Voxel-Based
Morphometry
John Ashburner
john@fil.ion.ucl.ac.uk
Functional Imaging Lab, 12 Queen
Square, London, UK.
Voxel-Based Morphometry
Produce a map of statistically significant
differences among populations of subjects.
e.g. compare a patient group with a control group.
or identify correlations with age, test-score etc.
The data are pre-processed to sensitise the tests
to regional tissue volumes.
Usually grey or white matter.
Can be done with SPM software package, or other
analysis tools.
Pre-processing for Voxel-Based
Morphometry (VBM)
Smoothing
Before convolution
Convolved with a circle
Convolved with a Gaussian
3
3
Units are mm of original grey matter per mm of
spatially normalised space
Pre-processed data for four subjects
Warped, Modulated Grey Matter
12mm FWHM Smoothed Version
Statistical Parametric Mapping…
–
group 1
voxel by voxel
modelling

parameter estimate
standard error
=
statistic image
or
SPM
group 2
SPM results...
Validity of Registration and Segmentation
Comparison with
Manually Drawn
ROIs.
 Good et al (2002).
NeuroImage 17:29-46.
 Not quite comparing
like-with-like.
Validity of the statistical tests
Residuals are not normally distributed.
Little impact on uncorrected statistics for
experiments comparing groups.
Invalidates experiments that compare one subject
with a group.
Corrections for multiple comparisons.
Mostly valid for corrections based on peak heights.
Not valid for corrections based on cluster extents.
SPM makes the inappropriate assumption that the
smoothness of the residuals is stationary.
• Bigger blobs expected in smoother regions.
Alzheimer’s Disease

Karas GB, Burton EJ, Rombouts SA, van Schijndel RA, O'Brien JT, Scheltens P, McKeith IG,
Williams D, Ballard C & Barkhof F. A comprehensive study of gray matter loss in patients with
Alzheimer's disease using optimized voxel-based morphometry. Neuroimage 18:895-907 (2003).
 Our results confirm earlier findings, but additionally we demonstrate global cortical atrophy with sparing of
the sensorimotor cortex, occipital poles, and cerebellum. Moreover, we show atrophy of the caudate head
nuclei and medial thalami. Our findings are in full agreement with the established
neuropathological descriptions, offering a comprehensive view of atrophy patterns in AD.

Gee J, Ding L, Xie Z, Lin M, DeVita C & Grossman M. Alzheimer's disease and frontotemporal
dementia exhibit distinct atrophy-behavior correlates. Academic Radiology 10:1392-1401 (2003).
 VBM provides an important first step in analyzing brain-behavior relations in vivo in patients
with neurodegenerative diseases. More refined analyses of brain morphology via high-dimensional normalization
methods that are capable of modeling local as well as global variability in neuroanatomical structure promise to
be even more informative.

Busatto GF, Garrido GE, Almeida OP, Castro CC, Camargo CH, Cid CG, Buchpiguel CA, Furuie S &
Bottino CM. A voxel-based morphometry study of temporal lobe gray matter reductions in
Alzheimer's disease. Neurobiol Aging 24:221-31 (2003).
 These VBM results confirm previous findings of temporal lobe atrophic changes in AD, and suggest that
these abnormalities may be confined to specific sites within that lobe, rather than showing a widespread
distribution

Testa C, Laakso MP, Sabattoli F, Rossi R, Beltramello A, Soininen H & Frisoni GB. A comparison
between the accuracy of voxel-based morphometry and hippocampal volumetry in Alzheimer's
disease. J Magn Reson Imaging 19:274-82 (2004).
 These results indicate that VBM is more accurate, but the combination of both methods provides the
highest accuracy for detection of hippocampal atrophy in AD.
Degeneration


Keller SS, Mackay CE, Barrick TR, Wieshmann UC, Howard MA & Roberts N. Voxel-based
morphometric comparison of hippocampal and extrahippocampal abnormalities in patients with left
and right hippocampal atrophy. Neuroimage 16:23-31 (2002).
 This work demonstrates methodological consistency between automated VBM and manual
stereological analysis of the hippocampus in group comparisons
Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ, Chan D, Crum WR, Rossor MN & Frackowiak
RS. Automatic differentiation of anatomical patterns in the human brain: validation with studies of
degenerative dementias. Neuroimage 17:29-46 (2002).
 We compared voxel-based morphometry (VBM) with independent accurate region-of-interest (ROI)
measurements of temporal lobe structures in order to validate the usefulness of this fully automated and
unbiased technique in Alzheimer's disease (AD) and semantic dementia (SD). In AD, ROI analyses appear more
sensitive to volume loss in the amygdalae, whereas VBM analyses appear more sensitive to right middle
temporal gyrus and regional hippocampal volume loss. In SD, ROI analyses appear more sensitive to left middle
and inferior temporal gyrus volume loss, whereas VBM appears more sensitive to regional hippocampal volume
loss. In addition the significance of volume reductions was generally less in VBM owing to more stringent
corrections for multiple comparisons. In conclusion, the automated technique detects a general trend of
atrophy similar to that of expertly labeled ROI measurements in AD and SD, although there
are discrepancies in the ranking of severity and in the significance of volume reductions that are more
marked in AD

