Fast in silico assessment of physical stress for peripheral nerves

  • Elisabetta Giannessi
  • Maria Rita Stornelli
  • Pier Nicola Sergi
Original Article


The level of physical stress rules the adaptative response of peripheral nerves, which is crucial to assess their physiological and pathological states. To this aim, in this work, different computational approaches were presented to model the stress response of in vitro peripheral nerves undergoing longitudinal stretch. More specifically, the effects of geometrical simplifications were studied with respect to the amount of computational time needed to obtain relevant information. Similarly, the variation of compressibility of the peripheral nervous tissue was investigated with respect to the variation of longitudinal stress and transversal stretch variations, and with reference to the computational time needed for simulations. Finally, the effect of small dimensional changes was investigated to better understand whether the variation of time was only due to the amount of nodes or elements. In conclusion, since fast in silico models, able to assess the nerve stress, could be a strategic advantage in case of time constraints or on-line evaluation (e.g., surgical interventions), a synergistic use of these approaches was proposed as a possible strategy to decrease the computational time needed for simulations from minutes to seconds.

Graphical Abstract

A synergistic approach involving both symmetry and tuning of compressibility allows the computational time to be considerably decreased


Peripheral nerves Physical stress Fast in silico models 



The authors thank the company “Desideri Luciano s.r.l” for biological specimens and Dr. Cesare Temporin for his valuable technical assistance in handling and dissection of peripheral nerves.

Supplementary material

11517_2018_1794_MOESM1_ESM.pdf (191 kb)
(PDF 191 KB)
11517_2018_1794_MOESM2_ESM.pdf (177 kb)
(PDF 176 KB)


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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Elisabetta Giannessi
    • 1
  • Maria Rita Stornelli
    • 1
  • Pier Nicola Sergi
    • 2
  1. 1.Department of Veterinary ScienceUniversity of PisaPisaItaly
  2. 2.Translational Neural Engineering Area, The Biorobotics InstituteSant’Anna School of Advanced StudiesPontederaItaly

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