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Influence of heterogeneous and anisotropic tissue conductivity on electric field distribution in deep brain stimulation

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Abstract

The aim was to quantify the influence of heterogeneous isotropic and heterogeneous anisotropic tissue on the spatial distribution of the electric field during deep brain stimulation (DBS). Three finite element tissue models were created of one patient treated with DBS. Tissue conductivity was modelled as (I) homogeneous isotropic, (II) heterogeneous isotropic based on MRI, and (III) heterogeneous anisotropic based on diffusion tensor MRI. Modelled DBS electrodes were positioned in the subthalamic area, the pallidum, and the internal capsule in each tissue model. Electric fields generated during DBS were simulated for each model and target-combination and visualized with isolevels at 0.20 (inner), and 0.05 V mm−1 (outer). Statistical and vector analysis was used for evaluation of the distribution of the electric field. Heterogeneous isotropic tissue altered the spatial distribution of the electric field by up to 4% at inner, and up to 10% at outer isolevel. Heterogeneous anisotropic tissue influenced the distribution of the electric field by up to 18 and 15% at each isolevel, respectively. The influence of heterogeneous and anisotropic tissue on the electric field may be clinically relevant in anatomic regions that are functionally subdivided and surrounded by multiple fibres of passage.

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References

  1. Pahwa R, Wilkinson SB, Overman J, Lyons KE (2005) Preoperative clinical predictors of response to bilateral subthalamic stimulation in patients with Parkinson’s disease. Stereotact Funct Neurosurg 832–3:80–83

    Article  Google Scholar 

  2. Isaias I U, Alterman R L, and Tagliati M (2008) Outcome predictors of pallidal stimulation in patients with primary dystonia: the role of disease duration. Brain 131Pt 7: 1895–902.

    Google Scholar 

  3. Vasques X, Cif L, Gonzalez V, Nicholson C, Coubes P (2009) Factors predicting improvement in primary generalized dystonia treated by pallidal deep brain stimulation. Mov Disord 246:846–853

    Article  Google Scholar 

  4. Vasques X, Cif L, Hess O, Gavarini S, Mennessier G, Coubes P (2009) Prognostic value of globus pallidus internus volume in primary dystonia treated by deep brain stimulation. J Neurosurg 1102:220–228

    Google Scholar 

  5. Hemm S, Wårdell K (2010) Stereotactic implantation of deep brain stimulation electrodes: a review of technical systems, methods and emerging tools. Med Biol Eng Comput 487:611–624

    Article  Google Scholar 

  6. Åström M, Zrinzo LU, Tisch S, Tripoliti E, Hariz MI, Wårdell K (2009) Method for patient-specific finite element modeling and simulation of deep brain stimulation. Med Biol Eng Comput 471:21–28

    Article  Google Scholar 

  7. Butson CR, Cooper SE, Henderson JM, McIntyre CC (2007) Patient-specific analysis of the volume of tissue activated during deep brain stimulation. Neuroimage 342:661–670

    Article  Google Scholar 

  8. Åström M, Tripoliti E, Hariz MI, Zrinzo LU, Martinez-Torres I, Limousin P, Wårdell K (2010) Patient-specific model-based investigation of speech intelligibility and movement during deep brain stimulation. Stereotact Funct Neurosurg 884:224–233

    Article  Google Scholar 

  9. Nicholson PW (1965) Specific impedance of cerebral white matter. Exp Neurol 134:386–401

    Article  Google Scholar 

  10. Sotiropoulos SN, Steinmetz PN (2007) Assessing the direct effects of deep brain stimulation using embedded axon models. J Neural Eng 42:107–119

    Article  Google Scholar 

  11. Tuch DS, Wedeen VJ, Dale AM, George JS, Belliveau JW (2001) Conductivity tensor mapping of the human brain using diffusion tensor MRI. Proc Natl Acad Sci USA 9820:11697–11701

