SPM Course

FIL SPM Course Oct 2012
Voxel-Based Morphometry
Ged Ridgway, FIL/WTCN
With thanks to John Ashburner
Overview
• Unified segmentation recap
• Voxel-based morphometry (VBM)
• Spatial normalisation with Dartel
Tissue segmentation
• High-resolution MRI reveals fine structural detail in the
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brain, but not all of it reliable or interesting
• Noise, intensity-inhomogeneity, vasculature, …
MR intensity is usually not quantitative (cf. relaxometry)
• fMRI time-series allow signal changes to be analysed
statistically, compared to baseline or global values
• Regional volumes of the three main tissue types: gray
matter, white matter and CSF, are well-defined and
potentially very interesting
Summary of unified segmentation
• Unifies tissue segmentation and spatial normalisation
• Principled Bayesian formulation: probabilistic generative model
• Gaussian mixture model with deformable tissue prior
probability maps (from segmentations in MNI space)
• The inverse of the transformation that aligns the TPMs can be
used to normalise the original image to standard space
[Or the rigid component can be used to initialise Dartel]
•
• Intensity non-uniformity (bias) is included in the model
Tissue intensity distributions (T1-w MRI)
Gaussian mixture model (GMM or MoG)
• Classification is based on a Mixture of Gaussians (MoG)
model fitted to the intensity probability density (histogram)
Frequency
Image Intensity
Modelling inhomogeneity
• MR images are corrupted by spatially smooth
intensity variations (worse at high field strength)
• A multiplicative bias correction field is modelled
as part of unified segmentation
Corrupted image
Bias Field
Corrected image
TPMs – Tissue prior
probability maps
• Each TPM indicates the
prior probability for a
particular tissue at each
point in MNI space
• Fraction of occurrences in
previous segmentations
• TPMs are warped to
match the subject
• The inverse transform
normalises to MNI space
Overview
• Unified segmentation
• Voxel-based morphometry (VBM)
• Spatial normalisation with Dartel
Computational neuroanatomy
• Quantitative analysis of variability in biological shape
• Can be univariate or multivariate, inferential or predictive
• Example applications
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Distinguish groups (e.g schizophrenics from healthy controls)
Model changes (e.g. in development or aging)
Characterise plasticity, e.g. when learning new skills
Find structural correlates (scores, traits, genetics, etc.)
Differentiate degenerative disease from healthy aging
• Evaluate subjects on drug treatments versus placebo
Voxel-Based Morphometry
• Most widely used method for computational anatomy
• VBM is essentially Statistical Parametric Mapping of
regional segmented tissue density or volume
• The exact interpretation of gray matter density or
volume is complicated, and depends on the
preprocessing steps used
• It is not interpretable as neuronal packing density or other
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cytoarchitectonic tissue properties
The hope is that changes in these microscopic properties may
lead to macro- or mesoscopic VBM-detectable differences
VBM methods overview
• Unified segmentation and spatial normalisation
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• More flexible groupwise normalisation using DARTEL
Volume-preserving transformation/warping
Gaussian smoothing
Optional computation of tissue totals/globals
Voxel-wise statistical analysis
VBM in pictures
Segment
Normalise
VBM in pictures
Segment
Normalise
Modulate
Smooth
VBM in pictures
Segment
Normalise
Modulate
Smooth
Voxel-wise statistics
 a1 xyz
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a 2 xyz
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 aNxyz
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  Y  X
 e xyz
xyz
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2

e xyz ~ N ( 0 ,  xyz V )
1

1
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X  
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0
0
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0
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0
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1
1 
VBM in pictures
beta_0001
con_0001
ResMS
spmT_0001
Segment
Normalise
Modulate
Smooth
Voxel-wise statistics
FWE < 0.05
Modulation
(“preserve amounts”)
