# NIDM-Results: a model to share brain mapping statistical results

Camille Maumet

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## To cite this version:

Camille Maumet. NIDM-Results: a model to share brain mapping statistical results. CBU Open Science Workshop 2016, Nov 2016, Cambridge, United Kingdom. . inserm-01887752

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Submitted on 4 Oct 2018

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# NIDM-Results: a model to share brain mapping statistical results

CBU Open Science Workshop 2016, Nov. 22nd

Camille Maumet

Neuroimaging Statistics, University of Warwick, UK

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# Agenda

* Background

- Reporting of neuroimaging results

- Neuroimaging meta-analyses

* The NeuroImaging Data Model

* Example of image-based meta-analysis
## Background

Reporting of neuroimaging results
Data sharing

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Raw data

Pre-processing

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Pre-processed data

Analysis

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<figure><img src="image_7.png" /></figure>

Results

Publication

<figure><img src="image_8.png" /></figure>

Publication
Data sharing

<figure><img src="image_9.png" /></figure>

Raw data

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OpenfMRI

Pre-processing

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Pre-processed data

<figure><img src="image_12.png" /></figure>

NITRC

Analysis

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Results

Publicly
available
data

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NEUROVAULT

Publication

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Publication

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brainmap.org

neurosynth.org
# Data sharing

BUT

< 5% of data
shared in public
repositories

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Raw data

Pre-processing

<figure><img src="image_19.png" /></figure>

Pre-processed data

Analysis

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<figure><img src="image_21.png" /></figure>

Results

Publication

<figure><img src="image_22.png" /></figure>

Publication

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OpenfMRI

<figure><img src="image_24.png" /></figure>

NITRC

Publicly
available
data

<figure><img src="image_25.png" /></figure>

NEUROVAULT

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brainmap.org

neurosynth.org
# Data sharing

* Barriers to neuroimaging data sharing

- Ethical considerations

- Privacy issues

- Psychological barriers

- Time consuming process

<figure><img src="image_27.png" /></figure>

Raw data

Pre-processing

<figure><img src="image_28.png" /></figure>

Pre-processed data

Analysis

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

Results

Publication

<figure><img src="image_31.png" /></figure>

Publication
# Data sharing

* Barriers to neuroimaging data sharing

- Ethical considerations

- Privacy issues

- Psychological barriers

- Time consuming process

* “Reporting” rather than “sharing”

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# Reporting of neuroimaging results

<figure><img src="image_33.png" /></figure>

Raw data

Pre-processing

<figure><img src="image_34.png" /></figure>

Pre-processed data

Analysis

<figure><img src="image_35.png" /></figure>

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Results

Publication

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Publication
# Reporting of neuroimaging results

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Raw data

Pre-processing

<figure><img src="image_39.png" /></figure>

Pre-processed data

Analysis

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Results

Publication

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Publication

## Publication

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /><figcaption>Figure
(selected slices)</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>Thresholded
statistics</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>Peak locations</figcaption></figure>
# Reporting of neuroimaging results

<figure><img src="image_47.png" /></figure>

Publication

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

Figure
(selected slices)

<figure><img src="image_50.png" /></figure>

Thresholded
statistics

<figure><img src="image_51.png" /></figure>

Peak locations

* Incomplete statistical results

* Ambiguous/incomplete methods

* Metadata is not searchable
## Background

### Neuroimaging meta-analyses
# Meta-analysis

Study 1

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Raw data

Pre-processing

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Pre-processed data

Analysis

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Results

Study 2

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Raw data

Pre-processing

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Pre-processed data

Analysis

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

Results

...</td>

Study n

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Raw data

Pre-processing

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Pre-processed data

Analysis

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Results

Meta-analysis

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Results
# Neuroimaging meta-analysis

* Rich fMRI literature
  > 30,000 articles (“fMRI” pubmed)

* Synthesize information across studies

* Increase statistical power
# Coordinate- or Image-Based?

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# Coordinate- or Image-Based?

<figure><img src="image_68.png" /></figure>

Coordinate-based meta-analysis

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# Coordinate- or Image-Based?

