- Email: [email protected]
Locating chronically implanted subdural electrodes using surface reconstruction
Clinical Neurophysiology 116 (2005) 1984–1987 www.elsevier.com/locate/clinph
Locating chronically implanted subdural electrodes using surface reconstruction John D. Huntera,*, Diana M. Hananb, Bryan F. Singerb, Samir Shaikhb, Katherine A. Brubakerb, Kurt E. Hecoxa, Vernon L. Towlea,b,c a
Department of Pediatrics, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA Department of Neurology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA c Department of Surgery, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
b
Accepted 11 March 2005
Abstract Objective: To determine the accuracy of locating subdural electrodes by means of 3-D surface rendering of CT scans. Methods: Open source software has been developed and posted on the web which segments the electrodes into 3-D surfaces and allows their 3-D locations to be exported to other EEG analysis programs. The accuracy of the technique was determined by studying 410 subdural electrodes implanted in four epilepsy patients. Accuracy was determined by comparing the locations from the rendering analysis to the locations of the same electrodes determined by conventional analysis of their appearance on individual CT slices. Results: The average accuracy of a study of 410 electrodes imaged in four patients repeated two times by three observers was 0.91 (G0.41) mm, with a maximum error of 3.3 mm, about half of the diameter of an electrode. Conclusions: The location of subdural electrodes can easily and quickly be determined within high-resolution CT scans through the use of 3-D rendering. Significance: This relatively fast and easy method for determining the location of subdural electrodes should facilitate their use in both clinical and research investigations. q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. Keywords: Subdural electrodes; 3-D localization; Source analysis; Epilepsy surgery; Surface rendering
1. Introduction The widespread inclusion of chronic depth and subdural recordings in the surgical work-up of intractable epilepsy patients has contributed to the efficacy and reduced the morbidity of modern epilepsy surgery (Bancaud et al., 1965; Berger et al., 1989; Burchiel et al., 1989; Goldring and Gregorie, 1984; Wyler et al., 1984; Wyllie et al., 1988). New approaches to the analysis of intracerebral recordings require knowledge of the 3-D locations of recording electrodes (Cuffin et al., 1991; Merlet and Gotman, 1999; Sutherling et al., 2001; Towle et al., 1999, 2003). To date, accurate determination of the location of implanted subdural electrodes is tedious and difficult, hindering the routine * Corresponding author. E-mail address: [email protected] (J.D. Hunter).
clinical use of advanced quantitative EEG techniques for patient care. We present here a procedure which segments the electrodes as 3-D surfaces within CT images, allowing the location and identification of hundreds of electrode locations within a few minutes. 2. Methods High-resolution CT scans (1 mm slice thickness) were loaded into the program and viewed as slices or 3-D surfaces (Fig. 1). Slices were displayed orthogonally or rotated in any direction to achieve an optimal view of the electrode arrays to facilitate identification of each electrode. Arbitrary 3-D viewports which contained three non-orthogonal slices were utilized. Images were rotated, translated, zoomed, and brightness and contrast adjusted, under interactive mouse control.
1388-2457/$30.00 q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. doi:10.1016/j.clinph.2005.03.027
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
1985
Fig. 1. A screenshot of the program revealing 3-D rendered arrays of subdural electrodes (top) and CT slices (below). The upper left panel is a 3D interactive view which supports interaction, electrode labeling, etc. The upper right is a 3D rendering window showing the segmented electrodes and other metallic hardware and identified electrodes (colored spheres). The three lower windows show 3 planar slices, with electrodes. One can pan and zoom all of these views arbitrarily and reposition the electrodes in them with 3-button mouse control.
Surfaces for the skin, skull and electrodes were superimposed with various colors and transparencies (Fig. 2), using features to align the different viewports for these 3-D surfaces with the CT image slices. To render the electrodes, skin or skull as 3-D surfaces, an intensity value for the isosurface (e.g. electrodes) to be reconstructed was selected, using an image sampling tool that collects averages over CT
regions selected with the mouse. To select these regions, the cursor was dragged over one or more of the electrodes, and the average intensity of the pixels sampled was computed. This could be adjusted up or down via the keyboard, as needed. This value was then used to render the surface of all of the electrodes and any other objects containing that intensity value (See Fig. 1). The rendering engine uses
Fig. 2. A detail of the rendering window illustrating how various surfaces can be individually rendered, colored, and superimposed using translucent and opaque displays. The electrodes and their tunneled leads are juxtaposed relative to the craniotomy.
