Neurosurgical Anatomy and Approaches to Simulation in Neurosurgical Training

  • Antonio Bernardo
  • Alexander I. Evins
Part of the Comprehensive Healthcare Simulation book series (CHS)


Quality of neurosurgical care and patient outcomes are inextricably linked to surgical and technical proficiency and a thorough working knowledge of microsurgical anatomy. Simulated neurosurgical training is essential for the development and refinement of technical skills prior to their use on a living patient. Recent biotechnological advances—including 3D microscopy and endoscopy, 3D printing, virtual reality, surgical simulation, surgical robotics, and advanced neuroimaging—have proved to reduce the learning curve, improve conceptual understanding of complex anatomy, and enhance visuospatial skills in neurosurgical training. For developing neurosurgeons, such tools can reduce the learning curve, improve conceptual understanding of complex anatomy, and enhance visuospatial skills. We explore the current and future roles and application of virtual reality and simulation in neurosurgical training.


Virtual reality Simulation Neurosurgery Surgical training Robotics Augmented reality Stereoscopic 3D 

Abbreviations and Acronyms






6 Degrees


Apparent diffusion coefficient


Augmented reality


Augmented reality and artificial intelligence


Computed tomography angiography


Fractional anisotropy


Functional magnetic resonance


Head-mounted displays


Magnetic resonance angiography


Operating microscope


Operating room


Red green blue


Simulation markup language


Virtual reality


Visualization tool kit


  1. 1.
    Cappabianca P, Magro F. The lesson of anatomy. Surg Neurol. 2009;71:597–89.CrossRefPubMedGoogle Scholar
  2. 2.
    Moon K, Filis AK, Cohen AR. The birth and evolution of neuroscience through cadaveric dissection. Neurosurgery. 2010;67:799–810.CrossRefPubMedGoogle Scholar
  3. 3.
    Aboud E, Al-Mefty O, Yaşargil MG. New laboratory model for neurosurgical training that simulates live surgery. J Neurosurg. 2002;97:1367–72.CrossRefPubMedGoogle Scholar
  4. 4.
    Kockro RA, Stadie A, Schwandt E, et al. A collaborative virtual reality environment for neurosurgical planning and training. Neurosurgery. 2007;61:379–91.CrossRefPubMedGoogle Scholar
  5. 5.
    Kin T, Nakatomi H, Shojima M, et al. A new strategic neurosurgical planning tool for brainstem cavernous malformations using interactive computer graphics with multimodal fusion images. J Neurosurg. 2012;117(1):78–88.CrossRefPubMedGoogle Scholar
  6. 6.
    Abhari K, Baxter JSH, Chen ECS, et al. Training for planning tumour resection: augmented reality and human factors. IEEE Trans Biomed Eng. 2015;62(6):1466–77.CrossRefPubMedGoogle Scholar
  7. 7.
    Moisi M, Tubbs RS, Page J, et al. Training medical novices in spinal microsurgery: does the modality matter? A pilot study comparing traditional microscopic surgery and a novel robotic optoelectronic visualization tool. Cureus. 2016;8(1):e469.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Ruisoto P, Juanes JA, Contador I, Mayoral P, Prats-Galino A. Experimental evidence for improved neuroimaging interpretation using three-dimensional graphic models. Anat Sci Educ. 2012;5(3):132–7.CrossRefPubMedGoogle Scholar
  9. 9.
    Weigl M, Stefan P, Abhari K. Intra-operative disruptions, surgeon’s mental workload, and technical performance in a full-scale simulated procedure. Surg Endosc. 2015;30(2):559–66.CrossRefPubMedGoogle Scholar
  10. 10.
    Valdés PA, Roberts DW, Lu F-K, Golby A. Optical technologies for intraoperative neurosurgical guidance. Neurosurg Focus. 2016;40(3):E8.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Healey AN, Sevdalis N, Vincent CA. Measuring intra-operative interference from distraction and interruption observed in the operating theatre. Ergonomics. 2006;49:589–604.CrossRefPubMedGoogle Scholar
