Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models

The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accura...

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Published inHuman brain mapping Vol. 34; no. 4; pp. 923 - 935
Main Authors Windhoff, Mirko, Opitz, Alexander, Thielscher, Axel
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2013
Wiley-Liss
John Wiley & Sons, Inc
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Abstract The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high‐quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy‐to‐use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well‐shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open‐source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.
AbstractList The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high‐quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy‐to‐use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well‐shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open‐source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.
The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high-quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy-to-use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well-shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open-source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs.The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high-quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy-to-use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well-shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open-source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs.
The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high-quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy-to-use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well-shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open-source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs.
The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high-quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy-to-use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well-shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open-source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT]
Author Thielscher, Axel
Windhoff, Mirko
Opitz, Alexander
AuthorAffiliation 1 High‐Field Magnetic Resonance Centre, MPI for Biological Cybernetics, Tübingen, Germany
AuthorAffiliation_xml – name: 1 High‐Field Magnetic Resonance Centre, MPI for Biological Cybernetics, Tübingen, Germany
Author_xml – sequence: 1
  givenname: Mirko
  surname: Windhoff
  fullname: Windhoff, Mirko
  organization: High-Field Magnetic Resonance Centre, MPI for Biological Cybernetics, Tübingen, Germany
– sequence: 2
  givenname: Alexander
  surname: Opitz
  fullname: Opitz, Alexander
  organization: High-Field Magnetic Resonance Centre, MPI for Biological Cybernetics, Tübingen, Germany
– sequence: 3
  givenname: Axel
  surname: Thielscher
  fullname: Thielscher, Axel
  email: axel.thielscher@tuebingen.mpg.de
  organization: High-Field Magnetic Resonance Centre, MPI for Biological Cybernetics, Tübingen, Germany
BackLink http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27158990$$DView record in Pascal Francis
https://www.ncbi.nlm.nih.gov/pubmed/22109746$$D View this record in MEDLINE/PubMed
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Issue 4
Keywords Nervous system diseases
Head
Transcranial magnetic stimulation
Radiodiagnosis
Central nervous system
structural magnetic resonance imaging
Nuclear magnetic resonance imaging
transcranial direct current stimulation
Encephalon
electric field calculation
Electric field
Direct current
Models
Language English
License http://onlinelibrary.wiley.com/termsAndConditions#vor
CC BY 4.0
Copyright © 2011 Wiley Periodicals, Inc.
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PublicationTitle Human brain mapping
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References Nitsche MA, Paulus W ( 2000): Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol 527( Pt 3): 633-639.
Rullmann M, Anwander A, Dannhauer M, Warfield SK, Duffy FH, Wolters CH ( 2009): EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model. Neuroimage 44: 399-410.
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Epstein BR, Foster KR ( 1983): Anisotropy in the dielectric properties of skeletal muscle. Med Biol Eng Comput 21: 51-55.
Salvador R, Silva S, Basser PJ, Miranda PC ( 2011): Determining which mechanisms lead to activation in the motor cortex: A modeling study of transcranial magnetic stimulation using realistic stimulus waveforms and sulcal geometry. Clin Neurophysiol 122: 748-758.
Datta A, Bikson M, Fregni F ( 2010): Transcranial direct current stimulation in patients with skull defects and skull plates: High-resolution computational FEM study of factors altering cortical current flow. Neuroimage 52: 1268-1278.
Wagner TA, Zahn M, Grodzinsky AJ, Pascual-Leone AP ( 2004): Three-dimensional head model simulation of transcranial magnetic stimulation. IEEE Trans Bio-Med Eng 51: 1586-1598.
Cerri G, De Leo R, Moglie F, Schiavoni A ( 1995): An accurate 3-D model for magnetic stimulation of the brain cortex. J Med Eng Technol 19: 7-16.
Miranda PC, Lomarev M, Hallett M ( 2006): Modeling the current distribution during transcranial direct current stimulation. Clin Neurophysiol 117: 1623-1629.
Thielscher A, Opitz A, Windhoff M ( 2011): Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation. Neuroimage 54: 234-243.
Smith SM ( 2002): Fast robust automated brain extraction. Hum Brain Mapp 17: 143-155.
Geuzaine C, Remacle J-F ( 2009): Gmsh: A three-dimensional finite element mesh generator with built-in pre- and post-processing facilities. Int J Numer Meth Eng 79: 1309-1331.
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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 98: 11697-11701.
Thielscher A, Kammer T ( 2004): Electric field properties of two commercial figure-8 coils in TMS: Calculation of focality and efficiency. Clin Neurophysiol 115: 1697-1708.
Güllmar D, Haueisen J, Reichenbach JR ( 2010): Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study. Neuroimage 51: 145-163.
Fischl B, Sereno MI, Dale AM ( 1999): Cortical surface-based analysis-II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9: 195-207.
Sparing R, Hesse MD, Fink GR ( 2009): Neuronavigation for transcranial magnetic stimulation (TMS): Where we are and where we are going. Cortex 46: 118-120.