1986
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
VTK, the Visualization Toolkit for 3-D visualization and radiographic image manipulation, which was developed by Kitware (Kitware, Inc., Clifton Park, NY), a private company funded by National Library of Medicine to develop high quality visualization libraries. The surface rendering pipeline consisted of a marching cubes algorithm followed by connectivity filters; the algorithms and filters utilized were vtkMarchingCubes and vtkPolyDataConnectivityFilter (Schroeder et al., 2002). The marching cubes algorithm builds one or more isosurfaces along the specified contour value. These isosurfaces are fed to a connectivity filter, which extracts cells that share common points. The outputs of this filter are regions, which include the electrodes as well as other pieces of surgical metal that are readily distinguishable by visual inspection, both because they have different geometries and because the electrodes lie on regular grids. The center of the undersurface of the electrodes (not the center of mass) which were in contact with the brain were marked by clicking on them, and this screen location was fed to a cell picker (vtkCellPicker) that returned world coordinates of the point under the mouse click position with a tolerance of 0.5% of the window width. If multiple isosurfaces were in the viewport (e.g. skin, bone, and electrodes), it was possible select which of the surfaces the picker intersected with. This enabled the placement of markers on the outer or inner surfaces of fiducial points on multiple surfaces for inter-image registration. When the electrodes were marked, spherical polygons with configurable radii and colors were added to the images, centered on the selected x, y, z, locations. These polygons were then labeled in a semi-automatic process to indicate the grid and electrode number. The user specified the label and the number of the initial electrode, and the program incremented the label for each successive electrode. This marker information (label, position, radius and color) was exported as an ASCII text CSV file which can be read by most data base and word processing packages. The application, which runs on Microsoft Windows, Linux and OS X, was developed solely with open source tools, and is available free at http://pbrain.sourceforge.net for non-commercial use. The Python front-end is a crossplatform, object oriented, interpreted language widely used in scientific computing, and enables hardware accelerated 3-D interaction using OpenGL.
3. Results The within- and between-observer reliability and accuracy of electrode localizations has been validated through two years of use, including repeated analysis by three different observers, as well as through comparison with intraoperative photographs taken both before and after the CT scans. To quantify the accuracy of the technique, an analysis of the variability and localization error of 410 subdural electrodes implanted in four different patients was
Table 1 Variability and accuracy of 410 electrode localizations from four patients, based on observations made by three independent observers Source
Mean (SD) (mm)
Maximum (mm)
Within observer variability Between observer variability Overall Accuracy
0.25 (G0.14) 0.31 (G0.13) 0.91 (G 0.41)
1.2 0.9 3.3
performed (Table 1). The patients gave their informed written consent. Each electrode was identified by three independent observers on two separate days. In this situation, by accuracy we mean the degree to which electrodes located by the rendering technique approximate the location of electrodes determined manually by careful visual inspection of the electrode as seen on individual slices. We calculated the Cartesian distance between the electrodes as determined by both of these techniques. A large Euclidian discrepancy between the two techniques would correspond to less accurate results. The electrodes were 6 mm in diameter with 10 mm center-to-center spacing, and were arranged in 8!8 arrays or 1!8 strips placed over the parietal, frontal and temporal lobes, according to the needs of the patients. The findings reveal that when high-resolution CT scans of 1–1.5 mm slice thickness are obtained, the average error of the technique is less than 1 mm. Although the error increases when lower resolution CT scans (5 mm slices) are utilized, it still appears to be less than the diameter of an electrode. In either case, the empirical error is less than the other sources of error in dipole localization studies (Towle et al., 1997), and should make this technique useful for testing and improving the utility and validity of multichannel intracerebral recordings.
