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Vol. 87, No. 4, 2009
Issue release date: August 2009
Free Access
Stereotact Funct Neurosurg 2009;87:229–240
(DOI:10.1159/000225976)

Automated 3-Dimensional Brain Atlas Fitting to Microelectrode Recordings from Deep Brain Stimulation Surgeries

Luján J.L.a · Noecker A.M.a · Butson C.R.a · Cooper S.E.b · Walter B.L.b · Vitek J.L.b, c · McIntyre C.C.a, b
aDepartment of Biomedical Engineering, bCenter for Neurological Restoration, and cDepartment of Neurosciences, Cleveland Clinic Foundation, Cleveland, Ohio, USA
email Corresponding Author

Abstract

Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.


 goto top of outline Key Words

  • Deep brain stimulation
  • Brain atlas
  • Microelectrode recordings
  • Stereotactic neurosurgery
  • Stereotactic target location

 goto top of outline Abstract

Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.

Copyright © 2009 S. Karger AG, Basel


 goto top of outline References
  1. 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–S258.
  2. Richter EO, Lozano AM: Deep brain stimulation for Parkinson’s disease and movement disorders; in Rosch PJ (ed): Bioelectromagnetic Medicine. New York, Dekker, 2004, pp 265–276.
  3. Starr PA, Christine CW, Theodosopoulos PV, Lindsey N, Byrd D, Mosley A, Marks WJ Jr: Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and magnetic resonance imaging-verified lead locations. J Neurosurg 2002;97:370–387.
  4. Tasker RR, Munz M, Junn FS, Kiss ZH, Davis K, Dostrovsky JO, Lozano AM: Deep brain stimulation and thalamotomy for tremor compared. Acta Neurochir 1997;68(suppl): 49–53.
  5. Gross RE, Krack P, Rodriguez-Oroz MC, Rezai AR, Benabid AL: Electrophysiological mapping for the implantation of deep brain stimulators for Parkinson’s disease and tremor. Mov Disord 2006;21(suppl 14):S259–S283.
  6. D’Haese PF, Cetinkaya E, Konrad PE, Kao C, Dawant BM: Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance. IEEE Trans Med Imaging 2005;24:1469–1478.
  7. Finnis KW, Starreveld YP, Parrent AG, Sadikot AF, Peters TM: Three-dimensional database of subcortical electrophysiology for image-guided stereotactic functional neurosurgery. IEEE Trans Med Imaging 2003;22:93–104.
  8. Mogilner AY, Benabid AL, Rezai AR: Chronic therapeutic brain stimulation: history, current clinical indications, and future prospects; in Rosch PJ (ed): Bioelectromagnetic Medicine. New York, Dekker, 2004, pp 133–151.
  9. Andrade-Souza YM, Schwalb JM, Hamani C, Eltahawy H, Hoque T, Saint-Cyr J, Lozano AM: Comparison of three methods of targeting the subthalamic nucleus for chronic stimulation in Parkinson’s disease. Neurosurgery 2005;56:360–368.
  10. Schaltenbrand G, Wahren W: Atlas for Stereotaxy of the Human Brain, ed 2. Chicago, Year Book Medical Publishers, 1977.
  11. Falkenberg JH, McNames J, Favre J, Burchiel KJ: Automatic analysis and visualization of microelectrode recording trajectories to the subthalamic nucleus: preliminary results. Stereotact Funct Neurosurg 2006;84:35–45.
  12. Littlechild P, Varma TR, Eldridge PR, Fox S, Forster A, Fletcher N, Steiger M, Byrne P, Tyler K, Flintham S: Variability in position of the subthalamic nucleus targeted by magnetic resonance imaging and microelectrode recordings as compared to atlas co-ordinates. Stereotact Funct Neurosurg 2003;80:82–87.
  13. Moran A, Bar-Gad I, Bergman H, Israel Z: Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure. Mov Disord 2006;21:1425–1431.
  14. Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tande D, Karachi C, Ayache N, Cornu P, Agid Y: A three-dimensional, histological and deformable atlas of the human basal ganglia. 1. Atlas construction based on immunohistochemical and MRI data. Neuroimage 2007;34:618–638.
  15. Halpern CH, Danish SF, Baltuch GH, Jaggi JL: Brain shift during deep brain stimulation surgery for Parkinson’s disease. Stereotact Funct Neurosurg 2008;86:37–43.
  16. Khan MF, Mewes K, Gross RE, Skrinjar O: Assessment of brain shift related to deep brain stimulation surgery. Stereotact Funct Neurosurg 2008;86:44–53.
  17. Winkler D, Tittgemeyer M, Schwarz J, Preul C, Strecker K, Meixensberger J: The first evaluation of brain shift during functional neurosurgery by deformation field analysis. J Neurol Neurosurg Psychiatry 2005;76:1161–1163.
  18. Ganser KA, Dickhaus H, Metzner R, Wirtz CR: A deformable digital brain atlas system according to Talairach and Tournoux. Med Image Anal 2004;8:3–22.
  19. Miocinovic S, Noecker AM, Maks CB, Butson CR, McIntyre CC: Cicerone: stereotactic neurophysiological recording and deep brain stimulation electrode placement software system. Acta Neurochir 2007;97 (suppl):561–567.

