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 in | Human brain mapping Vol. 34; no. 4; pp. 923 - 935 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.04.2013
Wiley-Liss John Wiley & Sons, Inc |
Subjects | |
Online Access | Get full text |
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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. |
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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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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. Rusconi E, Bestmann S ( 2009): On tickling brains to investigate minds. Cortex 45: 1021-1024. Renard Y, Pommier J ( 2010): Getfem++: A Generic Finite Element Library in C.++ Documentation, http://home.gna.org/getfem/. 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. 2009; 45 2009; 44 2002; 17 2009; 46 2009; 21 2009; 43 2010 2004; 23 1994; 66 2006 2011; 32 2011; 54 1997; 1 2001; 49 1995; 19 2011; 58 2007; 55 2006; 117 1999; 9 2009; 79 2007; 118 2010; 26 2004; 115 2004; 51 2009; 54 1993; 38 2000; 527 1983; 21 2008; 24 2008; 40 2010; 52 1994; 30 2010; 51 2011; 122 1992; 63 2001; 98 e_1_2_6_32_1 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_30_1 e_1_2_6_19_1 Renard Y (e_1_2_6_22_1) 2010 e_1_2_6_13_1 e_1_2_6_36_1 e_1_2_6_14_1 e_1_2_6_35_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_12_1 e_1_2_6_33_1 e_1_2_6_17_1 e_1_2_6_18_1 e_1_2_6_39_1 e_1_2_6_15_1 e_1_2_6_38_1 e_1_2_6_16_1 e_1_2_6_37_1 e_1_2_6_21_1 e_1_2_6_20_1 e_1_2_6_40_1 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_5_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_24_1 e_1_2_6_3_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_29_1 e_1_2_6_28_1 e_1_2_6_27_1 e_1_2_6_26_1 |
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. 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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.... |
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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 |
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