Skip to main content

Advertisement

Log in

Neuroimaging Technological Advancements for Targeting in Functional Neurosurgery

  • Neuroimaging (N Pavese, Section Editor)
  • Published:
Current Neurology and Neuroscience Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Ablations and particularly deep brain stimulation (DBS) of a variety of CNS targets are established therapeutic tools for movement disorders. Accurate targeting of the intended structure is crucial for optimal clinical outcomes. However, most targets used in functional neurosurgery are sub-optimally visualized on routine MRI. This article reviews recent neuroimaging advancements for targeting in movement disorders.

Recent Findings

Dedicated MRI sequences can often visualize to some degree anatomical structures commonly targeted during DBS surgery, including at 1.5-T field strengths. Due to recent technological advancements, MR images using ultra-high magnetic field strengths and new acquisition parameters allow for markedly improved visualization of common movement disorder targets. In addition, novel neuroimaging techniques have enabled group-level analysis of DBS patients and delineation of areas associated with clinical benefits. These areas might diverge from the conventionally targeted nuclei and may instead correspond to white matter tracts or hubs of functional networks.

Summary

Neuroimaging advancements have enabled improved direct visualization-based targeting as well as optimization and adjustment of conventionally targeted structures.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Lozano AM, Lipsman N. Probing and regulating dysfunctional circuits using deep brain stimulation. Neuron. 2013;77(3):406–24. https://doi.org/10.1016/j.neuron.2013.01.020.

    Article  CAS  PubMed  Google Scholar 

  2. Okun MS. Deep-brain stimulation for Parkinson’s disease. N Engl J Med. 2012;367(16):1529–38. https://doi.org/10.1056/NEJMct1208070.

    Article  CAS  PubMed  Google Scholar 

  3. Follett KA, Weaver FM, Stern M, Hur K, Harris CL, Luo P, et al. Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease. N Engl J Med. 2010;362(22):2077–91. https://doi.org/10.1056/NEJMoa0907083.

    Article  CAS  PubMed  Google Scholar 

  4. Lozano AM, Lipsman N, Bergman H, Brown P, Chabardes S, Chang JW, et al. Deep brain stimulation: current challenges and future directions. Nat Rev Neurol. 2019;15:148–60. https://doi.org/10.1038/s41582-018-0128-2.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Li Z, Zhang JG, Ye Y, Li X. Review on factors affecting targeting accuracy of deep brain stimulation electrode implantation between 2001 and 2015. Stereotact Funct Neurosurg. 2016;94(6):351–62. https://doi.org/10.1159/000449206.

    Article  PubMed  Google Scholar 

  6. Bot M, Schuurman PR, Odekerken VJJ, Verhagen R, Contarino FM, De Bie RMA, et al. Deep brain stimulation for Parkinson’s disease: defining the optimal location within the subthalamic nucleus. J Neurol Neurosurg Psychiatry. 2018;89(5):493–8. https://doi.org/10.1136/jnnp-2017-316907.

    Article  PubMed  Google Scholar 

  7. Ranjan M, Boutet A, Xu DS, Lozano CS, Kumar R, Fasano A, et al. Subthalamic nucleus visualization on routine clinical preoperative MRI scans: a retrospective study of clinical and image characteristics predicting its visualization. Stereotact Funct Neurosurg. 2018;96(2):120–6. https://doi.org/10.1159/000488397.

    Article  PubMed  Google Scholar 

  8. Abosch A, Yacoub E, Ugurbil K, Harel N. An assessment of current brain targets for deep brain stimulation surgery with susceptibility-weighted imaging at 7 tesla. Neurosurgery. 2010;67(6):1745–1756; discussion 56. 10.1227/NEU.0b013e3181f74105.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bejjani BP, Dormont D, Pidoux B, Yelnik J, Damier P, Arnulf I, et al. 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. https://doi.org/10.3171/jns.2000.92.4.0615.

    Article  CAS  PubMed  Google Scholar 

  10. Foltynie T, Zrinzo L, Martinez-Torres I, Tripoliti E, Petersen E, Holl E, et al. MRI-guided STN DBS in Parkinson’s disease without microelectrode recording: efficacy and safety. J Neurol Neurosurg Psychiatry. 2011;82(4):358–63. https://doi.org/10.1136/jnnp.2010.205542 These authors report MRI-guided STN DBS in Parkinson’s disease patients (without MERs) with good clinical benefits and very low morbidity.

