# Neuroinformatics techniques for provenance & data sharing

Camille Maumet

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Camille Maumet. Neuroinformatics techniques for provenance & data sharing. GlaxoSmithKline-Neurophysics Workshop on Skeptical Neuroimaging, Jan 2014, London, United Kingdom. . inserm-01887730

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Distributed under a Creative Commons Attribution 4.0 International License
Neuroinformatic techniques for
provenance & data sharing

Camille Maumet

GlaxoSmithKline - Neurophysics Workshop on
Skeptical Neuroimaging

January 14th, 2014
Outline

1. Data sharing: current practice in neuroimaging

2. How to become less skeptical?

3. Neuroinformatics techniques for provenance and data sharing
Outline

1. Data sharing: current practice in neuroimaging

2. How to become less skeptical?

3. Neuroinformatics techniques for provenance and data sharing
# Overview of a neuroimaging study

<figure><img src="image_3.png" /></figure>
# Neuroimaging and data sharing

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Data shared with my collaborators
# Sharing data with my collaborators

<figure><img src="image_6.png" /></figure>
# Neuroimaging and data sharing

<figure><img src="image_7.png" /></figure>
# A neuroimaging publication

* Methods section: metadata in free-form text.

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

## General technical implementation

A single scanner session included the four paradigms separately implemented with the same parameters: a simple block design alternated a rest condition as control and the language task, starting with rest, with a preliminary period of signal acquisition for MRI signal stabilization which was later discarded during data processing. Each paradigm included three 27-s blocks of each condition and had a total duration of 2 min 48 s. The scanner session, including the anatomical acquisition and the four language paradigms, had a duration of about 30-35 min. All subjects performed the tasks in the same order, as during the preparation step, in order to avoid the mix of auditory and visual tasks and the resulting complication for the child. Words required by the tasks were one-to-three-syllable words highly frequent in the lexicon of French 8 years old children (Lambert and Chesnet, 2001).

During the rest condition, a red cross was displayed on the projection screen, and children were asked “not to work” to “think

<table><caption>**Table 2**
Task comparisons (>) and conjunctions (C).</caption><thead><tr><th></th><th>Auditory language 1</th><th></th></tr><tr><th></th><th>Categ - Def</th><th></th></tr></thead><tbody><tr><td>Left Hemisphere</td><td></td><td></td></tr><tr><td>Inf frontal-Oper</td><td>--</td><td></td></tr><tr><td>Precentral</td><td>18-3.38<sup>(3)</sup></td><td></td></tr><tr><td>Mid frontal</td><td>33-3.66</td><td></td></tr><tr><td>SMA</td><td>--</td><td></td></tr><tr><td>Cingulate</td><td>--</td><td></td></tr><tr><td>Med sup frontal</td><td>174-4.69</td><td></td></tr><tr><td>Rol operculum</td><td>--</td><td></td></tr><tr><td>Insula</td><td>--</td><td></td></tr><tr><td>Sup temporal</td><td>--</td><td></td></tr><tr><td>Mid temporal</td><td>--</td><td></td></tr><tr><td>Inf parietal</td><td>--</td><td></td></tr><tr><td>Sup parietal</td><td>--</td><td></td></tr><tr><td>Postcentral</td><td>--</td><td></td></tr><tr><td>Inf occipital</td><td>--</td><td></td></tr></tbody></table>

* Results section:

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

<figure><img src="image_10.png" /></figure>

2D plot(s) of the detections

## General technical implementation

A single scanner session included the four paradigms separately implemented with the same parameters: a simple block design alternated a rest condition as control and the language task, starting with rest, with a preliminary period of signal acquisition for MRI signal stabilization which was later discarded during data processing. Each paradigm included three 27-s blocks of each condition and had a total duration of 2 min 48 s. The scanner session, including the anatomical acquisition and the four language paradigms, had a duration of about 30-35 min. All subjects performed the tasks in the same order, as during the preparation step, in order to avoid the mix of auditory and visual tasks and the resulting complication for the child. Words required by the tasks were one-to-three-syllable words highly frequent in the lexicon of French 8 years old children (Lambert and Chesnet, 2001).

During the rest condition, a red cross was displayed on the projection screen and children were asked “not to work”, to “think about nothing” and, because of the complexity of this instruction, to listen to the noise of the scanner and fix attention on the red cross.

