Group ICA of resting-state data: a comparison

Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages...
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Autor*in:	Schöpf, Veronika [verfasserIn] Windischberger, Christian [verfasserIn] Kasess, Christian H. [verfasserIn] Lanzenberger, Rupert [verfasserIn] Moser, Ewald [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	fMRI Group ICA Resting-state RSN Default mode

Übergeordnetes Werk:	Enthalten in: Magnetic resonance materials in physics, biology and medicine - Heidelberg : Springer, 1993, 23(2010), 5-6 vom: 03. Juni, Seite 317-325
Übergeordnetes Werk:	volume:23 ; year:2010 ; number:5-6 ; day:03 ; month:06 ; pages:317-325

Links:	Volltext

DOI / URN:	10.1007/s10334-010-0212-0

Katalog-ID:	SPR009481451

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520			\|a Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.
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matchkey_str	article:13528661:2010----::ruiafetnsaeaa
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bklnumber	33.07 35.25 44.64
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allfields	10.1007/s10334-010-0212-0 doi (DE-627)SPR009481451 (SPR)s10334-010-0212-0-e DE-627 ger DE-627 rakwb eng 610 570 530 ASE 610 ASE 33.07 bkl 35.25 bkl 44.64 bkl Schöpf, Veronika verfasserin aut Group ICA of resting-state data: a comparison 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times. fMRI (dpeaa)DE-He213 Group ICA (dpeaa)DE-He213 Resting-state (dpeaa)DE-He213 RSN (dpeaa)DE-He213 Default mode (dpeaa)DE-He213 Windischberger, Christian verfasserin aut Kasess, Christian H. verfasserin aut Lanzenberger, Rupert verfasserin aut Moser, Ewald verfasserin aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 23(2010), 5-6 vom: 03. Juni, Seite 317-325 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325 https://dx.doi.org/10.1007/s10334-010-0212-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.07 ASE 35.25 ASE 44.64 ASE AR 23 2010 5-6 03 06 317-325
spelling	10.1007/s10334-010-0212-0 doi (DE-627)SPR009481451 (SPR)s10334-010-0212-0-e DE-627 ger DE-627 rakwb eng 610 570 530 ASE 610 ASE 33.07 bkl 35.25 bkl 44.64 bkl Schöpf, Veronika verfasserin aut Group ICA of resting-state data: a comparison 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times. fMRI (dpeaa)DE-He213 Group ICA (dpeaa)DE-He213 Resting-state (dpeaa)DE-He213 RSN (dpeaa)DE-He213 Default mode (dpeaa)DE-He213 Windischberger, Christian verfasserin aut Kasess, Christian H. verfasserin aut Lanzenberger, Rupert verfasserin aut Moser, Ewald verfasserin aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 23(2010), 5-6 vom: 03. Juni, Seite 317-325 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325 https://dx.doi.org/10.1007/s10334-010-0212-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.07 ASE 35.25 ASE 44.64 ASE AR 23 2010 5-6 03 06 317-325
allfields_unstemmed	10.1007/s10334-010-0212-0 doi (DE-627)SPR009481451 (SPR)s10334-010-0212-0-e DE-627 ger DE-627 rakwb eng 610 570 530 ASE 610 ASE 33.07 bkl 35.25 bkl 44.64 bkl Schöpf, Veronika verfasserin aut Group ICA of resting-state data: a comparison 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times. fMRI (dpeaa)DE-He213 Group ICA (dpeaa)DE-He213 Resting-state (dpeaa)DE-He213 RSN (dpeaa)DE-He213 Default mode (dpeaa)DE-He213 Windischberger, Christian verfasserin aut Kasess, Christian H. verfasserin aut Lanzenberger, Rupert verfasserin aut Moser, Ewald verfasserin aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 23(2010), 5-6 vom: 03. Juni, Seite 317-325 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325 https://dx.doi.org/10.1007/s10334-010-0212-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.07 ASE 35.25 ASE 44.64 ASE AR 23 2010 5-6 03 06 317-325
