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
| Autor: | Pablo Rudomín, Enrique Contreras-Hernández, Mario Martín, Javier Béjar, Silvio Glusman, Gennaro Esposito, Ulises Cortés, Diógenes Chávez |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
| Rok vydání: | 2015 |
| Předmět: |
Signal processing
Dorsum Discovery and classification Computer science data analysis Biomedical Engineering Neuroscience (miscellaneous) neural signal processing Stimulation Sensory system Machine learning computer.software_genre capsaicin lcsh:RC321-571 Neural networks (Computer science) Informàtica::Aplicacions de la informàtica [Àrees temàtiques de la UPC] Methods medicine lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Selection (genetic algorithm) Spinal cord Cord Dorsum Potentials spontaneous neuronal activity business.industry spinal cord Tractament del senyal Computer Science Applications machine learning Nociception medicine.anatomical_structure Allodynia sorting of spontaneous cord dorsum potentials Neural Signals Processing Hyperalgesia Enginyeria biomèdica Artificial intelligence medicine.symptom business Neuroscience computer Medicina -- Informàtica |
| Zdroj: | Frontiers in Neuroinformatics, Vol 9 (2015) Recercat. Dipósit de la Recerca de Catalunya instname Frontiers in Neuroinformatics UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 1662-5196 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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