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Related Concept Videos

Brain Imaging01:14

Brain Imaging

612
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
612

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Source-based morphometry: a decade of covarying structural brain patterns.

Cota Navin Gupta1,2, Jessica A Turner3,4, Vince D Calhoun3,4,5

  • 1Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, US. cngupta@iitg.ac.in.

Brain Structure & Function
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

Source-based morphometry (SBM) is a powerful brain imaging analysis technique. This review highlights SBM

Keywords:
Biclustered independent component analysis (B-ICA)Independent component analysis (ICA)Multivariate analysisNonlinear independent component analysis (NICE)Source-based morphometry (SBM)Univariate analysisVoxel-based morphometry (VBM)

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Biostatistics

Background:

  • Source-based morphometry (SBM) is a data-driven linear multivariate approach.
  • SBM decomposes structural brain imaging data into covarying components and subject-specific parameters.
  • It is widely used to identify neuroanatomic differences in neuropsychiatric diseases.

Purpose of the Study:

  • To review and discuss brain imaging studies utilizing the SBM approach over the past decade.
  • To highlight the advantages of SBM over univariate analysis.
  • To present recent extensions and future directions of SBM.

Main Methods:

  • Review of published literature on Source-based Morphometry (SBM).
  • Discussion of SBM's statistical framework and its application in neuroimaging.
  • Exploration of advanced SBM variants like nonlinear SBM and B-ICA.

Main Results:

  • SBM effectively identifies neuroanatomic differences between healthy controls and patient groups.
  • Recent studies demonstrate successful applications of SBM in various neuropsychiatric conditions.
  • Extensions like nonlinear SBM and B-ICA offer enhanced analytical capabilities.

Conclusions:

  • Source-based morphometry is a valuable tool for neuroimaging research.
  • Continued development of SBM methods promises deeper insights into brain structure and disease.
  • Future work should explore novel applications and refine existing SBM techniques.