VBM Voxel-based morphometry

1
VBMVoxel-based morphometry

• Floris de Lange
• Most slides taken/adapted from
• Nicola Hobbs Marianne Novak
• http//www.fil.ion.ucl.ac.uk/mfd/

2
Overview

• Background (What is VBM?)
• Pre-processing steps
• Analysis
• Multiple comparisons
• Pros and cons of VBM
• Optional extras

3
What is VBM?

• VBM is a voxel-wise comparison of local tissue
  volumes within a group or across groups
• Whole-brain analysis, does not require a priori
  assumptions about ROIs unbiased way of
  localising structural changes
• Can be automated, requires little user
  intervention ? compare to manual ROI tracing

4
Basic Steps

1. Spatial normalisation (alignment) into standard
   space
2. Segmentation of tissue classes
3. Modulation - adjust for volume changes during
   normalisation
4. Smoothing - each voxel is a weighted average of
   surrounding voxels
5. Statistics - localise make inferences about
   differences

5
VBM Processing
6
(No Transcript)
7
Step 1 normalisation

• Aligns images by warping to standard stereotactic
  space
• Affine step translation, rotation, scaling,
  shearing
• Non-linear step
• Adjust for differences in
• head position/orientation in scanner
• global brain shape
• Any remaining differences (detectable by VBM) are
  due to smaller-scale differences in volume

8
(No Transcript)
9
Normalization linear transformations

• parameter affine transform
• 3 translations
• 3 rotations
• 3 zooms
• 3 shears
• Fits overall shape and size

10
Normalization nonlinear transformations
Deformations consist of a linear combination of
smooth basis functions These are the lowest
frequencies of a 3D discrete cosine transform
(DCT)
11
(No Transcript)
12
2. Tissue segmentation

• Aims to classify image as GM, WM or CSF
• Two sources of information
• a) Spatial prior probability maps
• b) Intensity information in the image itself

13
a) Spatial prior probability maps

• Smoothed average of GM from MNI
• Intensity at each voxel represents probability of
  being GM
• SPM compares the original image to this to help
  work out the probability of each voxel in the
  image being GM (or WM, CSF)

14
b) Image intensities

• Intensities in the image fall into roughly 3
  classes
• SPM can also assign a voxel to a tissue class by
  seeing what its intensity is relative to the
  others in the image
• Each voxel has a value between 0 and 1,
  representing the probability of it being in that
  particular tissue class
• Includes correction for image intensity
  non-uniformity

15
Bias correction

• The contrast of a scan may not be the same
  everywhere
• This makes it more difficult to partition the
  scan in different tissue types
• Bias correction estimates and removes this bias

16
Generative model

• Segmentation into tissue types
• Bias Correction
• Normalisation
• These steps cycled through until normalisation
  and segmentation criteria are met

17
(No Transcript)
18
Step 3 modulation

• Corrects for changes in volume induced by
  normalisation
• Voxel intensities are multiplied by the local
  value in the deformation field from
  normalisation, so that total GM/WM signal remains
  the same
• Allows us to make inferences about volume,
  instead of concentration

19
Modulation

• E.g. During normalisation TL in AD subject
  expands to double the size
• Modulation multiplies voxel intensities by
  Jacobian from normalisation process (halve
  intensities in this case).
• Intensity now represents relative volume at that
  point

20
Is modulation optional?

• Unmodulated data compares the proportion of
  grey or white matter to all tissue types within a
  region
• Hard to interpret
• Not useful for looking at e.g. the effects of
  degenerative disease
• Modulated data compares volumes
• Unmodulated data may be useful for highlighting
  areas of poor registration (perfectly registered
  unmodulated data should show no differences
  between groups)

21
(No Transcript)
22
Step 4 Smoothing

• Convolve with an isotropic Gaussian kernel
• Each voxel becomes weighted average of
  surrounding voxels
• Smoothing renders the data more normally
  distributed (Central Limit theorem)
• Required if using parametric statistics
• Smoothing compensates for inaccuracies in
  normalisation
• Makes mass univariate analysis more like
  multivariate analysis
• Filter size should match the expected effect size
• Usually between 8 14mm

