Skip to main content

Advertisement

SpringerLink
Log in

Elastic and viscoelastic mechanical properties of brain tissues on the implanting trajectory of sub-thalamic nucleus stimulation

  • Engineering and Nano-engineering Approaches for Medical Devices
  • Original Research
  • Published:
Journal of Materials Science: Materials in Medicine Aims and scope Submit manuscript

Abstract

Corresponding to pre-puncture and post-puncture insertion, elastic and viscoelastic mechanical properties of brain tissues on the implanting trajectory of sub-thalamic nucleus stimulation are investigated, respectively. Elastic mechanical properties in pre-puncture are investigated through pre-puncture needle insertion experiments using whole porcine brains. A linear polynomial and a second order polynomial are fitted to the average insertion force in pre-puncture. The Young’s modulus in pre-puncture is calculated from the slope of the two fittings. Viscoelastic mechanical properties of brain tissues in post-puncture insertion are investigated through indentation stress relaxation tests for six interested regions along a planned trajectory. A linear viscoelastic model with a Prony series approximation is fitted to the average load trace of each region using Boltzmann hereditary integral. Shear relaxation moduli of each region are calculated using the parameters of the Prony series approximation. The results show that, in pre-puncture insertion, needle force almost increases linearly with needle displacement. Both fitting lines can perfectly fit the average insertion force. The Young’s moduli calculated from the slope of the two fittings are worthy of trust to model linearly or nonlinearly instantaneous elastic responses of brain tissues, respectively. In post-puncture insertion, both region and time significantly affect the viscoelastic behaviors. Six tested regions can be classified into three categories in stiffness. Shear relaxation moduli decay dramatically in short time scales but equilibrium is never truly achieved. The regional and temporal viscoelastic mechanical properties in post-puncture insertion are valuable for guiding probe insertion into each region on the implanting trajectory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Foltynie T, Zrinzo L, Martinez-Torres I, Tripoliti E, Petersen E, Holl E, Aviles-Olmos I, Jahanshahi M, Hariz M, Limousin P. MRI-guided STN DBS in Parkinson’s disease without microelectrode recording: efficacy and safety. J Neurol Neurosurg Psychiatry. 2011;82(4):358–63.

    Article  Google Scholar 

  2. Shin M, Lefaucheur JP, Penholate MF, Brugieres P, Gurruchaga JM, Nguyen JP. Subthalamic nucleus stimulation in Parkinson’s disease: postoperative CT-MRI fusion images confirm accuracy of electrode placement using intraoperative multi-unit recording. Neurophysiol Clin. 2007;37(6):457–66.

    Article  Google Scholar 

  3. Benazzouz A, Breit S, Koudsie A, Pollak P, Krack P, Benabid AL. Intraoperative microrecordings of the subthalamic nucleus in Parkinson’s disease. Mov Disord. 2002;17(Suppl. 3):S145–49.

    Article  Google Scholar 

  4. Machado A, Rezai AR, Kopell BH, Gross RE, Sharan AD, Benabid AL. Deep brain stimulation for Parkinson’s disease: surgical technique and perioperative management. Mov Disord. 2006;21(Suppl.14):S247–58.

    Article  Google Scholar 

  5. Obuchi T, Katayama Y, Kobayashi K, Oshima H, Fukaya C, Yamamoto T. Direction and Predictive factors for the shift of brain structure during deep brain stimulation electrode implantation for advanced Parkinson’s disease. Neuromodulation. 2008;11(4):302–10.

    Article  Google Scholar 

  6. Bejjani BP, Dormont D, Pidoux B, Yelnik J, Damier P, Arnulf I, Bonnet AM, Marsault C, Agid Y, Philippon J, Cornu P. Bilateral subthalamic stimulation for Parkinson’s disease by using three-dimensional stereotactic magnetic resonance imaging and electrophysiological guidance. J Neurosurg. 2000;92(4):615–25.

    Article  Google Scholar 

  7. Polikov VS, Tresco PA, Reichert WM. Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods. 2005;148(1):1–18.

    Article  Google Scholar 

  8. Welkenhuysen M, Andrei A, Ameye L, Eberle W, Nuttin B. Effect of insertion speed on tissue response and insertion mechanics of a chronically implanted silicon-based neural probe. IEEE Trans Biomed Eng. 2011;58(11):3250–9.

    Article  Google Scholar 

  9. Casanova F, Carney PR, Sarntinoranont M. In vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain. J Neurosci Methods. 2014;237:79–89.

