Clinical Significance of Computational Brain Models in Neurorehabilitation
DOI:
https://doi.org/10.12775/mbs-2013-0003Keywords
neurorehabilitation, brain plasticity, computational modelsAbstract
Despite quick development of the newest neurorehabilitation methods and techniques there is a need for experimentally validated models of motor learning, neural control of movements, functional recovery, therapy control strategies.
Computational models are perceived as another way for optimization and objectivization of the neurorehabilitation. Fully understanding of the neural repair is needed for simulation of reorganization and remodeling of neural networks as the effect of neurorehabilitation. Better understanding can significantly influence both traditional forms of the therapy (neurosurgery, drug therapy, neurorehabilitation, etc.) and use of the advanced Assitive Technology (AT) solutions, e.g. brain-computer interfaces (BCIs) and neuroprostheses [49, 50] or artificial brain stimulation.
References
Shadgan B., Roig M., Hajghanbari B., et al.: Top-cited articles in rehabilitation. Arch Phys Med Rehabil 2010; 91(5): 806-815.[ http://dx.doi.org/10.1016/j.apmr.2010.01.011]
Levin M. F., Kleim J. A., Wolf S. L.: What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabil Neural Repair 2009; 23(4): 313-319.
French B., Thomas L. H., Leathley M. J. et al.: Repetitive task training for improving functional ability after stroke. Cochrane Database Sys Rev 2007; 4: CD006073.
Duch W., Nowak W., Meller J., et al.: Computational approach to understanding autism spectrum disorders. Computer Science Journal, 2012, 14(2): 47-61.
Duch W., Nowak W., Meller J., et al.: Consciousness and attention in autism spectrum disorders. Proceedings of Cracow Grid Workshop 2010, pp. 202-211, Cracow 2011.
Ascoli G. A.: Progress and perspectives in computational neuroanatomy. Anat Rec 1999; 257(6): 195-207.
Lansner A., Diesmann M. Virtues, pitfalls, and methodology of neuronal network modeling and simulations on supercomputers. [In:] Le Novère N. (ed.) Computational Systems Neurobiology, Springer, New York 2012, pp. 283-315.
Mikołajewska E., Mikołajewski D.: Role of brainstem within human body systems - computational approach. J Health Sci 2012; (2)1: 95-106.
Mikołajewska E., Mikołajewski D.: Consciousness disorders as the possible effect of brainstem activity failure - computational approach. J Health Sci 2012; (2)2: 7-18.
Gazzaniga M.S.: Neuroscience and the correct level of explanation for understanding mind. Trends in Cognitive Sciences 2010; 14(7): 297-292.
Duch W. Mind-brain relations from geometric perspective. [in print]
Mikołajewska E., Mikołajewski D.: Selected applications of computational models in medicine (article in Polish). Ann Acad Med Siles. 2011; 1-2: 78-87.
Wójcik G.M.: Modelowanie i eksploracja sieci neuronów biologicznych w GENESIS. Uniwersytet Marii Curie- Skłodowskiej, Lublin 2012.
O’Reilly R. C., Munakata Y.: Computational Explorations in Cognitive Neuroscience. Understanding the Mind by Simulating the Brain. MIT Press, Cambridge 2000.
Tadeusiewicz R. (red.) Neurocybernetyka teoretyczna. Wydawnictwo Uniwersytetu Warszawskiego, Warszawa 2009.
Hodgkin, A., Huxley, A. A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol., 1952, 117: 500-544.
Gorzelańczyk E. J., Huflejt M., Kniat J. et. al.: The functional model of memorizing pyramidal cell synapses complex in hippocampus (article in Polish). Bio- Algorithms and Med-Systems, 2005; 1-2: 217-220.
Naud R., Gerstner W.: The performance (and limits) of simple neuron models: generalizations of the leaky integrate-and-fire model. [In:] Le Novère N. (ed.) Computational Systems Neurobiology, Springer, New York 2012, pp. 163-192.
