Document Type : Full Research Paper

Authors

1 Assistant Professor, Bioelectric Group, Biomedical Engineering School, Science and Technology Branch, Islamic Azad University

2 Professor, Bioelectric Division, Biomedical Engineering School, Amirkabir University of Technology

3 Professor, Medical Physics Group, Medical Sciences School, Tarbiat Modares University

4 Associate Professor, Anesthesiology Grou, Medicine School, Shahid Beheshti University of Medical Sciences

10.22041/ijbme.2009.13384

Abstract

The procedure of pain formation embarks on primary sensory neurons and then ends in central nervous system which is the first stage in the dorsal horn of the spinal cord. Nowadays the great challenge of some researchers for pain control has been to elucidate the mechanisms that are able to switch the state of the dorsal horn of the spinal cord from an unwanted state to a favorite one. In order to achieve such an aim, a model of the function of the dorsal horn of the spinal cord is extracted in order to be able to control the created pains with changing the parameters of the aforementioned model. In this study a cybernetic model is presented with the aid of bifurcation methodologies and reconstructing the dynamics linked with the process of pain formation via clinical experiment that can express different states in the dorsal horn of the spinal cord as normal, suppressed, sensitized, the functionality of memory, the effect of other primary afferents and the effect of descending signals. Input signals in this model consist of thermal stimulation degree proportional to action potential firing rate from Ab afferents, inhibitory descending signals from midbrain and inhibitory or excitatory descending signal from thalamus and cortex and the output signal is the action potential firing rate from transmission cells in dorsal horn of the spinal cord proportional to pain level have been sensed. The significant and remarkable characteristic of this model is applying a cybernetical model based on a sequence of input-output data which can obviate the drawbacks of other models in which simplification and reduction of terms reduce the operation of components of a system. On the other hand, unlike previous models which have been modeled based on membrane (slow) potential, this model is based on the action potential firing rate from transmission cells of the dorsal horn of the spinal cord that has the adaptability with cellular recording as well as having a higher accuracy.

