Source-Based Morphometry: Data-Driven Multivariate Analysis of Structural Brain Imaging Data

Cota Navin Gupta; Jessica A. Turner; Vince Daniel Calhoun

doi:10.1007/978-1-4939-7647-8_7

Source-Based Morphometry: Data-Driven Multivariate Analysis of Structural Brain Imaging Data

Cota Navin Gupta, Jessica A. Turner, Vince Daniel Calhoun

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This chapter discusses a now established linear multivariate technique called source-based morphometry (SBM), a data-driven multivariate approach for decomposing structural brain imaging data into commonly covarying components and subject-specific loading parameters. It has been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of data-driven multivariate techniques over univariate analysis for imaging studies. We then discuss results from a range of recent imaging studies which have successfully applied this linear technique. We also present extensions of this framework such as nonlinear SBM, morphometric analysis using independent vector analysis (IVA), and related approaches such as parallel independent component analysis with reference (pICA-R). This chapter thus reviews a wide range of multivariate, data-driven approaches which have been successfully applied to brain imaging studies.

Original language	English (US)
Title of host publication	Neuromethods
Publisher	Humana Press Inc.
Pages	105-120
Number of pages	16
DOIs	https://doi.org/10.1007/978-1-4939-7647-8_7
State	Published - Jan 1 2018
Externally published	Yes

Publication series

Name	Neuromethods
Volume	136
ISSN (Print)	0893-2336
ISSN (Electronic)	1940-6045

Keywords

Genome-wide association
Independent component analysis (ICA)
Independent vector analysis (IVA)
Multivariate analysis
Nonlinear independent component analysis (NICE)
Source-based morphometry (SBM)
Univariate analysis
Voxel-based morphometry (VBM)

ASJC Scopus subject areas

Neuroscience(all)
Biochemistry, Genetics and Molecular Biology(all)
Pharmacology, Toxicology and Pharmaceutics(all)
Psychiatry and Mental health

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Cite this

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abstract = "This chapter discusses a now established linear multivariate technique called source-based morphometry (SBM), a data-driven multivariate approach for decomposing structural brain imaging data into commonly covarying components and subject-specific loading parameters. It has been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of data-driven multivariate techniques over univariate analysis for imaging studies. We then discuss results from a range of recent imaging studies which have successfully applied this linear technique. We also present extensions of this framework such as nonlinear SBM, morphometric analysis using independent vector analysis (IVA), and related approaches such as parallel independent component analysis with reference (pICA-R). This chapter thus reviews a wide range of multivariate, data-driven approaches which have been successfully applied to brain imaging studies.",

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