    Article  CAS  PubMed  Google Scholar 

  11. Hariz MI, Krack P, Melvill R, Jorgensen JV, Hamel W, Hirabayashi H, et al. A quick and universal method for stereotactic visualization of the subthalamic nucleus before and after implantation of deep brain stimulation electrodes. Stereotact Funct Neurosurg. 2003;80(1–4):96–101. https://doi.org/10.1159/000075167.

    Article  PubMed  Google Scholar 

  12. Patel NK, Plaha P, Gill SS. Magnetic resonance imaging-directed method for functional neurosurgery using implantable guide tubes. Neurosurgery. 2007;61(5 Suppl 2):358–365; discussion 65-6. https://doi.org/10.1227/01.neu.0000303994.89773.01.

    Article  Google Scholar 

  13. Hirabayashi H, Tengvar M, Hariz MI. Stereotactic imaging of the pallidal target. Mov Disord. 2002;17(Suppl 3):S130–4.

    Article  PubMed  Google Scholar 

  14. Reich CA, Hudgins PA, Sheppard SK, Starr PA, Bakay RA. A high-resolution fast spin-echo inversion-recovery sequence for preoperative localization of the internal globus pallidus. AJNR Am J Neuroradiol. 2000;21(5):928–31.

    CAS  PubMed  Google Scholar 

  15. Lozano CS, Ranjan M, Boutet A, Xu DS, Kucharczyk W, Fasano A, et al. Imaging alone versus microelectrode recording-guided targeting of the STN in patients with Parkinson’s disease. J Neurosurg. 2018;2018:1–6. https://doi.org/10.3171/2018.2.JNS172186.

    Article  Google Scholar 

  16. Spiegel EA, Wycis HT, Marks M, Lee AJ. Stereotaxic apparatus for operations on the human brain. Science. 1947;106(2754):349–50. https://doi.org/10.1126/science.106.2754.349.

    Article  CAS  PubMed  Google Scholar 

  17. Tasker RR. Simple localization for stereoencephalotomy using the “portable” central beam of the image intensifier. Confin Neurol. 1965;26(3):209–12.

    Article  CAS  PubMed  Google Scholar 

  18. Pallavaram S, Yu H, Spooner J, D'Haese PF, Bodenheimer B, Konrad PE, et al. Intersurgeon variability in the selection of anterior and posterior commissures and its potential effects on target localization. Stereotact Funct Neurosurg. 2008;86(2):113–9. https://doi.org/10.1159/000116215.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Aviles-Olmos I, Kefalopoulou Z, Tripoliti E, Candelario J, Akram H, Martinez-Torres I, et al. Long-term outcome of subthalamic nucleus deep brain stimulation for Parkinson’s disease using an MRI-guided and MRI-verified approach. J Neurol Neurosurg Psychiatry. 2014;85(12):1419–25. https://doi.org/10.1136/jnnp-2013-306907.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zrinzo L, Hariz M, Hyam JA, Foltynie T, Limousin P. Letter to the editor: a paradigm shift toward MRI-guided and MRI-verified DBS surgery. J Neurosurg. 2016;124(4):1135–7. https://doi.org/10.3171/2015.9.JNS152061.

    Article  PubMed  Google Scholar 

  21. Forstmann BU, Isaacs BR, Temel Y. Ultra high field MRI-guided deep brain stimulation. Trends Biotechnol. 2017;35(10):904–7. https://doi.org/10.1016/j.tibtech.2017.06.010.

    Article  CAS  PubMed  Google Scholar 

  22. Springer E, Dymerska B, Cardoso PL, Robinson SD, Weisstanner C, Wiest R, et al. Comparison of routine brain imaging at 3 T and 7 T. Investig Radiol. 2016;51(8):469–82. https://doi.org/10.1097/RLI.0000000000000256.

    Article  Google Scholar 

  23. Chandran AS, Bynevelt M, Lind CR. Magnetic resonance imaging of the subthalamic nucleus for deep brain stimulation. J Neurosurg. 2016;124(1):96–105. https://doi.org/10.3171/2015.1.JNS142066.

