Mathematical and Computational Models for Pain: A Systematic Review

Abstract

Objective There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain. Methods The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant. Results Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment. Conclusions Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.

Muscle contractions and pain sensation accompanying high-frequency electroporation pulses

“…In our case, this would mean that stimulation of the muscle and the resulting muscle contraction activates/excites the large fibers and thus, reduces the excitation of the nociceptive (pain) fibers, i.e., the gates close. However, the theories and models of pain are still evolving and need further validation 81 .…”

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

“…In sum, the importance of building multi-modal network models has been discussed in the context of emerging field of network physiology (Bashan et al, 2012;Ivanov PCh, 2021) but also in the context of precision medicine and digital twins (Coorey et al, 2021;Björnsson et al, 2019;Lonsdale et al, 2022). Some promising and valuable computational models have been already developed in the field of human pain [see e.g., (Lang et al, 2021;Ionescu et al, 2019)] and a review of such models could help to understand, predict and analyze nociceptive system function and pain perception. Accordingly, we are very optimistic that the domain of multiscale and network-oriented computational pain research will continue to grow and contribute to our understanding of human pain signaling and perception in health and disease states.…”

“…The development of computational methods in the last decades allowed for construction of computational models that promote a better understanding of pain mechanisms [see e.g., the recent systematic review of Lang et al (2021)]. Those models span from single neuron levels (Chrysostomidou et al, 2021;Le Franc and Le Masson 2010;Balachandar and Prescott 2018;Tigerholm et al, 2014) over spinal synaptic effects (Tanaka et al, 2021) to brain imaging analysis (Morton et al, 2016).…”

A computational model of the integration of noxious and innocuous input in the dorsal horn

“…In a 2021 review by Lang et al, the authors examined the field of mathematical and computational models of pain [17]. Thirteen mathematical models were identified.…”