4. Discussion We have demonstrated that subdural electrodes can be accurately located with an 80–90% reduction in time compared to manual identification of the electrodes on CT slices. Knowledge of the location of recording electrodes is increasingly important for investigations in which electrophysiologic sources are modeled. Several different strategies have been employed to locate implanted electrodes, including intraoperative photographs, frameless stereotactic wands, skull films, and CT, MRI and ultrasound during surgery (Barnett et al., 1993; Bootsveld et al., 1993; Grzeszczuk et al., 1992; Winkler et al., 2000; Towle et al., 2003). The 3-D location of scalp EEG electrodes has also been determined using radiologic localizers, infrared camera arrays, and CT (Homan et al., 1987; Myslobodsky and Bar-Ziv, 1989; Towle et al., 2003). Winkler’s (2000) analysis revealed that 39% of electrodes were difficult to identify on CT slices, largely because their relevant context is not easily viewed. We have found that this problem is
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
immediately solved using the non-orthogonal slice feature and the rendered image. The only ambiguous situation we encountered was when two electrodes were located directly on top of each other. Viewing the intraoperative photographs usually solved this problem. In our experience, the possible misplacement of an electrode by one-half of its diameter would not likely influence the interpretation of EEG data. We are not sure that the discrepancy between the two techniques is not due to deviations from reality by our ‘gold standard’ as evidenced by Winkler’s findings, above, and that the rendering technique is more accurate than conventional analysis. One limitation of our technique is that it does not currently allow visualization of the electrodes relative to cortical gyri. Even so, because the electrode localizations can be obtained so quickly and easily, we have found it useful to visualize the location of chronic subdural electrodes as an aid to surgical decision making.
Acknowledgements Supported in part by NIH NS40514 and The Brain Research Foundation. We are indebted to Jake Reimer and Anna Hill for aiding in the development and validation of this research project. Portions of these data were presented at the annual meeting of the American Epilepsy Society, December, 2004, New Orleans.
References Bancaud J, Talairach J, Bonis A. La ste´re´o-E´lectroence´phalographie dans l’epilepsie. Informations neurophysiopathologiques apporte´es par l’investigation fonctionelle ste´re´otaxique. Paris: Masson; 1965. Barnett GH, Kormos DW, Steiner CP, Morris H. Registration of EEG electrodes with three-dimensional neuroimaging using a frameless, armless stereotactic wand. Stereotact Funct Neurosurg 1993;61:32–8. Berger MS, Kincaid J, Ojemann GA, Lettich E. Brain mapping techniques to maximize resection, safety, and seizure control in children with brain tumors. Neurosurgery 1989;25:786–92. Bootsveld K, Traber F, Kaiser WA, Layer G, Elger CE, Hufnagel A, Gieseke J, Reiser M. Intracranial ECoG electrodes. Location
1987
determination using three-dimensional reconstruction of MR data of the brain as a component of the presurgical diagnosis of epilepsy. Radiologe 1993;33:185–8. Burchiel KJ, Clarke H, Ojemann GA, Dacey RG, Winn HR. Use of stimulation mapping and corticography in the excision of arteriovenous malformations in sensorimotor and language-related neocortex. Neurosurgery 1989;24:322–7. Cuffin BN, Cohen D, Yunokuchi K, Maniewski R, Purcell C, Cosgrove GR, et al. Tests of EEG localization accuracy using implanted sources in the human brain. Ann Neurol 1991;29:132–8. Goldring S, Gregorie EM. Surgical management of epilepsy using epidural recordings to localize the seizure focus-Review of 100 cases. J Neurosurg 1984;60:457–66. Grzeszczuk R, Tan KK, Levin DN, Pelizzari CA, Hu XP, Chen GTY, et al. Retrospective fusion of radiographic and MR data for localization of subdural electrodes. J Comput Assist Tomogr 1992;16:764–73. Homan RW, Herman J, Purdy P. Cerebral location of international 10–20 system electrode placement. Electroencephalogr Clin Neurophysiol 1987;66:376–82. Merlet I, Gotman J. Reliability of dipole models of epileptic spikes. Clin Neurophysiol 1999;110:1013–28. Myslobodsky MS, Bar-Ziv J. Locations of occipital EEG electrodes verified by computed-tomography. Electroencephalogr Clin Neurophysiol 1989;72:362–6. Schroeder W, Martin K, Lorenson B. The visualization toolkit: an object oriented approach to 3D graphics. 