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
Fractal analysis of spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments has revealed that these potentials are generated by ongoing structured (non-random) neuronal activity. Studies aimed to disclose the changes produced by nociceptive stimulation on the funct...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-08-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2015.00021/full |
| _version_ | 1818611728732127232 |
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| author | Mario eMartin Enrique eContreras-Hernàndez Javier eBéjar Gennaro eEsposito Diógenes eChávez Silvio eGlusman Ulises eCortés Pablo eRudomin |
| author_facet | Mario eMartin Enrique eContreras-Hernàndez Javier eBéjar Gennaro eEsposito Diógenes eChávez Silvio eGlusman Ulises eCortés Pablo eRudomin |
| author_sort | Mario eMartin |
| collection | DOAJ |
| description | Fractal analysis of spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments has revealed that these potentials are generated by ongoing structured (non-random) neuronal activity. Studies aimed to disclose the changes produced by nociceptive stimulation on the functional organization of the neuronal networks generating these potentials 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 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 method is evaluated by analyzing the effects on the probabilities of generation of different types of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat.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. |
| first_indexed | 2024-12-16T15:34:57Z |
| format | Article |
| id | doaj.art-c7316ac9bbee4ea99a69a0da57e3f302 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-16T15:34:57Z |
| publishDate | 2015-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-c7316ac9bbee4ea99a69a0da57e3f3022022-12-21T22:26:13ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962015-08-01910.3389/fninf.2015.00021155638A 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 stimulationMario eMartin0Enrique eContreras-Hernàndez1Javier eBéjar2Gennaro eEsposito3Diógenes eChávez4Silvio eGlusman5Ulises eCortés6Pablo eRudomin7Universitat Politècnica de CatalunyaCenter for Research and Advanced Studies, National Polytechnic InstituteUniversitat Politècnica de CatalunyaUniversitat Politècnica de CatalunyaCenter for Research and Advanced Studies, National Polytechnic InstituteCenter for Research and Advanced Studies, National Polytechnic InstituteUniversitat Politècnica de CatalunyaCenter for Research and Advanced Studies, National Polytechnic InstituteFractal analysis of spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments has revealed that these potentials are generated by ongoing structured (non-random) neuronal activity. Studies aimed to disclose the changes produced by nociceptive stimulation on the functional organization of the neuronal networks generating these potentials 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 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 method is evaluated by analyzing the effects on the probabilities of generation of different types of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat.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.http://journal.frontiersin.org/Journal/10.3389/fninf.2015.00021/fulldata analysisspontaneous neuronal activityNeural Signals ProcessingDiscovery and classificationCord Dorsum Potentials |
| spellingShingle | Mario eMartin Enrique eContreras-Hernàndez Javier eBéjar Gennaro eEsposito Diógenes eChávez Silvio eGlusman Ulises eCortés Pablo eRudomin 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 Frontiers in Neuroinformatics data analysis spontaneous neuronal activity Neural Signals Processing Discovery and classification Cord Dorsum Potentials |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | data analysis spontaneous neuronal activity Neural Signals Processing Discovery and classification Cord Dorsum Potentials |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2015.00021/full |
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