Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology...
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Transcript of Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology...
![Page 1: Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology meeting 14 april 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062409/56649f575503460f94c7cc6f/html5/thumbnails/1.jpg)
Voxel-based morphometry
The methods and the interpretation (SPM based)
Harma MeffertMethodology meeting
14 april 2009
![Page 2: Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology meeting 14 april 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062409/56649f575503460f94c7cc6f/html5/thumbnails/2.jpg)
Outline
• General preprocessing steps
• Preprocessing
• Comparison two recent tools
• Data analysis
• Discussion about ‘ISSUES’
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General preprocessing steps …
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anatomicalscan
VBM
segmentation smoothingnormalisation
General preprocessing steps
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VBM Normalisation step; a closer look
1. Determine parameters
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VBM Normalisation step; a closer look
1. Determine parameters
2. Deform brain to fit template
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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
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Preprocessing …
Protocols and toolboxes
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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
• …
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Standard VBM – SPM99 / SPM2
Mechelli et al. 2005
Normalisation
Segmentation
Gray matter White matter
smoothing
Analysis
smoothing
Analysis
modulation modulation
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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
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VBM with unified segmentation – SPM5
Tissue classification, image registration and bias correction within one model
Normalisation / segmentation
smoothing
Analysis
modulation
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VBM5 toolbox in SPM5
Noise reduction with Markov Random Field
MRF prior probability
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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
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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
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Comparison two recent tools…
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VBM5 vs SPM5
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Data analysis …
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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
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Voxel-based morphometry …
The Issues!
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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?
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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
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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
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Issue 4: How do you assess the quality of segmentation
• VBM5 has the option to chack sample homogeneity
• Furthermore it is visual inspection
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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.•
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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