allfieldsGer	10.1007/s10334-010-0212-0 doi (DE-627)SPR009481451 (SPR)s10334-010-0212-0-e DE-627 ger DE-627 rakwb eng 610 570 530 ASE 610 ASE 33.07 bkl 35.25 bkl 44.64 bkl Schöpf, Veronika verfasserin aut Group ICA of resting-state data: a comparison 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times. fMRI (dpeaa)DE-He213 Group ICA (dpeaa)DE-He213 Resting-state (dpeaa)DE-He213 RSN (dpeaa)DE-He213 Default mode (dpeaa)DE-He213 Windischberger, Christian verfasserin aut Kasess, Christian H. verfasserin aut Lanzenberger, Rupert verfasserin aut Moser, Ewald verfasserin aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 23(2010), 5-6 vom: 03. Juni, Seite 317-325 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325 https://dx.doi.org/10.1007/s10334-010-0212-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.07 ASE 35.25 ASE 44.64 ASE AR 23 2010 5-6 03 06 317-325
allfieldsSound	10.1007/s10334-010-0212-0 doi (DE-627)SPR009481451 (SPR)s10334-010-0212-0-e DE-627 ger DE-627 rakwb eng 610 570 530 ASE 610 ASE 33.07 bkl 35.25 bkl 44.64 bkl Schöpf, Veronika verfasserin aut Group ICA of resting-state data: a comparison 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times. fMRI (dpeaa)DE-He213 Group ICA (dpeaa)DE-He213 Resting-state (dpeaa)DE-He213 RSN (dpeaa)DE-He213 Default mode (dpeaa)DE-He213 Windischberger, Christian verfasserin aut Kasess, Christian H. verfasserin aut Lanzenberger, Rupert verfasserin aut Moser, Ewald verfasserin aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 23(2010), 5-6 vom: 03. Juni, Seite 317-325 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325 https://dx.doi.org/10.1007/s10334-010-0212-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.07 ASE 35.25 ASE 44.64 ASE AR 23 2010 5-6 03 06 317-325
language	English
source	Enthalten in Magnetic resonance materials in physics, biology and medicine 23(2010), 5-6 vom: 03. Juni, Seite 317-325 volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325
sourceStr	Enthalten in Magnetic resonance materials in physics, biology and medicine 23(2010), 5-6 vom: 03. Juni, Seite 317-325 volume:23 year:2010 number:5-6 day:03 month:06 pages:317-325
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	fMRI Group ICA Resting-state RSN Default mode
dewey-raw	610
isfreeaccess_bool	false
container_title	Magnetic resonance materials in physics, biology and medicine
authorswithroles_txt_mv	Schöpf, Veronika @@aut@@ Windischberger, Christian @@aut@@ Kasess, Christian H. @@aut@@ Lanzenberger, Rupert @@aut@@ Moser, Ewald @@aut@@
publishDateDaySort_date	2010-06-03T00:00:00Z
hierarchy_top_id	308449711
dewey-sort	3610
id	SPR009481451
language_de	englisch
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author	Schöpf, Veronika
spellingShingle	Schöpf, Veronika ddc 610 bkl 33.07 bkl 35.25 bkl 44.64 misc fMRI misc Group ICA misc Resting-state misc RSN misc Default mode Group ICA of resting-state data: a comparison
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title	Group ICA of resting-state data: a comparison
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title_full	Group ICA of resting-state data: a comparison
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author_browse	Schöpf, Veronika Windischberger, Christian Kasess, Christian H. Lanzenberger, Rupert Moser, Ewald
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title_sort	group ica of resting-state data: a comparison
title_auth	Group ICA of resting-state data: a comparison
abstract	Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.
abstractGer	Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.
abstract_unstemmed	Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.
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container_issue	5-6
title_short	Group ICA of resting-state data: a comparison
url	https://dx.doi.org/10.1007/s10334-010-0212-0
remote_bool	true
author2	Windischberger, Christian Kasess, Christian H. Lanzenberger, Rupert Moser, Ewald
author2Str	Windischberger, Christian Kasess, Christian H. Lanzenberger, Rupert Moser, Ewald
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doi_str	10.1007/s10334-010-0212-0
up_date	2024-07-04T02:14:45.844Z
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score	7.399351

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Group ICA of resting-state data: a comparison

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