3rd ed.: Pearson Education, Inc.; 2002. Sutherling WW, Akhtari M, Mamelak AN, Mosher J, Arthur D, Sands S, Weiss P, Lopez N, DiMauro M, Flynn E, Leahy R. Dipole localization of human induced focal afterdischarge seizure in simultaneous magnetoencephalography and electrocorticography. Brain Topogr 2001;14:101–16. Towle VL, Khorasani L, Berkson EM, Syed I, Pelizzari CH, Jewett D, Spire J-P. Human somatosensory evoked potential dipoles: how accurate are the localizations? Proc IEEE-EMBS 1997;19:2616–21. Towle VL, Carder RK, Khorasanil L, Lindberg D. Electrocorticographic coherence patterns. J Clin Neurophysiol 1999;16:528–47. Towle VL, Khorasani L, Uftring S, Pelizzari C, Erickson RK, Spire J-P, Hoffmann K, Chu D, Scherg M. Noninvasive identification of the human central sulcus: a comparison of gyral morphology, functional MRI dipole localization and direct cortical mapping. NeuroImage 2003; 19:684–97. Winkler PA, Vollmar C, Krishnan KG, Pfluger T, Bru¨ckmann H, Noachtar S. Usefulness of 3-D reconstructed images of the human cerebral cortex for localization of subdural electrodes in epilepsy surgery. Epilepsy Res 2000;41:169–78. Wyler AR, Ojemann GA, Lettich E, Ward AA. Subdural strip electrodes for localizing epileptogenic foci. J Neurosurg 1984;60:1195–200. Wyllie E, Lu¨ders H, Morris III HH, Lesser RP, Dinner DS, Rothner AD, et al. Subdural electrodes in the evaluation for epilepsy surgery in children and adults. Neuropediatrics 1988;19:80–6.
Locating chronically implanted subdural electrodes using surface reconstruction John D. Huntera,*, Diana M. Hananb, Bryan F. Singerb, Samir Shaikhb, Katherine A. Brubakerb, Kurt E. Hecoxa, Vernon L. Towlea,b,c a
Department of Pediatrics, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA Department of Neurology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA c Department of Surgery, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
b
Accepted 11 March 2005
Abstract Objective: To determine the accuracy of locating subdural electrodes by means of 3-D surface rendering of CT scans. Methods: Open source software has been developed and posted on the web which segments the electrodes into 3-D surfaces and allows their 3-D locations to be exported to other EEG analysis programs. The accuracy of the technique was determined by studying 410 subdural electrodes implanted in four epilepsy patients. Accuracy was determined by comparing the locations from the rendering analysis to the locations of the same electrodes determined by conventional analysis of their appearance on individual CT slices. Results: The average accuracy of a study of 410 electrodes imaged in four patients repeated two times by three observers was 0.91 (G0.41) mm, with a maximum error of 3.3 mm, about half of the diameter of an electrode. Conclusions: The location of subdural electrodes can easily and quickly be determined within high-resolution CT scans through the use of 3-D rendering. Significance: This relatively fast and easy method for determining the location of subdural electrodes should facilitate their use in both clinical and research investigations. q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. Keywords: Subdural electrodes; 3-D localization; Source analysis; Epilepsy surgery; Surface rendering
1. Introduction The widespread inclusion of chronic depth and subdural recordings in the surgical work-up of intractable epilepsy patients has contributed to the efficacy and reduced the morbidity of modern epilepsy surgery (Bancaud et al., 1965; Berger et al., 1989; Burchiel et al., 1989; Goldring and Gregorie, 1984; Wyler et al., 1984; Wyllie et al., 1988). New approaches to the analysis of intracerebral recordings require knowledge of the 3-D locations of recording electrodes (Cuffin et al., 1991; Merlet and Gotman, 1999; Sutherling et al., 2001; Towle et al., 1999, 2003). To date, accurate determination of the location of implanted subdural electrodes is tedious and difficult, hindering the routine * Corresponding author. E-mail address: [email protected] (J.D. Hunter).
