Brain Tissue Responses to Guide Cannula Insertion and Replacement of a Microrecording Electrode with a Definitive DBS Electrode
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Abstract
The objective is to achieve a better understanding of mechanisms of guide cannula insertion into brains and the replacement of a microrecording electrode with a definitive deep brain stimulation (DBS) electrode during the sub-thalamic nucleus (STN) DBS surgery. Guide cannula insertions are investigated by single-insertion experiments that insert points within labeled cortical surfaces using three cylindrical needles at different insertion velocities. Moreover, the replacement of a microrecording electrode with a definitive DBS electrode is investigated by two-insertion experiments that successively insert points within labeled areas twice using the same needle and absolute insertion depth. The results show that in single-insertion experiments, variations in cortical surface movements and profile patterns of needle axial forces are different at different insertion stages. Needle axial forces at each insertion stage can be modeled by their individual equations. Needles with larger diameters will cause greater dimpling depths/puncture forces. Variations in dimpling depths/puncture forces exhibit undulation with increasing insertion velocities. Dimpling depths positively correlate to puncture forces. In two-insertion experiments, needle axial force and its increasing slope during the second insertion are lower than the first. Actual insertion depth during the second insertion is greater than the first for the same absolute insertion depth. These results can directly guide the mechanical processes of the STN-DBS surgery to minimize insertion traumas and targeting errors.
Keywords
Cortical surface movement Needle axial force Dimpling depth Puncture force Actual needle insertion depthNotes
Acknowledgements
This work is partially supported by the National Science Foundation of China (Grant No. 51375268) and Independent Innovation Foundation of Shandong University (Grant No. 2012ZD009). We would like to thank the experienced neurosurgeon in Qilu hospital for his help and thank Catherine Lei for her proofreading.
Compliance with Ethical Standards
Conflicts of interest
The authors have no conflicts of interest.
References
- 1.Foltynie, T., Zrinzo, L., Martinez-Torres, I., Tripoliti, E., Petersen, E., Holl, E., et al. (2011). MRI-guided STN DBS in Parkinson’s disease without microelectrode recording: Efficacy and safety. Journal of Neurology, Neurosurgery and Psychiatry, 82(4), 358–363.CrossRefGoogle Scholar
- 2.Obuchi, T., Katayama, Y., Kobayashi, K., Oshima, H., Fukaya, C., & Yamamoto, T. (2008). Direction and predictive factors for the shift of brain structure during deep brain stimulation electrode implantation for advanced Parkinson’s disease. Neuromodulation, 11(4), 302–310.CrossRefGoogle Scholar
- 3.Machado, A., Rezai, A. R., Kopel, B. H., Gross, R. E., Sharan, A. D., & Benabid, A. L. (2006). Deep brain stimulation for Parkinson’s disease: Surgical technique and perioperative management. Movement Disorder, 21(14), 247–258.CrossRefGoogle Scholar
- 4.Sterio, D., Zonenshayn, M., Mogilner, A. Y., Rezai, A. R., Kiprovski, K., Kelly, P. J., et al. (2002). Neurophysiological refinement of subthalamic nucleus targeting. Neurosurgery, 50(1), 58–67.Google Scholar
- 5.D’Haese, P. F., Pallavaram, S., Konrad, P. E., Neimat, J., Fitzpatrick, J. M., & Dawant, B. (2008). Clinical accuracy of a customized stereotactic platform for deep brain stimulation after accounting for brain shift. Stereotactic and Functional Neurosurgery, 88(2), 44–53.Google Scholar
- 6.Amirnovin, R., Williams, Z. M., Cosgrove, G. R., & Eskandar, E. N. (2006). Experience with microelectrode guided subthalamic nucleus deep brain stimulation. Neurosurgery, 58(1), 96–102.Google Scholar
- 7.Fiegele, T., Feuchtner, G., Sohm, F., Bauer, R., Anton, J. V., Gotwald, T., et al. (2008). Accuracy of stereotactic electrode placement in deep brain stimulation by intraoperative computed tomography. Parkinsonism & Related Disorders, 14(8), 595–599.CrossRefGoogle Scholar
- 8.Bejjani, B. P., Dormont, D., Pidoux, B., Yelnik, J., Damier, P., Arnulf, I., et al. (2000). Bilateral subthalamic stimulation for Parkinson’s disease by using three-dimensional stereotactic magnetic resonance imaging and electrophysiological guidance. Journal of Neurosurgery, 92(4), 615–625.CrossRefGoogle Scholar