Keywords

Main Subjects

[1]     Doubell T.P., Mannion R.J., Woolf C.J., The dorsal horn State-dependent sensory processing, plasticity and the generation of pain. In: The textbook of Pain 4 Edition (Edited by: Wall PD, Melzack D). London: Churchill Livingstone 1999; 165-182.
[2]     Brown A., Rose P., Snow P., The morphology of spinocervical tract neurones revealed by intracellular injection of horseradish peroxidase (cat), J. Physiol., 1977; 270(3): 747–764.
[3]     Woolf C.J., Central terminations of cutaneous mechanoreceptive afferents in the rat lumbar spinal cord, J. Comp. Neurol, 1987; 261: 105-119.
[4]     Semba K., Masarachia P., Malamed S., Jacquin M., Harris S., Yang G., Egger M.D., An electron microscopic study of terminals of rapidly adapting mechanoreceptive afferent fibers in the cat spinal cord, J. Comp. Neurol, 1985; 232: 229–240.
[5]     Sugiura Y., Terui N., Hosoya Y., Difference in distribution of central terminals between visceral and somatic unmyelinated (C) primary afferent fibers, J. Neurophysiol, 1989; 62: 834-840.
[6]     Light A.R., Trevino D.L., Perl E.R., Morphological features of functionally defined neurons in the marginal zone and substantia gelatinosa of the spinal dorsal horn, Journal of Comparative Neurology, 1979; 186: 151–172.
[7]     Alvarez F.J., Kavookjian A.M., Light A.R., Synaptic interactions between GABAimmunoreactive profiles and the terminalsof functionally defined myelinated nociceptors in the monkey and cat spinal cord, J. Neurosci, 1992; 12: 2901-2917.
[8]     Maxwell D.J., Réthelyi M., Ultrastructure and synaptic connections of cutaneous afferent fibres in the spinal cord, Trends Neurosci, 1987; 10: 117- 123.
[9]     Shortland P., Woolf C.J., Morphology and somatotopy of the central arborizations of rapidly adapting glabrous skin afferents in the rat lumbar spinal cord, J. Comp. Neurol, 1993; 329: 491-511.
[10] Guyton A., Text book of Medical Physiology, 2000.
[11] Swett J.E., Woolf C.J., The somatotopic organization of primary afferent terminals in the superficial laminae of the dorsal horn of the rat spinal cord, J. Comp. Neurol, 1985; 231: 66-77.
[12] Maslany S., Crockett D.P., Egger M.D., Organization of cutaneous primary afferent fibers projecting to the dorsal horn in the rat: WGA-HRP versus B-HRP, Brain Res, 1992; 569: 123-135.
[13] Gerber G., Randic M., Excitatory amino acidmediated components of synaptically evoked input from dorsal roots to deep dorsal horn neurons in the rat spinal cord slice, Neurosci Lett, 1989; 106: 211- 219.
[14] Gerber G., Cerne R., Randic M., Participation of excitatory amino acid receptors in the slow excitatory synaptic transmission in rat spinal dorsal horn, Brain Res, 1991; 561: 236-251.
[15] Torebjork E.H., Ochoa J.L., Specific sensations evoked by activity in single identified sensory units in man, Acta Physiol Scand 1980; 110: 445–7.
[16] Melzack R., Wall P.D., Pain mechanisms: a new theory, Science, 1965; 150 (699): 971-9.
[17] Haeri M., Asemani D., Gharibzadeh S., Modelling of Pain Using Artificial Neural Networks, Journal of Theoretical Biology, 2003; 220: 277-284.
[18] Britton F., Skevington M., A mathematical model of the gate control theory of pain, Journal of Theoretical Biology, 1989; 137: 91-105.
[19] Britton F., Skevington M., Chaplajn M., Mathematical modeling of acute pain, J. Bio. Systems, 1995; 3: 1119-24.
[20] Britton F., Skevington M., On the Mathematical Modelling of Pain, Neurochemical Research, 1996; 21 (9): 1133-1140.
[21] Chaplain M., Britton F., Skevington M., The role of N-methyl-D-aspartate (NMDA) receptors in windup: a mathematical model, IMA Journal of Mathematics Applied in Medicine and Biology, 1996; 13: 193-205.
[22] Prince M., Campbell J., Picton P., A Computational Model of Acute Pain, 18th European Simulation Multiconference, Magdeburg, Germany, 2004; pp. 117-122.
[23] Schiavenato M., Byers J., Scovanner P., Neonatal pain facial expression evaluating the primal face of pain, Pain, 2008: 460 471.
[24] Stevens B., Johnston C., Physiological responses of premature infants to a painful stimulus, Nurse. Res. 1994; 43: 226–31.
[25] Warnock F., Sandrin D., Comprehensive description of newborn stress behavior in response to acute pain, Pain, 2004; 107: 242-55.
[26] Pantic M., Facial expression analysis thesis, Delft University of Technology, 2001.
[27] Tasdelen B., S. Helvaci, H. Kaleagasi, A. Ozge, Artificial Neural Network Analysis for Prediction of Headache Prognosis in Elderly Patients, Turk J Med Sci., 2009; 39 (1): 5-12.
[28] Nobutomo M., Asayo K., Modeling of Superficial Pain using ANNs, ICCAS2005, Korea, 2005.
[29] Simon D., Craig K.D., Gosselin F., Belin P., Rainville P., Recognition and discrimination of prototypical dynamic expressions of pain and emotions, Pain, 2008; 135: 55-64.
[30] Burchiel K.J., Design of an Artificial Neural Network for Diagnosis of Facial Pain Syndromes, Stereotactic and Functional Neurosurgery, 2006; 84 (5-6): 212-220.
[31] Derighetti M., Feedback control in anaesthesia, PhD thesis, Swiss Federal Institute of Technology, Zurich, 1999.
[32] Mahfouf M., Asbury A.J., Linkens D.A., Physiological Modeling and Fuzzy Control of Anesthesia via Vaporization of Isoflurane by Liquid Infusion, International Journal of Simulation- Systems, Science and Technology, 2001; 2 (1): 55- 66.
[33] Richard H., Studies of pain in human subjects, In: The textbook of Pain 4 Edition (Edited by: Wall PD, Melzack D). London: Churchill Livingstone, 1999; pp. 385-401.
[34] Kenshalo D.R., Bergen D.C., A device to measure cutaneous temperature sensitivity in humans and subhuman species, J Appl Physiol, 1975; 39: 1038- 1040.
[35] McCaffery M., Beebe A., Pain: Clinical Manual for Nursing Practice, Baltimore, 1993; Mosby Company.
[36] Melzack R., Katz J., Pain measurement in persons in pain, In: The textbook of Pain 4 Edition (Edited by: Wall PD, Melzack D). London: Churchill Livingstone, 1999; pp. 409-423.
[37] Closs S.J., Barr B., Briggs M., A comparison of five pain assessment scales for nursing home residents with varying degrees of Cognitive impairment, J. Pain and Symptom Manage, 2004; 27(3):196-205.
[38] Hansson P., Lundeberg T., Transcutaneous electrical nerve stimulation, vibration and acupuncture as pain-relieving measures, In: Textbook of Pain, edited by Wall PD and Melzack R. Edinburgh: Churchill Livingstone, 1999; pp. 1341–1351.
[39] Johnson S. M., Transcutaneous electrical nerve stimulation, in: Electrotherapy, Evidence-Based Practice, Kitchen S., 11th edition, Churchill Livingstone, 2001; 259 – 286.
[40] Novin medical eng. company, designer & manufacture of physiotherapy equipment, http://www.novinmed.com, 10-2010, 2007.
[41] Andre P., Mauderli Charles J., Vierck J.r., Relationships Between Skin Temperature and Temporal Summation of Heat and Cold Pain, J Neurophysiol, 2003; 90: 100-109.
[42] Online documentation for MATLAB and The MathWorks products: http://www.mathworks.com/ access/helpdesk/help/toolbox/curvefit/CURVEFIT. PDF., 10-2010, 2007.
[43] Frank H., The ciba collection of medical illustrations: nervous system, CIBA pharmaceutical company, 1983.
[44] Francis Heylighen, Cliff Joslyn, Cybernetics and Second-Order Cybernetics, Encyclopedia of Physical Science & Technology, 3rd ed., Academic Press, New York, 2001; (Modelling Dynamics, 7- 8).
[45] Price D., Physiological and Neurological Mechanisms of Pain, Raven Press, New York, 1988.
[46] Gadsby G., Electroanalgesia: Historical and Contemporary Developments, selections from the PhD Thesis, 1998.
[47] Gerstner W., Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge Univ Pr., 2002.
[48] Eugene M., Izhikevich, simple model of spiking neurons, IEEE Transactions on Neural Networks, 2003; 14:1569- 1572
[49] LaMotte R.H., Campbell J.N., Comparison of responses of warm and nociceptive C-fiber afferents in monkey with human judgments of thermal pain. Journal of neurophysiology, 1978; 41(2): 509-28.
[50] Hodgkin A.L., Huxley A.F., A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiology, 1952; 117: 500-544.
[51] Hodgkin A.L., Huxley A.F., The component of membrane conductance in the giant axon of Loligo, J. Physiology, 1952; 116: 449-472.