TY - JOUR
T1 - Source-based morphometry
T2 - a decade of covarying structural brain patterns
AU - Gupta, Cota Navin
AU - Turner, Jessica A.
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was supported by NIH 1R01MH094524 to Drs. Turner and Calhoun, as well as P20GM103472, 1R01EB006841, R01EB005846 and?NSF grant 1539067?to Dr. Calhoun. The first author acknowledges support from the Indian Institute of Technology Guwahati startup grant during this work.
Funding Information:
This work was supported by NIH 1R01MH094524 to Drs. Turner and Calhoun, as well as P20GM103472, 1R01EB006841, R01EB005846 and NSF grant 1539067 to Dr. Calhoun. The first author acknowledges support from the Indian Institute of Technology Guwahati startup grant during this work.
Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - Biclustered independent component analysis (B-ICA)
KW - Independent component analysis (ICA)
KW - Multivariate analysis
KW - Nonlinear independent component analysis (NICE)
KW - Source-based morphometry (SBM)
KW - Univariate analysis
KW - Voxel-based morphometry (VBM)
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U2 - 10.1007/s00429-019-01969-8
DO - 10.1007/s00429-019-01969-8
M3 - Review article
C2 - 31701266
AN - SCOPUS:85074822228
VL - 224
SP - 3031
EP - 3044
JO - Brain Structure and Function
JF - Brain Structure and Function
SN - 1863-2653
IS - 9
ER -