TY - CHAP
T1 - Source-Based Morphometry
T2 - Data-Driven Multivariate Analysis of Structural Brain Imaging Data
AU - Gupta, Cota Navin
AU - Turner, Jessica A.
AU - Calhoun, Vince Daniel
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Genome-wide association
KW - Independent component analysis (ICA)
KW - Independent vector analysis (IVA)
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 - 10.1007/978-1-4939-7647-8_7
DO - 10.1007/978-1-4939-7647-8_7
M3 - Chapter
AN - SCOPUS:85042905671
T3 - Neuromethods
SP - 105
EP - 120
BT - Neuromethods
PB - Humana Press Inc.
ER -