    External Resources

  20. Guo T, Finnis KW, Parrent AG, Peters TM: Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries. Comput Aided Surg 2006;11:231–239.
  21. Nowinski WL, Liu J, Thirunavuukarasuu A: Quantification and visualization of the three-dimensional inconsistency of the subthalamic nucleus in the Schaltenbrand-Wahren brain atlas. Stereotact Funct Neurosurg 2006;84:46–55.
  22. 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;57:319–330.
  23. Maks CB, Butson CR, Walter BL, Vitek JL, McIntyre CC: Deep brain stimulation activation volumes and their association with neurophysiological mapping and therapeutic outcomes. J Neurol Neurosurg Psychiatry, E-pub ahead of print.
  24. Butson CR, Cooper SE, Henderson JM, McIntyre CC: Patient-specific analysis of the volume of tissue activated during deep brain stimulation. Neuroimage 2007;34:661–670.
  25. Boyd S, Vanderberghe L: Convex Optimization. Cambridge, Cambridge University Press, 2004.
  26. Saint-Cyr JA, Hoque T, Pereira LC, Dostrovsky JO, Hutchison WD, Mikulis DJ, Abosch A, Sime E, Lang AE, Lozano AM: Localization of clinically effective stimulating electrodes in the human subthalamic nucleus on magnetic resonance imaging. J Neurosurg 2002;97:1152–1166.
  27. Yelnik J, Damier P, Demeret S, Gervais D, Bardinet E, Bejjani BP, Francois C, Houeto JL, Arnule I, Dormont D, Galanaud D, Pidoux B, Cornu P, Agid Y: Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method. J Neurosurg 2003;99:89–99.
  28. Herzog J, Fietzek U, Hamel W, Morsnowski A, Steigerwald F, Schrader B, Weinert D, Pfister G, Muller D, Mehdorn HM, Deuschl G, Volkmann J: Most effective stimulation site in subthalamic deep brain stimulation for Parkinson’s disease. Mov Disord 2004;19:1050–1054.
  29. Anheim M, Batir A, Fraix V, Silem M, Chabardès S, Seigneuret E, Krack P, Benabid AL, Pollak P: Improvement in Parkinson disease by subthalamic nucleus stimulation based on electrode placement: effects of reimplantation. Arch Neurol 2008;65:612–616.
  30. Castro FJ, Pollo C, Meuli R, Maeder P, Cuisenaire O, Cuadra MB, Villemure JG, Thiran JP: A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms. IEEE Trans Med Imaging 2006;25:1440–1450.
  31. Lemaire JJ, Coste J, Ouchchane L, Caire F, Nuti C, Derost P, Cristini V, Gabrillargues J, Hemm S, Durif F, Chazal J: Brain mapping in stereotactic surgery: a brief overview from the probabilistic targeting to the patient-based anatomic mapping. Neuroimage 2007;37(suppl 1):S109–S115.
  32. Guo T, Parrent A, Peters T: Surgical targeting accuracy analysis of six methods for subthalamic nucleus deep brain stimulation. Comput Aided Surg 2007;12:325–334.
  33. Vitek JL, Bakay RA, Hashimoto T, Kaneoke Y, Mewes K, Zhang JY, Rye D, Starr P, Baron M, Turner R, DeLong MR: Microelectrode-guided pallidotomy: technical approach and its application in medically intractable Parkinson’s disease. J Neurosurg 1998;88:1027–1043.
  34. Acar F, Miller JP, Berk MC, Anderson G, Burchiel KJ: Safety of anterior commissure-posterior commissure-based target calculation of the subthalamic nucleus in functional stereotactic procedures. Stereotact Funct Neurosurg 2007;85:287–291.
  35. Xue Z, Shen D, Karacali B, Stern J, Rottenberg D, Davatzikos C: Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms. Neuroimage 2006;33:855–866.
  36. Fletcher R: Practical Methods of Optimization. Chichester, Wiley, 1987.
  37. Gill PR, Murray W, Wright MH: The Levenberg-Marquardt method; in Gill PR, Murray W, Wright MH: Practical Optimization. London, Academic Press, 1981, pp 136–137.
  38. Hock W, Schittkowski K: A comparative performance evaluation of 27 nonlinear programming codes. Computing 1983;30:335.