Description of the detections

Table

<table><caption>**Table 2**
Task comparisons (>) and conjunctions (C). Peak locations, cluster extent-Z-score (p=0.001 unc.; k= 10).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Auditory language tasks</th><th colspan="2">Visual language tasks</th></tr><tr><th>Categ - Def</th><th>Def- Categ</th><th>Categ C Def</th><th>Ph-s - Ph-d</th><th>Ph-s</th></tr></thead><tbody><tr><td>Left Hemisphere</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Inf frontal-Oper</td><td>--</td><td>--</td><td>348-4.10<sup>(4)</sup></td><td>--</td><td>82<sup>(5)</sup></td></tr><tr><td>Precentral</td><td>18-3.38<sup>(3)</sup></td><td>--</td><td>348-5.09</td><td>--</td><td>82<sup>(5)</sup></td></tr><tr><td>Mid frontal</td><td>33-3.66</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>SMA</td><td>--</td><td>--</td><td>1433-5.48</td><td>--</td><td>35<sup>(6)</sup></td></tr><tr><td>Cingulate</td><td>--</td><td>--</td><td>1433-5.08<sup>(2)</sup></td><td>--</td><td>--</td></tr><tr><td>Med sup frontal</td><td>174-4.69</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>Rol operculum</td><td>--</td><td>--</td><td>--</td><td>36-4.31</td><td>--</td></tr><tr><td>Insula</td><td>--</td><td>--</td><td>396-4.87<sup>(8)</sup></td><td>--</td><td>58<sup>(9)</sup></td></tr><tr><td>Sup temporal</td><td>--</td><td>--</td><td>351-3.81<sup>(1)</sup></td><td>--</td><td>91<sup>(10)</sup></td></tr><tr><td>Mid temporal</td><td>--</td><td>1658-4.67<sup>(3)</sup></td><td>351-5.61<sup>(2)</sup></td><td>--</td><td>10<sup>(11)</sup></td></tr><tr><td>Inf parietal</td><td>--</td><td>1658-5.18<sup>(6)</sup></td><td>--</td><td>--</td><td>--</td></tr><tr><td>Sup parietal</td><td>--</td><td>--</td><td>--</td><td>--</td><td>97<sup>(12)</sup></td></tr><tr><td>Postcentral</td><td>--</td><td>--</td><td>--</td><td>--</td><td>97<sup>(12)</sup></td></tr><tr><td>Sup occipital</td><td>--</td><td>--</td><td>--</td><td>146-4.43</td><td>14<sup>(13)</sup></td></tr><tr><td>Mid occipital</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>Inf occipital</td><td>--</td><td>--</td><td>--</td><td>--</td><td>14<sup>(14)</sup></td></tr><tr><td>Fusiform</td><td>--</td><td>--</td><td>--</td><td>397-5.44</td><td>14<sup>(15)</sup></td></tr></tbody></table>

Table of local maxima
# Outline

1. Data sharing: current practice in neuroimaging

2. How to become less skeptical?

3. Neuroinformatics techniques for provenance and data sharing
# Reproducibility

<figure><img src="image_11.png" /></figure>

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

Acquisition

<figure><img src="image_13.png" /></figure>

Raw data

<figure><img src="image_14.png" /></figure>

Pre-processing

<figure><img src="image_15.png" /></figure>

Pre-processed data

<figure><img src="image_16.png" /></figure>

Statistical
analysis

<figure><img src="image_17.png" /></figure>

Estimates / Statistic

<figure><img src="image_18.png" /></figure>

Publication

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

<figure><img src="image_20.png" /></figure>

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

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

Acquisition

<figure><img src="image_23.png" /></figure>

?

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

Pre-processing

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

?

<figure><img src="image_26.png" /></figure>

Statistical
analysis

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

?

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

Publication

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

New conclusions
# Full provenance

<figure><img src="image_30.png" /></figure>
# Meta-analysis: analyzing the analyses

* Coordinate-Based Meta-Analysis (CBMA)

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

Paper 1

<figure><img src="image_32.png" /></figure>

Paper 2

...

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

Paper n

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

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

Detection

New
results!

* Image-Based Meta-Analysis (IBMA).

<figure><img src="image_36.png" /></figure>

Study 1

<figure><img src="image_37.png" /></figure>

Study 2

...

