Due to the lack of data shared when reporting neuroimaging results, most neuroimaging meta-analyses are based on peak coordinate data. However, the best practice is an image-based meta-analysis that combines full image data of the effect estimates and standard errors derived from each study. The Neuroimaging Data Model (NIDM) is an ongoing effort, supported by the INCF, to provide a domain-specific extension of the W3C PROV-DM. In this talk, I will review our recent progress in extending NIDM to share the statistical results of a neuroimaging study and our interactions with existing software packages (SPM, FSL, AFNI, Neurovault.org).

1. Supporting image-based meta-analysis with NIDM:
Standardized reporting of neuroimaging results
INCF NIDASH Task force
Camille Maumet
University of Warwick, Neuroimaging Statistics, Coventry, UK.
Oct. 8th 2014

2. Agenda
• Context
– NIDM and the INCF NIDASH Task force
– Meta-analysis use-case
– Data sharing environment
• NIDM for meta-analysis
– NIDM-Results
– Implementation
– Future directions
• Conclusions
2

3. CONTEXT
3

4. CONTEXT
INCF NIDASH Task Force
4

5. 5
International Neuroinformatics
Coordinating Facility
Program on Digital
Brain Atlasing
2 Task Forces
– Neuroimaging (NIDASH)
– Electrophysiology

6. NIDM working group
• NIDASH Task force
– “Standards for Data Sharing aims to develop
generic standards and tools to facilitate the
recording, sharing, and reporting of
neuroscience metadata, in order to improve
practices for the archiving and sharing of
neuroscience data.”
• BIRN Derived Data Working Group
6

7. From XCEDE to NIDM
• XML-Based Clinical Experiment Data
Exchange Schema (XCEDE): www.xcede.org
– Describes subject, study, activation
– Limited provenance encoding
– Initiative of the BIRN
• XCEDE-DM
• NeuroImaging Data Model (NIDM):
www.nidm.nidash.org
7

8. NIDM: Neuroimaging Data Model
Source: Poline et al, Frontiers in Neuroinformatics (2012).
8

9. NIDM: Neuroimaging Data Model
• Based on PROV-DM
• First applications
– Description of the dataset-experiment hierarchy
– Freesurfer volumes
– DICOM terms
9

10. CONTEXT
Meta-analysis use case
10
Results
Meta-analysis
Results
Results

11. Why meta-analyses?
Data
acquisition Analysis
Experiment Raw data Results
Data
acquisition Analysis
…
Experiment Raw data Results
• Increase statistical power
• Combine information across studies
Results
Meta-analysis
11

12. Data analysis in neuroimaging
Analysis
Data
acquisition
Experimen t Results
Publication
Raw data Paper
Imaging
data
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500MB/subject
0MB < 0.5MB
20GB
2.5GB/subject
100GB
[ ~2GB for stats]

13. Data analysis in neuroimaging
Table of local maxima
(quantitative)
13
Paper
Publication
?
Detection images
(qualitative)
Peaks
(quantitative)
< 0.5MB

14. Coordinate- or Image-Based meta-analysis?
14
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

15. CONTEXT
Data sharing environment
15

16. Acquisition
Pre-processing
Statistical
analysis
Data sharing tools
Publication
16

17. Three major software packages
17
~80%
Automatically created with Neurotrends based on over
16 000 journal articles; Source:
http://neurotrends.herokuapp.com/static/img/temporal/pkg-prop-year.png

18. Summary of the problem
• Use-case: Support meta-analysis
• Machine-readable format describing
neuroimaging results
• Easiness for the end-user
• Integrate with existing neuroimaging software
packages (SPM, FSL, AFNI,…)
• Extend previous work: NIDM
18

19. NIDM FOR META-ANALYSIS
19

20. NIDM FOR META-ANALYSIS
NIDM-Results
20

21. Neuroimaging Data Model
21

22. NIDM-Results
22

23. 23
NIDM-Results

24. 24
NIDM-Results: SPM-specific

25. 25
NIDM-Results: FSL-specific

26. Standardization across software
• Model of the error
– Prob. distribution:
– Variance:
– Dependence:
26
Gaussian
Non-­‐Parametric
heterogeneous
Independent
noise
homogeneous
Compound
Symmetry
Serially
correlated
Arbitrarily
correlated
…
global
local
regularized

