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Computational anatomy with the SPM software.

John Ashburner1

  • 1Wellcome Trust Centre for Neuroimaging, London, UK. john@fil.ion.ucl.ac.uk

Magnetic Resonance Imaging
|March 3, 2009
PubMed
Summary
This summary is machine-generated.

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This review covers computational methods for analyzing brain anatomy variations using Statistical Parametric Mapping (SPM). It highlights voxel-based morphometry and multivariate approaches for potential computer-assisted diagnoses.

Area of Science:

  • Neuroimaging
  • Computational anatomy
  • Medical image analysis

Background:

  • Neuroanatomical variability is crucial for understanding brain function and disease.
  • Statistical Parametric Mapping (SPM) is a widely used software package for analyzing neuroimaging data.
  • Existing methods often focus on univariate approaches for volumetric analysis.

Purpose of the Study:

  • To provide an overview of computational procedures for examining neuroanatomical variability.
  • To focus on approaches applicable with the SPM software package.
  • To discuss the potential of these methods for computer-assisted diagnoses.

Main Methods:

  • Explanation of Statistical Parametric Mapping (SPM) in functional imaging.
  • Detailed discussion of volumetry, particularly voxel-based morphometry (VBM).

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  • Exploration of pre-processing steps within the SPM software for VBM.
  • Comparison of univariate and multivariate approaches for modeling anatomical variability.

    • Main Results:

    • SPM facilitates the examination of neuroanatomical variability.
    • Voxel-based morphometry (VBM) is a key technique within SPM for volumetric analysis.
    • Multivariate approaches may offer more accurate modeling of overall brain form compared to univariate methods.
    • Correction for global measures like total brain volume is common in univariate studies.

    Conclusions:

    • Computational methods, especially VBM within SPM, are valuable for studying brain anatomy.
    • Multivariate modeling of anatomical variability shows promise for enhancing diagnostic accuracy.
    • Further development of these computational tools could significantly impact computer-assisted neurological diagnoses.