Attene M ( 2010): A lightweight approach to repairing digitized polygon meshes. Visual Comput 26: 1393-1406.
Basser PJ, Mattiello J, LeBihan D ( 1994): MR diffusion tensor spectroscopy and imaging. Biophys J 66: 259-267.
Schöberl J ( 1997): NETGEN An advancing front 2D/3D-mesh generator based on abstract rules. Comput Visual Sci 1: 41-52.
Toschi N, Welt T, Guerrisi M, Keck ME ( 2009): Transcranial magnetic stimulation in heterogeneous brain tissue: Clinical impact on focality, reproducibility and true sham stimulation. J Psychiatr Res 43: 255-264.
Sack AT, Cohen Kadosh R, Schuhmann T, Moerel M, Walsh V, Goebel R ( 2009): Optimizing functional accuracy of TMS in cognitive studies: A comparison of methods. J Cogn Neurosci 21: 207-221.
Salinas FS, Lancaster JL, Fox PT ( 2009): 3D modeling of the total electric field induced by transcranial magnetic stimulation using the boundary element method. Phys Med Biol 54: 3631-3647.
Wang W, Eisenberg SR ( 1994): A three-dimensional finite element method for computing magnetically induced currents in tissues. IEEE Trans Magn 30: 5015-5023.
Dale AM, Fischl B, Sereno MI ( 1999): Cortical surface-based analysis-I. Segmentation and surface reconstruction. Neuroimage 9: 179-194.
Hart FX, Dunfee WR ( 1993): In vivo measurement of the low-frequency dielectric spectra of frog skeletal muscle. Phys Med Biol 38: 1099-1112.
Logothetis NK, Kayser C, Oeltermann A ( 2007): In vivo measurement of cortical impedance spectrum in monkeys: Implications for signal propagation. Neuron 55: 809-823.
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, Luca MD, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, Stefano ND, Brady JM, Matthews PM ( 2004): Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23: S208-S219.
Kloppel S, Baumer T, Kroeger J, Koch MA, Buchel C, Munchau A, Siebner HR ( 2008): The cortical motor threshold reflects microstructural properties of cerebral white matter. Neuroimage 40: 1782-1791.
Toschi N, Welt T, Guerrisi M, Keck ME ( 2008): A reconstruction of the conductive phenomena elicited by transcranial magnetic stimulation in heterogeneous brain tissue. Phys Med 24: 80-86.
Wagner T, Rushmore J, Eden U, Valero-Cabre A ( 2009): Biophysical foundations underlying TMS: Setting the stage for an effective use of neurostimulation in the cognitive neurosciences. Cortex 45: 1025-1034.
Dannhauer M, Lanfer B, Wolters CH, Knosche TR ( 2011): Modeling of the human skull in EEG source analysis. Hum Brain Mapp 32: 1383-1399.
McConnell KA, Nahas Z, Shastri A, Lorberbaum JP, Kozel FA, Bohning DE, George MS ( 2001): The transcranial magnetic stimulation motor threshold depends on the distance from coil to underlying cortex: A replication in healthy adults comparing two methods of assessing the distance to cortex. Biol Psychiatr 49: 454-459.
Heller L, van Hulsteyn DB ( 1992): Brain stimulation using electromagnetic sources: Theoretical aspects. Biophys J 63: 129-138.
Opitz A, Windhoff M, Heidemann R, Turner R, Thielscher A: ( 2011): How the brain tissue shapes the electric field induced by transcranial magnetic stimulation. Neuroimage 58: 849-859.
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2010; 51
2011; 122
1992; 63
2001; 98
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References_xml – reference: Sparing R, Hesse MD, Fink GR ( 2009): Neuronavigation for transcranial magnetic stimulation (TMS): Where we are and where we are going. Cortex 46: 118-120.
– reference: Kloppel S, Baumer T, Kroeger J, Koch MA, Buchel C, Munchau A, Siebner HR ( 2008): The cortical motor threshold reflects microstructural properties of cerebral white matter. Neuroimage 40: 1782-1791.
– reference: Thielscher A, Opitz A, Windhoff M ( 2011): Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation. Neuroimage 54: 234-243.
– reference: Epstein BR, Foster KR ( 1983): Anisotropy in the dielectric properties of skeletal muscle. Med Biol Eng Comput 21: 51-55.
– reference: Güllmar D, Haueisen J, Reichenbach JR ( 2010): Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study. Neuroimage 51: 145-163.
– reference: Rullmann M, Anwander A, Dannhauer M, Warfield SK, Duffy FH, Wolters CH ( 2009): EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model. Neuroimage 44: 399-410.
– reference: Cerri G, De Leo R, Moglie F, Schiavoni A ( 1995): An accurate 3-D model for magnetic stimulation of the brain cortex. J Med Eng Technol 19: 7-16.
– reference: Datta A, Bikson M, Fregni F ( 2010): Transcranial direct current stimulation in patients with skull defects and skull plates: High-resolution computational FEM study of factors altering cortical current flow. Neuroimage 52: 1268-1278.
– reference: Salinas FS, Lancaster JL, Fox PT ( 2009): 3D modeling of the total electric field induced by transcranial magnetic stimulation using the boundary element method. Phys Med Biol 54: 3631-3647.