- 9.Shin, M., Lefaucheur, J. P., Penholate, M. F., Brugieres, P., Gurruchaga, J. M., & Nguyen, J. P. (2007). Subthalamic nucleus stimulation in Parkinson’s disease: Postoperative CT-MRI fusion images confirm accuracy of electrode placement using intraoperative multi-unit recording. Neurophysiologie Clinique/Clinical Neurophysiology, 37(6), 457–466.CrossRefGoogle Scholar
- 10.Benazzouz, A., Breit, S., Koudsie, A., Pollak, P., Krack, P., & Benabid, A. L. (2002). Intraoperative microrecordings of the subthalamic nucleus in Parkinson’s disease. Movement Disorder, 17(Suppl. 3), S145–S149.CrossRefGoogle Scholar
- 11.Wester, K., & Krakenes, J. (2001). Vertical displacement of the brain and the target area during open stereotaxic neurosurgery. Acta Neurochirurgica, 143(6), 603–606.CrossRefGoogle Scholar
- 12.Pollo, C., Vingerhoets, F., Pralong, E., Ghika, J., Maeder, P., Meuli, R., et al. (2007). Localization of electrodes in the subthalamic nucleus on magnetic resonance imaging. Journal of Neurosurgery, 106(1), 36–44.CrossRefGoogle Scholar
- 13.Casanova, F., Carney, P. R., & Sarntinoranont, M. (2014). In-vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain. Journal of Neuroscience Methods, 237(30), 79–89.CrossRefGoogle Scholar
- 14.Sharp, A. A., Ortega, A. M., Restrepo, D., Curran-Everett, D., & Gall, K. (2009). In vivo penetration mechanics and mechanical properties of mouse brain tissue at micrometer scales. IEEE Transaction Biomedical Engineering, 56(1), 45–53.CrossRefGoogle Scholar
- 15.Welkenhuysen, M., Andrei, A., Ameye, L., Eberle, W., & Nuttin, B. (2011). Effect of insertion speed on tissue response and insertion mechanics of a chronically implanted silicon-based neural probe. IEEE Transaction Biomedical Engineering, 58(11), 3250–3259.CrossRefGoogle Scholar
- 16.Fekete, Z., Nemeth, A., Marton, G., Ulbert, I., & Pongracz, A. (2015). Experimental study on the mechanical interaction between silicon neural microprobes and rat dura mater during insertion. Journal of Materrials Science: Materials in Meddicine, 26, 70.Google Scholar
- 17.Polikov, V. S., Tresco, P. A., & Reichert, W. M. (2005). Response of brain tissue to chronically implanted neural electrode. Journal of Neuroscience Methods, 148(1), 1–18.CrossRefGoogle Scholar
- 18.Edell, D. J., Toi, V. V., McNeil, V. M., & Clark, L. D. (1992). Factors influencing the biocompatibility of insertable silicon microshafts in cerebral cortex. IEEE Transaction Biomedical Engineering, 39, 635–643.CrossRefGoogle Scholar
- 19.Szarowski, D. H., Andersen, M. D., Retterer, S., Spence, A. J., Isaacson, M., Craighead, H. G., et al. (2003). Brain responses to micro-machined silicon devices. Brain Research, 983, 23–25.CrossRefGoogle Scholar
- 20.Biran, R., Martin, D. C., & Tresco, P. A. (2005). Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays. Experimental Neurology, 195(1), 115–126.CrossRefGoogle Scholar
- 21.Jensen, W., Yoshida, K., & Hofmann, U. G. (2006). In-vivo implant mechanics of flexible, silicon-based ACREO microelectrode arrays in rat cerebral cortex. IEEE Transaction Biomedical Engineering, 53(5), 934–940.CrossRefGoogle Scholar
- 22.Balachandran, R., Welch, E. B., Dawant, B. M., & Fitzpatrick, J. M. (2010). Effect of MR distortion on targeting for deep-brain stimulation. IEEE Transaction Biomedical Engineering, 57(7), 1729–1735.CrossRefGoogle Scholar
- 23.Khan, M. F., Mewes, K., Gross, R. E., & Skrinjar, O. (2007). Assessment of brain shift related to deep brain stimulation surgery. Stereotactic and Functional Neurosurgery, 86(1), 44–53.CrossRefGoogle Scholar
- 24.Andrei, A., Welkenhuysen, M., Nuttin, B., & Eberle, W. (2012). A response surface model predicting the in vivo insertion behavior of micromachined neural implants. Journal of Neural Engineering. doi: 10.1088/1741-2560/9/1/016005.Google Scholar
- 25.Brett, P. N., Parker, T. J., Harrison, A. J., Thomas, T. A., & Carr, A. (1997). Simulation of resistance forces acting on surgical needles. Proceedings of the Institution of Mechanical Engineers, 211(4), 335–347.CrossRefGoogle Scholar
- 26.Paulsen, K. D., Miga, M. I., Kennedy, F. E., Hoopes, P. J., Hartov, A., & Roberts, D. W. (1999). A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery. IEEE Transaction Biomedical Engineering, 46(2), 213–225.CrossRefGoogle Scholar