<figure><img src="image_38.png" /></figure>

Study n

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

<figure><img src="image_40.png" /></figure>

Detection

New
results!
# How to become less skeptical?

* Reproducibility
  - Confirm results by re-running an analysis

* Provenance
  - Needed for reproducibility
  - Avoid selection bias.

* Meta-analysis
  - Strengthen results by combining studies.

* What do we need?
  - Sharing data, meta-data and provenance.
# Data sharing: obstacles

* Psychological

– “My” data

* Ethical constraints

* Technical: difficulties to share data with enough metadata to be really useful

– *Available data versus usable data.*

“Less than a few percents of acquired neuroimaging data is available in public repositories” [Poline 2012]
Outline

1. Data sharing: current practice in neuroimaging

2. How to become less skeptical?

3. Neuroinformatics techniques for provenance and data sharing
# Data sharing tools

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

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

Acquisition

<figure><img src="image_44.png" /></figure>

Raw data

<figure><img src="image_45.png" /></figure>

Pre-processing

<figure><img src="image_46.png" /></figure>

Pre-processed data

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

Statistical
analysis

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

Detection

Estimates / Statistic

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

Publication

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

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

OpenfMRI

ADNI

BIDN
Biomedical Informatics
Research Network

<figure><img src="image_52.png" /></figure>

LORIS

<figure><img src="image_53.png" /></figure>

XNAT

<figure><img src="image_54.png" /></figure>

HID

NeuroVault

A public repository of brain activation maps

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brainmap.org

SumsDB

<figure><img src="image_56.png" /></figure>

neurosynth.org

<figure><img src="image_57.png" /></figure>

SPM

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

Nipype:
Neuroimaging in Python
Pipelines and Interfaces

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

<figure><img src="image_60.png" /></figure>

LONI
PIPELINE
# A standard format for meta-data

* Sharing data across the data sharing tools...

* First attempt of an agnostic format: XML-Based Clinical Experiment Data Exchange Schema (XCEDE): www.xcede.org

- Describes subject, study, activation

- Limited provenance encoding

- Initiative of the BIRN

* NeuroImaging Data Model NI-DM: www.nidm.nidash.org

- Based on web-semantic tools.

- Initiative of the BIRN and INCF
Three major players

* Bottom-up approach.

* Lean on existing analysis software (SPM, FSL, AFNI) to disseminate the standard.

<figure><img src="image_61.png" /></figure>

Automatically created with [Neurotrends](https://neurotrends.com/) based on over 16 000 journal articles
# Work in progress

* Define a format to represent the results of a neuroimaging study with a focus on meta-analysis.

<table><thead><tr><th>Term name</th><th>Definition</th><th>Example</th><th>BIRNLex or NIDM Concept ID</th></tr></thead><tbody><tr><td>BonferroniCorrection</td><td>Bonferroni correction for multiple statistical tests</td><td></td><td>nidl:nidl_80</td></tr><tr><td>chi-squareStatistic</td><td>A statistical parameter drawn from a chi-square statistic</td><td></td><td>nidl:nidl_81</td></tr><tr><td>FDR</td><td>False Discovery Rate correction</td><td></td><td>nidl:nidl_82</td></tr><tr><td>FWER</td><td>Family-wise Error Rate correction</td><td></td><td>nidl:nidl_83</td></tr><tr><td>Scan Image</td><td>An image that is the output of an MRI or CT or PET scan</td><td></td><td>nidl:nidl_84</td></tr><tr><td>SliceOrder</td><td>The temporal order in which the 2D slices were acquired by the imaging systems</td><td></td><td>nidl:nidl_85</td></tr><tr><td>Voxel</td><td>volumetric pixel</td><td></td><td>nidl:nidl_86</td></tr><tr><td>Z-Statistic</td><td>A statistical parameter drawn from a normal or z distribution</td><td></td><td>nidl:nidl_87</td></tr><tr><td>extentThresh</td><td>Minimum cluster size used when thresholding a statistic image</td><td>5voxels</td><td>nidl:nidl_88</td></tr><tr><td>errorDegreesOfFreedom</td><td>Degrees of freedom of the error.</td><td></td><td>73 nidl:nidl_89</td></tr><tr><td>effectDegreesOfFreedom</td><td>Degrees of freedom of the effect.</td><td></td><td>1 nidl:nidl_90</td></tr><tr><td>StatisticMap</td><td>A map (2D or 3D structured dataset) whose value at each location is a statistic.</td><td></td><td>nidl:nidl_91</td></tr><tr><td>voxelSize</td><td>3D size of a voxel measured in voxelsUnits.</td><td>[2 2 4]</td><td>nidl:nidl_92</td></tr><tr><td>cluster</td><td>A group of neighboring image elements (voxels or vertices)</td><td></td><td>nidl:nidl_93</td></tr><tr><td>qValueFDR</td><td>p-value adjusted for the search volume, controlling for the False Discovery Rate</td><td></td><td>0.000154 nidl:nidl_94</td></tr><tr><td>pValueFWE</td><td>p-value adjusted for the search volume, controlling for the Familywise Error Rate</td><td></td><td>0.00554 nidl:nidl_95</td></tr><tr><td>pValueUncorrected</td><td>Uncorrected p-value</td><td></td><td>0.0542 nidl:nidl_96</td></tr></tbody></table>

