation. Annals of Biomedical Engineering 35(5):847-857, 2007. [PDF – 534 KB]
Mitsis, G.D., R. Zhang, B.D. Levine and V.Z. Marmarelis. Cerebral hemodynamics during orthostatic stress assessed by nonlinear modeling. Journal of Applied Physiology 101:354-366, 2006. [PDF – 670 KB]
Marmarelis, V.Z. and T.W. Berger. General methodology for nonlinear modeling of neural systems with Poisson point-process inputs. Mathematical Biosciences 196:1-13, 2005. [PDF - 301 KB]
Marmarelis, V.Z. Nonlinear Dynamic Modeling of Physiological Systems, Wiley, New York, 2004.
Mitsis, G.D., M.J. Poulin, P.A. Robbins, and V.Z. Marmarelis. Nonlinear modeling of the dynamic effects of arterial pressure and CO2 variations on cerebral blood flow in healthy humans. IEEE Transactions on Biomedical Engineering 51(11):1932-1943, 2004. [PDF - 717 KB]
Gholmieh G., S.H. Courellis, V.Z. Marmarelis and T.W. Berger. Detection and classification of neurotoxins using a novel short-term plasticity quantification method. Biosensors & Bioelectronics 18(12):1467-1478, 2003. [PDF - 606 KB]
Gholmieh, G., S.H. Courellis, V.Z. Marmarelis and T.W. Berger. An efficient method for studying short-term plasticity with random impulse train stimuli. Journal of Neuroscience Methods 21(2):111-127, 2002. [PDF - 531KB]
Mitsis G.D., R. Zhang, B.D. Levine and V.Z. Marmarelis. Modeling of nonlinear physiological systems with fast and slow dynamics. II. Application to cerebral autoregulation. Annals of Biomedical Engineering 30(4):555-565, 2002. [PDF - 434 KB]
Mitsis G.D. and V.Z. Marmarelis. Modeling of nonlinear physiological systems with fast and slow dynamics. I. Methodology. Annals of Biomedical Engineering 30(2):272-281, 2002. [PDF - 155 KB]
Berger, T.W., M. Baudry, R.D. Brinton, J-S. Liaw, V.Z. Marmarelis, Y. Park, B.J. Sheu and A.R. Tanguay, Jr. Brain-implantable biomimetic electronics as the next era in neural prosthetics. Proceedings of the IEEE, 89:993-1012, 2001. [PDF - 498 KB]
Gholmieh, G., W. Soussou, S.H. Courellis, V.Z. Marmarelis, T.W. Berger and M. Baudry. A biosensor for detecting changes in cognitive processing based on nonlinear systems analysis. Biosensors and Bioelectronics, 16(7-8): 491-501, 2001. [PDF - 468 KB]
Alataris, K., T.W. Berger & V.Z. Marmarelis. A novel network for nonlinear modeling of neural systems with arbitrary point-process inputs. Neural Networks, 13(2):255-266, 2000. [PDF - 668 KB]
Shehada, R.W., V.Z. Marmarelis, H.N. Mansour & W.S. Grundfest. Laser-induced fluorescence attenuation spectroscopy: Detection of hypoxia. IEEE Transactions on Biomedical Engineering 47(3):301-312, 2000. [PDF - 184 KB]
Iatrou, M., T.W. Berger & V.Z. Marmarelis. Application of a novel modeling method to the nonstationay properties of potentiation in the rabbit hippocampus. Annals of Biomedical Engineering27(5):581-591, 1999. [PDF - 158 KB]
Marmarelis, V.Z., M. Juusola & A.S. French. Principal dynamic mode analysis of nonlinear transduction in a spider mechanoreceptor. Annals of Biomedical Engineering27:391-402, 1999.
Marmarelis, V.Z., K.H. Chen, N.H. Holstein-Rathlou & D.J. Marsh. Nonlinear analysis of renal autoregulation in rats using principal dynamic modes. Annals of Biomedical Engineering 27:23-31, 1999.
Iatrou, M., T.W. Berger and V.Z. Marmarelis. Application of a novel modeling method to the nonstationary properties of potentiation in the rabbit hippocampus. Annals of Biomedical Engineering, 27:581-591, 1999.
Marmarelis, V.Z. & X. Zhao. Volterra models and three-layer perceptrons. IEEE Transactions on Biomedical Engineering 8(6):1421-1433, 1997.
Marmarelis, V.Z. Modeling methodology for nonlinear physiological systems. Annals of Biomedical Engineering 25:239-251, 1997.
Marmarelis, V.Z. (Ed.) Advanced Methods of Physiological System Modeling: Volume III., Plenum, New York, 1994.
Marmarelis, V.Z. & M.E. Orme. Modeling of neural systems by use of neuronal modes. IEEE Transactions on Biomedical Engineering 40(11):1149-1158, 1993.
Marmarelis, V.Z. Identification of nonlinear biological systems using Laguerre expansions of kernels. Annals of Biomedical Engineering 21:573-589, 1993.
Marmarelis, V.Z. Wiener analysis of nonlinear feedback in sensory system. Annals of Biomedical Engineering 19:345-382, 1991.
Marmarelis, V.Z. Signal transformation and coding in neural systems. IEEE Transactions on Biomedical Engineering 36(1):15-24, 1989.
Marmarelis, V.Z. (Ed.) Advanced Methods of Physiological System Modeling: Volume II., Plenum, New York, 1989.
Marmarelis, V.Z. Advanced Methods of Physiological System Modeling: Volume I., Biomedical Simulations Resource, Los Angeles, California, 1987.