23
Smoothing
SMOOTH WITH 8MM KERNEL
24
VBM Analysis

• What does the SPM show in VBM?
• Cross-sectional VBM
• Multiple comparison corrections
• Pros and cons of VBM
• Optional extras

25
VBM Cross-sectional analysis overview

• T1-weighted MRI from one or more groups at a
  single time point
• Analysis compares (whole or part of) brain volume
  between groups, or correlates volume with another
  measurement at that time point
• Generates map of voxel intensities represent
  volume of, or probability of being in, a
  particular tissue class

26
What is the question in VBM analysis?
Control
AD

• Take a single voxel, and ask are the
  intensities in the AD images significantly
  different to those in the control images for this
  particular voxel?
• eg is the GM intensity (volume) lower in the AD
  group cf controls?
• ie do a simple t-test on the voxel intensities

27
Statistical Parametric Maps (SPM)

• Repeat this for all voxels
• Highlights all voxels where intensities (volume)
  are significantly different between groups the
  SPM

• SPM showing regions where Huntingtons patients
  have lower GM intensity than controls
• Colour bar shows the t-value

28
VBM correlation

• Correlate images and test scores (eg Alzheimers
  patients with memory score)
• SPM shows regions of GM or WM where there are
  significant associations between intensity
  (volume) and test score

• V ß1(test score) ß2(age) ß3(gender)
  ß4(global volume) µ e
• Contrast of interest is whether ß1 (slope of
  association between intensity test score) is
  significantly different to zero

29
Correcting for Multiple Comparisons

• 200,000 voxels per scan ie 200,000 t-tests
• If you do 200,000 t-tests at plt0.05, by chance
  10,000 will be false positives
• Bad practice
• A strict Bonferroni correction would reduce the p
  value for each test to 0.00000025
• However, voxel intensities are not independent,
  but correlated with their neighbours
• Bonferroni is therefore too harsh a correction
  and will lose true results

30
Familywise Error

• SPM uses Gaussian Random Field theory (GRF)1
• Using FWE, plt0.05 5 of ALL our SPMs will
  contain a false positive voxel
• This effectively controls the number of false
  positive regions rather than voxels
• Can be thought of as a Bonferroni-type
  correction, allowing for multiple non-independent
  tests
• Good a safe way to correct
• Bad but we are probably missing a lot of true
  positives

1 http//www.mrc-cbu.cam.ac.uk/Imaging/Common/rand
omfields.shtml
31
False Discovery Rate
q value
FDR
qlt0.05

• FDR more recent
• It controls the expected proportion of false
  positives among suprathreshold voxels only
• Using FDR, qlt0.05 we expect 5 of the voxels
  for each SPM to be false positives (1,000 voxels)
• Bad less stringent than FWE so more false
  positives
• Good fewer false negatives (ie more true
  positives)
• But assumes independence of voxels avoid.?

Voxel
32
VBM Pros
1. Objective analysis 2. Do not need priors
more exploratory 3. Automated
VBM Cons

• 1. SPM normalization procedure is rather crude
• Not ideal for subcortical (well-delineated)
  structures
• More difficult to pick up differences in areas
  with high inter-subject variance low signal to
  noise ratio

33

• Standard preprocessing areas of decreased volume
  in depressed subjects

DARTEL preprocessing areas of decreased volume
in depressed subjects
34
(No Transcript)
35
Resources and references

• http//www.fil.ion.ucl.ac.uk/spm (the SPM
  homepage)
• http//imaging.mrc-cbu.cam.ac.uk/imaging/CbuImag
  ing (neurimaging wiki homepage)
• http//www.mrc-cbu.cam.ac.uk/Imaging/Common/rando
  mfields.shtml (for multiple comparisons info)
• Ashburner J, Friston KJ. Voxel-based
  morphometry--the methods. Neuroimage 2000 11
  805-821 (the original VBM paper)
• Good CD, Johnsrude IS, Ashburner J, Henson RN,
  Friston KJ, Frackowiak RS. A voxel-based
  morphometric study of ageing in 465 normal adult
  human brains. Neuroimage 2001 14 21-36 (the
  optimised VBM paper)
• Ridgway GR, Henley SM, Rohrer JD, Scahill RI,
  Warren JD, Fox NC. Ten simple rules for reporting
  voxel-based morphometry studies. Neuroimage 2008.