OHBM 2016: Practical intensity based meta-analysis
1 like296 views
This document discusses image-based meta-analysis of neuroimaging data. It notes that image-based meta-analysis using a mixed-effects GLM is the gold standard approach but is difficult to implement in practice. When only contrast estimates are available, a random effects GLM is a practical alternative. Few tools currently exist for methods based on Z-maps, but some are being developed. The document emphasizes the importance of data sharing and standardized reporting to facilitate image-based meta-analysis.
![Units of contrast estimates
• Contrast Estimation
– Linear combination of parameter estimates
– Final statistics invariant to scale
• e.g. [ 1 1 1 1 ] gives same T’s & P’s as [ ¼ ¼ ¼ ¼ ]
Units depend on contrast vector
– Rule for contrasts to preserve units
• Positive elements sum to 1
• Negative elements sum to -1
Pre-processed
data
Data scaling
Scaled pre-
proc. data
Model
parameter
estimation Parameter
estimates
Contrast
estimation Contrast and
std. err. maps
9
Contrast
estimation](https://image.slidesharecdn.com/ohbm2016maumetpracticalintensitybasedmeta-analysis-160627131859/85/OHBM-2016-Practical-intensity-based-meta-analysis-17-320.jpg)
![3dMEMA_result+tlrc.BRIK[[0]]
[from contrast & stat maps]
Which images for IBMA?
Contrast & std. err.
maps
Statistic map
E.g. Z-map
Contrast map
SPM FSL AFNI
con_0001.nii
[SPM.mat]
cope1.nii
varcope1.nii (squared)
3dMEMA_result+tlrc.BRIK[[1]]spmT_0001.nii tstat1.nii.gz
zstat1.nii.gz
3dMEMA_result+tlrc.BRIK[[0]]con_0001.nii cope1.nii
11](https://image.slidesharecdn.com/ohbm2016maumetpracticalintensitybasedmeta-analysis-160627131859/85/OHBM-2016-Practical-intensity-based-meta-analysis-19-320.jpg)
OHBM 2016: Practical intensity based meta-analysis
- 1. Practical Intensity-Based Meta-Analysis Camille Maumet OHBM Neuroimaging Meta-Analysis Educational course 26 June 2016
- 2. Coordinate- or Image-Based? 2 Acquisition Analysis Experiment Raw data Results Acquisition Analysis Experiment Raw data Results … Publication Publication Paper Paper
- 3. Coordinate- or Image-Based? 2 Acquisition Analysis Experiment Raw data Results Acquisition Analysis Experiment Raw data Results … Publication Publication Paper Paper Coordinate-based meta-analysis Coordinate-based meta-analysis
- 4. Image-based meta-analysis Shared results Data sharing Coordinate- or Image-Based? 2 Acquisition Analysis Experiment Raw data Results Acquisition Analysis Experiment Raw data Results … Publication Publication Paper Paper Coordinate-based meta-analysis Coordinate-based meta-analysis Image-based meta-analysis
- 6. Inference Detections (subject-level) Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps 4
- 7. Inference Detections (subject-level) Inference Detections (subject-level) Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … 4
- 8. Inference Detections (study-level) Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps 4
- 9. Inference Detections (study-level) Inference Detections (study-level) Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps 4
- 10. Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Model fitting and estimation Contrast and std. err. maps Inference Detections (meta-analysis) 4
- 11. Inference Detections (subject-level) Inference Detections (subject-level) Inference Detections (study-level) Inference Detections (study-level) Meta-analysis levelStudy levelSubject level Image-based meta-analysis Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Model fitting and estimation Contrast and std. err. maps Inference Detections (meta-analysis) 4
- 12. Image-based meta-analysis • Gold standard: Third-level Mixed-Effects GLM • Requirements – study-level Contrast estimates and Standard error maps. – Same units Contrast and std. err. maps 5
- 13. Units of contrast estimates Pre-processed data Model fitting and estimation Contrast and std. err. maps 6
- 14. Units of contrast estimates Pre-processed data Model fitting and estimation Contrast and std. err. maps 6 Pre-processed data Data scaling Scaled pre- proc. data Model parameter estimation Parameter estimates Contrast estimation Contrast and std. err. maps
- 15. Units depend on mean estimation and scaling target. Units of contrast estimates Pre-processed data Data scaling Scaled pre- proc. data Model parameter estimation Parameter estimates Contrast estimation Contrast and std. err. maps Data scaling 7
- 16. Units of contrast estimates Y = β + Units depend on scaling of explanatory variables Pre-processed data Data scaling Scaled pre- proc. data Model parameter estimation Parameter estimates Contrast estimation Contrast and std. err. maps Model parameter estimation 8