    Article  Google Scholar 

  10. Jensen W, Yoshida K, Hofmann UG. In-vivo implant mechanics of flexible, silicon-based ACREO microelectrode arrays in rat cerebral cortex. IEEE Trans Biomed Eng. 2006;53(5):934–40.

    Article  Google Scholar 

  11. Fekete Z, Nemeth A, Marton G, Ulbert I, Pongracz A. Experimental study on the mechanical interaction between silicon neural microprobes and rat dura mater during insertion. J Mater Sci: Mater Med. 2015;26:70

    Google Scholar 

  12. Miller K. How to test very soft biological tissues in extension? J Biomech. 2001;34(5):651–7.

    Article  Google Scholar 

  13. Miller K, Chinzei K, Orssengo G, Bednarz P. Mechanical properties of brain tissue in-vivo: experiment and computer simulation. J Biomech. 2000;33(11):1369–76.

    Article  Google Scholar 

  14. Balachandran R, Welch EB, Dawant BM, Fitzpatrick JM. Effect of MR Distortion on Targeting for Deep-Brain Stimulation. IEEE Trans Biomed Eng. 2010;57(7):1729–35.

    Article  Google Scholar 

  15. Fiegele T, Feuchtner G, Sohm F, Bauer R, Anton JV, Gotwald T, Twerdy K, Eisner W. Accuracy of stereotactic electrode placement in deep brain stimulation by intraoperative computed tomography. Parkinsonism Relat Disord. 2008;14(8):595–9.

    Article  Google Scholar 

  16. Finan JD, Elkin BS, Pearson EM, Kalbian IL, Morrison B 3rd. Viscoelastic properties of the rat brain in the sagittal plane: effects of anatomical structure and age. Ann Biomed Eng. 2012;40(1):70–8.

    Article  Google Scholar 

  17. Soza G, Grosso R, Nimsky C, Hastreiter P, Fahlbusch R, Greiner G. Determination of the elasticity parameters of brain tissue with combined simulation and registration. Int J Med Robot. 2005;1(3):87–95.

    Article  Google Scholar 

  18. Subbaroyan J, Martin DC, Kipke DR. A finite-element model of the mechanical effects of implantable microelectrodes in the cerebral cortex. J Neural Eng. 2005;2(4):103–13.

    Article  Google Scholar 

  19. Gefen A, Gefen N, Zhu QL, Raghupathi R, Margulies SS. Age-dependent changes in material properties of the brain and braincase of the rat. J Neurotrauma. 2003;20(11):1163–77.

    Article  Google Scholar 

  20. Elkin BS, Ilankova A, Morrison B 3rd. Dynamic, regional mechanical properties of the porcine brain: indentation in the coronal plane. J Biomech Eng. 2011;133(7):071009

    Article  Google Scholar 

  21. Prevost TP, Balakrishnan A, Suresh S, Socrate S. Biomechanics of brain tissue. Acta Biomater. 2011;7(1):83–95.

    Article  Google Scholar 

  22. Elkin BS, Ilankovan AI, Morrison B 3rd. A detailed viscoelastic characterization of the P17 and adult rat brain. J Neurotrauma. 2011;28:2235-44.

    Article  Google Scholar 

  23. Miller K. Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. J Biomech. 1999;32(5):531–7.

    Article  Google Scholar 

  24. Dommelen JAW, Sande TPJ, Hrapko M, Peters GWM. Mechanical properties of brain tissue by indentation: interregional variation. J Mech Behav Biomed Mater. 2010;3(2):158–66.

    Article  Google Scholar 

  25. Prange MT, Margulies SS. Regional, directional, and age-dependent properties of the brain undergoing large deformation. J Biomech Eng. 2002;124(2):244–52.

    Article  Google Scholar 

  26. Miller K, Chinzei K. Mechanical properties of brain tissue in tension. J Biomech. 2002;35(4):483–90.

    Article  Google Scholar 

  27. Pervin F, Chen WW. Dynamic mechanical response of bovine gray matter and white matter brain tissues under compression. J Biomech. 2009;42(6):731–5.

    Article  Google Scholar 

  28. Prevost TP, Jin G, Moya MA, Alam HB, Suresh S, Socrate S. Dynamic mechanical response of brain tissue in indentation in vivo, in situ and in vitro. Acta Biomater. 2011;7(12):4090–101.

    Article  Google Scholar 

  29. Abolhassani N, Patel R, Moallem M. Needle insertion into soft tissue: a survey. Med Eng Phys. 2007;29(4):413–31.

    Article  Google Scholar 

  30. Okamura AM, Simone C, O’Leary MD. Force modeling for needle insertion into soft tissue. IEEE Trans Biomed Eng. 2004;51(10):1707–16.