Native
1
intensity = tissue
density
1
• Multiplication of the warped
(normalised) tissue intensities so
that their regional or global
volume is preserved
Unmodulated
• Can detect differences in
completely registered areas
• Otherwise, we “preserve
1
1
1
1
concentrations”, and are detecting
mesoscopic effects that remain
after approximate registration has
removed the macroscopic effects
• Flexible (not necessarily “perfect”)
Modulated
registration may not leave any
such differences
2/3
1/3
1/3
2/3
Modulation
(“preserve amounts”)
• Top shows “unmodulated”
data (wc1), with intensity or
concentration preserved
• Intensities are constant
• Below is “modulated” data
(mwc1) with amounts or
totals preserved
• The voxel at the cross-hairs
brightens as more tissue is
compressed at this point
Smoothing
• The analysis will be most sensitive to effects that match
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the shape and size of the kernel
The data will be more Gaussian and closer to a
continuous random field for larger kernels
• Usually recommend >= 6mm
Results will be rough and noise-like if too little
smoothing is used
Too much will lead to distributed, indistinct blobs
• Usually recommend <= 12mm
Smoothing as a locally weighted ROI
• VBM > ROI: no subjective (or arbitrary) boundaries
• VBM < ROI: harder to interpret blobs & characterise error
Interpreting findings
Thinning
Contrast
Mis-register
Mis-register
Thickening
Folding
Mis-classify
Interpreting findings
VBM is sometimes described as
“unbiased whole brain volumetry”
Regional variation in
registration accuracy
Segmentation problems, issues
with analysis mask
Intensity, folding, etc.
But significant blobs probably still indicate meaningful
systematic effects!
Adjustment for “nuisance” variables
• Anything which might explain some variability
in regional volumes of interest should be
considered
• Age and gender are obvious and commonly used
• Consider age+age2 to allow quadratic effects
• Site or scanner if more than one
(NB factor, not covariate!)
• Interval in longitudinal studies
• Some “12-month” intervals end up months longer…
• Total grey matter volume often used for VBM
• Changes interpretation when correlated with local
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volumes (shape is a multivariate concept…)
Total intracranial volume (TIV/ICV) sometimes
more useful/interpretable, see also
Barnes et al., (2010), NeuroImage 53(4):1244-55
Longitudinal VBM
• The simplest method for longitudinal VBM is to use
cross-sectional preprocessing, but longitudinal statistics
• Standard preprocessing not optimal, but unbiased
• Non-longitudinal statistics would inflate false positive rates
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• (Estimates of standard errors would be too small)
Simplest longitudinal statistical analysis: two-stage summary
statistic approach (common in fMRI)
• Within subject longitudinal differences or beta estimates from linear
regressions against time
Longitudinal VBM variations
• Intra-subject registration over time is much more
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accurate than inter-subject normalisation
A simple approach is to apply one set of normalisation
parameters (e.g. estimated from baseline images) to
both baseline and repeat(s)
• Draganski et al (2004) Nature 427: 311-312
More sophisticated approaches use nonlinear withinsubject registration, e.g. with HDW or new toolbox
• E.g. Kipps et al (2005) JNNP 76:650
• Beware of bias from asymmetries! (Thomas et al 2009)
doi:10.1016/j.neuroimage.2009.05.097
Overview
• Unified segmentation
• Voxel-based morphometry (VBM)
• Spatial normalisation with Dartel
Spatial normalisation with DARTEL
• VBM is crucially dependent on registration performance
• Limited flexibility (low DoF) registration has been criticised
• Inverse transformations are useful, but not always well-defined
• More flexible registration requires careful modelling and
regularisation (prior belief about reasonable warping)
MNI/ICBM templates/priors are not universally representative
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• The DARTEL toolbox combines several methodological
advances to address these limitations
• Evaluations show DARTEL performs at state-of-the art
• E.g. Klein et al., (2009) NeuroImage 46(3):786-802
…
Part of
Fig.1 in
Klein et al.
Part of
Fig.5 in
Klein et al.
