Voxel-based morphometry

The methods and the interpretation (SPM based)

Harma MeffertMethodology meeting

14 april 2009

Outline

• General preprocessing steps

• Preprocessing

• Comparison two recent tools

• Data analysis

• Discussion about ‘ISSUES’

General preprocessing steps …

anatomicalscan

VBM

segmentation smoothingnormalisation

General preprocessing steps

VBM Normalisation step; a closer look

1. Determine parameters

VBM Normalisation step; a closer look

1. Determine parameters

2. Deform brain to fit template

Unmodulated

Modulated

VBM Normalisation step; a closer look

1. Determine parameters

2. Deform brain to fit template

3. Unmodulated (concentration)

4. Modulated (volumetric)

Unmodulated * Volume before warping / Volume after warping

Preprocessing …

Protocols and toolboxes

Overview ‘toolboxes’ and protocols

• Standard VBM – SPM99 / SPM2

• Optimised VBM – SPM99 / SPM2

• VBM with unified segmentation – SPM5

• VBM2 toolbox for SPM2

• VBM5 toolbox for SPM5

• Dartell

• …

Standard VBM – SPM99 / SPM2

Mechelli et al. 2005

Normalisation

Segmentation

Gray matter White matter

smoothing

Analysis

smoothing

Analysis

modulation modulation

Optimised VBM – SPM99 / SPM2

Mechelli et al. 2005

Segmentation

Gray matter White matter

smoothing

Analysis

smoothing

Analysis

Normalisation to GM template

Normalisation to WM template

Apply norm. par. to raw image

Apply norm. par. to raw image

modulationmodulation

VBM with unified segmentation – SPM5

Tissue classification, image registration and bias correction within one model

Normalisation / segmentation

smoothing

Analysis

modulation

VBM5 toolbox in SPM5

Noise reduction with Markov Random Field

MRF prior probability

Summary: Segmentation and Normalisation

Options and considerations:– Normalisation before segmentation– Optimized order (norm segm norm)– Unified segmentation (SPM5)– Unified segmentation with the use of customized

priors (VBM5)– Unified segmentation without the use of priors for

tissue classification (VBM5)– Hidden Markov Random Field (VBM5)– Center of mass as origin doesn’t work

Summary: Modulation

Options, considerations and questions– Unmodulated ≈ ‘concentration’– Modulated ≈ ‘volume’– Modulation of …

• non-linear effects only• affine and non-linear effects (no correction for

brain size afterwards)

– Smoothing– Less smoothing in modulated images

Comparison two recent tools…

VBM5 vs SPM5

Data analysis …

Data-analysis: Considerations

• Corrections for multiple comparisons with local maxima of the t statistic

• GLM with SPM, SnPM, machine learning algorithms

• Global or localized inferences? Use of covariates

• Non-stationary cluster extent correction

Voxel-based morphometry …

The Issues!

Issue 1: Unmodulated images…

• Compatible with modulated images?

• Just registration errors?

• Very dependend on used toolbox?

• Normalisation proces: Adding or removing voxels… how does that happen?

Issue 2: Covariates

• If you modulate for both affine and non-linear effects you do not have to correct for global brain size….

• If global brain size is correlated with ‘treatment’ it is not a good covariate because it will mask ‘treatment’ effects

Issue 3: What do the tissue labels mean

• If you add up probabilities in one voxel across different tissue types they can be >1

• Could you use white and gray maps to determine the relative amount of gray for example

Issue 4: How do you assess the quality of segmentation

• VBM5 has the option to chack sample homogeneity

• Furthermore it is visual inspection

Literature

• Ashburner, J. and K. J. Friston (2000). "Voxel-based morphometry--the methods." Neuroimage 11(6 Pt 1): 805-21.

•• Ashburner, J. and K. J. Friston (2001). "Why voxel-based morphometry should be used." Neuroimage 14(6):

1238-43.•• Ashburner, J. and K. J. Friston (2005). "Unified segmentation." Neuroimage 26(3): 839-51.•• Bookstein, F. L. (2001). ""Voxel-based morphometry" should not be used with imperfectly registered images."

Neuroimage 14(6): 1454-62.•• Devlin, J. T. and R. A. Poldrack (2007). "In praise of tedious anatomy." Neuroimage 37(4): 1033-41; discussion

1050-8.•• Good, C. D., I. S. Johnsrude, et al. (2001). "A voxel-based morphometric study of ageing in 465 normal adult

human brains." Neuroimage 14(1 Pt 1): 21-36.•• Mechelli, A., C. J. Price, et al. (2005). "Voxel-based morphometry of the human brain: Methods and applications."

Current Medical Imaging Reviews 1(2): 105-113.•• Ridgway, G. R., S. M. Henley, et al. (2008). "Ten simple rules for reporting voxel-based morphometry studies."

Neuroimage 40(4): 1429-35.•• Ridgway, G. R., R. Omar, et al. (2009). "Issues with threshold masking in voxel-based morphometry of atrophied

brains." Neuroimage 44(1): 99-111.•

NeuroImaging Center – Social Brain lab:

1. Prof. Dr. Christian Keysers

2. Dr. Valeria Gazzola

3. MSc. Jojanneke Bastiaansen

4. Other members of the lab

Department of Psychiatry, UMCG

Prof. Dr. Hans den Boer

FPC Dr. S. van Mesdag

1. Dr. Arnold Bartels

2. Dr. Marinus Spreen

3. Research department