University of Warwick, Warwick Manufacturing Group & Department of Statistics, Coventry, UK.
Camille Maumet and Thomas E. Nichols
IBMA: An SPM toolbox for Neuroimaging Image-Based Meta-Analysis
Agenda
• Meta-analyses in Neuroimaging – Why? – Coordinate-Based or Image-Based?
• Image-Based Meta-Analysis – Gold standard – Other approaches
• Validity of IBMA approaches in neuroimaging
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Meta-Analyses in Neuroimaging
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Why meta-analyses?
• Power increase • Combine information across studies
Data acquisition Analysis
Experiment Raw data Results
Data acquisition Analysis
Experiment Raw data Results
… Results
Meta-analysis
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Data analysis in neuroimaging Analysis
Results Experiment
Data acquisition
Raw data Paper
Publication
MRI acquisition parameters
Task design and timing
Description of
participants
Mental processes
studied
Imaging data
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500MB/subject 20GB
2.5GB/subject 100GB
[ ~2GB for stats]
< 0.5MB 0MB
Data processing and analysis procedure
Meta data
Coordinate-Based Meta-Analysis
Table of local maxima (quantitative)
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Paper
Publication
?
Detection images (qualitative)
Peaks (quantitative)
< 0.5MB
Coordinate- or Image-Based?
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Data acquisition Analysis
Experiment Raw data Results
Data acquisition Analysis
Experiment Raw data Results
…
Publication
Publication
Paper
Paper
Coordinate-based meta-analysis
Image-based meta-analysis
Shared results Data sharing
Image-Based Meta-Analysis
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Meta-analysis level Study level Subject level
Meta-analysis gold standard
Pre-processed data S
ubje
ct 1
Model fitting and estimation Contrast and
std. err. maps
Inference Detections
(subject-level)
Model fitting and estimation Pre-processed
data Sub
ject
n
Contrast and std. err. maps
…
Inference Detections
(subject-level)
Model fitting and estimation Contrast and
std. err. maps
Inference Detections (study-level)
Pre-processed data S
ubje
ct 1
Model fitting and estimation Contrast and
std. err. maps
Model fitting and estimation Pre-processed
data Sub
ject
n
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)
Inference Detections (study-level)
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Image-based Meta-analysis
• Gold standard: • But…
– Units will depend on: • The scaling of the data (subject-level) • The scaling of the predictor(s) that are involved in the
selected contrast (subject- and study-level) • The scaling of the selected contrast (subject- and study-
level).
– Contrast estimates and standard error maps are rarely shared…
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Third-level Mixed-Effects GLM
Image-Based Meta-Analysis
• Other (sub-optimal) statistics available: – Based on z-statistic:
• Fishers’s; Stouffer’s; “Stouffers’s MFX” – Based on z-statistic + sample size
• Weighted Stouffer’s
– Based on contrast estimates only: • RFX GLM;
– Based on contrast estimates and standard error • Fixed-Effects GLM
• Based on restrictive assumptions, robustness to violation need to be further studied
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IBMA toolbox
• Plugin for • Available on github:
https://github.com/NeuroimagingMetaAnalysis/ibma
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Validity of IBMA approaches in neuroimaging
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Meta-analysis of 21 pain studies
• Data – 21 studies investigated pain in healthy subjects
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Conclusion
• Towards Image-Based meta-analysis. • In practice, it is difficult to use the gold
standard Third-level Mixed-Effects General Linear Model.
• IBMA toolbox provides alternative approaches.
• Further investigation: two-sample analysis…
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Acknowledgements Q & A
We gratefully acknowledge the use of MRI data from the Tracey pain group, FMRIB, Oxford. This work is supported by the
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