    External Resources

  39. Powell MJD: Variable metric methods for constrained optimization; in Bachem A, Grotschel M, Korte B (eds): Mathematical Programming: The State of the Art. Bonn, Springer, 1983, pp 288–311.
  40. Collobert R, Bengio S: A gentle hessian for efficient gradient descent. Proc IEEE Int Conf on Acoust Speech Signal Processing, 2004, Montreal, vol 5, pp 517–520.

 goto top of outline Author Contacts

Cameron C. McIntyre, PhD
Department of Biomedical Engineering, Cleveland Clinic Foundation
9500 Euclid Avenue ND20
Cleveland, OH 44195 (USA)
Tel. +1 216 445 3264, Fax +1 216 444 9198, E-Mail mcintyc@ccf.org


 goto top of outline Article Information

Published online: June 26, 2009
Number of Print Pages : 12
Number of Figures : 8, Number of Tables : 2, Number of References : 40


 goto top of outline Publication Details

Stereotactic and Functional Neurosurgery

Vol. 87, No. 4, Year 2009 (Cover Date: August 2009)

Journal Editor: Roberts D.W. (Lebanon, N.H.)
ISSN: 1011-6125 (Print), eISSN: 1423-0372 (Online)

For additional information: http://www.karger.com/SFN


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.



 goto top of outline Author Contacts

Cameron C. McIntyre, PhD
Department of Biomedical Engineering, Cleveland Clinic Foundation
9500 Euclid Avenue ND20
Cleveland, OH 44195 (USA)
Tel. +1 216 445 3264, Fax +1 216 444 9198, E-Mail mcintyc@ccf.org


 goto top of outline Article Information

Published online: June 26, 2009
Number of Print Pages : 12
Number of Figures : 8, Number of Tables : 2, Number of References : 40


 goto top of outline Publication Details

Stereotactic and Functional Neurosurgery

Vol. 87, No. 4, Year 2009 (Cover Date: August 2009)

Journal Editor: Roberts D.W. (Lebanon, N.H.)
ISSN: 1011-6125 (Print), eISSN: 1423-0372 (Online)

