Wikipedia

Voxel-based morphometry

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][2] In traditional morphometry, volume of the whole brain or its subparts is measured by drawing regions of interest (ROIs) on images from 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.

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

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.[3] 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, rather than differences in cortical thickness.[4][5]

History

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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.[6]

One of the first VBM studies and one that came to attention in mainstream media was a study on the hippocampus brain structure of London taxicab drivers.[7] 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.[8] 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[9]—one of the most cited articles in the journal NeuroImage.[10] 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.[11]

For brain asymmetry

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Usually VBM is performed for examining differences across subjects, but it may also be used to examine neuroanatomical differences between hemispheres detecting brain asymmetry.[12][13] A technical procedure for such an investigation may use the following steps:[14][15]

  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 white and grey matter templates from 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

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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.[16]

See also

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

References

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  1. ^ Ashburner, John; Friston, Karl J. (June 2000). "Voxel-Based Morphometry—The Methods". NeuroImage. 11 (6): 805–21. CiteSeerX 10.1.1.114.9512. doi:10.1006/nimg.2000.0582. PMID 10860804. S2CID 16777465.
  2. ^ Wright, I.C.; McGuire, P.K.; Poline, J.B.; Travere, J.M.; Murray, R.M.; Frith, C.D.; Frackowiak, R.S.; Friston, K.J. (1995). "A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia". NeuroImage. 2 (4): 244–52. doi:10.1006/nimg.1995.1032. PMID 9343609.
  3. ^ John Ashburner (October 2001). "Computational anatomy with the SPM software". Magnetic Resonance Imaging. 27 (8): 1163–74. doi:10.1016/j.mri.2009.01.006. PMID 19249168.
  4. ^ Natalie L. Voets; Morgan G. Hough; Gwenaëlle Douaud; Paul M. Matthews; Anthony James; Louise Winmill; Paula Webster; Stephen Smith (2008). "Evidence for abnormalities of cortical development in adolescent-onset schizophrenia". NeuroImage. 43 (4): 665–75. doi:10.1016/j.neuroimage.2008.08.013. PMID 18793730. S2CID 1341760.
  5. ^ Anderson M. Winkler; Peter Kochunov; John Blangero; Laura Almasy; Karl Zilles; Peter T. Fox; Ravindranath Duggirala; David C. Glahn (2010). "Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies". NeuroImage. 53 (3): 1135–46. doi:10.1016/j.neuroimage.2009.12.028. PMC 2891595. PMID 20006715.
  6. ^ Simone, Maria Stefania De; Scarpazza, Cristina (14 July 2016). "Voxel-based morphometry: current perspectives". Neuroscience and Neuroeconomics: 1. doi:10.2147/NAN.S66439. Retrieved 19 May 2016.
  7. ^ Eleanor A. Maguire, David G. Gadian, Ingrid S. Johnsrude, Catriona D. Good, John Ashburner, Richard S. J. Frackowiak, and Christopher D. Frith (2000). "Navigation-related structural change in the hippocampi of taxi drivers". Proceedings of the National Academy of Sciences. 97 (8): 4398–4403. Bibcode:2000PNAS...97.4398M. doi:10.1073/pnas.070039597. PMC 18253. 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:
  8. ^ Catriona D. Good, Ingrid S. Johnsrude, John Ashburner, Richard N.A. Henson, Karl J. Friston and Richard S.J. Frackowiak (July 2001). "A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains" (PDF). NeuroImage. 14 (1): 21–36. doi:10.1006/nimg.2001.0786. PMID 11525331. S2CID 6392260.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  9. ^ John Ashburner and Karl J. Friston (June 2000). "Voxel-Based Morphometry—The Methods" (PDF). NeuroImage. 11 (6): 805–821. CiteSeerX 10.1.1.114.9512. doi:10.1006/nimg.2000.0582. PMID 10860804. S2CID 16777465.
  10. ^ The number of citations is apparent from a search with Google Scholar (2007-12-07) [1].
  11. ^ Yasuhiro Kawasaki, Michio Suzuki, Ferath Kherif, Tsutomu Takahashi, Shi-Yu Zhou, Kazue Nakamura, Mie Matsui, Tomiki Sumiyoshi, Hikaru Seto and Masayoshi Kurachi (January 2007). "Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls". NeuroImage. 34 (1): 235–242. doi:10.1016/j.neuroimage.2006.08.018. PMID 17045492. S2CID 1951358.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  12. ^ K.E. Watkins, T. Paus, J.P. Lerch, A. Zijdenbos, D.L. Collins, P. Neelin, J. Taylor, Keith J. Worsley and Alan C. Evans (September 2001). "Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans". Cerebral Cortex. 11 (9): 868–877. doi:10.1093/cercor/11.9.868. PMID 11532891.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  13. ^ E. Luders, C. Gaser, L. Jancke & G. Schlaug (June 2004). "A voxel-based approach to gray matter asymmetries". NeuroImage. 22 (2): 656–664. CiteSeerX 10.1.1.58.3228. doi:10.1016/j.neuroimage.2004.01.032. PMID 15193594. S2CID 3061292.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  14. ^ Catriona D. Good, Ingrid Johnsrude, John Ashburner, Richard N. A. Henson, Karl J. Friston and Richard Frackowiak (September 2001). "Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains NeuroImage". NeuroImage. 14 (3): 685–700. CiteSeerX 10.1.1.420.7705. doi:10.1006/nimg.2001.0857. PMID 11506541. S2CID 16235256.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  15. ^ F. Kurth, C. Gaser, E. Luders (2015). "A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM)". Nature Protocols. 10 (2): 293–304. doi:10.1038/nprot.2015.014. PMID 25591011.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  16. ^ Scarpazza C, De Simone M (July 2016). "Voxel-based morphometry: current perspectives". Neuroscience and Neuroeconomics. 2016 (5): 19–35. doi:10.2147/NAN.S66439.

Further reading

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