  12. 12.
    Christian CK, Gustafson ML, Roth EM, et al. A prospective study of patient safety in the operating room. Surgery. 2006;139:159–73.CrossRefPubMedGoogle Scholar
  13. 13.
    Etchells E, O’Neill C, Bernstein M. Patient safety in surgery: error detection and prevention. World J Surg. 2003;27:936–42.CrossRefPubMedGoogle Scholar
  14. 14.
    Schreuder HW, Wolswijk R, Zweemer RP, Schijven MP, Verheijen RH. Training and learning robotic surgery, time for a more structured approach: a systematic review. BJOG. 2012;119:137–49.CrossRefPubMedGoogle Scholar
  15. 15.
    Maertens H, Madani A, Landry T, Vermassen F, Van Herzeele I, Aggarwal R. Systematic review of e-learning for surgical training. Br J Surg. 2016;103:1428–37.CrossRefPubMedGoogle Scholar
  16. 16.
    Urgun K, Toktas ZO, Akakin A, Yilmaz B, Sahin S, Kilic TA. Very quickly prepared, colored silicone material for injecting into cerebral vasculature for anatomical dissection: a novel and suitable material for both fresh and non-fresh cadavers. Turk Neurosurg. 2016;26(4):568–73.PubMedGoogle Scholar
  17. 17.
    O’Donnell RD, Eggemeier FT. Workload assessment methodology. In: Handbook of perception and human performance. Cognitive processes and performance, vol. 2. New York: Wiley; 1986. p. 42.1–4.Google Scholar
  18. 18.
    Selye H. The evolution of the stress concept. Am Sci. 1973;61:692–9.PubMedGoogle Scholar
  19. 19.
    Satava RM. Historical review of surgical simulation-a personal prospective. World J Surg. 2008;32:141.CrossRefPubMedGoogle Scholar
  20. 20.
    Hohl BL, Neal DW, Kleinhenz DT, Hoh DJ, Mocco J, Barker FGII. Higher complications and no improvement in mortality in the ACGME resident duty-hour restriction era: an analysis of more than 107.000 neurosurgical trauma patients in Nationwide inpatient sample database. Neurosurgery. 2012;70:1369–82.CrossRefGoogle Scholar
  21. 21.
    Selden NR, Barbaro N, Origitano TC, Burchiel KJ. Fundamental skills for entering neurosurgery residents: report of a Pacific region “boot camp” pilot course, 2009. Neurosurgery. 2011;68:759–64.CrossRefPubMedGoogle Scholar
  22. 22.
    Bohnen HG, Gaillard AW. The effects of sleep loss in a combined tracking and time estimation task. Ergonomics. 1994;37:1021–30.CrossRefPubMedGoogle Scholar
  23. 23.
    Mascord DJ, Heath RA. Behavioral and physiological indices of fatigue in a visual tracking task. J Saf Res. 1992;23:19–25.CrossRefGoogle Scholar
  24. 24.
    Borghini G, Astolfi L, Vecchiato G, Mattia D, Babiloni F. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev. 2014;44:58–75.CrossRefPubMedGoogle Scholar
  25. 25.
    Muns A, Meixensberger J, Lindner D. Evaluation of a novel phantom-based neurosurgical training system. Surg Neurol Int. 2014;5:173.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Patel A, Koshy N, Ortega-Barnett J, Chan HC, Kuo Y, Luciano C, et al. Neurological tactile discrimination training with haptic-based virtual reality simulation. Neurol Res. 2014;36:1035–9.CrossRefPubMedGoogle Scholar
  27. 27.
    Ofek E, Pizov R, Bitterman N. From a radial operating theatre to a self-contained operating table. Anaesthesia. 2006;61:548–52.CrossRefPubMedGoogle Scholar
  28. 28.
    Ganju A, Aoun SG, Daou MR, Ahmadieh TY, Chang Wang L, et al. The role of simulation in neurosurgical education: a survey of 99 United States neurosurgery program directors. World Neurosurg. 2013;80:e1–8.CrossRefPubMedGoogle Scholar
  29. 29.
    Kshettry VR, Mullin JP, Schlenk R, Recinos PF, Benzel EC. The role of laboratory dissection training in neurosurgical residency: results of a national survey. World Neurosurg. 2014;82:554–9.CrossRefPubMedGoogle Scholar