    Article  PubMed  Google Scholar 

  24. Cheng CH, Huang HM, Lin HL, Chiou SM. 1.5T versus 3T MRI for targeting subthalamic nucleus for deep brain stimulation. Br J Neurosurg. 2014;28(4):467–70. https://doi.org/10.3109/02688697.2013.854312.

    Article  PubMed  Google Scholar 

  25. Lefranc M, Derrey S, Merle P, Tir M, Constans JM, Montpellier D, et al. High-resolution 3-dimensional T2*-weighted angiography (HR 3-D SWAN): an optimized 3-T magnetic resonance imaging sequence for targeting the subthalamic nucleus. Neurosurgery. 2014;74(6):615–26. https://doi.org/10.1227/NEU.0000000000000319 This study demonstrates improved STN visualization with 3-T MRI and optimized acquisition parameters.

    Article  PubMed  Google Scholar 

  26. Warnke P. Deep brain stimulation: awake or asleep: it comes with a price either way. J Neurol Neurosurg Psychiatry. 2018;89(7):672. https://doi.org/10.1136/jnnp-2017-315710.

    Article  PubMed  Google Scholar 

  27. Ostrem JL, Ziman N, Galifianakis NB, Starr PA, Luciano MS, Katz M, et al. Clinical outcomes using ClearPoint interventional MRI for deep brain stimulation lead placement in Parkinson’s disease. J Neurosurg. 2016;124(4):908–16. https://doi.org/10.3171/2015.4.JNS15173.

    Article  PubMed  Google Scholar 

  28. Duchin Y, Shamir RR, Patriat R, Kim J, Vitek JL, Sapiro G et al. Patient-specific anatomical model for deep brain stimulation based on 7 tesla MRI. PLoS One 2018;13(8):e0201469. 10.1371/journal.pone.0201469.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Forstmann BU, de Hollander G, van Maanen L, Alkemade A, Keuken MC. Towards a mechanistic understanding of the human subcortex. Nat Rev Neurosci. 2016;18(1):57–65. https://doi.org/10.1038/nrn.2016.163.

    Article  CAS  PubMed  Google Scholar 

  30. Kraff O, Quick HH. 7T: physics, safety, and potential clinical applications. J Magn Reson Imaging. 2017;46(6):1573–89. https://doi.org/10.1002/jmri.25723.

    Article  PubMed  Google Scholar 

  31. Ewert S, Plettig P, Li N, Chakravarty MM, Collins DL, Herrington TM, et al. Toward defining deep brain stimulation targets in MNI space: a subcortical atlas based on multimodal MRI, histology and structural connectivity. Neuroimage. 2018;170:271–82. https://doi.org/10.1016/j.neuroimage.2017.05.015.

    Article  PubMed  Google Scholar 

  32. Wiggins GC, Polimeni JR, Potthast A, Schmitt M, Alagappan V, Wald LL. 96-channel receive-only head coil for 3 Tesla: design optimization and evaluation. Magn Reson Med. 2009;62(3):754–62. https://doi.org/10.1002/mrm.22028.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ugurbil K. Magnetic resonance imaging at ultrahigh fields. IEEE Trans Biomed Eng. 2014;61(5):1364–79. https://doi.org/10.1109/TBME.2014.2313619.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Massey LA, Miranda MA, Zrinzo L, Al-Helli O, Parkes HG, Thornton JS, et al. High resolution MR anatomy of the subthalamic nucleus: imaging at 9.4 T with histological validation. Neuroimage. 2012;59(3):2035–44. https://doi.org/10.1016/j.neuroimage.2011.10.016.

    Article  CAS  PubMed  Google Scholar 

  35. Alkemade A, de Hollander G, Keuken MC, Schafer A, Ott DVM, Schwarz J, et al. Comparison of T2*-weighted and QSM contrasts in Parkinson's disease to visualize the STN with MRI. PLoS One. 2017;12(4):e0176130. https://doi.org/10.1371/journal.pone.0176130 This study shows that QSM is optimal to visualize the STN compared to the more commonly used T2W*-weighted sequences.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Liu T, Eskreis-Winkler S, Schweitzer AD, Chen W, Kaplitt MG, Tsiouris AJ, et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping. Radiology. 2013;269(1):216–23. https://doi.org/10.1148/radiol.13121991.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tullo S, Devenyi GA, Patel R, Park MTM, Collins DL, Chakravarty MM. Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci Data. 2018;5:180107. https://doi.org/10.1038/sdata.2018.107.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Keuken MC, Bazin PL, Crown L, Hootsmans J, Laufer A, Muller-Axt C, et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. Neuroimage. 2014;94:40–6. https://doi.org/10.1016/j.neuroimage.2014.03.032.