Thieben MJ, Duggins AJ, Good CD, Gomes L, Mahant N, Richards F, McCusker E & Frackowiak RS.
The distribution of structural neuropathology in pre-clinical Huntington's disease. Brain 125:181528 (2002).
 This suggests that VBM may be useful in monitoring cross-sectional and longitudinal changes in
brain structure in pre-clinical Huntington's disease and for determining the efficacy of neuroprotective
agents.
Schizophrenia & Bipolar Disorder

Moorhead TW, Job DE, Whalley HC, Sanderson TL, Johnstone EC & Lawrie SM. Voxel-based
morphometry of comorbid schizophrenia and learning disability: analyses in normalized and native
spaces using parametric and nonparametric statistical methods. Neuroimage 22:188-202 (2004).
 Overall, these VBM results replicate previous ROI findings and are compatible with the view that
comorbid learning disability with schizophrenia is a severe form of schizophrenia, rather than a consequence of
learning disability. VBM has the facility to compare grey matter distributions in this structurally diverse
cohort.


Kubicki M, Shenton ME, Salisbury DF, Hirayasu Y, Kasai K, Kikinis R, Jolesz FA & McCarley RW.
Voxel-based morphometric analysis of gray matter in first episode schizophrenia. Neuroimage
17:1711-9 (2002).
 These findings suggest both the promise and utility of VBM in evaluating gray matter abnormalities.
They further suggest the importance of comparing VBM findings with more traditional ROI
analyses until the reasons for the differences between methods are determined.
Job DE, Whalley HC, McConnell S, Glabus M, Johnstone EC & Lawrie SM. Structural gray matter
differences between first-episode schizophrenics and normal controls using voxel-based
morphometry. Neuroimage 17:880-9 (2002).
 These results are compatible with and extend the relevant findings of the previous
volumetric ROI analysis, when allowing for the differences between the methods and interpretation of
their results.

Lyoo IK, Kim MJ, Stoll AL, Demopulos CM, Parow AM, Dager SR, Friedman SD, Dunner DL, Renshaw
PF. Frontal lobe gray matter density decreases in bipolar I disorder. Biol Psychiatry 55:648-51
(2004).
 The observation of a gray matter density decrease in the left anterior cingulate, which processes emotions, in
bipolar subjects is consistent with prior reports that used region-of-interest analytic
methods.
Other...

Luders E, Gaser C, Jancke L & Schlaug G. A voxel-based approach to gray matter asymmetries.
NeuroImage 22:656-664 (2004).
 Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional
ROI-based morphometric techniques, the results of our analyses support the use of VBM for
examinations of GM asymmetries.

White NS, Alkire MT& Haier RJ. A voxel-based morphometric study of nondemented adults with
Down Syndrome. Neuroimage 20:393-403 (2003).
 While these results are generally consistent with prior ROI-based imaging studies of
nondemented DS individuals, the present findings provide additional understanding of the three-dimensional
topography of DS morphology throughout the brain. The consistency of these findings with prior imaging
reports demonstrates the utility of the VBM technique for investigating the neuroanatomy of DS.

Ettinger U, Kumari V, Chitnis XA, Corr PJ, Sumich AL, Rabe-Hesketh S, Crawford TJ & Sharma T.
Relationship between brain structure and saccadic eye movements in healthy humans. Neuroscience
Letters 328: 225-228 (2003).
 VBM replicated this finding: gain was correlated with grey matter in the left cerebellar hemisphere and
vermis. These findings agree with previous studies on the role of the cerebellar vermis in saccadic gain
and support the validity of structural neuroimaging methods in elucidating the neural correlates of
saccadic eye movements.
Lesions & Malformations

Mehta S, Grabowski TJ, Trivedi Y & Damasio H. Evaluation of voxel-based morphometry for focal
lesion detection in individuals. Neuroimage 20:1438-54 (2003).
 Performance metrics revealed that (1) for this application, VBM had low sensitivity; (2) detection
sensitivity was altered by model parameterization; (3) underperformance was due to the adverse
influence of lesions on the preprocessing steps and to insufficient statistical power; and (4)
VBM could not satisfactorily delineate the spatial extent of lesions, even in simulations that
VBM is not a suitable stand-alone
technique for detecting or spatially characterizing focal lesions.
avoided preprocessing artifacts. In its current form,


