Voxel-based morphometry

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Example of a VBM analysis on patients who experience cluster headaches.

Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images.[1]

In traditional

brain scanning and calculating the volume enclosed. However, this is time consuming and can only provide measures of rather large areas. Smaller differences in volume may be overlooked. The value of VBM is that it allows for comprehensive measurement of differences, not just in specific structures, but throughout the entire brain. VBM registers every brain to a template, which gets rid of most of the large differences in brain anatomy among people. Then the brain images are smoothed so that each voxel
represents the average of itself and its neighbors. Finally, the image volume is compared across brains at every voxel.

However, VBM can be sensitive to various artifacts, which include misalignment of brain structures, misclassification of tissue types, differences in folding patterns and in cortical thickness.[2] All these may confound the statistical analysis and either decrease the sensitivity to true volumetric effects, or increase the chance of false positives. For the cerebral cortex, it has been shown that volume differences identified with VBM may reflect mostly differences in surface area of the cortex, than in cortical thickness.[3][4]

History

Over the past two decades, hundreds of studies have shed light on the neuroanatomical structural correlates of neurological and psychiatric disorders. Many of these studies were performed using voxel-based morphometry (VBM), a whole-brain technique for characterizing between groups' regional volume and tissue concentration differences from structural magnetic resonance imaging (MRI) scans.[5]

One of the first VBM studies and one that came to attention in mainstream media was a study on the

taxicab drivers.[6]
The VBM analysis showed the back part of the posterior hippocampus was on average larger in the taxi drivers compared to control subjects while the anterior hippocampus was smaller. London taxi drivers need good spatial navigational skills and scientists have usually associated hippocampus with this particular skill.

Another famous VBM paper was a study on the effect of age on gray and white matter and CSF of 465 normal adults.[7] The VBM analysis showed global gray matter was decreased linearly with age, especially for men, whereas global white matter did not decline with age.

A key description of the methodology of voxel-based morphometry is Voxel-Based Morphometry—The Methods[8]—one of the most cited articles in the journal NeuroImage.[9] The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may also be used, e.g., for classifying patients from healthy.[10]

For brain asymmetry

Usually VBM is performed for examining differences across subjects, but it may also be used to examine neuroanatomical differences between hemispheres detecting brain asymmetry.[11][12] A technical procedure for such an investigation may use the following steps:[13]

  1. Construction of a study-specific brain image template with a balanced set of left and right handed and males and females.
  2. Construction of
    segmentation
    .
  3. Construction of symmetric grey and white matter templates by averaging right and left cerebral hemispheres.
  4. Segmentation and extraction of brain image, e.g., removal of scalp tissue in the image.
  5. Spatial normalization to the symmetric templates
  6. Correction for volume change (applying a
    Jacobian determinant
    )
  7. Spatial smoothing (intensity in each voxel is a local weighted average generally expressed as GM, WM, CSF concentration).
  8. Actual statistical analysis by the general linear model, i.e., statistical parametric mapping.

The outcome of these steps is a statistical parametric map, highlighting all voxels of the brain where intensities (volume or GM concentration depending on whether the modulation step has been applied or not) in a group images are significantly lower/higher than those in the other group under investigation.

Compared to the region of interest approach

Before the advent of VBM, the manual delineation of region of interest was the gold standard for measuring the volume of brain structures. However, compared to the region of interest approach, VBM presents a large number of advantages that explain its wide popularity within the neuroimaging community. Indeed, it is an automated and relatively easy-to–use, time-efficient, whole-brain tool that could detect the focal microstructural differences in brain anatomy in vivo between groups of individuals without requiring any a priori decision concerning which structure to evaluate. Moreover, VBM exhibits comparable accuracy to manual volumetry. Indeed, several studies have shown good correspondence between the two techniques, providing confidence in the biological validity of the VBM approach.[14]

See also

Wikibooks has a book on the topic of: SPM/VBM

References

  1. S2CID 16777465
    .
  2. PMID 19249168
    .
  3. S2CID 1341760
    .
  4. PMID 20006715
    .
  5. doi:10.2147/NAN.S66439
    . Retrieved 19 May 2016.
  6. PMID 10716738.{{cite journal}}: CS1 maint: multiple names: authors list (link
    ) Commentary on the original article in the same issue: News story with an interview of the researcher:
  7. S2CID 6392260.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  8. S2CID 16777465
    .
  9. ^ The number of citations is apparent from a search with Google Scholar (2007-12-07) [1].
  10. S2CID 1951358.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  11. PMID 11532891.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  12. S2CID 3061292.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  13. S2CID 16235256.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  14. doi:10.2147/NAN.S66439
    .

Further reading
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