Endler L., Stefan M. I., Edelstein S. J., Le Novère N.: Using chemical kinetics to model neuronal signalling pathways. [In:] Le Novère N. (ed.) Computational Systems Neurobiology, Springer, New York 2012, pp. 81-117.
Brown S.-A., Holmes R. M., Loew L. M.: Spatial organization and diffusion in neuronal signaling. [In:] Le Novère N. (ed.) Computational Systems Neurobiology, Springer, New York 2012, pp. 133-161.
Manola L., Holsheimer J.: Motor cortex stimulation: role of computer modeling. Acta Neurochir Suppl. 2007; 97: 497-503. [http://dx.doi.org/10.1007/978-3-211-33081-4_57]
Tononi G., Sporns O., Edelman G.M.: A measure for brain complexity: Relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA. 1994; 91: 5033-5037.[ http://dx.doi.org/10.1073/pnas.91.11.5033]
Balduzzi D., Tononi G.: Integrated information in discrete dynamical systems: motivation and theoretical framework. PloS Computational Biology. 2008; 4(6): e1000091.[ http://dx.doi.org/10.1371/journal.pcbi.1000091]
Seth A.K., Izhikevich E., Reeke G.N., Edelman G.M.: Theories and measures of consciousness: An extended framework. PNAS. 2006; 103(28): 10799-10804.[ http://dx.doi.org/10.1073/pnas.0604347103]
Gamez D., Aleksander I. Accuracy and performance of the state-based Φ and liveliness measures of information integration. Conscious Cogn. 2011; 20(4): 1403-1424.[ http://dx.doi.org/10.1016/j.concog.2011.05.016]
Dobosz K., Duch W. Understanding neurodynamical systems via Fuzzy Symbolic Dynamics. Neural Networks, 2010; 23: 487-496.[ http://dx.doi.org/10.1016/j.neunet.2009.12.005]
Duch W., Dobosz K. Visualization for understanding of neurodynamical systems. Cognitive Neurodynamics, 2011; 5(2): 145-160. [http://dx.doi.org/10.1007/s11571-011-9153-1]
Łęski S., Kublik E., Świejkowski D. A., Wróbel A., Wójcie D. K.: Extracting meaningful components of neural dynamics with ICA and iCSD. J Comput. Neurosci. 2010; 29: 459-473. [http://dx.doi.org/10.1007/s10827-009-0203-1]
Ipek M., Hilal H., Nese T., Aynur M., Gazanfer E.: Neuronal plasticity in a case with total hemispheric lesion. J Med Life. 2011; 4(3): 291-294.
Scott P., Cowan A.I., Stricker C.: Quantifying impacts of short-term plasticity on neuronal information transfer. Phys Rev E Stat Nonlin Soft Matter Phys. 2012; 85(4-1): 041921. [http://dx.doi.org/10.1103/PhysRevE.85.041921]
Xing C., Hayakawa K., Lok J., Arai K., Lo E.H.: Injury and repair in the neurovascular unit. Neurol Res. 2012; 34(4): 325-330.
Scholz J., Klein M.C., Behrens T.E., Johansen-Berg H. Training induces changes in white-matter architecture. Nat Neurosci. 2009;12(11): 1370-1371. [http://dx.doi.org/10.1038/nn.2412]
Mikołajewska E., Mikołajewski D.: Implikacje neuroplastyczności mózgu na modelowanie procesów umysłowych człowieka. Kognitywistyka i Media w Edukacji, 2010, 2: 199-207.
