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

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Brain Morphometry

Part of the book series: Neuromethods ((NM,volume 136))

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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Acknowledgments

This work was supported by NIH 1R01MH094524 (to JT and VDC) as well as P20GM103472, 1R01EB006841, and R01EB005846 (to VDC).

Author information

Author notes

  1. Jessica A. Turner and Vince D. Calhoun contributed equally to this work.

Authors and Affiliations

  1. The Mind Research Network, Albuquerque, NM, USA

    Cota Navin Gupta, Jessica A. Turner & Vince D. Calhoun

  2. Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, USA

    Jessica A. Turner

  3. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA

    Vince D. Calhoun

  4. Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati, India

    Cota Navin Gupta

Authors
  1. Cota Navin Gupta
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  2. Jessica A. Turner
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  3. Vince D. Calhoun
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Corresponding author

Correspondence to Vince D. Calhoun .

Editor information

Editors and Affiliations

  1. IRCCS Santa Lucia Foundation, Laboratory of Neuropsychiatry, Rome, Italy

    Gianfranco Spalletta

  2. Division of Neuropsychiatry Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA

    Fabrizio Piras

  3. IMT School for Advanced Studies–Lucca, Lucca, Italy

    Tommaso Gili

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Gupta, C.N., Turner, J.A., Calhoun, V.D. (2018). Source-Based Morphometry: Data-Driven Multivariate Analysis of Structural Brain Imaging Data. In: Spalletta, G., Piras, F., Gili, T. (eds) Brain Morphometry. Neuromethods, vol 136. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7647-8_7

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  • DOI: https://doi.org/10.1007/978-1-4939-7647-8_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7645-4

  • Online ISBN: 978-1-4939-7647-8

  • eBook Packages: Springer Protocols

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