27. Error models : SPM, FSL and AFNI
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1st level 2nd level
Gaussian
Serial.
corr.
global
Homogeneous
local
Gaussian
Independent
noise
Homogeneous
local
Gaussian
Homogeneous
local
Serial.
corr.
regularized
Gaussian
Homogeneous
local
Serial.
corr.
local
Gaussian
Independent
noise
Heterogeneous
local
Gaussian
Independent
noise
Hetero-­‐
or
Homogeneous
local

28. Error models: non-parametric
28
2nd level: Sign-flipping
Heterogeneous
local
Independent
noise
NonParametric
Symmetric
2nd level: Label permutation
Homogeneous
local
Exchangeable
noise
local
NonParametric

29. Terms
• “Flat” ontology
• Terms re-use:
– Interaction with STATO (statistical terms)
– Dublin Core (file formats)
– But also: NCIT, OBI…
• Work-in-progress
• http://tinyurl.com/nidm-results/terms
• Aim: include the terms in Neurolex.
29

30. Queries
• For each contrast get name, contrast file,
statistic file and type of statistic used.
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prefix
prov:
<http://www.w3.org/ns/prov#>
prefix
nidm:
<http://www.incf.org/ns/nidash/nidm#>
SELECT
?contrastName
?contrastFile
?statType
?statFile
WHERE
{
?cid
a
nidm:ContrastMap
;
nidm:contrastName
?contrastName
;
prov:atLocation
?contrastFile
.
?cea
a
nidm:ContrastEstimation
.
?cid
prov:wasGeneratedBy
?cea
.
?sid
a
nidm:StatisticMap
;
nidm:statisticType
?statType
;
prov:atLocation
?statFile
.
}
More queries: http://tinyurl.com/nidm-results/query

31. NIDM FOR META-ANALYSIS
Implementation
31

32. Implementation
• NIDM export
– SPM12 (natively)
– Scripts for FSL:
https://github.com/incf-nidash/nidm-results_fsl
– First contact with AFNI developers
32

33. NIDM FOR META-ANALYSIS
Future directions
33

34. Next steps and future plans
• Extend NIDM-Results implementation:
– AFNI
– SnPM, Randomise
• Refine the terms and definitions.
34

35. Next steps and future plans
• NIDM import for
Neurovault
35

36. NIDM-Results and NIDM-Workflow
• NIDM-Results: effort on standardization
across software
– A small number of generic activities
– A few software-specific entities
• NIDM-Workflow: a detailed view of all
software-specific processes.
36

37. CONCLUSION
37

38. Conclusion
• NIDM-Results: standardized reporting of
neuroimaging results
– Use-case: Meta-analysis
– Terms: http://tinyurl.com/nidm-results/terms/
– Specification: http://nidm.nidash.org
– Implementation in SPM12 & FSL
• Next steps
– Refine the terms, AFNI and SnPM/Randomise models
– Integration with Neurovault
– Build apps
38

39. Resources
• Github: https://github.com/incf-nidash
• Specifications: http://nidm.nidash.org
39

40. Acknowledgements
40
Thank you! To all the INCF
NIDASH task force members.
NIDM working group
Tibor Auer, Gully Burns, Fariba Fana, Guillaume Flandin, Satrajit Ghosh,
Chris Gorgolewski, Karl Helmer, David Keator, Camille Maumet,
Nolan Nichols, Thomas Nichols, Jean-Baptiste Poline, Jason Steffener,
Jessica Turner.
INCF NIDASH - Other members
David Kennedy, Cameron Craddock, Stephan Gerhard,
Yaroslav Halchenko, Michael Hanke, Christian Haselgrove, Arno Klein,
Daniel Marcus, Franck Michel, Simon Milton, Russell Poldrack,
Rich Stoner.
This work is supported by the

41. 41
Q & A
NIDM Resources
• Github: https://github.com/incf-nidash
• Specifications: http://nidm.nidash.org