1. Voxel-based Morphometric Analysis
Martinos Center for Biomedical Imaging,
Massachusetts General Hospital,
Charlestown, MA, United States

2. Outline
Voxel-based Morphometry
Surface-based Analysis
Comparisons 2

3. Voxel-based Morphometry

4. Anatomical Changes GM
Subject 1
88 yo
Subject 2
19 yo

5. Voxel-based Morphometry (VBM)
How do the sizes of gray/white matter and CSF structures change between subjects/populations? GM WM
CSF
Subject 1
88 yo
Subject 2
19 yo

6. Voxel-based Morphometry (VBM)
How to define a volume without defining a boundary?
How to compare regions without defining a region? WM
CSF

7. Non-linear Spatial Normalization
Subject 2
(Target)
Subject 1
Subject 1  Subject 2
CSF
Eyes move closer together
Lips curl and get wider

8. Non-linear Spatial Normalization
Subject 2
(Target)
Subject 1
Subject 1  Subject 2
CSF
Eyes move closer together
Lips curl and get wider

9. Keep Track of Changes in Size
CSF
Voxel on the left side of mouth does not change size
Voxel on the right gets much larger
Eyes do not change size
Quantification: Gray Matter “Density”

10. Jacobian Map of change in volume at each voxel in target space
Subject 2
(Target)
Subject 2
(Target)
Subject 1
CSF
Map of change in volume at each voxel in target space
Eyes are 0
Left side of lips are 0 (black)
Right side of lips are yellow (expansion)

11. Normalization and Segmentation
Individual T1
(Template Space)
Jacobian
Individual T1
Spatial
Normalization
Expansion
Compression
Group Template
(Target)
Segmentations
CSF WM GM
Values between 0 and 1
“Density”, Partial Volume
Note: in FSL, Segmentations computed in native space
“Unified Segmentation”, Ashburner and Friston, NI, 2005
“Optimized VBM” Good, et al, NI, Douaud, et al, Brain

12. Modulation and Smoothing
(Template Space)
GM Segmentation
(Concentration)
Multiply
3D Smooth
GM Density
Jacobian

13. Aging Gray Matter Volume Study
Statistical Maps (SPM8/VBM8) p<.01
Positive Age Correlation
Negative Age Correlation
GM Density

14. Surface-based Analysis

15. Surface-based Analysis: Cortex
Outer layer of gray matter
White/Gray Surface
Pial Surface

16. Cortical Thickness pial surface
Distance between white and pial surfaces along normal vector.
1-5mm

17. IndividualThickness Maps
46 yo
88 yo
18 yo
Male
Female
Salat, et al, 2004, Cerebral Cortex

18. A Surface-Based Coordinate System
Common space for group analysis (like Talairach). Fischl, et al, 1998, NI.

19. Surface Spatial Smoothing
5 mm apart in 3D
25 mm apart on surface!
Kernel much larger
Averaging with other tissue types (WM, CSF)
Averaging with other functional areas

20. Aging Thickness Study N=40 p<.01 Positive Age Correlation
Negative Age Correlation
p<.01

21. VBM and Thickness vs Age
VBM (SPM8/VBM8)
Thickness (FreeSurfer 5.0)
p<.01

22. Comparisons Thickness does not require modulation
False positive rates are much higher in VBM because of Jacobian modulation (same for surface-based when area or volume are used; Greve and Fischl, submitted)
Thickness is independent of registration.
VBM – harder to interpret because “density” is a mixture of thickness, surface area, gyrification, registration, and volume-based smoothing.
VBM allows subcortical analysis
Young (20)
Old (80) 22

23. Which is better? Still an open question
Voets, et al, 2008, NI – mixed results
Hutton, et al, 2009, NI – voxel-based cortical thickness (VBCT) was more sensitive to aging than VBM
False Positive Rate considerations 23

24. VBM Software Statistical Parametric Mapping (SPM)
uses the VBM toolbox
dbm.neuro.uni-jena.de/vbm
FMRIB Software Library (FSL) 24

25. Thanks! 25

27. VBM Summary Spatial Normalization Volume = Jacobian X Segmentation
Strengths
Cortical and subcortical
Gray Matter, White Matter, CSF
Easy to use
Weaknesses
Volume metric derived from normalization
Sensitive to registration and segmentation errors
Segmentation is atlas-dependent 27

28. Surface-based Summary
Thickness (can also use area and volume)
Strengths
Surface-based Normalization
Surface-based Smoothing
Thickness independent of normalization
Surface extraction atlas-independent
Weaknesses
No subcortical, White Matter, or CSF
More complicated to analyze 28