clinical use of advanced quantitative EEG techniques for patient care. We present here a procedure which segments the electrodes as 3-D surfaces within CT images, allowing the location and identification of hundreds of electrode locations within a few minutes. 2. Methods High-resolution CT scans (1 mm slice thickness) were loaded into the program and viewed as slices or 3-D surfaces (Fig. 1). Slices were displayed orthogonally or rotated in any direction to achieve an optimal view of the electrode arrays to facilitate identification of each electrode. Arbitrary 3-D viewports which contained three non-orthogonal slices were utilized. Images were rotated, translated, zoomed, and brightness and contrast adjusted, under interactive mouse control.
1388-2457/$30.00 q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. doi:10.1016/j.clinph.2005.03.027
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
1985
Fig. 1. A screenshot of the program revealing 3-D rendered arrays of subdural electrodes (top) and CT slices (below). The upper left panel is a 3D interactive view which supports interaction, electrode labeling, etc. The upper right is a 3D rendering window showing the segmented electrodes and other metallic hardware and identified electrodes (colored spheres). The three lower windows show 3 planar slices, with electrodes. One can pan and zoom all of these views arbitrarily and reposition the electrodes in them with 3-button mouse control.
Surfaces for the skin, skull and electrodes were superimposed with various colors and transparencies (Fig. 2), using features to align the different viewports for these 3-D surfaces with the CT image slices. To render the electrodes, skin or skull as 3-D surfaces, an intensity value for the isosurface (e.g. electrodes) to be reconstructed was selected, using an image sampling tool that collects averages over CT
regions selected with the mouse. To select these regions, the cursor was dragged over one or more of the electrodes, and the average intensity of the pixels sampled was computed. This could be adjusted up or down via the keyboard, as needed. This value was then used to render the surface of all of the electrodes and any other objects containing that intensity value (See Fig. 1). The rendering engine uses
Fig. 2. A detail of the rendering window illustrating how various surfaces can be individually rendered, colored, and superimposed using translucent and opaque displays. The electrodes and their tunneled leads are juxtaposed relative to the craniotomy.
1986
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
VTK, the Visualization Toolkit for 3-D visualization and radiographic image manipulation, which was developed by Kitware (Kitware, Inc., Clifton Park, NY), a private company funded by National Library of Medicine to develop high quality visualization libraries. The surface rendering pipeline consisted of a marching cubes algorithm followed by connectivity filters; the algorithms and filters utilized were vtkMarchingCubes and vtkPolyDataConnectivityFilter (Schroeder et al., 2002). The marching cubes algorithm builds one or more isosurfaces along the specified contour value. These isosurfaces are fed to a connectivity filter, which extracts cells that share common points. The outputs of this filter are regions, which include the electrodes as well as other pieces of surgical metal that are readily distinguishable by visual inspection, both because they have different geometries and because the electrodes lie on regular grids. The center of the undersurface of the electrodes (not the center of mass) which were in contact with the brain were marked by clicking on them, and this screen location was fed to a cell picker (vtkCellPicker) that returned world coordinates of the point under the mouse click position with a tolerance of 0.5% of the window width. If multiple isosurfaces were in the viewport (e.g. skin, bone, and electrodes), it was possible select which of the surfaces the picker intersected with. This enabled the placement of markers on the outer or inner surfaces of fiducial points on multiple surfaces for inter-image registration. When the electrodes were marked, spherical polygons with configurable radii and colors were added to the images, centered on the selected x, y, z, locations. These polygons were then labeled in a semi-automatic process to indicate the grid and electrode number. The user specified the label and the number of the initial electrode, and the program incremented the label for each successive electrode. This marker information (label, position, radius and color) was exported as an ASCII text CSV file which can be read by most data base and word processing packages. The application, which runs on Microsoft Windows, Linux and OS X, was developed solely with open source tools, and is available free at http://pbrain.sourceforge.net for non-commercial use. The Python front-end is a crossplatform, object oriented, interpreted language widely used in scientific computing, and enables hardware accelerated 3-D interaction using OpenGL.