    Article  Google Scholar 

  12. Tuch DS, Wedeen VJ, Dale AM, George JS, Belliveau JW (1999) Conductivity mapping of biological tissue using diffusion MRI. Ann N Y Acad Sci 888:314–316

    Article  PubMed  CAS  Google Scholar 

  13. Haueisen J, Tuch DS, Ramon C, Schimpf PH, Wedeen VJ, George JS, Belliveau JW (2002) The influence of brain tissue anisotropy on human EEG and MEG. Neuroimage 151:159–166

    Article  Google Scholar 

  14. Walckiers G, Fuchs B, Thiran JP, Mosig JR, Pollo C (2010) Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation. J Neurosci Methods 1861:90–96

    Article  Google Scholar 

  15. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S (2004) Fiber tract-based atlas of human white matter anatomy. Radiology 2301:77–87

    Article  Google Scholar 

  16. Chaturvedi A, Butson CR, Lempka SF, Cooper SE, McIntyre CC (2010) Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions. Brain Stimul 32:65–67

    Article  Google Scholar 

  17. Andreuccetti D, Fossi R, Petrucci C (2005) Dielectric properties of body tissue. Italian National Research Council, Institute for Applied Physics, Florence. http://niremf.ifac.cnr.it/tissprop/

  18. Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S (2006) DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 812:106–116

    Article  Google Scholar 

  19. Cheng D K (1989) Field and Wave Electromagnetics. Vol. ISBN 0-201-52820-7. 1989: Addison-Wesley Publishing Company Inc., New York

  20. Kindlmann G (2004) Superquadric tensor glyphs. In: Proceedings IEEE TVCG/EG symposium on visualization, pp 147–154

  21. Ennis DB, Kindlman G, Rodriguez I, Helm PA, McVeigh ER (2005) Visualization of tensor fields using superquadric glyphs. Magn Reson Med 531:169–176

    Article  Google Scholar 

  22. Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R (2002) Processing and visualization for diffusion tensor MRI. Med Image Anal 62:93–108

    Article  Google Scholar 

  23. Rattay F (1986) Analysis of models for external stimulation of axons. IEEE Trans Biomed Eng 3310:974–977

    Article  Google Scholar 

  24. Butson CR, McIntyre CC (2005) Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clin Neurophysiol 11610:2490–2500

    Article  Google Scholar 

  25. Butson CR, Maks CB, McIntyre CC (2006) Sources and effects of electrode impedance during deep brain stimulation. Clin Neurophysiol 1172:447–454

    Article  Google Scholar 

  26. Yousif N, Bayford R, Wang S, Liu X (2008) Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation. Neuroscience 1523:683–691

    Article  Google Scholar 

  27. Kuncel AM, Grill WM (2004) Selection of stimulus parameters for deep brain stimulation. Clin Neurophysiol 11511:2431–2441

    Article  Google Scholar 

  28. Kuncel AM, Cooper SE, Grill WM (2008) A method to estimate the spatial extent of activation in thalamic deep brain stimulation. Clin Neurophysiol 1199:2148–2158

    Article  Google Scholar 

  29. Hemm S, Mennessier G, Vayssiere N, Cif L, El Fertit H, Coubes P (2005) Deep brain stimulation in movement disorders: stereotactic coregistration of two-dimensional electrical field modeling and magnetic resonance imaging. J Neurosurg 1036:949–955

    Google Scholar 

  30. Mikos A, Bowers D, Noecker AM, McIntyre CC, Won M, Chaturvedi A, Foote KD, Okun (2011) Patient-specific analysis of the relationship between the volume of tissue activated during DBS and verbal fluency. Neuroimage 54(Suppl 1):S238–S246

    Article  PubMed  Google Scholar 

  31. Frankemolle AM, Wu J, Noecker AM, Voelcker-Rehage C, Ho JC, Vitek JL, McIntyre CC, Alberts JL (2010) Reversing cognitive-motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming. Brain 133(Pt 3):746–761