    Article  CAS  PubMed  Google Scholar 

  39. Dammann P, Kraff O, Wrede KH, Ozkan N, Orzada S, Mueller OM, et al. Evaluation of hardware-related geometrical distortion in structural MRI at 7 Tesla for image-guided applications in neurosurgery. Acad Radiol. 2011;18(7):910–6. https://doi.org/10.1016/j.acra.2011.02.011.

    Article  PubMed  Google Scholar 

  40. Kanowski M, Voges J, Buentjen L, Stadler J, Heinze HJ, Tempelmann C. Direct visualization of anatomic subfields within the superior aspect of the human lateral thalamus by MRI at 7T. AJNR Am J Neuroradiol. 2014;35(9):1721–7. https://doi.org/10.3174/ajnr.A3951.

    Article  CAS  PubMed  Google Scholar 

  41. Plantinga BR, Temel Y, Duchin Y, Uludag K, Patriat R, Roebroeck A, et al. Individualized parcellation of the subthalamic nucleus in patients with Parkinson’s disease with 7T MRI. Neuroimage. 2018;168:403–11. https://doi.org/10.1016/j.neuroimage.2016.09.023 This study demonstrates that the STN can be parcellated at 7 T into sub-regions with preferential white matter connectivity.

    Article  PubMed  Google Scholar 

  42. Tourdias T, Saranathan M, Levesque IR, Su J, Rutt BK. Visualization of intra-thalamic nuclei with optimized white-matter-nulled MPRAGE at 7T. Neuroimage. 2014;84:534–45. https://doi.org/10.1016/j.neuroimage.2013.08.069 These authors report that 7 T can be used to visualize intra-thalamic nuclei, confirmed to be anatomically accurate.

    Article  PubMed  Google Scholar 

  43. Verhagen R, Schuurman PR, van den Munckhof P, Contarino MF, de Bie RM, Bour LJ. Comparative study of microelectrode recording-based STN location and MRI-based STN location in low to ultra-high field (7.0 T) T2-weighted MRI images. J Neural Eng. 2016;13(6):066009. https://doi.org/10.1088/1741-2560/13/6/066009.

    Article  PubMed  Google Scholar 

  44. Duchin Y, Abosch A, Yacoub E, Sapiro G, Harel N. Feasibility of using ultra-high field (7 T) MRI for clinical surgical targeting. PLoS One. 2012;7(5):e37328. https://doi.org/10.1371/journal.pone.0037328 These authors report that image distortions at 7 T is comparable to 1.5 T for DBS targets.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. O'Gorman RL, Jarosz JM, Samuel M, Clough C, Selway RP, Ashkan K. CT/MR image fusion in the postoperative assessment of electrodes implanted for deep brain stimulation. Stereotact Funct Neurosurg. 2009;87(4):205–10. https://doi.org/10.1159/000225973.

    Article  PubMed  Google Scholar 

  46. Dula AN, Virostko J, Shellock FG. Assessment of MRI issues at 7 T for 28 implants and other objects. AJR Am J Roentgenol. 2014;202(2):401–5. https://doi.org/10.2214/AJR.13.10777.

    Article  PubMed  Google Scholar 

  47. Feng DX, McCauley JP, Morgan-Curtis FK, Salam RA, Pennell DR, Loveless ME, et al. Evaluation of 39 medical implants at 7.0 T. Br J Radiol. 2015;88(1056):20150633. https://doi.org/10.1259/bjr.20150633.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Boutet A, Hancu I, Saha U, Crawley A, Xu DS, Ranjan M, et al. 3-Tesla MRI of deep brain stimulation patients: safety assessment of coils and pulse sequences. J Neurosurg. 2019;2019:1–9. https://doi.org/10.3171/2018.11.JNS181338.

    Article  Google Scholar 

  49. Hancu I, Boutet A, Fiveland E, Ranjan M, Prusik J, Dimarzio M, et al. On the (non-)equivalency of monopolar and bipolar settings for deep brain stimulation fMRI studies of Parkinson’s disease patients. J Magn Reson Imaging. 2018. https://doi.org/10.1002/jmri.26321.