Gitelman DR, Ashburner J, Friston KJ, Tyler LK & Price CJ. Voxel-based morphometry of herpes
simplex encephalitis. Neuroimage 13:623-31 (2001).
 We found that, despite problems in normalizing and segmenting these severely distorted
brains, VBM was able to identify correctly a number of the regional gray matter abnormalities in HSE. The
results, while consistent with the well-known histopathology of the disease, also demonstrate
potential difficulties with this method.
Wilke M, Kassubek J, Ziyeh S, Schulze-Bonhage A & Huppertz HJ. Automated detection of gray
matter malformations using optimized voxel-based morphometry: a systematic approach.
NeuroImage 20:330-343 (2003).
 However, a number of approaches failed to perform well. The reasons for these failures and the implications
this has for other studies are discussed. We conclude that voxel-based morphometry is able to
detect cortical malformations with a high degree of accuracy. However, specific problems seem to
arise when using an optimized protocol for voxel-based morphometry, indicating that this protocol may not be
optimal for all voxel-based studies on brain morphology. Our approach, involving systematic alterations of
parameters and evaluation, may be useful for other studies.
Words of Caution

Bookstein FL. "Voxel-Based Morphometry" Should Not Be Used with Imperfectly Registered
Images. NeuroImage 14:1454-1462 (2001).
 John Ashburner and Karl Friston (2000) introduced a standardized method of "voxel-based morphometry"
(VBM) for comparisons of local concentrations of gray matter between two groups of subjects. Segmented
images of gray matter from grossly normalized high-resolution images are smoothed and their group
differences analyzed by the now-conventional voxelwise Worsley approach to Gaussian random fields of
unfortunate interaction between the algorithm's
spatial normalization and voxelwise comparison steps, whereby several obvious quantitative
differences. This comment concerns an
confounds are injected at the core of the inference engine the authors put forward. Specifically, the
statistics of the resulting voxelwise comparisons are uninformative about group differences wherever the
spatial normalization algorithm has failed to register on any robustly appearing image gradient. The method of
Ashburner and Friston is defensible only far from all image gradients.

Salmond CH, Ashburner J, Vargha-Khadem F, Connelly A, Gadian DG & Friston KJ. Distributional
assumptions in voxel-based morphometry. Neuroimage 17:1027-30 (2002).
 In brief, our results indicate that nonnormality in the error terms can be an issue in VBM. However, in
balanced designs, provided the data are smoothed with a 4-mm FWHM kernel, nonnormality is sufficiently
attenuated to render the tests valid. Unbalanced designs appear to be less robust to violations
of normality: a significant number of false positives arise at a smoothing of 4 and 8 mm
when comparing a single subject to a group. This is despite the fact that conventional group
comparisons appear to be robust, remaining valid even with no smoothing.
Methodology References
 I. C. Wright, P. K. McGuire, J.-B. Poline, J. M. Travere, R. M. Murray, C. D. Frith, R. S. J.
Frackowiak & K. J. Friston.A Voxel-Based Method for the Statistical Analysis of Gray and
White Matter Density Applied to Schizophrenia. NeuroImage 2:244-252 (1995).
 I. C. Wright, Z. R. Ellison, T. Sharma, K. J. Friston, R. M. Murray & P. K. Mcguire. Mapping of
Grey Matter Changes in Schizophrenia. Schizophrenia Research 35:1-14 (1999).
 J. Ashburner & K. J. Friston. Voxel-Based Morphometry - The Methods. NeuroImage 11:805821 (2000).
 J. Ashburner & K. J. Friston. Why Voxel-Based Morphometry Should Be Used. NeuroImage
14:1238-1243 (2001).
 C. D. Good, I. S. Johnsrude, J. Ashburner and R. N. A Henson, K. J. Friston & R. S. J.
Frackowiak. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains.
NeuroImage 14:21-36 (2001).
 C. D. Good, R. I. Scahill, N. C. Fox, J. Ashburner, K. J. Friston, D. Chan, W. R. Crum, M. N.
Rossor & R. S. J. Frackowiak. Automatic Differentiation of Anatomical Patterns in the Human
Brain: Validation with Studies of Degenerative Dementias. NeuroImage 17:29-46 (2002).
 W.R. Crum, L.D. Griffin, D.L.G. Hill & D.J. Hawkes. Zen and the art of medical image
registration: correspondence, homology, and quality. NeuroImage 20:1425-1437 (2003).
 http://www.fil.ion.ucl.ac.uk/spm
Serial Scans
Early
Late
Difference
Data from the
Dementia Research
Group, Queen Square.
Regions of expansion and contraction
Relative
volumes
encoded in
Jacobian
determinants
of the
deformations.
Late
Warped early
Early
Difference
Late CSF
Relative volumes
Early CSF
CSF “modulated” by
relative volumes
Late CSF - modulated CSF
Late CSF - Early CSF
Smoothed