For additional information: http://www.karger.com/SFN


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. 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–S258.
  2. Richter EO, Lozano AM: Deep brain stimulation for Parkinson’s disease and movement disorders; in Rosch PJ (ed): Bioelectromagnetic Medicine. New York, Dekker, 2004, pp 265–276.
  3. Starr PA, Christine CW, Theodosopoulos PV, Lindsey N, Byrd D, Mosley A, Marks WJ Jr: Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and magnetic resonance imaging-verified lead locations. J Neurosurg 2002;97:370–387.
  4. Tasker RR, Munz M, Junn FS, Kiss ZH, Davis K, Dostrovsky JO, Lozano AM: Deep brain stimulation and thalamotomy for tremor compared. Acta Neurochir 1997;68(suppl): 49–53.
  5. Gross RE, Krack P, Rodriguez-Oroz MC, Rezai AR, Benabid AL: Electrophysiological mapping for the implantation of deep brain stimulators for Parkinson’s disease and tremor. Mov Disord 2006;21(suppl 14):S259–S283.
  6. D’Haese PF, Cetinkaya E, Konrad PE, Kao C, Dawant BM: Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance. IEEE Trans Med Imaging 2005;24:1469–1478.
  7. Finnis KW, Starreveld YP, Parrent AG, Sadikot AF, Peters TM: Three-dimensional database of subcortical electrophysiology for image-guided stereotactic functional neurosurgery. IEEE Trans Med Imaging 2003;22:93–104.
  8. Mogilner AY, Benabid AL, Rezai AR: Chronic therapeutic brain stimulation: history, current clinical indications, and future prospects; in Rosch PJ (ed): Bioelectromagnetic Medicine. New York, Dekker, 2004, pp 133–151.
  9. Andrade-Souza YM, Schwalb JM, Hamani C, Eltahawy H, Hoque T, Saint-Cyr J, Lozano AM: Comparison of three methods of targeting the subthalamic nucleus for chronic stimulation in Parkinson’s disease. Neurosurgery 2005;56:360–368.
  10. Schaltenbrand G, Wahren W: Atlas for Stereotaxy of the Human Brain, ed 2. Chicago, Year Book Medical Publishers, 1977.
  11. Falkenberg JH, McNames J, Favre J, Burchiel KJ: Automatic analysis and visualization of microelectrode recording trajectories to the subthalamic nucleus: preliminary results. Stereotact Funct Neurosurg 2006;84:35–45.
  12. Littlechild P, Varma TR, Eldridge PR, Fox S, Forster A, Fletcher N, Steiger M, Byrne P, Tyler K, Flintham S: Variability in position of the subthalamic nucleus targeted by magnetic resonance imaging and microelectrode recordings as compared to atlas co-ordinates. Stereotact Funct Neurosurg 2003;80:82–87.
  13. Moran A, Bar-Gad I, Bergman H, Israel Z: Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure. Mov Disord 2006;21:1425–1431.
  14. Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tande D, Karachi C, Ayache N, Cornu P, Agid Y: A three-dimensional, histological and deformable atlas of the human basal ganglia. 1. Atlas construction based on immunohistochemical and MRI data. Neuroimage 2007;34:618–638.
  15. Halpern CH, Danish SF, Baltuch GH, Jaggi JL: Brain shift during deep brain stimulation surgery for Parkinson’s disease. Stereotact Funct Neurosurg 2008;86:37–43.
  16. Khan MF, Mewes K, Gross RE, Skrinjar O: Assessment of brain shift related to deep brain stimulation surgery. Stereotact Funct Neurosurg 2008;86:44–53.
  17. Winkler D, Tittgemeyer M, Schwarz J, Preul C, Strecker K, Meixensberger J: The first evaluation of brain shift during functional neurosurgery by deformation field analysis. J Neurol Neurosurg Psychiatry 2005;76:1161–1163.
  18. Ganser KA, Dickhaus H, Metzner R, Wirtz CR: A deformable digital brain atlas system according to Talairach and Tournoux. Med Image Anal 2004;8:3–22.
  19. Miocinovic S, Noecker AM, Maks CB, Butson CR, McIntyre CC: Cicerone: stereotactic neurophysiological recording and deep brain stimulation electrode placement software system. Acta Neurochir 2007;97 (suppl):561–567.