<figure><img src="image_70.png" /></figure>

Coordinate-based meta-analysis

<figure><img src="image_71.png" /></figure>

Image-based meta-analysis

<figure><img src="image_72.png" /></figure>
# The Neuroimaging Data Model
(NIDM)
# INCF Neuroimaging Task Force

* International collaboration

- 13 labs, >12 tools

- Weekly teleconferences, focused workshops, GitHub

- Open

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# NIDM: a set of specifications to describe neuroimaging data

## NIDM Experiment

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OpenfMRI

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XNAT

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INS

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HUMAN
CONNECTOME
PROJECT

Data Acquisition

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## NIDM Workflow

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NITRC

SPM

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## NIDM Results

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Design of the experiment and processed data

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Model fitting

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Inference

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Statistical Model

Image Processing

## NIDM Core Vocabulary

## PROV Family of Specifications

<figure><img src="image_90.png" /></figure>

SPARQL

Semantic Web Technologies

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RDF

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# NIDM: a set of specifications to describe neuroimaging data

<figure><img src="image_93.png" /></figure>

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## NIDM Core Vocabulary

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# NIDM-Results

* Metadata selected according to

– Meta-analysis use-case

– Best practices

– Neuroimaging software

* **Automatically** generated by the neuroimaging software packages.
# NIDM-Results

**NIDM-Results pack:** Compressed file containing a NIDM-Results serialization and some or all of the referenced image data files.

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /><figcaption>NIDM-Results
graph</figcaption></figure>

<figure><img src="image_99.png" /><figcaption>Standard error
map</figcaption></figure>

<figure><img src="image_100.png" /><figcaption>Statistic map
E.g. Z-map</figcaption></figure>

<figure><img src="image_101.png" /><figcaption>Contrast map</figcaption></figure>

<figure><img src="image_102.png" /><figcaption>Design matrix
 png and csv)</figcaption></figure>
NIDM-Results

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /></figure>

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Design of the experiment, pre-processed data...

Statistical estimation

<figure><img src="image_106.png" /></figure>

Effects, standard error and statistical maps...

Inference

<figure><img src="image_107.png" /></figure>

Thresholded maps, list of peaks and clusters...
<figure><img src="image_108.png" /></figure>

Specification

nidm:'Error Model': Model used to describe the random variation of the error term as part of parameter estimation, including specification of the error probability distribution, its variance and dependence both spatially and across observations.

nidm:'Error Model' is a prov:'Entity' used by nidm:'Model Parameter Estimation'.

A nidm:'Error Model' has attributes:

* rdfs:label: (OPTIONAL) Human readable description of the nidm:'Error Model'.

* nidm:'dependence Map-Wise Dependence': (OPTIONAL) Property that associates an 'Error Parameter Map-Wise Dependence' to the dependence of an 'Error Model'. (range nidm:'Error Parameter Map-Wise Dependence' such as nidm:'Constant Parameter', nidm:'Independent Parameter', nidm:'Regularized Parameter').

* nidm:'error Variance Homogeneous': (OPTIONAL) A boolean value reflecting how the variance of the error is modeled during parameter estimation; TRUE for constant variance over all observations in the model, FALSE for heterogeneous variance. (range xsd:boolean).

* nidm:'has Error Dependence': (OPTIONAL) Property that associates a covariance structure representing the dependence structure of the error, used as part of model estimation with an 'Error Model'. (range obo:'covariance structure' such as nidm:'Exchangeable Error', nidm:'Independent Error', obo:'Toepitz covariance structure', obo:'compound symmetry covariance structure', obo:'unstructured covariance structure').

* nidm:'has Error Distribution': (OPTIONAL) Property that associates a Probability distribution used to model the error with an ErrorModel. (range obo:'probability distribution' such as obo:'continuous probability distribution', obo:'discrete probability distribution').

* nidm:'variance Map-Wise Dependence': (OPTIONAL) Property that associates an 'Error Parameter Map-Wise Dependence' to the variance of an 'Error Model'. (range nidm:'Error Parameter Map-Wise Dependence' such as nidm:'Constant Parameter', nidm:'Independent Parameter', nidm:'Regularized Parameter').