## Vocabulary

<figure><img src="image_62.png" /></figure>

## Data model
# Neuroimaging terms

* Define a vocabulary to support the format.

<table><thead><tr><th>Term name</th><th>Definition</th><th>Example</th><th>BIRNLex or NIDM Concept ID</th><th>synonyms and related urls</th><th>Parent term</th></tr></thead><tbody><tr><td>cluster</td><td>A group of neighboring image elements (voxels or vertices)</td><td></td><td>nidm:nidm_93</td><td>http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C43 or http://purl.obolibrary.org/obo/OBI_0000251</td><td></td></tr><tr><td>qValueFDR</td><td>p-value adjusted for the search volume, controlling for the False Discovery Rate</td><td>0.000154</td><td>nidm:nidm_94</td><td>http://purl.obolibrary.org/obo/OBI_0001442</td><td>p-value i.e. nidm:nidm_0011</td></tr><tr><td>pValueUncorrected</td><td>p-value adjusted for the search volume, controlling</td><td>0.00542</td><td>nidm:nidm_96</td><td>http://purl.obolibrary.org/obo/OBI_0001442</td><td>p-value i.e. nidm:nidm_0011</td></tr><tr><td>SelectionProcedure</td><td>Procedure to select the values that are being reported</td><td></td><td>nidm:nidm_97</td><td></td><td></td></tr><tr><td>clusterSizeIn Voxels</td><td>Number of voxels contained in a cluster.</td><td>18</td><td>nidm:nidm_98</td><td></td><td></td></tr><tr><td>softwareVersion</td><td>Name and Number specifying software version.</td><td>SPM99, SPM2, SPM5, SPM8, SPM12b, FSL5.0.0</td><td>nidm:nidm_99</td><td></td><td></td></tr><tr><td>softwareRevision</td><td>Software revision number.</td><td>v5417</td><td>nidm:nidm_100</td><td></td><td>nidm:Software</td></tr><tr><td>clusterSizeIn Vertices</td><td>Number of vertices contained in a cluster.</td><td>10</td><td>nidm:nidm_101</td><td></td><td></td></tr><tr><td>clusterSizeIn Resels</td><td>Number of resels contained in a cluster.</td><td>13</td><td>nidm:nidm_102</td><td></td><td></td></tr><tr><td>voxelUnits</td><td>Units associated to each dimensions of some N-dimensional data.</td><td>{"mm" "mm" "s"}</td><td>nidm:nidm_103</td><td></td><td></td></tr><tr><td>ReselSizeInWorldUnits</td><td>Volume of a resel, a resolution element, expressed in units. It expresses the smoothness of the noise, with smoother images having larger resels.</td><td></td><td>nidm:nidm_104</td><td>http://en.wikipedia.org/wiki/Resel</td><td></td></tr><tr><td>Map</td><td>2D or 3D structured dataset.</td><td></td><td>nidm:nidm_106</td><td></td><td></td></tr><tr><td>fileName</td><td>Name associated with a file (without path).</td><td></td><td>nidm:nidm_107</td><td></td><td></td></tr><tr><td>numberOfDimensions</td><td>Number of Dimensions of some N-dimensional data.</td><td>3</td><td>nidm:nidm_108</td><td></td><td></td></tr><tr><td>dimensions</td><td>Dimensions of some N-dimensional data.</td><td>[64 64 20]</td><td>nidm:nidm_109</td><td></td><td></td></tr><tr><td>coordinateSystem</td><td>Type of coordinate system.</td><td>nidm:mniCoordinateSystem</td><td>nidm:nidm_110</td><td></td><td></td></tr><tr><td>searchVolumeIn Voxels</td><td>Total number of voxels within the search volume.</td><td>68656</td><td>nidm:nidm_111</td><td>Synonyms of nidm:volumeInVoxels</td><td></td></tr><tr><td>searchVolumeIn Resels</td><td>Total number of resels within the search volume.</td><td>151.3</td><td>nidm:nidm_112</td><td>Synonyms of nidm:volumeInResels</td><td></td></tr></tbody></table>
Data model

