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 proceedingChapter

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 languageEnglish (US)
Title of host publicationNeuromethods
PublisherHumana Press Inc.
Pages105-120
Number of pages16
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Publication series

NameNeuromethods
Volume136
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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