    Article  Google Scholar 

  31. Prange MT, Meaney DF, Margulies SS. Defining brain mechanical properties: effects of region, direction, and species. Stapp Car Crash J. 2000;44:205–13.

    Google Scholar 

  32. Nicolle S, Lounis M, Willinger R. Shear properties of brain tissue over a frequency range relevant for automotive impact situations: new experimental results. Stapp Car Crash J. 2004;48:239-58.

    Google Scholar 

  33. D’Haese PF, Pallavaram S, Niermann K, Spooner J, Kao C, Konrad PE, Dawant BM. Automatic selection of DBS target points using multiple electrophysiological atlases. Med Image Comput Comput Assist Interv. 2005;8(Pt 2):427–34.

    Google Scholar 

  34. Garo A, Hrapko M, Dommelen JAW, Peters GWM. Towards a reliable characterisation of the mechanical behaviour of brain tissue: the effects of post-mortem time and sample preparation. Biorheology. 2007;44(1):51–8.

    Google Scholar 

  35. Sharp AA, Ortega AM, Restrepo D, Curran-Everett D, Gall K. In vivo penetration mechanics and mechanical properties of mouse brain tissue at micrometer scales. IEEE Trans Biomed Eng. 2009;56(1):45–53.

    Article  Google Scholar 

  36. Zhang M, Zheng YP, Mak AFT. Estimating the effective Young’s modulus of soft tissues from indentation tests-nonlinear finite element analysis of effects of friction and large deformation. Med Eng Phys. 1997;19(6):512–7.

    Article  Google Scholar 

  37. Fischer-Cripps AC. Introduction to Contact Mechanics. New York: Springer-Verelag; 2000.

    Google Scholar 

  38. Hayes WC, Keer LM, Herrmann G, Mockros LF. A mathematical analysis for indentation tests of articular cartilage. J Biomech. 1972;5(5):541–51.

    Article  Google Scholar 

  39. Finan JD, Fox PM, Morrison B 3rd. Non-ideal effects in indentation testing of soft tissues. Biomech Model Mechanobiol. 2014;13(3):573–84.

    Article  Google Scholar 

  40. Bonferroni CE. Il calcolo delle assicurazioni su gruppi diteste. Studi in onore del Professore Salvatore Ortu Carboni. 1935; 13–60.

  41. Walsh EK, Furniss WW, Schettini A. On measurement of brain elastic response in vivo. Am J Physiol Regul Integr Comp Physiol. 1977;232(1):R27-R30.

    Google Scholar 

  42. Kobayashi Y, Sato T, Fujie MG. Modeling of friction force based on relative velocity between liver tissue and needle for needle insertion simulation. 31st Annual International Conference of the IEEE EMBS. 2009:5274-8.

  43. Nagashima T, Shirakuni T, Rapoport SI. A two-dimensional, finite element analysis of vasogenic brain edema. Nerol. Med. Chir. 1990;30:1-9.

    Article  Google Scholar 

  44. Lee H, Bellamkonda RV, Sun W, Levenston ME. Biomechanical analysis of silicon microelectrode-induced strain in the brain. J Neural Eng. 2005;2(4):81–9.

    Article  Google Scholar 

  45. DiMaio SP, Salcudean SE. Needle insertion modeling and simulation. IEEE trans Robot Automation. 2003;19(5):864–75.

    Article  Google Scholar 

  46. Andrei A, Welkenhuysen M, Nuttin B, Eberle W. A response surface model predicting the in vivo insertion behavior of micromachined neural implants. J Neural Eng. 2012;9:016005

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Science Foundation of China (Grant No. 51375268) and Independent Innovation Foundation of Shandong University (Grant No. 2012ZD009). Thanks for the help of the experienced neurosurgeon in Qilu hospital.

Author information

Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, Department of Mechanical Engineering, Shandong University, Jinan, 250061, PR China

    Yan Li, Jianxin Deng & Jun Zhou

  2. Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, 250012, PR China

    Xueen Li

Authors
  1. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianxin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xueen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jianxin Deng or Jun Zhou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Deng, J., Zhou, J. et al. Elastic and viscoelastic mechanical properties of brain tissues on the implanting trajectory of sub-thalamic nucleus stimulation. J Mater Sci: Mater Med 27, 163 (2016). https://doi.org/10.1007/s10856-016-5775-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10856-016-5775-5

Keywords

Search

Navigation