    Article  PubMed  Google Scholar 

  50. Haast RAM, Ivanov D, Uludag K. The impact of B1+ correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T. Hum Brain Mapp. 2018;39(6):2412–25. https://doi.org/10.1002/hbm.24011.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Keuken MC, Isaacs BR, Trampel R, van der Zwaag W, Forstmann BU. Visualizing the human subcortex using ultra-high field magnetic resonance imaging. Brain Topogr. 2018;31(4):513–45. https://doi.org/10.1007/s10548-018-0638-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Yarach U, Luengviriya C, Stucht D, Godenschweger F, Schulze P, Speck O. Correction of B 0-induced geometric distortion variations in prospective motion correction for 7T MRI. MAGMA. 2016;29(3):319–32. https://doi.org/10.1007/s10334-015-0515-2.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Dimov AV, Gupta A, Kopell BH, Wang Y. High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping. J Neurosurg. 2018:1–8. https://doi.org/10.3171/2018.3.JNS172145.

  54. Liu C, Li W, Tong KA, Yeom KW, Kuzminski S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging. 2015;42(1):23–41. https://doi.org/10.1002/jmri.24768.

    Article  PubMed  Google Scholar 

  55. Nolte IS, Gerigk L, Al-Zghloul M, Groden C, Kerl HU. Visualization of the internal globus pallidus: sequence and orientation for deep brain stimulation using a standard installation protocol at 3.0 Tesla. Acta Neurochir. 2012;154(3):481–94. https://doi.org/10.1007/s00701-011-1242-8.

    Article  PubMed  Google Scholar 

  56. Cobzas D, Sun H, Walsh AJ, Lebel RM, Blevins G, Wilman AH. Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. J Magn Reson Imaging. 2015;42(6):1601–10. https://doi.org/10.1002/jmri.24951.

    Article  PubMed  Google Scholar 

  57. Visser E, Keuken MC, Forstmann BU, Jenkinson M. Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7T data at young and old age. Neuroimage. 2016;139:324–36. https://doi.org/10.1016/j.neuroimage.2016.06.039.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Keuken MC, Bazin PL, Backhouse K, Beekhuizen S, Himmer L, Kandola A, et al. Effects of aging on T(1), T(2)*, and QSM MRI values in the subcortex. Brain Struct Funct. 2017;222(6):2487–505. https://doi.org/10.1007/s00429-016-1352-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Keuken MC, Bazin PL, Schafer A, Neumann J, Turner R, Forstmann BU. Ultra-high 7T MRI of structural age-related changes of the subthalamic nucleus. J Neurosci. 2013;33(11):4896–900. https://doi.org/10.1523/JNEUROSCI.3241-12.2013.

    Article  CAS  PubMed  Google Scholar 

  60. Langkammer C, Schweser F, Shmueli K, Kames C, Li X, Guo L, et al. Quantitative susceptibility mapping: report from the 2016 reconstruction challenge. Magn Reson Med. 2018;79(3):1661–73. https://doi.org/10.1002/mrm.26830.

    Article  CAS  PubMed  Google Scholar 

  61. Zrinzo L, Zrinzo LV, Tisch S, Limousin PD, Yousry TA, Afshar F, Hariz MI Stereotactic localization of the human pedunculopontine nucleus: atlas-based coordinates and validation of a magnetic resonance imaging protocol for direct localization. Brain. 2008;131(Pt 6):1588–1598. https://doi.org/10.1093/brain/awn075.

    Article  PubMed  Google Scholar 

  62. Akram H, Dayal V, Mahlknecht P, Georgiev D, Hyam J, Foltynie T, et al. Connectivity derived thalamic segmentation in deep brain stimulation for tremor. Neuroimage Clin. 2018;18:130–42. https://doi.org/10.1016/j.nicl.2018.01.008.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Coenen VA, Allert N, Madler B. A role of diffusion tensor imaging fiber tracking in deep brain stimulation surgery: DBS of the dentato-rubro-thalamic tract (drt) for the treatment of therapy-refractory tremor. Acta Neurochir 2011;153(8):1579–1585; discussion 85. 10.1007/s00701-011-1036-z.

