Dezhdar et al. - 2015 - A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors.pdf (1.23 MB)
A probabilistic model for estimating the depth and threshold temperature of C-fiber nociceptors
journal contribution
posted on 2023-06-09, 00:10 authored by Tara Dezhdar, Rabih A Moshourab, Ingo Fründ, Gary R Lewin, Michael SchmukerMichael SchmukerThe subjective experience of thermal pain follows the detection and encoding of noxious stimuli by primary afferent neurons called nociceptors. However, nociceptor morphology has been hard to access and the mechanisms of signal transduction remain unresolved. In order to understand how heat transducers in nociceptors are activated in vivo, it is important to estimate the temperatures that directly activate the skin-embedded nociceptor membrane. Hence, the nociceptor's temperature threshold must be estimated, which in turn will depend on the depth at which transduction happens in the skin. Since the temperature at the receptor cannot be accessed experimentally, such an estimation can currently only be achieved through modeling. However, the current state-of-the-art model to estimate temperature at the receptor suffers from the fact that it cannot account for the natural stochastic variability of neuronal responses. We improve this model using a probabilistic approach which accounts for uncertainties and potential noise in system. Using a data set of 24 C-fibers recorded in vitro, we show that, even without detailed knowledge of the bio-thermal properties of the system, the probabilistic model that we propose here is capable of providing estimates of threshold and depth in cases where the classical method fails.
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- Published
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- Published version
Journal
Scientific ReportsISSN
2045-2322Publisher
Nature PublishingExternal DOI
Volume
5Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-01-28First Open Access (FOA) Date
2016-01-28First Compliant Deposit (FCD) Date
2016-01-28Usage metrics
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