DARTEL Transformations
• Estimate (and regularise) a flow u
• Think syrup rather than elastic
• 3 (x,y,z) parameters per 1.5mm3 voxel
• 10^6 degrees of freedom vs. 10^3 DF
for old discrete cosine basis functions
• Scaling and squaring is used to
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generate deformations
Inverse simply integrates -u
DARTEL objective function
• Likelihood component (matching)
• Specific for matching tissue segments to their mean
• Multinomial distribution (cf. Gaussian)
• Prior component (regularisation)
• A measure of deformation (flow) roughness = ½uTHu
• Need to choose H and a balance between the two terms
• Defaults usually work well (e.g. even for AD)
• Though note that changing models (priors) can change results
Simultaneous registration of GM to GM and
WM to WM, for a group of subjects
Subject 1
Grey matter
White matter
Grey matter
White matter
Grey matter
White matter
Grey matter
Template
Grey matter
White matter
White matter
Subject 2
Subject 4
Subject 3
DARTEL average
template evolution
Template
1
Rigid average
(Template_0)
Average of
mwc1 using
segment/DCT
Template
6
Summary
• VBM performs voxel-wise statistical analysis on
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smoothed (modulated) normalised tissue segments
SPM8 performs segmentation and spatial normalisation
in a unified generative model
• Based on Gaussian mixture modelling, with DCT-warped
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spatial priors, and multiplicative bias field
The new segment toolbox includes non-brain priors and more
flexible/precise warping of them
• Subsequent (currently non-unified) use of DARTEL
improves normalisation for VBM
• And probably also fMRI...
EXTRA MATERIAL
Mathematical advances in
computational anatomy
• VBM is well-suited to find focal volumetric differences
• Assumes independence among voxels
• Not very biologically plausible
• But shows differences that are easy to interpret
• Some anatomical differences can not be localised
• Need multivariate models
• Differences in terms of proportions among measurements
• Where would the difference between male and female faces
be localised?
Mathematical advances in
computational anatomy
• In theory, assumptions about structural covariance
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among brain regions are more biologically plausible
• Form influenced by spatio-temporal modes of gene expression
Empirical evidence, e.g.
• Mechelli, Friston, Frackowiak & Price. Structural covariance in
the human cortex. Journal of Neuroscience 25:8303-10 (2005)
• Recent introductory review:
• Ashburner & Klöppel. “Multivariate models of inter-subject
anatomical variability”. NeuroImage 56(2):422-439 (2011)
Summary of extra material
• VBM uses the machinery of SPM to localise patterns in
regional volumetric variation
• Use of “globals” as covariates is a step towards multivariate
modelling of volume and shape
• More advanced approaches typically benefit from the
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same preprocessing methods
• New segmentation and DARTEL close to state of the art
• Though possibly little or no smoothing
Elegant mathematics related to transformations
(diffeomorphism group with Riemannian metric)
VBM – easier interpretation – complementary role
Historical bibliography of VBM
• A Voxel-Based Method for the Statistical Analysis of
Gray and White Matter Density… Wright, McGuire,
Poline, Travere, Murrary, Frith, Frackowiak and Friston
(1995 (!)) NeuroImage 2(4)
• Rigid reorientation (by eye), semi-automatic scalp editing and
segmentation, 8mm smoothing, SPM statistics, global covars.
• Voxel-Based Morphometry – The Methods. Ashburner
and Friston (2000) NeuroImage 11(6 pt.1)
• Non-linear spatial normalisation, automatic segmentation
• Thorough consideration of assumptions and confounds
Historical bibliography of VBM
• A Voxel-Based Morphometric Study of Ageing… Good,
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Johnsrude, Ashburner, Henson and Friston (2001)
NeuroImage 14(1)
• Optimised GM-normalisation (“a half-baked procedure”)
Unified Segmentation. Ashburner and Friston (2005)
NeuroImage 26(3)
• Principled generative model for segmentation using
deformable priors
• A Fast Diffeomorphic Image Registration Algorithm.
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Ashburner (2007) Neuroimage 38(1)
• Large deformation normalisation
Computing average shaped tissue probability templates.
Ashburner & Friston (2009) NeuroImage 45(2): 333-341