Source-based morphometry: a decade of covarying structural brain patterns
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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.
KeywordsIndependent 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)
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.
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Conflict of interest
The authors declare that they have no competing interests.
This is a review article and human participants were not involved in this work.
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