Source-based morphometry: a decade of covarying structural brain patterns

Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject...
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Autor*in:	Gupta, Cota Navin [verfasserIn] Turner, Jessica A. [verfasserIn] Calhoun, Vince D. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Independent component analysis (ICA) Source-based morphometry (SBM) Multivariate analysis Voxel-based morphometry (VBM) Univariate analysis Nonlinear independent component analysis (NICE) Biclustered independent component analysis (B-ICA)

Übergeordnetes Werk:	Enthalten in: Anatomy and embryology - Berlin : Springer, 1891, 224(2019), 9 vom: 07. Nov., Seite 3031-3044
Übergeordnetes Werk:	volume:224 ; year:2019 ; number:9 ; day:07 ; month:11 ; pages:3031-3044

Links:	Volltext

DOI / URN:	10.1007/s00429-019-01969-8

Katalog-ID:	SPR005736595

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spelling	10.1007/s00429-019-01969-8 doi (DE-627)SPR005736595 (SPR)s00429-019-01969-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.34 bkl Gupta, Cota Navin verfasserin aut Source-based morphometry: a decade of covarying structural brain patterns 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future. Independent component analysis (ICA) (dpeaa)DE-He213 Source-based morphometry (SBM) (dpeaa)DE-He213 Multivariate analysis (dpeaa)DE-He213 Voxel-based morphometry (VBM) (dpeaa)DE-He213 Univariate analysis (dpeaa)DE-He213 Nonlinear independent component analysis (NICE) (dpeaa)DE-He213 Biclustered independent component analysis (B-ICA) (dpeaa)DE-He213 Turner, Jessica A. verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Anatomy and embryology Berlin : Springer, 1891 224(2019), 9 vom: 07. Nov., Seite 3031-3044 (DE-627)253389798 (DE-600)1458423-2 1432-0568 nnns volume:224 year:2019 number:9 day:07 month:11 pages:3031-3044 https://dx.doi.org/10.1007/s00429-019-01969-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_711 44.34 ASE AR 224 2019 9 07 11 3031-3044
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allfieldsGer	10.1007/s00429-019-01969-8 doi (DE-627)SPR005736595 (SPR)s00429-019-01969-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.34 bkl Gupta, Cota Navin verfasserin aut Source-based morphometry: a decade of covarying structural brain patterns 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future. Independent component analysis (ICA) (dpeaa)DE-He213 Source-based morphometry (SBM) (dpeaa)DE-He213 Multivariate analysis (dpeaa)DE-He213 Voxel-based morphometry (VBM) (dpeaa)DE-He213 Univariate analysis (dpeaa)DE-He213 Nonlinear independent component analysis (NICE) (dpeaa)DE-He213 Biclustered independent component analysis (B-ICA) (dpeaa)DE-He213 Turner, Jessica A. verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Anatomy and embryology Berlin : Springer, 1891 224(2019), 9 vom: 07. Nov., Seite 3031-3044 (DE-627)253389798 (DE-600)1458423-2 1432-0568 nnns volume:224 year:2019 number:9 day:07 month:11 pages:3031-3044 https://dx.doi.org/10.1007/s00429-019-01969-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_711 44.34 ASE AR 224 2019 9 07 11 3031-3044
allfieldsSound	10.1007/s00429-019-01969-8 doi (DE-627)SPR005736595 (SPR)s00429-019-01969-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.34 bkl Gupta, Cota Navin verfasserin aut Source-based morphometry: a decade of covarying structural brain patterns 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future. Independent component analysis (ICA) (dpeaa)DE-He213 Source-based morphometry (SBM) (dpeaa)DE-He213 Multivariate analysis (dpeaa)DE-He213 Voxel-based morphometry (VBM) (dpeaa)DE-He213 Univariate analysis (dpeaa)DE-He213 Nonlinear independent component analysis (NICE) (dpeaa)DE-He213 Biclustered independent component analysis (B-ICA) (dpeaa)DE-He213 Turner, Jessica A. verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Anatomy and embryology Berlin : Springer, 1891 224(2019), 9 vom: 07. Nov., Seite 3031-3044 (DE-627)253389798 (DE-600)1458423-2 1432-0568 nnns volume:224 year:2019 number:9 day:07 month:11 pages:3031-3044 https://dx.doi.org/10.1007/s00429-019-01969-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_711 44.34 ASE AR 224 2019 9 07 11 3031-3044
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topic_facet	Independent component analysis (ICA) Source-based morphometry (SBM) Multivariate analysis Voxel-based morphometry (VBM) Univariate analysis Nonlinear independent component analysis (NICE) Biclustered independent component analysis (B-ICA)
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author	Gupta, Cota Navin
spellingShingle	Gupta, Cota Navin ddc 610 bkl 44.34 misc Independent component analysis (ICA) misc Source-based morphometry (SBM) misc Multivariate analysis misc Voxel-based morphometry (VBM) misc Univariate analysis misc Nonlinear independent component analysis (NICE) misc Biclustered independent component analysis (B-ICA) Source-based morphometry: a decade of covarying structural brain patterns
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topic_title	610 ASE 44.34 bkl Source-based morphometry: a decade of covarying structural brain patterns Independent component analysis (ICA) (dpeaa)DE-He213 Source-based morphometry (SBM) (dpeaa)DE-He213 Multivariate analysis (dpeaa)DE-He213 Voxel-based morphometry (VBM) (dpeaa)DE-He213 Univariate analysis (dpeaa)DE-He213 Nonlinear independent component analysis (NICE) (dpeaa)DE-He213 Biclustered independent component analysis (B-ICA) (dpeaa)DE-He213
topic	ddc 610 bkl 44.34 misc Independent component analysis (ICA) misc Source-based morphometry (SBM) misc Multivariate analysis misc Voxel-based morphometry (VBM) misc Univariate analysis misc Nonlinear independent component analysis (NICE) misc Biclustered independent component analysis (B-ICA)
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title	Source-based morphometry: a decade of covarying structural brain patterns
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title_full	Source-based morphometry: a decade of covarying structural brain patterns
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title_sort	source-based morphometry: a decade of covarying structural brain patterns
title_auth	Source-based morphometry: a decade of covarying structural brain patterns
abstract	Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.
abstractGer	Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.
abstract_unstemmed	Abstract In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.
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title_short	Source-based morphometry: a decade of covarying structural brain patterns
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