Supporting image-based meta-analysis with NIDM: Standardized … 2014. 10. 9. · From XCEDE to NIDM...

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

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CONTEXT

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INCF NIDASH Task Force CONTEXT

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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

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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

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NIDM: Neuroimaging Data Model

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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

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Meta-analysis use case CONTEXT

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Results

Meta-analysis

Results

Results

Why meta-analyses?

•  Increase statistical power •  Combine information across studies

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

Data analysis in neuroimaging

Table of local maxima (quantitative)

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Paper

Publication

?

Detection images (qualitative)

Peaks (quantitative)

< 0.5MB

Coordinate- or Image-Based meta-analysis?

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…

Publication

Publication

Paper

Paper

Coordinate-based meta-analysis

Image-based meta-analysis

Shared results Data sharing

Data sharing environment CONTEXT

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Acquisition

Pre-processing

Statistical analysis

Data sharing tools

Publication

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Three major software packages

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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

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NIDM FOR META-ANALYSIS

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NIDM-Results NIDM FOR META-ANALYSIS

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Neuroimaging Data Model

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NIDM-Results

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NIDM-Results

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NIDM-Results: SPM-specific

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NIDM-Results: FSL-specific

Standardization across software

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

–  Dependence:

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heterogeneous

Independent noise

homogeneous

Compound Symmetry

Serially correlated

Arbitrarily correlated

Gaussian Non-‐Parametric …

global

local

regularized

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


Heterogeneous local


Hetero-‐ or Homogeneous

local

Error models: non-parametric

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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.

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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

Implementation NIDM FOR META-ANALYSIS

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Implementation

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

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

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Future directions NIDM FOR META-ANALYSIS

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Next steps and future plans

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

•  Refine the terms and definitions.

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Next steps and future plans •  NIDM import for

Neurovault

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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.

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CONCLUSION

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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

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

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Acknowledgements

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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

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Q & A

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

NIDM Resources

Supporting image-based meta-analysis with NIDM: Standardized … 2014. 10. 9. · From XCEDE to NIDM...

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