- 27.Miller, K. (1999). Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. Journal of Biomechnics, 32(5), 531–537.CrossRefGoogle Scholar
- 28.Finan, J. D., Elkin, B. S., Pearson, E. M., Kalbian, I. L., & Morrison, B. Ш. (2012). Viscoelastic properties of the rat brain in the sagittal plane: Effects of anatomical structure and age. Annals of Biomedical Engineering, 40(1), 70–78.CrossRefGoogle Scholar
- 29.Elkin, B. S., Ilankova, A., & Morrison, B. Ш. (2011). Dynamic, regional mechanical properties of the porcine brain: Indentation in the coronal plane. Journal of Biomechnical Engineering, 133(7), 071009.CrossRefGoogle Scholar
- 30.Casanova, F., Carney, P. R., & Sarntinoranont, M. (2014). Effect of needle insertion speed on tissue injury, stress, and backflow distribution for convection-enhanced delivery in the rat brain. PLoS ONE, 9, e94919.CrossRefGoogle Scholar
- 31.Howard, M. A., Abkes, B. A., Ollendieck, M. C., Noh, M. D., Ritter, R. C., & Gillies, G. T. (1999). Measurement of the force required to move a neurosurgical probe through in vivo human brain tissue. IEEE Transaction Biomedical Engineering, 46(7), 891–894.CrossRefGoogle Scholar
- 32.Prevost, T. P., Jin, G., Moya, M. A., Alam, H. B., Suresh, S., & Socrate, S. (2011). Dynamic mechanical response of brain tissue in indentation in vivo, in situ and in vitro. Acta Biomaterialia, 7(12), 4090–4101.CrossRefGoogle Scholar
- 33.Prange, M. T., Meaney, D. F., & Margulies, S. S. (2000). Defining brain mechanical properties: Effects of region, direction, and species. Stapp Car Crash Journal, 44, 205–213.Google Scholar
- 34.Prange, M. T., & Margulies, S. S. (2002). Regional, directional, and age-dependent properties of the brain undergoing large deformation. Journal of Biomechnical Engineering, 124(2), 244–252.CrossRefGoogle Scholar
- 35.Nicolle, S., Lounis, M., & Willinger, R. (2004). Shear properties of brain tissue over a frequency range relevant for automotive impact situations: New experimental results. Stapp Car Crash Journal, 48, 239–258.Google Scholar
- 36.Jin, X., Yang, K. H., & King, A. I. (2011). Mechanical properties of bovine pia–arachnoid complex in shear. Journal of Biomechnics, 44, 467–474.CrossRefGoogle Scholar
- 37.Prevost, T. P., Balakrishnan, A., Suresh, S., & Socrate, S. (2011). Biomechanics of brain tissue. Acta Biomaterialia, 7(1), 83–95.CrossRefGoogle Scholar
- 38.Coats, B., & Margulies, S. S. (2006). Material properties of porcine parietal cortex. Journal of Biomechnics, 39(13), 2521–2525.CrossRefGoogle Scholar
- 39.Miller, K., & Chinzei, K. (1997). Constitutive modelling of brain tissue: Experiment and theory. Journal of Biomechnics, 30, 1115–1121.CrossRefGoogle Scholar
- 40.Garo, A., Hrapko, M., Dommelen, J. A. W., & Peters, G. W. M. (2007). Towards a reliable characterisation of the mechanical behaviour of brain tissue: The effects of post-mortem time and sample preparation. Biorheology, 44, 51–58.Google Scholar
- 41.Casanova, F., Carney, P. R., & Sarntinoranont, M. (2012). Influence of needle insertion speed on backflow for convection-enhanced delivery. Journal of Biomechnical Engineering, 134, 0410061–0410068.Google Scholar
- 42.Bjornsson, C. S., Oh, S. J., Al-Kofahi, Y. A., Lim, Y. J., Smith, K. L., Turner, J. N., et al. (2006). Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion. Journal of Neural Engineering, 3, 196–207.CrossRefGoogle Scholar
- 43.Okamura, A. M., Simone, C., & O’Leary, M. D. (2004). Force modeling for needle insertion into soft tissue. IEEE Transaction Biomedical Engineering, 51(10), 1707–1716.CrossRefGoogle Scholar
- 44.Casanova, F., Carney, P. R., & Sarntinoranont, M. (2014). In vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain. Journal of Neuroscience Methods, 237, 79–89.CrossRefGoogle Scholar
- 45.Hing, J. T., Brooks, A. D., & Desai, J. P. (2007). A biplanar fluoroscopic approach for the measurement, modeling. Medical Image Analysis, 11, 62–78.CrossRefGoogle Scholar
- 46.Salleh, F. H. M., Arif, S. H., Zainudin, S., & Raih, M. F. (2015). Reconstructing gene regulatory networks from knock-out data using Gaussian noise model and Pearson correlation coefficient. Computational Biology and Chemistry, 59, 3–14.CrossRefGoogle Scholar