ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Relationships among motor unit discharge parameters used to estimate synaptic inputs to motoneurons.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Pathophysiology of voluntary motor commands at the level of the spinal motoneuron in patients with multiple sclerosis.medRxiv : the preprint server for health sciences · 2025Article
- A novel protocol to investigate the monoaminergic relationship between descending nociceptive and motor unit neuromodulation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
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4 authors.
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Abstract
The motor unit, consisting of a single α-motoneuron and the muscle fibers it innervates, is a neuromechanical transducer that transforms neural inputs from afferent, spinal, and descending sources into motoneuron discharge patterns and resulting muscle forces. The neural inputs that converge on the motoneuron constitute the motor command and are classified into three types: excitatory, inhibitory, and neuromodulatory. Motoneurons have complex and malleable input/output functions that depend on the mixture of excitatory, inhibitory, and neuromodulatory input. Recently, a reverse engineering paradigm was developed to identify temporal features of motoneuron discharge that can estimate aspects of excitatory, inhibitory, and neuromodulatory input. However, the common parameters used are sensitive to more than one type of input. The purpose of this study was to explore relationships among the reverse engineering parameters and to determine if the parameter set can be reduced to a smaller number of dimensions that summarize patterns in the data. We performed principal component (PC) analysis on seven reverse engineering parameters and found PCs corresponding to the amplification aspect of neuromodulation, the prolongation aspect of neuromodulation, and the pattern of inhibition.
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