    Article  PubMed  Google Scholar 

  64. Kincses ZT, Szabo N, Valalik I, Kopniczky Z, Dezsi L, Klivenyi P, et al. Target identification for stereotactic thalamotomy using diffusion tractography. PLoS One. 2012;7(1):e29969. 10.1371/journal.pone.0029969.

    Article  Google Scholar 

  65. Pouratian N, Zheng Z, Bari AA, Behnke E, Elias WJ, Desalles AA. Multi-institutional evaluation of deep brain stimulation targeting using probabilistic connectivity-based thalamic segmentation. J Neurosurg. 2011;115(5):995–1004. https://doi.org/10.3171/2011.7.JNS11250.

    Article  PubMed  Google Scholar 

  66. See AAQ, King NKK. Improving surgical outcome using diffusion tensor imaging techniques in deep brain stimulation. Front Surg. 2017;4:54. https://doi.org/10.3389/fsurg.2017.00054.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Vanegas-Arroyave N, Lauro PM, Huang L, Hallett M, Horovitz SG, Zaghloul KA, et al. Tractography patterns of subthalamic nucleus deep brain stimulation. Brain. 2016;139(Pt 4):1200–10. https://doi.org/10.1093/brain/aww020.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Horn A, Reich M, Vorwerk J, Li N, Wenzel G, Fang Q et al. Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol. 2017;82(1):67–78. https://doi.org/10.1002/ana.24974. This study shows that normative data can be used to predict clinical improvement in Parkinson’s disease patients based on connectivity associated with the volume of tissue activated.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Coenen VA, Allert N, Paus S, Kronenburger M, Urbach H, Madler B. Modulation of the cerebello-thalamo-cortical network in thalamic deep brain stimulation for tremor: a diffusion tensor imaging study. Neurosurgery. 2014;75(6):657–69. https://doi.org/10.1227/NEU.0000000000000540.

    Article  PubMed  Google Scholar 

  70. Coenen VA, Varkuti B, Parpaley Y, Skodda S, Prokop T, Urbach H, et al. Postoperative neuroimaging analysis of DRT deep brain stimulation revision surgery for complicated essential tremor. Acta Neurochir. 2017;159(5):779–87. https://doi.org/10.1007/s00701-017-3134-z.

    Article  PubMed  Google Scholar 

  71. Schlaepfer TE, Bewernick BH, Kayser S, Madler B, Coenen VA. Rapid effects of deep brain stimulation for treatment-resistant major depression. Biol Psychiatry. 2013;73(12):1204–12. https://doi.org/10.1016/j.biopsych.2013.01.034.

    Article  PubMed  Google Scholar 

  72. Sammartino F, Krishna V, King NK, Lozano AM, Schwartz ML, Huang Y, et al. Tractography-based ventral intermediate nucleus targeting: novel methodology and intraoperative validation. Mov Disord. 2016;31(8):1217–25. https://doi.org/10.1002/mds.26633.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Johansen-Berg H, Behrens TE, Sillery E, Ciccarelli O, Thompson AJ, Smith SM, et al. Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cereb Cortex. 2005;15(1):31–9. https://doi.org/10.1093/cercor/bhh105.

    Article  PubMed  Google Scholar 

  74. Krishna V, Sammartino F, Agrawal P, Changizi BK, Bourekas E, Knopp MV, et al. Prospective tractography-based targeting for improved safety of focused ultrasound thalamotomy. Neurosurgery. 2019;84(1):160–8. https://doi.org/10.1093/neuros/nyy020.

    Article  PubMed  Google Scholar 

  75. Riva-Posse P, Choi KS, Holtzheimer PE, Crowell AL, Garlow SJ, Rajendra JK, et al. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol Psychiatry. 2018;23(4):843–9. https://doi.org/10.1038/mp.2017.59.

    Article  CAS  PubMed  Google Scholar 

  76. Tyagi H, Apergis-Schoute AM, Akram H, Foltynie T, Limousin P, Drummond LM, et al. A randomized trial directly comparing ventral capsule and anteromedial subthalamic nucleus stimulation in obsessive-compulsive disorder: clinical and imaging evidence for dissociable effects. Biol Psychiatry. 2019;85:726–34. https://doi.org/10.1016/j.biopsych.2019.01.017.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Anthofer J, Steib K, Fellner C, Lange M, Brawanski A, Schlaier J. The variability of atlas-based targets in relation to surrounding major fibre tracts in thalamic deep brain stimulation. Acta Neurochir. 2014;156(8):1497–504. https://doi.org/10.1007/s00701-014-2103-z.

