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?

7

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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