Cuntz H., Forstner F., Borst A., et al.: One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Comput Biol 2010; 6(8) pii: e1000877.[ http://dx.doi.org/10.1371/journal.pcbi.1000877]
Brown K.M., Gillette T.A., Ascoli G.A.: Quantifying neuronal size: summing up trees and splitting the branch difference. Semin Cell Dev Biol 2008; 19(6): 485-493.[ http://dx.doi.org/10.1016/j.semcdb.2008.08.005]
Cuntz H., Borst A., Segev I.: Optimization principles of dendritic structure. Theor Biol Med Model 2007; 4: 21. [http://dx.doi.org/10.1186/1742-4682-4-21]
Sjöström P.J., Rancz E.A., Roth A., et al.: Dendritic excitability and synaptic plasticity. Physiol Rev 2008; 88(2): 769-840.[ http://dx.doi.org/10.1152/physrev.00016.2007]
Leibold C., van Hemmen J.L.: Synaptic plasticity determines the character of interaural-time-difference representation. Neurocomputing 2003; 52-54:321-326[http://dx.doi.org/10.1016/S0925-2312(02)00800-7]
Grzyb B.J., Chinellato E., Wojcik G.M., Kaminski W.A. Which model to use for the liquid state machine? IJCNN, IEEE, 2010, 1018-1024.
Kaminski W.A., Wojcik G.M. Liquid state machine built of hodgkin-huxley neurons. Informatica, 2004, 15(1): 39-44.
Wojcik G.M., Kaminski W.A. Liquid state machine and its separation ability as function of electrical parameters of cell. Neurocomputing, 2007, 70(13-15): 2593-2697.[ http://dx.doi.org/10.1016/j.neucom.2006.12.015]
Wojcik G.M.: Self-organising criticality in the simulated models of the rat cortical microcircuits. Neurocomputing, 2012, 79: 61-67.[ http://dx.doi.org/10.1016/j.neucom.2011.10.004]
Wojcik G.M.: Electrical parameters influence on the dynamics of the hodgkin-huxley liquid state machine. Neurocomputing, 2011, 79: 68-78.
Faisal A.A., Selen L.P.J., Wolpert D.M.: Noise in the nervous system. Nat Rev Neurosci., 2008, 9(4): 292-303.[ http://dx.doi.org/10.1038/nrn2258]
Faisal A.A.: Noise in neurons and other constraints. [In:] Le Novère N. (ed.) Computational Systems Neurobiology, Springer, New York 2012, pp. 227-257.
Eldar A., Elowitz M.B.: Functional roles for noise in genetic circuits. Nature 2010; 467(7312): 167-173.
Duch W., Nowak W., Meller J., Osiński G., Dobosz K., Mikołajewski D., Wójcik G.M.: Consciousness and attention in autism spectrum disorders. Proceedings of Cracow Grid Workshop 2010, pp. 202-211, 2011.
Majka P., Kublik E., Furga G., Wójcik D.K.: Common Atlas Format and 3D Brain Atlas Reconstructor, the infrastructure for constructing 3D brain atlases. Neuroinformatics. 2012; 10: doi 10.1007/s12021-011-9138-6.[ http://dx.doi.org/10.1007/s12021-011-9138-6]
Mikołajewska E., Mikołajewski D.: Neuroprostheses for increasing disabled patients' mobility and control. Adv Clin Exp Med. 2012; 21(2): 263-272.
Durka P. J., Kuś R., Żygierewicz J. i wsp.: User-centered design of brain-computer interfaces: OpenBCI.pl and BCI Appliancess. Bulletin of the Polish Academy of Sciences, 2012, http://brain.fuw.edu.pl~durka/papers/OpenBCI_and_BCI_Appliance.pdf -access 31.07.2012.
van den Brand R., Heutschi J., Barraud Q., et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science. 2012;336(6085):1182-1185.
Dominici N., Keller U., Vallery H., et al. Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders. Nature Medicine. 2012; doi:10.1038/nm.2845.[ http://dx.doi.org/10.1038/nm.2845]
Lewandowski R., Roszkowski K., Lewandowska M.A.: Personalized medicine in oncology: vision or realistic concept? (article in Polish) Contemporary Oncology (2011) vol. 15; 1 (1-6)
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