    Article  PubMed  Google Scholar 

  78. Nowacki A, Schlaier J, Debove I, Pollo C. Validation of diffusion tensor imaging tractography to visualize the dentatorubrothalamic tract for surgical planning. J Neurosurg. 2018:1–10. https://doi.org/10.3171/2017.9.JNS171321.

    Article  PubMed  Google Scholar 

  79. Miocinovic S, Somayajula S, Chitnis S, Vitek JL. History, applications, and mechanisms of deep brain stimulation. JAMA Neurol. 2013;70(2):163–71. https://doi.org/10.1001/2013.jamaneurol.

    Article  PubMed  Google Scholar 

  80. Ewert S, Horn A, Finkel F, Li N, Kuhn AA, Herrington TM. Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei. Neuroimage. 2019;184:586–98. https://doi.org/10.1016/j.neuroimage.2018.09.061.

    Article  PubMed  Google Scholar 

  81. Horn A, Li N, Dembek TA, Kappel A, Boulay C, Ewert S, et al. Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. Neuroimage. 2019;(184):293–316. https://doi.org/10.1016/j.neuroimage.2018.08.068 The authors report a streamline easy-to-use MATLAB-based platform to perform deep brain stimulation analysis.

    Article  PubMed  Google Scholar 

  82. Chaturvedi A, Lujan JL, McIntyre CC. Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation. J Neural Eng. 2013;10(5):056023. https://doi.org/10.1088/1741-2560/10/5/056023.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Schmidt C, Grant P, Lowery M, van Rienen U. Influence of uncertainties in the material properties of brain tissue on the probabilistic volume of tissue activated. IEEE Trans Biomed Eng. 2013;60(5):1378–87. https://doi.org/10.1109/TBME.2012.2235835.

    Article  PubMed  Google Scholar 

  84. Boutet A, Ranjan M, Zhong J, Germann J, Xu D, Schwartz ML, et al. Focused ultrasound thalamotomy location determines clinical benefits in patients with essential tremor. Brain. 2018;141(12):3405–14. https://doi.org/10.1093/brain/awy278.

    Article  PubMed  Google Scholar 

  85. Miguel EC, Lopes AC, McLaughlin NCR, Noren G, Gentil AF, Hamani C, et al. Evolution of gamma knife capsulotomy for intractable obsessive-compulsive disorder. Mol Psychiatry. 2019;24(2):218–40. https://doi.org/10.1038/s41380-018-0054-0.

    Article  PubMed  Google Scholar 

  86. Akram H, Sotiropoulos SN, Jbabdi S, Georgiev D, Mahlknecht P, Hyam J, et al. Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson’s disease. Neuroimage. 2017;158:332–45. https://doi.org/10.1016/j.neuroimage.2017.07.012.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Dembek TA, Barbe MT, Astrom M, Hoevels M, Visser-Vandewalle V, Fink GR, et al. Probabilistic mapping of deep brain stimulation effects in essential tremor. Neuroimage Clin. 2017;13:164–73. https://doi.org/10.1016/j.nicl.2016.11.019 This study demonstrates how to compute a probabilistic map of clinical outcomes using statistically validated techniques.

    Article  PubMed  Google Scholar 

  88. Eisenstein SA, Koller JM, Black KD, Campbell MC, Lugar HM, Ushe M, et al. Functional anatomy of subthalamic nucleus stimulation in Parkinson disease. Ann Neurol. 2014;76(2):279–95 10.1002/ana.24204.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Butson CR, Cooper SE, Henderson JM, Wolgamuth B, McIntyre CC. Probabilistic analysis of activation volumes generated during deep brain stimulation. Neuroimage. 2011;54(3):2096–104. https://doi.org/10.1016/j.neuroimage.2010.10.059.

    Article  PubMed  Google Scholar 

  90. King NKK, Krishna V, Sammartino F, Bari A, Reddy GD, Hodaie M, et al. Anatomic targeting of the optimal location for thalamic deep brain stimulation in patients with essential tremor. World Neurosurg. 2017;107:168–74. https://doi.org/10.1016/j.wneu.2017.07.136.