– reference: Wagner TA, Zahn M, Grodzinsky AJ, Pascual-Leone AP ( 2004): Three-dimensional head model simulation of transcranial magnetic stimulation. IEEE Trans Bio-Med Eng 51: 1586-1598.
– reference: Toschi N, Welt T, Guerrisi M, Keck ME ( 2009): Transcranial magnetic stimulation in heterogeneous brain tissue: Clinical impact on focality, reproducibility and true sham stimulation. J Psychiatr Res 43: 255-264.
– reference: Miranda PC, Lomarev M, Hallett M ( 2006): Modeling the current distribution during transcranial direct current stimulation. Clin Neurophysiol 117: 1623-1629.
– reference: Hart FX, Dunfee WR ( 1993): In vivo measurement of the low-frequency dielectric spectra of frog skeletal muscle. Phys Med Biol 38: 1099-1112.
– reference: Opitz A, Windhoff M, Heidemann R, Turner R, Thielscher A: ( 2011): How the brain tissue shapes the electric field induced by transcranial magnetic stimulation. Neuroimage 58: 849-859.
– reference: Heller L, van Hulsteyn DB ( 1992): Brain stimulation using electromagnetic sources: Theoretical aspects. Biophys J 63: 129-138.
– reference: Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, Luca MD, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, Stefano ND, Brady JM, Matthews PM ( 2004): Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23: S208-S219.
– reference: Basser PJ, Mattiello J, LeBihan D ( 1994): MR diffusion tensor spectroscopy and imaging. Biophys J 66: 259-267.
– reference: Renard Y, Pommier J ( 2010): Getfem++: A Generic Finite Element Library in C.++ Documentation, http://home.gna.org/getfem/.
– reference: Geuzaine C, Remacle J-F ( 2009): Gmsh: A three-dimensional finite element mesh generator with built-in pre- and post-processing facilities. Int J Numer Meth Eng 79: 1309-1331.
– reference: Toschi N, Welt T, Guerrisi M, Keck ME ( 2008): A reconstruction of the conductive phenomena elicited by transcranial magnetic stimulation in heterogeneous brain tissue. Phys Med 24: 80-86.
– reference: Logothetis NK, Kayser C, Oeltermann A ( 2007): In vivo measurement of cortical impedance spectrum in monkeys: Implications for signal propagation. Neuron 55: 809-823.
– reference: Thielscher A, Kammer T ( 2004): Electric field properties of two commercial figure-8 coils in TMS: Calculation of focality and efficiency. Clin Neurophysiol 115: 1697-1708.
– reference: Salvador R, Silva S, Basser PJ, Miranda PC ( 2011): Determining which mechanisms lead to activation in the motor cortex: A modeling study of transcranial magnetic stimulation using realistic stimulus waveforms and sulcal geometry. Clin Neurophysiol 122: 748-758.
– reference: Wang W, Eisenberg SR ( 1994): A three-dimensional finite element method for computing magnetically induced currents in tissues. IEEE Trans Magn 30: 5015-5023.
– reference: Fischl B, Sereno MI, Dale AM ( 1999): Cortical surface-based analysis-II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9: 195-207.
– reference: McConnell KA, Nahas Z, Shastri A, Lorberbaum JP, Kozel FA, Bohning DE, George MS ( 2001): The transcranial magnetic stimulation motor threshold depends on the distance from coil to underlying cortex: A replication in healthy adults comparing two methods of assessing the distance to cortex. Biol Psychiatr 49: 454-459.
– reference: Schöberl J ( 1997): NETGEN An advancing front 2D/3D-mesh generator based on abstract rules. Comput Visual Sci 1: 41-52.
– reference: Sack AT, Cohen Kadosh R, Schuhmann T, Moerel M, Walsh V, Goebel R ( 2009): Optimizing functional accuracy of TMS in cognitive studies: A comparison of methods. J Cogn Neurosci 21: 207-221.
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Snippet The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas....
SourceID pubmedcentral
proquest
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SourceType Open Access Repository
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StartPage 923
SubjectTerms Adult
Biological and medical sciences
Brain - physiology
Brain Mapping
Computer Simulation
electric field calculation
Electric Stimulation
Female
Finite Element Analysis
Head
Humans
Image Processing, Computer-Assisted
Investigative techniques, diagnostic techniques (general aspects)
Magnetic Resonance Imaging
Male
Medical sciences
Models, Neurological
Nervous system
Nervous system involvement in other diseases. Miscellaneous
Neurology
Radiodiagnosis. Nmr imagery. Nmr spectrometry
structural magnetic resonance imaging
transcranial direct current stimulation
Transcranial Magnetic Stimulation
Title Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models
URI https://api.istex.fr/ark:/67375/WNG-6HBCTKVT-H/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.21479
https://www.ncbi.nlm.nih.gov/pubmed/22109746
https://www.proquest.com/docview/1317497977
https://www.proquest.com/docview/1317833404
https://pubmed.ncbi.nlm.nih.gov/PMC6870291
Volume 34
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