Lamina specific loss of inhibition may lead to distinct neuropathic manifestations: a computational modeling approach
Prada, Erick Javier A.; Bustillos, Ricardo José S.; Huerta, Mónica Karel; Martínez, Antonio D’Alessandro
http://dx.doi.org/10.1590/2446-4740.0734
Res. Biomed. Eng., vol.31, n2, p.133-147, 2015
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Abstract
Introduction: It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods: In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results: Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion: By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.
Keywords
Computational pain modeling, Inhibitory control, Superficial dorsal horn circuit, Network model.
References
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Farajidavar A, Towhidkhah F, Mirhashemi A, Gharibzadeh S, Behbehani K. Computational modeling of Aβ fiber wind-up. In: Proceedings of the 28th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC); 2006 Aug 30-Sept 3; New York, USA. New York: IEEE; 2013. p. 4975-8
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Hopfield JJ. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America. 1984; 81(10):3088-92. http://dx.doi.org/10.1073/pnas.81.10.3088. PMid:6587342
Horch KW, Dhillon GS. Neuroprosthetics: theory and practice. New York: World Scientific; 2004
Inquimbert P, Rodeau JL, Schlichter R. Differential contribution of GABAergic and glycinergic components to inhibitory synaptic transmission in lamina II and laminae III-IV of the young rat spinal cord. The European Journal of Neuroscience. 2007; 26(10):2940-9. http://dx.doi.org/10.1111/j.1460-9568.2007.05919.x. PMid:18001289
Kato G, Kawasaki Y, Koga K, Uta D, Kosugi M, Yasaka T, Yoshimura M, Ji RR, Strassman AM. Organization of intralaminar and translaminar neuronal connectivity in the superficial spinal dorsal horn. The Journal of Neuroscience. 2009; 29(16):5088-99. http://dx.doi.org/10.1523/JNEUROSCI.6175-08.2009. PMid:19386904
Keller AF, Beggs S, Salter MW, De Koninck Y. Transformation of the output of spinal lamina I neurons after nerve injury and microglia stimulation underlying neuropathic pain. Molecular Pain. 2007; 3(1):27. http://dx.doi.org/10.1186/1744-8069-3-27. PMid:17900333
Kosugi M, Kato G, Lukashov S, Pendse G, Puskar Z, Kozsurek M, Strassman AM. Subpopulation-specific patterns of intrinsic connectivity in mouse superficial dorsal horn as revealed by laser scanning photostimulation. The Journal of Physiology. 2013; 591(7):1935-49. http://dx.doi.org/10.1113/jphysiol.2012.244210. PMid:23297304
Lavertu G, Côté SL, De Koninck Y. Enhancing K-Cl co-transport restores normal spinothalamic sensory coding in a neuropathic pain model. Brain. 2014; 137(3):724-38. http://dx.doi.org/10.1093/brain/awt334. PMid:24369380
Lu Y, Dong H, Gao Y, Gong Y, Ren Y, Gu N, Zhou S, Xia N, Sun YY, Ji RR, Xiong L. A feed-forward spinal cord glycinergic neural circuit gates mechanical allodynia. The Journal of Clinical Investigation. 2013; 123(9):4050-62. http://dx.doi.org/10.1172/JCI70026. PMid:23979158
Lu Y, Perl ER. A specific inhibitory pathway between substantia gelatinosa neurons receiving direct C-fiber input. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2003; 23(25):8752-8. PMid:14507975
Lu Y, Perl ER. Modular organization of excitatory circuits between neurons of the spinal superficial dorsal horn (laminae I and II). The Journal of Neuroscience 2005; 25(15):3900-7. http://dx.doi.org/10.1523/JNEUROSCI.0102-05.2005. PMid:15829642
Ma Q. Population coding of somatic sensations. Neuroscience Bulletin. 2012; 28(2):91-9. http://dx.doi.org/10.1007/s12264-012-1201-2. PMid:22466120
Meisner JG, Marsh AD, Marsh DR. Loss of GABAergic interneurons in laminae I-III of the spinal cord dorsal horn contributes to reduced GABAergic tone and neuropathic pain after spinal cord injury. Journal of Neurotrauma. 2010; 27(4):729-37. http://dx.doi.org/10.1089/neu.2009.1166. PMid:20059302
Melzack R, Wall PD. Pain mechanisms: a new theory. Science 1965; 150(3699):971-9. http://dx.doi.org/10.1126/science.150.3699.971. PMid:5320816
Mendell LM. Computational functions of neurons and circuits signaling injury: relationship to pain behavior. Proceedings of the National Academy of Sciences of the United States of America. 2011; 108(Suppl 3):15596-601. http://dx.doi.org/10.1073/pnas.1012195108. PMid:21368123
Millán MJ. The induction of pain: an integrative review. Progress in Neurobiology. 1999; 57(1):1-164. http://dx.doi.org/10.1016/S0301-0082(98)00048-3. PMid:9987804
Miraucourt LS, Dallel R, Voisin DL. Glycine inhibitory dysfunction turns touch into pain through PKCgamma interneurons. PLoS One. 2007; 2(11):e1116. http://dx.doi.org/10.1371/journal.pone.0001116. PMid:17987109
Polgár E, Durrieux C, Hughes DI, Todd AJ. A quantitative study of inhibitory interneurons in laminae I-III of the mouse spinal dorsal horn. PLoS One. 2013; 8(10):e78309. http://dx.doi.org/10.1371/journal.pone.0078309. PMid:24205193
Prada EJA, Bustillos RJS, Castillo C, Huerta M. New trends in computational modeling: a neuroid-based retina model. In: Proceedings of the 35th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC); 2013 Jul 3-7; Osaka, Japan. Osaka: IEEE; 2013. p. 4561-4
Prada EJA, Bustillos RJS, Castillo C, Huerta M. The neuroid: a novel and simplified neuron-model. In: Proceedings of the 34th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC); 2012 Aug 28-Sep 1; San Diego, CA. San Diego: IEEE; 2012. p. 1234-7
Prada EJA, Bustillos RJS. The implementation of the neuroid in the gate control system leads to new ideas about pain processing. Revista Brasileira de Engenharia Biomédica. 2013; 29(3):254-61
Prescott SA, Ma Q, De Koninck Y. Normal and abnormal coding of somatosensory stimuli causing pain. Nature Neuroscience. 2014; 17(2):183-91. http://dx.doi.org/10.1038/nn.3629. PMid:24473266
Prescott SA, Ratté S. Pain processing by spinal microcircuits: afferent combinatorics. Current Opinion in Neurobiology. 2012; 22(4):631-9. http://dx.doi.org/10.1016/j.conb.2012.02.010. PMid:22409855
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