University of Warwick, Neuroimaging Statistics, Coventry, UK.

Camille Maumet

Supporting image-based meta-analysis with NIDM: Standardized reporting of neuroimaging results

INCF NIDASH Task force

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

CONTEXT

3

INCF NIDASH Task Force CONTEXT

4

5

International Neuroinformatics Coordinating Facility

Program on DigitalBrain Atlasing

2 Task Forces –  Neuroimaging (NIDASH) –  Electrophysiology

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

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

NIDM: Neuroimaging Data Model

8

Source: Poline et al, Frontiers in Neuroinformatics (2012).

NIDM: Neuroimaging Data Model

•  Based on PROV-DM •  First applications

–  Description of the dataset-experiment hierarchy –  Freesurfer volumes –  DICOM terms

9

Meta-analysis use case CONTEXT

10

Results

Meta-analysis

Results

Results

Why meta-analyses?

•  Increase statistical power •  Combine information across studies

Data acquisition Analysis

Experiment Raw data Results

Data acquisition Analysis

Experiment Raw data Results

… Results

Meta-analysis

11

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

12

500MB/subject 20GB

2.5GB/subject 100GB

[ ~2GB for stats]

< 0.5MB 0MB

Data processing and analysis procedure

Meta data

Data analysis in neuroimaging

Table of local maxima (quantitative)

13

Paper

Publication

?

Detection images (qualitative)

Peaks (quantitative)

< 0.5MB

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

Data sharing environment CONTEXT

15

Acquisition

Pre-processing

Statistical analysis

Data sharing tools

Publication

16

Three major software packages

17

Automatically created with Neurotrends based on over 16 000 journal articles; Source: http://neurotrends.herokuapp.com/static/img/temporal/pkg-prop-year.png

~80%

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

NIDM FOR META-ANALYSIS

19

NIDM-Results NIDM FOR META-ANALYSIS

20

Neuroimaging Data Model

21

NIDM-Results

22

23

NIDM-Results

24

NIDM-Results: SPM-specific

25

NIDM-Results: FSL-specific

Standardization across software

•  Model of the error –  Prob. distribution: –  Variance:

–  Dependence:

26

heterogeneous  

Independent  noise  

homogeneous  

Compound  Symmetry  

Serially  correlated  

Arbitrarily  correlated  

Gaussian   Non-­‐Parametric   …  

global  

local  

regularized  

Error models : SPM, FSL and AFNI

27

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  

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  

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

Queries •  For each contrast get name, contrast file,

statistic file and type of statistic used.

30

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

Implementation NIDM FOR META-ANALYSIS

31

Implementation

•  NIDM export –  SPM12 (natively) –  Scripts for FSL:

https://github.com/incf-nidash/nidm-results_fsl –  First contact with AFNI developers

32

Future directions NIDM FOR META-ANALYSIS

33

Next steps and future plans

•  Extend NIDM-Results implementation: –  AFNI –  SnPM, Randomise

•  Refine the terms and definitions.

34

Next steps and future plans •  NIDM import for

Neurovault

35

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

CONCLUSION

37

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

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

39

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

Q & A

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

NIDM Resources