  30. 30.
    Wehbe-Janek H, Colbert CY, Govednik-Horny C, White BAA, Thomas S, Shabahang M. Residents’ perspectives of the value of a simulation curriculum in a general surgery residency program: a multimethod study of stakeholder feedback. Surgery. 2012;151(6):815–21.CrossRefPubMedGoogle Scholar
  31. 31.
    Breimer GE, Bodani V, Looi T, Drake JM. Design and evaluation of a new synthetic brain simulator for endoscopic third ventriculostomy. J Neurosurg. 2015;15(1):82–8.Google Scholar
  32. 32.
    Congress of Neurological Surgeons. Congress Quarterly.; 2016 Accessed 1 Dec 2016.
  33. 33.
    Cleary DR, Siler DA, Whitney N, Selden NR. A microcontroller-based simulation of dural venous sinus injury for neurosurgical training. J Neurosurg. 2017:1–7.Google Scholar
  34. 34.
    Grandjean E. Fatigue in industry. Br J Ind Med. 1979;36:175–86.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Grandjean E. Fitting the task to the man: a textbook of occupational ergonomics. 4th ed: Taylor & Francis; 1988. philadelphia, PAGoogle Scholar
  36. 36.
    Johns MW, Chapman R, Crowley K, Tucker A. A new method for assessing the risks of drowsiness while driving. Somnologie. 2008;12:66–74.CrossRefGoogle Scholar
  37. 37.
    Hull L, Arora S, Kassab E, Kneebone R, Sevdalis N. Assessment of stress and teamwork in the operating room: an exploratory study. Am J Surg. 2011;201:24–30.CrossRefPubMedGoogle Scholar
  38. 38.
    Arora S, Sevdalis N, Nestel D, Woloshynowych M, Darzi A, Kneebone R. The impact of stress on surgical performance: a systematic review of the literature. Surgery. 2010;147:318–30. e1-e6CrossRefPubMedGoogle Scholar
  39. 39.
    Wetzel CM, Kneebone RL, Woloshynowych M, et al. The effects of stress on surgical performance. Am J Surg. 2006;191:5–10.CrossRefPubMedGoogle Scholar
  40. 40.
    Cinaz B, La Marca R, Arnrich B, Tröster G Monitoring of mental workload levels. Proceedings of the IADIS International Conference e-Healt. pp. 189–193. 2010.Google Scholar
  41. 41.
    Yurko YY, Scerbo MW, Prabhu AS, Acker CE, Stefanidis D. Higher mental workload is associated with poorer laparoscopic performance as measured by the NASA-TLX tool. Sim Healthcare. 2010;5:267–71.CrossRefGoogle Scholar
  42. 42.
    Zheng B, Cassera MA, Martinec DV, Spaun GO, Swanstrom LL. Measuring mental workload during the performance of advanced laparoscopic tasks. Surg Endosc. 2010;24:45–50.CrossRefPubMedGoogle Scholar
  43. 43.
    Hart SG, Staveland LE. Development of NASA-TLX: results of empirical and theoretical research. In: Human Mental Workload. Amsterdam: Elsevier; 1988. p. 139–83.CrossRefGoogle Scholar
  44. 44.
    Montero PN, Acker CE, Heniford BT, et al. Single incision laparoscopic surgery (SILS) is associated with poorer performance and increased surgeon workload compared with standard laparoscopy. Am Surg. 2011;77:73–7.PubMedGoogle Scholar
  45. 45.
    Carswell C, Clarke D, Seales W. Assessing mental workload during laparoscopic surgery. Surg Innov. 2005;12:80–90.CrossRefPubMedGoogle Scholar
  46. 46.
    Carter FJ, Schijven MP, Aggarwal R, et al. Consensus guidelines for validation of virtual reality surgical simulators. Surg Endosc. 2005;19(12):1523–32.CrossRefPubMedGoogle Scholar
  47. 47.
    Das P, Goyal T, Xue A, Kalatoor S, Guillaume D. Simulation training in neurological surgery. Austin Neurosurg Open Access. 2014;1(1):1004–10.Google Scholar
  48. 48.
    Anichini G, Evins AI, Boeris D, Stieg PE, Bernardo A. Three-dimensional endoscope-assisted surgical approach to the foramen magnum and craniovertebral junction: minimizing bone resection with the aid of the endoscope. World Neurosurg. 2014;82(6):e797–805.CrossRefPubMedGoogle Scholar
  49. 49.
