title: A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in neuronal connectivity induced by nociceptive stimulation.
abstract: Previous studies aimed to disclose the functional organization of the neuronal networks involved in the generation of the spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments used predetermined templates to select specific classes of spontaneous CDPs. Since this procedure was time consuming and required continuous supervision, it was limited to the analysis of two specific types of CDPs (negative CDPs and negative positive CDPs), thus excluding potentials that may reflect activation of other neuronal networks of presumed functional relevance. We now present a novel procedure based in machine learning that allows the efficient and unbiased selection of a variety of spontaneous CDPs with different shapes and amplitudes. The reliability and performance of the present method is evaluated by analyzing the effects on the probabilities of generation of different classes of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat, a procedure known to induce a state of central sensitization leading to allodynia and hyperalgesia. The results obtained with the selection method presently described allowed detection of spontaneous CDPs with specific shapes and amplitudes that are assumed to represent the activation of functionally coupled sets of dorsal horn neurones that acquire different, structured configurations in response to nociceptive stimuli. These changes are considered as responses tending to adequate transmission of sensory information to specific functional requirements as part of homeostatic adjustments.
authors.affiliation: Department of Computer Science, Universitat Politècnica de Catalunya. BarcelonaTech Catalonia, Spain.
authors.forename: Mario
authors.initials: M
authors.lastname: Martin
authors.affiliation: Department of Physiology, Biophysics and Neurosciences, Center for Research and Advanced Studies, National Polytechnic Institute Mexico City, Mexico.
authors.forename: Enrique
authors.initials: E
authors.lastname: Contreras-Hernández
authors.affiliation: Department of Computer Science, Universitat Politècnica de Catalunya. BarcelonaTech Catalonia, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Béjar
authors.affiliation: Department of Computer Science, Universitat Politècnica de Catalunya. BarcelonaTech Catalonia, Spain.
authors.forename: Gennaro
authors.initials: G
authors.lastname: Esposito
authors.affiliation: Department of Physiology, Biophysics and Neurosciences, Center for Research and Advanced Studies, National Polytechnic Institute Mexico City, Mexico.
authors.forename: Diógenes
authors.initials: D
authors.lastname: Chávez
authors.affiliation: Department of Physiology, Biophysics and Neurosciences, Center for Research and Advanced Studies, National Polytechnic Institute Mexico City, Mexico.
authors.forename: Silvio
authors.initials: S
authors.lastname: Glusman
authors.affiliation: Department of Computer Science, Universitat Politècnica de Catalunya. BarcelonaTech Catalonia, Spain ; Barcelona Supercomputing Center Catalonia, Spain.
authors.forename: Ulises
authors.initials: U
authors.lastname: Cortés
authors.affiliation: Department of Physiology, Biophysics and Neurosciences, Center for Research and Advanced Studies, National Polytechnic Institute Mexico City, Mexico ; El Colegio Nacional Mexico City, Mexico.
authors.forename: Pablo
authors.initials: P
authors.lastname: Rudomin
keywords: capsaicin; machine learning; neural signal processing; sorting of spontaneous cord dorsum potentials; spinal cord