EXAMPLE 19: Error Model: Ordinary least squares

@prefix nidm_ErrorModel: <http://purl.org/nidash/nidm#NIDM_0000023> .
@prefix nidm_hasErrorDistribution: <http://purl.org/nidash/nidm#NIDM_0000101> .
@prefix nidm_errorVarianceHomogeneous: <http://purl.org/nidash/nidm#NIDM_0000094> .
@prefix nidm_varianceMapWiseDependence: <http://purl.org/nidash/nidm#NIDM_0000126> .
@prefix nidm_hasErrorDependence: <http://purl.org/nidash/nidm#NIDM_0000100> .
@prefix nidm_dependenciesMapWiseDependence: <http://purl.org/nidash/nidm#NIDM_0000089> .
@prefix nidm_IndependentParameter: <http://purl.org/nidash/nidm#NIDM_0000073> .
@prefix nidm_IndependentError: <http://purl.org/nidash/nidm#NIDM_0000048> .
@prefix obo_normaldistribution: <http://purl.obolibrary.org/obo/STATO_0000227> .

niiri:error_model_id a prov:Entity , nidm_ErrorModel: ;
nidm_hasErrorDistribution: obo_normaldistribution: ;
nidm_errorVarianceHomogeneous: "true"^^xsd:boolean ;
nidm_varianceMapWiseDependence: nidm_IndependentParameter: ;
nidm_hasErrorDependence: nidm_IndependentError: ;
nidm_dependenciesMapWiseDependence: nidm_IndependentParameter: .

<figure><img src="image_109.png" /></figure>

Definition

<figure><img src="image_110.png" /></figure>

Attributes

<figure><img src="image_111.png" /></figure>

Examples
# Harmonisation across software

## Model of the error

- Prob. distribution:

<figure><img src="image_112.png" /><figcaption>Gaussian</figcaption></figure>

<figure><img src="image_113.png" /><figcaption>Non-Parametric</figcaption></figure>

- Variance:

<figure><img src="image_114.png" /><figcaption>homogeneous</figcaption></figure>

<figure><img src="image_115.png" /><figcaption>heterogeneous</figcaption></figure>

- Dependence:

<figure><img src="image_116.png" /><figcaption>Independent noise</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>Compound Symmetry</figcaption></figure>

<figure><img src="image_118.png" /><figcaption>Serially correlated</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>Arbitrarily correlated</figcaption></figure>

...

<figure><img src="image_120.png" /><figcaption>global</figcaption></figure>

<figure><img src="image_121.png" /><figcaption>local</figcaption></figure>

<figure><img src="image_122.png" /><figcaption>regularized</figcaption></figure>
# Error models: SPM, FSL and AFNI

## Subject level

SPM

<figure><img src="image_123.png" /></figure>

Gaussian

<figure><img src="image_124.png" /></figure>

Homogeneous
local

<figure><img src="image_125.png" /></figure>

Serial. corr.
global

<figure><img src="image_126.png" /></figure>

<figure><img src="image_127.png" /></figure>

Gaussian

<figure><img src="image_128.png" /></figure>

Homogeneous
local

<figure><img src="image_129.png" /></figure>

Serial. corr.
regularized

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

Gaussian

<figure><img src="image_132.png" /></figure>

Homogeneous
local

<figure><img src="image_133.png" /></figure>

Serial. corr.
local

## Group level

SPM

one sample
t-test

<figure><img src="image_134.png" /></figure>

FLAME 1

<figure><img src="image_135.png" /></figure>

Gaussian

<figure><img src="image_136.png" /></figure>

Homogeneous
local

<figure><img src="image_137.png" /></figure>

Independent
noise

<figure><img src="image_138.png" /></figure>

Gaussian

<figure><img src="image_139.png" /></figure>

Heterogeneous
local

<figure><img src="image_140.png" /></figure>

Independent
noise

<figure><img src="image_141.png" /></figure>

3dttest++

<figure><img src="image_142.png" /></figure>

Gaussian

<figure><img src="image_143.png" /></figure>

Homogeneous
local

<figure><img src="image_144.png" /></figure>

Independent
noise
# An image-based meta-analysis with NIDM-Results
# SPM export to NIDM

