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

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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).

Published in: Health & Medicine
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Supporting image-based meta-analysis with NIDM: Standardized reporting of neuroimaging results

  1. 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. 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. 3. CONTEXT 3
  4. 4. CONTEXT INCF NIDASH Task Force 4
  5. 5. 5 International Neuroinformatics Coordinating Facility Program on Digital Brain Atlasing 2 Task Forces – Neuroimaging (NIDASH) – Electrophysiology
  6. 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. 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. 8. NIDM: Neuroimaging Data Model Source: Poline et al, Frontiers in Neuroinformatics (2012). 8
  9. 9. NIDM: Neuroimaging Data Model • Based on PROV-DM • First applications – Description of the dataset-experiment hierarchy – Freesurfer volumes – DICOM terms 9
  10. 10. CONTEXT Meta-analysis use case 10 Results Meta-analysis Results Results
  11. 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. 12. Data analysis in neuroimaging Analysis Data acquisition Experimen t Results Publication Raw data Paper Imaging data 12 500MB/subject 0MB < 0.5MB 20GB 2.5GB/subject 100GB [ ~2GB for stats]
  13. 13. Data analysis in neuroimaging Table of local maxima (quantitative) 13 Paper Publication ? Detection images (qualitative) Peaks (quantitative) < 0.5MB
  14. 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. 15. CONTEXT Data sharing environment 15
  16. 16. Acquisition Pre-processing Statistical analysis Data sharing tools Publication 16
  17. 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. 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. 19. NIDM FOR META-ANALYSIS 19
  20. 20. NIDM FOR META-ANALYSIS NIDM-Results 20
  21. 21. Neuroimaging Data Model 21
  22. 22. NIDM-Results 22
  23. 23. 23 NIDM-Results
  24. 24. 24 NIDM-Results: SPM-specific
  25. 25. 25 NIDM-Results: FSL-specific
  26. 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. 27. 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
  28. 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. 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. 30. 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
  31. 31. NIDM FOR META-ANALYSIS Implementation 31
  32. 32. Implementation • NIDM export – SPM12 (natively) – Scripts for FSL: https://github.com/incf-nidash/nidm-results_fsl – First contact with AFNI developers 32
  33. 33. NIDM FOR META-ANALYSIS Future directions 33
  34. 34. Next steps and future plans • Extend NIDM-Results implementation: – AFNI – SnPM, Randomise • Refine the terms and definitions. 34
  35. 35. Next steps and future plans • NIDM import for Neurovault 35
  36. 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. 37. CONCLUSION 37
  38. 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. 39. Resources • Github: https://github.com/incf-nidash • Specifications: http://nidm.nidash.org 39
  40. 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. 41 Q & A NIDM Resources • Github: https://github.com/incf-nidash • Specifications: http://nidm.nidash.org

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