    External Resources

  20. Guo T, Finnis KW, Parrent AG, Peters TM: Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries. Comput Aided Surg 2006;11:231–239.
  21. Nowinski WL, Liu J, Thirunavuukarasuu A: Quantification and visualization of the three-dimensional inconsistency of the subthalamic nucleus in the Schaltenbrand-Wahren brain atlas. Stereotact Funct Neurosurg 2006;84:46–55.
  22. 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;57:319–330.
  23. Maks CB, Butson CR, Walter BL, Vitek JL, McIntyre CC: Deep brain stimulation activation volumes and their association with neurophysiological mapping and therapeutic outcomes. J Neurol Neurosurg Psychiatry, E-pub ahead of print.
  24. Butson CR, Cooper SE, Henderson JM, McIntyre CC: Patient-specific analysis of the volume of tissue activated during deep brain stimulation. Neuroimage 2007;34:661–670.
  25. Boyd S, Vanderberghe L: Convex Optimization. Cambridge, Cambridge University Press, 2004.
  26. Saint-Cyr JA, Hoque T, Pereira LC, Dostrovsky JO, Hutchison WD, Mikulis DJ, Abosch A, Sime E, Lang AE, Lozano AM: Localization of clinically effective stimulating electrodes in the human subthalamic nucleus on magnetic resonance imaging. J Neurosurg 2002;97:1152–1166.
  27. Yelnik J, Damier P, Demeret S, Gervais D, Bardinet E, Bejjani BP, Francois C, Houeto JL, Arnule I, Dormont D, Galanaud D, Pidoux B, Cornu P, Agid Y: Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method. J Neurosurg 2003;99:89–99.
  28. Herzog J, Fietzek U, Hamel W, Morsnowski A, Steigerwald F, Schrader B, Weinert D, Pfister G, Muller D, Mehdorn HM, Deuschl G, Volkmann J: Most effective stimulation site in subthalamic deep brain stimulation for Parkinson’s disease. Mov Disord 2004;19:1050–1054.
  29. Anheim M, Batir A, Fraix V, Silem M, Chabardès S, Seigneuret E, Krack P, Benabid AL, Pollak P: Improvement in Parkinson disease by subthalamic nucleus stimulation based on electrode placement: effects of reimplantation. Arch Neurol 2008;65:612–616.
  30. Castro FJ, Pollo C, Meuli R, Maeder P, Cuisenaire O, Cuadra MB, Villemure JG, Thiran JP: A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms. IEEE Trans Med Imaging 2006;25:1440–1450.
  31. Lemaire JJ, Coste J, Ouchchane L, Caire F, Nuti C, Derost P, Cristini V, Gabrillargues J, Hemm S, Durif F, Chazal J: Brain mapping in stereotactic surgery: a brief overview from the probabilistic targeting to the patient-based anatomic mapping. Neuroimage 2007;37(suppl 1):S109–S115.
  32. Guo T, Parrent A, Peters T: Surgical targeting accuracy analysis of six methods for subthalamic nucleus deep brain stimulation. Comput Aided Surg 2007;12:325–334.
  33. Vitek JL, Bakay RA, Hashimoto T, Kaneoke Y, Mewes K, Zhang JY, Rye D, Starr P, Baron M, Turner R, DeLong MR: Microelectrode-guided pallidotomy: technical approach and its application in medically intractable Parkinson’s disease. J Neurosurg 1998;88:1027–1043.
  34. Acar F, Miller JP, Berk MC, Anderson G, Burchiel KJ: Safety of anterior commissure-posterior commissure-based target calculation of the subthalamic nucleus in functional stereotactic procedures. Stereotact Funct Neurosurg 2007;85:287–291.
  35. Xue Z, Shen D, Karacali B, Stern J, Rottenberg D, Davatzikos C: Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms. Neuroimage 2006;33:855–866.
  36. Fletcher R: Practical Methods of Optimization. Chichester, Wiley, 1987.
  37. Gill PR, Murray W, Wright MH: The Levenberg-Marquardt method; in Gill PR, Murray W, Wright MH: Practical Optimization. London, Academic Press, 1981, pp 136–137.
  38. Hock W, Schittkowski K: A comparative performance evaluation of 27 nonlinear programming codes. Computing 1983;30:335.

    External Resources

  39. Powell MJD: Variable metric methods for constrained optimization; in Bachem A, Grotschel M, Korte B (eds): Mathematical Programming: The State of the Art. Bonn, Springer, 1983, pp 288–311.
  40. Collobert R, Bengio S: A gentle hessian for efficient gradient descent. Proc IEEE Int Conf on Acoust Speech Signal Processing, 2004, Montreal, vol 5, pp 517–520.