3. Results The within- and between-observer reliability and accuracy of electrode localizations has been validated through two years of use, including repeated analysis by three different observers, as well as through comparison with intraoperative photographs taken both before and after the CT scans. To quantify the accuracy of the technique, an analysis of the variability and localization error of 410 subdural electrodes implanted in four different patients was
Table 1 Variability and accuracy of 410 electrode localizations from four patients, based on observations made by three independent observers Source
Mean (SD) (mm)
Maximum (mm)
Within observer variability Between observer variability Overall Accuracy
0.25 (G0.14) 0.31 (G0.13) 0.91 (G 0.41)
1.2 0.9 3.3
performed (Table 1). The patients gave their informed written consent. Each electrode was identified by three independent observers on two separate days. In this situation, by accuracy we mean the degree to which electrodes located by the rendering technique approximate the location of electrodes determined manually by careful visual inspection of the electrode as seen on individual slices. We calculated the Cartesian distance between the electrodes as determined by both of these techniques. A large Euclidian discrepancy between the two techniques would correspond to less accurate results. The electrodes were 6 mm in diameter with 10 mm center-to-center spacing, and were arranged in 8!8 arrays or 1!8 strips placed over the parietal, frontal and temporal lobes, according to the needs of the patients. The findings reveal that when high-resolution CT scans of 1–1.5 mm slice thickness are obtained, the average error of the technique is less than 1 mm. Although the error increases when lower resolution CT scans (5 mm slices) are utilized, it still appears to be less than the diameter of an electrode. In either case, the empirical error is less than the other sources of error in dipole localization studies (Towle et al., 1997), and should make this technique useful for testing and improving the utility and validity of multichannel intracerebral recordings.
4. Discussion We have demonstrated that subdural electrodes can be accurately located with an 80–90% reduction in time compared to manual identification of the electrodes on CT slices. Knowledge of the location of recording electrodes is increasingly important for investigations in which electrophysiologic sources are modeled. Several different strategies have been employed to locate implanted electrodes, including intraoperative photographs, frameless stereotactic wands, skull films, and CT, MRI and ultrasound during surgery (Barnett et al., 1993; Bootsveld et al., 1993; Grzeszczuk et al., 1992; Winkler et al., 2000; Towle et al., 2003). The 3-D location of scalp EEG electrodes has also been determined using radiologic localizers, infrared camera arrays, and CT (Homan et al., 1987; Myslobodsky and Bar-Ziv, 1989; Towle et al., 2003). Winkler’s (2000) analysis revealed that 39% of electrodes were difficult to identify on CT slices, largely because their relevant context is not easily viewed. We have found that this problem is
J.D. Hunter et al. / Clinical Neurophysiology 116 (2005) 1984–1987
immediately solved using the non-orthogonal slice feature and the rendered image. The only ambiguous situation we encountered was when two electrodes were located directly on top of each other. Viewing the intraoperative photographs usually solved this problem. In our experience, the possible misplacement of an electrode by one-half of its diameter would not likely influence the interpretation of EEG data. We are not sure that the discrepancy between the two techniques is not due to deviations from reality by our ‘gold standard’ as evidenced by Winkler’s findings, above, and that the rendering technique is more accurate than conventional analysis. One limitation of our technique is that it does not currently allow visualization of the electrodes relative to cortical gyri. Even so, because the electrode localizations can be obtained so quickly and easily, we have found it useful to visualize the location of chronic subdural electrodes as an aid to surgical decision making.
Acknowledgements Supported in part by NIH NS40514 and The Brain Research Foundation. We are indebted to Jake Reimer and Anna Hill for aiding in the development and validation of this research project. Portions of these data were presented at the annual meeting of the American Epilepsy Society, December, 2004, New Orleans.
