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

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