    Article  PubMed  Google Scholar 

  91. Nowinski WL, Belov D, Pollak P, Benabid AL. Statistical analysis of 168 bilateral subthalamic nucleus implantations by means of the probabilistic functional atlas. Neurosurgery. 2005;57(4 Suppl):319–30 discussion -30.

    PubMed  Google Scholar 

  92. Schupbach WMM, Chabardes S, Matthies C, Pollo C, Steigerwald F, Timmermann L, et al. Directional leads for deep brain stimulation: opportunities and challenges. Mov Disord. 2017;32(10):1371–5. https://doi.org/10.1002/mds.27096.

    Article  PubMed  Google Scholar 

  93. Hellerbach A, Dembek TA, Hoevels M, Holz JA, Gierich A, Luyken K, et al. DiODe: directional orientation detection of segmented deep brain stimulation leads: a sequential algorithm based on CT imaging. Stereotact Funct Neurosurg. 2018;96(5):335–41. https://doi.org/10.1159/000494738.

    Article  PubMed  Google Scholar 

  94. Hunsche S, Neudorfer C, Majdoub FE, Maarouf M, Sauner D. Determining the rotational orientation of directional deep brain stimulation leads employing flat-panel computed tomography. Oper Neurosurg (Hagerstown). 2019;16(4):465–70. https://doi.org/10.1093/ons/opy163.

    Article  Google Scholar 

  95. Reinacher PC, Kruger MT, Coenen VA, Shah M, Roelz R, Jenkner C, et al. Determining the orientation of directional deep brain stimulation electrodes using 3D rotational fluoroscopy. AJNR Am J Neuroradiol. 2017;38(6):1111–6. https://doi.org/10.3174/ajnr.A5153.

    Article  CAS  PubMed  Google Scholar 

  96. Sitz A, Hoevels M, Hellerbach A, Gierich A, Luyken K, Dembek TA, et al. Determining the orientation angle of directional leads for deep brain stimulation using computed tomography and digital x-ray imaging: a phantom study. Med Phys. 2017;44(9):4463–73. https://doi.org/10.1002/mp.12424.

    Article  PubMed  Google Scholar 

  97. Anderson DN, Osting B, Vorwerk J, Dorval AD, Butson CR. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J Neural Eng. 2018;15(2):026005. https://doi.org/10.1088/1741-2552/aaa14b.

    Article  PubMed  Google Scholar 

  98. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105–24. https://doi.org/10.1016/j.neuroimage.2013.04.127.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–65. https://doi.org/10.1152/jn.00338.2011.

    Article  PubMed  Google Scholar 

  100. Horn A. The impact of modern-day neuroimaging on the field of deep brain stimulation. Curr Opin Neurol. 2019:1. https://doi.org/10.1097/WCO.0000000000000679.

  101. Baldermann JC, Melzer C, Zapf A, Kohl S, Timmermann L, Tittgemeyer M, et al. Connectivity profile predictive of effective deep brain stimulation in obsessive-compulsive disorder. Biol Psychiatry. 2019;85:735–43. https://doi.org/10.1016/j.biopsych.2018.12.019.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alfonso Fasano.

Ethics declarations

Conflict of Interest

Alexandre Boutet, Robert Gramer, Christopher J. Steele, Gavin J. B. Elias, Jürgen Germann, Ricardo Maciel, Walter Kucharczyk each declare no potential conflicts of interest. Ludvic Zrinzo reports honoraria for presenting educational material at meetings from Medtronic and Boston Scientific, outside the submitted work. Andres M. Lozano reports personal fees from Medtronic, St Jude, and Boston Scientific and Functional Neuromodulation, during the conduct of the study, and grants from GE Healthcare, outside the submitted work. Alfonso Fasano reports grants, personal fees, and non-financial support from Abbvie; grants, personal fees, and non-financial support from Medtronic; grants and personal fees from Boston Scientific; personal fees from Sunovion; personal fees from Chiesi farmaceutici; personal fees from UCB; and grants and personal fees from Ipsen, outside the submitted work.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Neuroimaging

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boutet, A., Gramer, R., Steele, C.J. et al. Neuroimaging Technological Advancements for Targeting in Functional Neurosurgery. Curr Neurol Neurosci Rep 19, 42 (2019). https://doi.org/10.1007/s11910-019-0961-8

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11910-019-0961-8

Keywords

Navigation