References Bancaud J, Talairach J, Bonis A. La ste´re´o-E´lectroence´phalographie dans l’epilepsie. Informations neurophysiopathologiques apporte´es par l’investigation fonctionelle ste´re´otaxique. Paris: Masson; 1965. Barnett GH, Kormos DW, Steiner CP, Morris H. Registration of EEG electrodes with three-dimensional neuroimaging using a frameless, armless stereotactic wand. Stereotact Funct Neurosurg 1993;61:32–8. Berger MS, Kincaid J, Ojemann GA, Lettich E. Brain mapping techniques to maximize resection, safety, and seizure control in children with brain tumors. Neurosurgery 1989;25:786–92. Bootsveld K, Traber F, Kaiser WA, Layer G, Elger CE, Hufnagel A, Gieseke J, Reiser M. Intracranial ECoG electrodes. Location
1987
determination using three-dimensional reconstruction of MR data of the brain as a component of the presurgical diagnosis of epilepsy. Radiologe 1993;33:185–8. Burchiel KJ, Clarke H, Ojemann GA, Dacey RG, Winn HR. Use of stimulation mapping and corticography in the excision of arteriovenous malformations in sensorimotor and language-related neocortex. Neurosurgery 1989;24:322–7. Cuffin BN, Cohen D, Yunokuchi K, Maniewski R, Purcell C, Cosgrove GR, et al. Tests of EEG localization accuracy using implanted sources in the human brain. Ann Neurol 1991;29:132–8. Goldring S, Gregorie EM. Surgical management of epilepsy using epidural recordings to localize the seizure focus-Review of 100 cases. J Neurosurg 1984;60:457–66. Grzeszczuk R, Tan KK, Levin DN, Pelizzari CA, Hu XP, Chen GTY, et al. Retrospective fusion of radiographic and MR data for localization of subdural electrodes. J Comput Assist Tomogr 1992;16:764–73. Homan RW, Herman J, Purdy P. Cerebral location of international 10–20 system electrode placement. Electroencephalogr Clin Neurophysiol 1987;66:376–82. Merlet I, Gotman J. Reliability of dipole models of epileptic spikes. Clin Neurophysiol 1999;110:1013–28. Myslobodsky MS, Bar-Ziv J. Locations of occipital EEG electrodes verified by computed-tomography. Electroencephalogr Clin Neurophysiol 1989;72:362–6. Schroeder W, Martin K, Lorenson B. The visualization toolkit: an object oriented approach to 3D graphics. 3rd ed.: Pearson Education, Inc.; 2002. Sutherling WW, Akhtari M, Mamelak AN, Mosher J, Arthur D, Sands S, Weiss P, Lopez N, DiMauro M, Flynn E, Leahy R. Dipole localization of human induced focal afterdischarge seizure in simultaneous magnetoencephalography and electrocorticography. Brain Topogr 2001;14:101–16. Towle VL, Khorasani L, Berkson EM, Syed I, Pelizzari CH, Jewett D, Spire J-P. Human somatosensory evoked potential dipoles: how accurate are the localizations? Proc IEEE-EMBS 1997;19:2616–21. Towle VL, Carder RK, Khorasanil L, Lindberg D. Electrocorticographic coherence patterns. J Clin Neurophysiol 1999;16:528–47. Towle VL, Khorasani L, Uftring S, Pelizzari C, Erickson RK, Spire J-P, Hoffmann K, Chu D, Scherg M. Noninvasive identification of the human central sulcus: a comparison of gyral morphology, functional MRI dipole localization and direct cortical mapping. NeuroImage 2003; 19:684–97. Winkler PA, Vollmar C, Krishnan KG, Pfluger T, Bru¨ckmann H, Noachtar S. Usefulness of 3-D reconstructed images of the human cerebral cortex for localization of subdural electrodes in epilepsy surgery. Epilepsy Res 2000;41:169–78. Wyler AR, Ojemann GA, Lettich E, Ward AA. Subdural strip electrodes for localizing epileptogenic foci. J Neurosurg 1984;60:1195–200. Wyllie E, Lu¨ders H, Morris III HH, Lesser RP, Dinner DS, Rothner AD, et al. Subdural electrodes in the evaluation for epilepsy surgery in children and adults. Neuropediatrics 1988;19:80–6.