    Raspelli S, Pallavicini F, Carelli L, et al. Validating the neuro VR-based virtual version of the multiple errands test: preliminary results. Presence Teleop Virt. 2012;21(1):31–42.CrossRefGoogle Scholar
  50. 50.
    UIC BVIS Students. Surgical simulation and augmented reality.; 2016 Accessed 1 Dec 2016.
  51. 51.
    Willaert WIM, Aggarwal R, Van Herzeele I, Cheshire NJ, Vermassen FE. Recent advancements in medical simulation: patient-specific virtual reality simulation. World J Surg. 2012;36(7):1703–12.CrossRefPubMedGoogle Scholar
  52. 52.
    Kockro RA, Reisch R, Serra L, Goh LC, Lee E, Stadie AT. Image-guided neurosurgery with 3-dimensional multimodal imaging data on a stereoscopic monitor. Neurosurgery. 2013;72:A78–88.CrossRefGoogle Scholar
  53. 53.
    Barsom EZ, Graafland M, Schijven MP. Systematic review on the effectiveness of augmented reality applications in medical training. Surg Endosc. 2016;30:4174–83.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Doulgeris JD, Gonzalez-Blohm SA, Filis AK, Shea Thomas M, Aghayev K, Vrionis FD. Robotics in neurosurgery: evolution, current challenges, and compromises. Cancer Control. 2015;22(3):352–9.CrossRefPubMedGoogle Scholar
  55. 55.
    Goetz J, Engineering. New technology may help surgeons save lives. Accessed 1 Dec 2016.
  56. 56.
    Espadaler JM, Conesa G. (2011) Navigated repetitive transcranial magnetic stimulation (TMS) for language mapping: a new tool for surgical planning. In: Duffau H. (eds) Brain Mapp. Springer, Vienna.Google Scholar
  57. 57.
    De Notaris M, Palma K, Serra L, et al. A three-dimensional computer-based perspective of the skull base. World Neurosurg. 2014;82(6):S41–8.CrossRefPubMedGoogle Scholar
  58. 58.
    Christian E, Yu C, Apuzzo MLJ. Focused ultrasound: relevant history and prospects for the addition of mechanical energy to the neurosurgical armamentarium. World Neurosurg. 2014;82(3–4):354–65.CrossRefPubMedGoogle Scholar
  59. 59.
    Robison RA, Liu CY, Apuzzo MLJ. Man, mind, and machine: the past and future of virtual reality simulation in neurologic surgery. World Neurosurg. 2011;76(5):419–30.CrossRefPubMedGoogle Scholar
  60. 60.
    Hochman JB, Kraut J, Kazmerik K, Unger BJ. Generation of a 3D printed temporal bone model with internal fidelity and validation of the mechanical construct. Otolaryngol Head Neck Surg. 2013;150(3):448–54.CrossRefPubMedGoogle Scholar
  61. 61.
    Lobel DA, Elder JB, Schirmer CM, Bowyer MW, Rezai AR. A novel craniotomy simulator provides a validated method to enhance education in the management of traumatic brain injury. Neurosurgery. 2013;73(Suppl 1):57–65.CrossRefPubMedGoogle Scholar
  62. 62.
    Hooten KG, Lister JR, Lombard G, et al. Mixed reality ventriculostomy simulation. Neurosurgery. 2014;10:576–81.CrossRefPubMedGoogle Scholar
  63. 63.
    Ramaswamy A, Monsuez B, Tapus A. Saferobots: a model-driven approach for designing robotic software architectures. Collab Technolog Syst. 2014:131–4.Google Scholar
  64. 64.
    Dharmendra, La G, Saxena K. AUC based software defect prediction for object-oriented systems. e-Learning. 2016;64(57)Google Scholar
  65. 65.
    Lee B, Liu CY, Apuzzo MLJ. Quantum computing: a prime modality in Neurosurgery’s future. World Neurosurg. 2012;78(5):404–8. 3CrossRefPubMedGoogle Scholar
  66. 66.
    Sabbadin M. Interaction and rendering with harvested 3D data. 2016.Google Scholar
  67. 67.
    Kurzhals K, Burch M, Pfeiffer T, Weiskopf D. Eye tracking in computer-based visualization. Comput Sci Eng. 2015;17(5):64–71.CrossRefGoogle Scholar
  68. 68.
    DeFanti TA, Sandin DJ, Cruz-Neira CA. “Room” with a “view”. IEEE Spectr. 1993;30(10):30–3.CrossRefGoogle Scholar
  69. 69.
    Lemole GM, Banerjee PP, Luciano C, Neckrysh S, Charbel FT. Virtual reality in neurosurgical education. Neurosurgery. 2007;61(1):142–9.CrossRefPubMedGoogle Scholar