- 17. Units of contrast estimates • Contrast Estimation – Linear combination of parameter estimates – Final statistics invariant to scale • e.g. [ 1 1 1 1 ] gives same T’s & P’s as [ ¼ ¼ ¼ ¼ ] Units depend on contrast vector – Rule for contrasts to preserve units • Positive elements sum to 1 • Negative elements sum to -1 Pre-processed data Data scaling Scaled pre- proc. data Model parameter estimation Parameter estimates Contrast estimation Contrast and std. err. maps 9 Contrast estimation
- 18. Image-based Meta-analysis • Gold standard: • But… – Units will depend on: • The scaling of the data (subject-level) • The scaling of the predictor(s) (subject- and study-level) • The scaling of the contrast (subject- and study-level). – Contrast estimates and standard error maps are rarely shared… 10 Third-level Mixed-Effects GLM
- 19. 3dMEMA_result+tlrc.BRIK[[0]] [from contrast & stat maps] Which images for IBMA? Contrast & std. err. maps Statistic map E.g. Z-map Contrast map SPM FSL AFNI con_0001.nii [SPM.mat] cope1.nii varcope1.nii (squared) 3dMEMA_result+tlrc.BRIK[[1]]spmT_0001.nii tstat1.nii.gz zstat1.nii.gz 3dMEMA_result+tlrc.BRIK[[0]]con_0001.nii cope1.nii 11
- 20. Image-based meta-analyses based on Z • Fisher's – Sum of −log P-values (from T/Z’s converted to P’s) • Stouffer’s – Average Z, rescaled to N(0,1) • “Stouffer's Random Effects (RFX)” – Submit Z’s to one-sample t-test 12 (Slide adapted from Thomas Nichols, OHBM 2015) Statistic map E.g. Z-map
- 21. • Weighted Stouffer’s – Z’s from bigger studies get bigger weight 13 (Slide adapted from Thomas Nichols, OHBM 2015) Statistic map E.g. Z-map Image-based meta-analyses based on Z + N + N
- 22. • Random Effects (RFX) GLM – Analyze per-study contrasts as “data” based on Con’s + SE’s • Fixed-Effects (FFX) GLM – Don’t estimate variance, just take from first level 14 (Slide adapted from Thomas Nichols, OHBM 2015) Image-based meta-analyses based on Con’s Contrast map Contrast & std. err. maps
- 23. Image-Based Meta-Analysis In practice! • Not all of these options are easily used 15 Meta-Analysis Method Inputs Neuroimaging Implementation ‘Gold Standard’ MFX Con’s + SE’s FSL’s FEAT SPM spm_mfx AFNI 3dMEMA RFX GLM Stouffer’s RFX Con’s Z’s FSL, SPM, AFNI, etc… FFX GLM Fisher’s Stouffer’s Stouffer’s Weighted Con’s +SE’s Z’s Z’s Z’s + N’s n/a (Slide from Thomas Nichols, OHBM 2015)
- 24. Self Promotion Alert: IBMA toolbox • SPM Extension • Still in beta! – But welcome all feedback • Available on GitHub https://github.com/NeuroimagingMetaAnalysis/ibma 16
- 25. Meta-analysis of 21 pain studies • Results – GLM methods similar – Z-based methods similar – But FFX Z methods more sensitive (as expected) RFX Data: Tracey pain group, FMRIB, Oxford. 17
- 26. Share image data supporting neuroimaging results
- 27. Share your statistic maps http://neurovault.org 19
- 28. Share your statistic maps http://neurovault.org 20
- 29. From SPM & FSL NIDM-Results http://nidm.nidash.org/getting-started/ 21
- 30. • When data available, Image-Based preferred to Coordinate-Based meta-analysis Conclusions For more on NIDM-Results Maumet et al., Poster 1851 - Tuesday 12:45-14:45 “NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI” 22
- 31. Conclusions • When data available, Image-Based preferred to Coordinate-Based meta-analysis • In practice, it is difficult to use the gold standard Mixed-Effects GLM For more on NIDM-Results Maumet et al., Poster 1851 - Tuesday 12:45-14:45 “NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI” 22
- 32. Conclusions • When data available, Image-Based preferred to Coordinate-Based meta-analysis • In practice, it is difficult to use the gold standard Mixed-Effects GLM • When only contrast estimates are available, RFX GLM is a practical & valid approach For more on NIDM-Results Maumet et al., Poster 1851 - Tuesday 12:45-14:45 “NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI” 22
- 33. Conclusions • When data available, Image-Based preferred to Coordinate-Based meta-analysis • In practice, it is difficult to use the gold standard Mixed-Effects GLM • When only contrast estimates are available, RFX GLM is a practical & valid approach • Few tools for Z-based IBMA, but underway… For more on NIDM-Results Maumet et al., Poster 1851 - Tuesday 12:45-14:45 “NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI” 22
- 34. Conclusions • When data available, Image-Based preferred to Coordinate-Based meta-analysis • In practice, it is difficult to use the gold standard Mixed-Effects GLM • When only contrast estimates are available, RFX GLM is a practical & valid approach • Few tools for Z-based IBMA, but underway… • Data sharing tools: NeuroVault, NIDM-Results For more on NIDM-Results Maumet et al., Poster 1851 - Tuesday 12:45-14:45 “NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI” 22
- 35. Thank you! This work is supported by the