SPM

<figure><img src="image_145.png" /></figure>

* Available as part of SPM12
# FSL export to NIDM

<figure><img src="image_146.png" /></figure>

$ nidmfs1 ds107.gfeat -g Control 49

<figure><img src="image_147.png" /></figure>

<figure><img src="image_148.png" /></figure>

* Install via pip

* To be included in the next FSL release
<figure><img src="image_149.png" /></figure>

**NEUROVAULT**

A public repository of unthresholded statistical maps, parcellations, and atlases of the human brain

### What is it?

A place where researchers can publicly store and share unthresholded statistical maps, parcellations, and atlases produced by MRI and PET studies.

### Why use it?

* Interactive visualization

* A permanent URL

* Publicly shareable

* Improves meta-analyses

### Supported by

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>

Stanford
University

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# A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts

Contributed by ChrisFiloGorgolewski

Krzysztof J. Gorgolewski, Dan Lurie, Sebastian Urchs, Judy A. Kipping, R. Cameron Craddock, Michael P. Milham, Daniel S. Margulies, Jonathan Smallwood

Link to the paper

3D View

File

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Options

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>

<figure><img src="image_156.png" /></figure>

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Images

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entries

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<table><thead><tr><th>View</th><th>ID</th><th>Name</th><th>Type</th></tr></thead><tbody><tr><td></td><td>25</td><td>fALFF: Past</td><td>Z map</td></tr><tr><td></td><td>26</td><td>fALFF: Future</td><td>Z map</td></tr><tr><td></td><td>27</td><td>fALFF: Past &amp; Future</td><td>Z map</td></tr><tr><td></td><td>28</td><td>fALFF: Positive</td><td>Z map</td></tr><tr><td></td><td>29</td><td>fALFF: Negative</td><td>T map</td></tr><tr><td></td><td>30</td><td>fALFF: Positive &amp; Negative</td><td>Z map</td></tr><tr><td></td><td>31</td><td>fALFF: Social Cognition</td><td>Z map</td></tr></tbody><tfoot><tr><td colspan="4">Showing 1 to 7 of 30 entries</td></tr><tr><td colspan="4">First Previous Next Last</td></tr></tfoot></table>
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## Example of meta-analysis

<figure><img src="image_160.png" /></figure>

### Coordinate-based meta-analysis

<figure><img src="image_161.png" /></figure>

### Image-based meta-analysis

<figure><img src="image_162.png" /></figure>
Conclusion
# Conclusion

NIDM-Results: a model for full reporting of
mass-univariate results

Future work

- Model extensions (e.g. permutation)

- Ecosystem of tools

- NIDM-Experiment (BIDS),
- NIDM-Workflows
**Acknowledgements**

Thank you! To all the INCF NIDASH task force members.

**Warwick neurodata sharing** Tom Nichols, Alex Bowring, Ruth Pauli, Peter Williams, Tom Maullin-Sapey.

**NIDM working group** Tibor Auer, Samir Das, Fariba Fana, Guillaume Flandin, Satra Ghosh, Tristan Glatard, Chris Gorgolewski, Karl Helmer, David Keator, Nolan Nichols, Jean-Baptiste Poline, Vanessa Sochat, Jason Steffener, Jessica Turner.

**SPM** Guillaume Flandin.

**FSL** Mark Jenkinson, Matthew Webster, Paul McCarthy, Eugene Duff, Steve Smith.

**AFNI** Gang Chen, Richard Reynolds, Ziad Saad, Robert Cox.

**Meta-analysis dataset** Tracey group at FMRIB.

This work is supported by the **wellcome** trust
Q & A

Maumet, C. et al. Sharing brain mapping statistical results with the neuroimaging data model. *Sci. Data* 3:160102
doi: [10.1038/sdata.2016.102](https://doi.org/10.1038/sdata.2016.102) (2016).

<figure><img src="image_163.png" /></figure>