    Article  PubMed  Google Scholar 

  32. Geddes LA, Baker LE (1967) The specific resistance of biological material–a compendium of data for the biomedical engineer and physiologist. Med Biol Eng 53:271–293

    Article  Google Scholar 

  33. Schwan HP, Kay CF (1957) The conductivity of living tissues. Ann N Y Acad Sci 656:1007–1013

    Article  Google Scholar 

  34. Latikka J, Kuurne T, Eskola H (2001) Conductivity of living intracranial tissues. Phys Med Biol 466:1611–1616

    Article  Google Scholar 

  35. Hemm S, Vayssiere N, Mennessier G, Cif L, Zanca M, Ravel P, Frerebeau P, Coubes P (2004) Evolution of brain impedance in dystonic patients treated by GPi electrical stimulation. Neuromodulation 7(2):75

    Article  Google Scholar 

  36. Gabriel C, Gabriel S, Corthout E (1996) The dielectric properties of biological tissues: I literature survey. Phys Med Biol 4111:2231–2249

    Article  Google Scholar 

  37. Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 4111:2251–2269

    Article  Google Scholar 

  38. Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: III parametric models for the dielectric spectrum of tissues. Phys Med Biol 4111:2271–2293

    Article  Google Scholar 

  39. McIntyre CC, Mori S, Sherman DL, Thakor NV, Vitek JL (2004) Electric field and stimulating influence generated by deep brain stimulation of the subthalamic nucleus. Clin Neurophysiol 1153:589–595

    Article  Google Scholar 

  40. Landman BA, Wan H, Bogovic JA, Bazin PL, Prince JL (2010) Resolution of crossing fibers with constrained compressed sensing using traditional diffusion tensor MRI. Proc Soc Photo Opt Instrum Eng 7623:76231H

    PubMed  Google Scholar 

  41. Wei XF, Grill WM (2005) Current density distributions, field distributions and impedance analysis of segmented deep brain stimulation electrodes. J Neural Eng 24:139–147

    Article  Google Scholar 

  42. Johnson MD, McIntyre CC (2008) Quantifying the neural elements activated and inhibited by globus pallidus deep brain stimulation. J Neurophysiol 1005:2549–2563

    Article  Google Scholar 

  43. Grant PF, Lowery MM (2009) Electric field distribution in a finite-volume head model of deep brain stimulation. Med Eng Phys 319:1095–1103

    Article  Google Scholar 

  44. Lemaire JJ, Coste J, Ouchchane L, Caire F, Nuti C, Derost P, Cristini V, Gabrillargues J, Hemm S, Durif F, Chazal J (2007) Brain mapping in stereotactic surgery: a brief overview from the probabilistic targeting to the patient-based anatomic mapping. Neuroimage 37(Suppl 1):S109–S115

    Article  PubMed  Google Scholar 

  45. Åström M, Johansson JD, Hariz MI, Eriksson O, Wårdell K (2006) The effect of cystic cavities on deep brain stimulation in the basal ganglia: a simulation-based study. J Neural Eng 32:132–138

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Simone Hemm-Ode, PhD, Göran Salerud, PhD, and Mats Andersson, PhD, for valuable input, and Associate professor Eva Enqvist, for statistical contributions. This study was financially supported by the Swedish Foundation for Strategic Research (SSF), Swedish Research Council (VR, grant number 621-2008-3013), and Swedish Governmental Agency for Innovation Systems (VINNOVA, group grant number 311-2006-7661).

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None of the authors reported any conflicts of interest.

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Correspondence to Mattias Åström.

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Åström, M., Lemaire, JJ. & Wårdell, K. Influence of heterogeneous and anisotropic tissue conductivity on electric field distribution in deep brain stimulation. Med Biol Eng Comput 50, 23–32 (2012). https://doi.org/10.1007/s11517-011-0842-z

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