  70. 70.
    Besharati Tabrizi L, Mahvash M. Augmented reality–guided neurosurgery: accuracy and intraoperative application of an image projection technique. J Neurosurg. 2015;123(1):206–11.CrossRefPubMedGoogle Scholar
  71. 71.
    Pun T, Roth P, Bologna G, Moustakas K, Tzovaras D. Image and video processing for visually handicapped people. EURASIP J Image Video Process. 2007;2007:1–12.CrossRefGoogle Scholar
  72. 72.
    Kersten-Oertel M, Gerard I, Drouin S, et al. Augmented reality in neurovascular surgery: feasibility and first uses in the operating room. Int J Comput Assist Radiol Surg. 2015;10(11):1823–36.CrossRefPubMedGoogle Scholar
  73. 73.
    Barry Issenberg S, Mcgaghie WC, Petrusa ER, Lee Gordon D, Features SRJ. Uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10–28.CrossRefPubMedGoogle Scholar
  74. 74.
    Kirkman MA, Ahmed M, Albert AF, Wilson MH, Nandi D, Sevdalis N. The use of simulation in neurosurgical education and training. J Neurosurg. 2014;121(2):228–46. 6CrossRefPubMedGoogle Scholar
  75. 75.
    Choudhury N, Gélinas-Phaneuf N, Delorme S, Del Maestro R. Fundamentals of neurosurgery: virtual reality tasks for training and evaluation of technical skills. World Neurosurg. 2013;80(5):e9–e19.CrossRefPubMedGoogle Scholar
  76. 76.
    Bajka M, Tuchschmid S, Bachofen D, Fink D, Szekely G, Harders M. Hysteroskopie: Operations training in der Virtuellen Realität. Geburtshilfe Frauenheilkd. 2008;68(S 01). S43.Google Scholar
  77. 77.
    Morris D, Sewell C, Barbagli F, Salisbury K, Blevins NH, Girod S. Visuohaptic simulation of bone surgery for training and evaluation. IEEE Comput Graph Appl. 2006;26(6):48–57.CrossRefPubMedGoogle Scholar
  78. 78.
    Steuer J. Defining virtual reality: dimensions determining telepresence. J Commun. 1992;42(4):73–93.CrossRefGoogle Scholar
  79. 79.
    Burdea GC, Lin MC, Ribarsky W, Watson B. Guest editorial: special issue on Haptics, virtual, and augmented reality. IEEE Trans Vis Comput Graph. 2005;11(6):611–3.CrossRefPubMedGoogle Scholar
  80. 80.
    Bernardo A, Preul MC, Zabramski JM, Spetzler RF. A three-dimensional interactive virtual dissection model to simulate Transpetrous surgical avenues. Neurosurgery. 2003;52:499–505.CrossRefPubMedGoogle Scholar
  81. 81.
    Evans CH, Schenarts KD. Evolving educational techniques in surgical training. Surg Clin North Am. 2016;96:71–88.CrossRefPubMedGoogle Scholar
  82. 82.
    Willis RE, Van Sickle KR. Current status of simulation-based training in graduate medical education. Surg Clin North Am. 2015;95:767–79.CrossRefPubMedGoogle Scholar
  83. 83.
    Gasco J, Holbrook TJ, Patel A, et al. Neurosurgery simulation in residency training. Neurosurgery. 2013;73:S39–45.CrossRefGoogle Scholar
  84. 84.
    Schirmer CM, Mocco J, Elder JB. Evolving virtual reality simulation in neurosurgery. Neurosurgery. 2013;73:S127–37.CrossRefGoogle Scholar
  85. 85.
    Dimou S, Battisti RA, Hermens DF, Lagopoulos JA. Systematic review of functional magnetic resonance imaging and diffusion tensor imaging modalities used in presurgical planning of brain tumour resection. Neurosurg Rev. 2012;36(2):205–14.CrossRefPubMedGoogle Scholar
  86. 86.
    Romano A, D’Andrea G, Minniti G, et al. Pre-surgical planning and MR-tractography utility in brain tumour resection. Eur Radiol. 2009;19(12):2798–808.CrossRefPubMedGoogle Scholar
  87. 87.
    Yoshino M, Kin T, Ito A, et al. Combined use of diffusion tensor tractography and multifused contrast-enhanced FIESTA for predicting facial and cochlear nerve positions in relation to vestibular schwannoma. J Neurosurg. 2015;123(6):1480–8.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Weill Cornell Medicine, Neurological SurgeryNew YorkUSA

Personalised recommendations