Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation

This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the asse...
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Autor*in:	Cvetković, Mario [verfasserIn] Šušnjara, Anna [verfasserIn] Poljak, Dragan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach

Übergeordnetes Werk:	Enthalten in: Engineering analysis with boundary elements - Amsterdam [u.a.] : Elsevier Science, 1989, 150, Seite 662-671
Übergeordnetes Werk:	volume:150 ; pages:662-671

DOI / URN:	10.1016/j.enganabound.2023.02.036

Katalog-ID:	ELV061386960

Internformat


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245	1	0	\|a Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
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520			\|a This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center.
650		4	\|a Method of moments
650		4	\|a Stochastic collocation method
650		4	\|a Transcranial magnetic stimulation (TMS)
650		4	\|a Homogeneous human brain model
650		4	\|a Surface integral equation approach
700	1		\|a Šušnjara, Anna \|e verfasserin \|4 aut
700	1		\|a Poljak, Dragan \|e verfasserin \|4 aut
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allfields	10.1016/j.enganabound.2023.02.036 doi (DE-627)ELV061386960 (ELSEVIER)S0955-7997(23)00094-2 DE-627 ger DE-627 rda eng 690 620 VZ 50.03 bkl Cvetković, Mario verfasserin (orcid)0000-0003-4889-7796 aut Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center. Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach Šušnjara, Anna verfasserin aut Poljak, Dragan verfasserin aut Enthalten in Engineering analysis with boundary elements Amsterdam [u.a.] : Elsevier Science, 1989 150, Seite 662-671 Online-Ressource (DE-627)320515486 (DE-600)2013898-2 (DE-576)259271462 0955-7997 nnns volume:150 pages:662-671 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 150 662-671
spelling	10.1016/j.enganabound.2023.02.036 doi (DE-627)ELV061386960 (ELSEVIER)S0955-7997(23)00094-2 DE-627 ger DE-627 rda eng 690 620 VZ 50.03 bkl Cvetković, Mario verfasserin (orcid)0000-0003-4889-7796 aut Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center. Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach Šušnjara, Anna verfasserin aut Poljak, Dragan verfasserin aut Enthalten in Engineering analysis with boundary elements Amsterdam [u.a.] : Elsevier Science, 1989 150, Seite 662-671 Online-Ressource (DE-627)320515486 (DE-600)2013898-2 (DE-576)259271462 0955-7997 nnns volume:150 pages:662-671 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 150 662-671
allfields_unstemmed	10.1016/j.enganabound.2023.02.036 doi (DE-627)ELV061386960 (ELSEVIER)S0955-7997(23)00094-2 DE-627 ger DE-627 rda eng 690 620 VZ 50.03 bkl Cvetković, Mario verfasserin (orcid)0000-0003-4889-7796 aut Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center. Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach Šušnjara, Anna verfasserin aut Poljak, Dragan verfasserin aut Enthalten in Engineering analysis with boundary elements Amsterdam [u.a.] : Elsevier Science, 1989 150, Seite 662-671 Online-Ressource (DE-627)320515486 (DE-600)2013898-2 (DE-576)259271462 0955-7997 nnns volume:150 pages:662-671 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 150 662-671
allfieldsGer	10.1016/j.enganabound.2023.02.036 doi (DE-627)ELV061386960 (ELSEVIER)S0955-7997(23)00094-2 DE-627 ger DE-627 rda eng 690 620 VZ 50.03 bkl Cvetković, Mario verfasserin (orcid)0000-0003-4889-7796 aut Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center. Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach Šušnjara, Anna verfasserin aut Poljak, Dragan verfasserin aut Enthalten in Engineering analysis with boundary elements Amsterdam [u.a.] : Elsevier Science, 1989 150, Seite 662-671 Online-Ressource (DE-627)320515486 (DE-600)2013898-2 (DE-576)259271462 0955-7997 nnns volume:150 pages:662-671 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 150 662-671
allfieldsSound	10.1016/j.enganabound.2023.02.036 doi (DE-627)ELV061386960 (ELSEVIER)S0955-7997(23)00094-2 DE-627 ger DE-627 rda eng 690 620 VZ 50.03 bkl Cvetković, Mario verfasserin (orcid)0000-0003-4889-7796 aut Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center. Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach Šušnjara, Anna verfasserin aut Poljak, Dragan verfasserin aut Enthalten in Engineering analysis with boundary elements Amsterdam [u.a.] : Elsevier Science, 1989 150, Seite 662-671 Online-Ressource (DE-627)320515486 (DE-600)2013898-2 (DE-576)259271462 0955-7997 nnns volume:150 pages:662-671 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 150 662-671
language	English
source	Enthalten in Engineering analysis with boundary elements 150, Seite 662-671 volume:150 pages:662-671
sourceStr	Enthalten in Engineering analysis with boundary elements 150, Seite 662-671 volume:150 pages:662-671
format_phy_str_mv	Article
bklname	Methoden und Techniken der Ingenieurwissenschaften
institution	findex.gbv.de
topic_facet	Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach
dewey-raw	690
isfreeaccess_bool	false
container_title	Engineering analysis with boundary elements
authorswithroles_txt_mv	Cvetković, Mario @@aut@@ Šušnjara, Anna @@aut@@ Poljak, Dragan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320515486
dewey-sort	3690
id	ELV061386960
language_de	englisch
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author	Cvetković, Mario
spellingShingle	Cvetković, Mario ddc 690 bkl 50.03 misc Method of moments misc Stochastic collocation method misc Transcranial magnetic stimulation (TMS) misc Homogeneous human brain model misc Surface integral equation approach Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
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topic_title	690 620 VZ 50.03 bkl Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation Method of moments Stochastic collocation method Transcranial magnetic stimulation (TMS) Homogeneous human brain model Surface integral equation approach
topic	ddc 690 bkl 50.03 misc Method of moments misc Stochastic collocation method misc Transcranial magnetic stimulation (TMS) misc Homogeneous human brain model misc Surface integral equation approach
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title	Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
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title_full	Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
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title_sort	deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
title_auth	Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
abstract	This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center.
abstractGer	This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center.
abstract_unstemmed	This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center.
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title_short	Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation
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author2	Šušnjara, Anna Poljak, Dragan
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up_date	2024-07-06T17:35:19.109Z
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Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation

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