* Based on PROV-DM a W3C recommendation to encode provenance.

www.w3.org/TR/prov-dm/

<figure><img src="image_63.png" /></figure>
Data model

<figure><img src="image_64.png" /></figure>
# Data model: activities

<figure><img src="image_65.png" /></figure>
Data model: agent

software_id

prov:type nidm:spm

prov:type prov:SoftwareAgent

prov:label SPM

nidm:softwareVersion SPM12b

nidm:softwareRevision 5765

<figure><img src="image_66.png" /></figure>
Data model: entities

<figure><img src="image_67.png" /><figcaption>**Table 2**
Task comparisons (>) and conjunctions (C). Peak locations, cluster extent-Z-score (p<0.001 unci; k = 10).</figcaption></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="2">Auditory language tasks</th><th colspan="3">Visual language tasks</th></tr><tr><th>Categ - Def</th><th>Def- Categ</th><th>Categ C Def</th><th>Ph-s > Ph-d</th><th>Ph</th></tr></thead><tbody><tr><td>Left Hemisphere</td><td></td><td></td><td>348-4.10<sup>(4)</sup></td><td></td><td>82</td></tr><tr><td>Inf frontal-Oper</td><td>--</td><td>--</td><td>--</td><td>--</td><td>82</td></tr><tr><td>Precentral</td><td>18-3.38<sup>(5)</sup></td><td>--</td><td>348-5.09</td><td>--</td><td>82</td></tr><tr><td>Mid frontal</td><td>33-3.66</td><td>--</td><td>--</td><td>--</td><td>35</td></tr><tr><td>SMA</td><td>--</td><td>--</td><td>1433-5.48</td><td>--</td><td>--</td></tr><tr><td>Cingulate</td><td>--</td><td>--</td><td>1433-5.08<sup>(3)</sup></td><td>--</td><td>--</td></tr><tr><td>Med sup frontal</td><td>174-4.69</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>Roi operculum</td><td>--</td><td>--</td><td>--</td><td>36-4.31</td><td>--</td></tr><tr><td>Insula</td><td>--</td><td>--</td><td>396-4.87<sup>(8)</sup></td><td>--</td><td>58</td></tr><tr><td>Sup temporal</td><td>--</td><td>--</td><td>351-3.81<sup>(1)</sup></td><td>--</td><td>91</td></tr><tr><td>Mid temporal</td><td>--</td><td>1658-4.67<sup>(3)</sup></td><td>351-5.61<sup>(2)</sup></td><td>--</td><td>10</td></tr><tr><td>Inf parietal</td><td>--</td><td>1658-5.18<sup>(8)</sup></td><td>--</td><td>--</td><td>97</td></tr><tr><td>Sup parietal</td><td>--</td><td>--</td><td>--</td><td>--</td><td>97</td></tr><tr><td>Postcentral</td><td>--</td><td>--</td><td>--</td><td>--</td><td>146-4.43</td></tr><tr><td>Sup occipital</td><td>--</td><td>--</td><td>--</td><td>--</td><td>146</td></tr><tr><td>Mid occipital</td><td>--</td><td>--</td><td>--</td><td>--</td><td>146</td></tr><tr><td>Inf occipital</td><td>--</td><td>--</td><td>--</td><td>--</td><td>146</td></tr><tr><td>Fusiform</td><td>--</td><td>--</td><td>--</td><td>397-5.44</td><td>146</td></tr></tbody></table>
Data model: entities

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

* Data sharing is one key to reduce skepticism.

* There is already a number of technical solutions for data sharing in neuroimaging.

* A meta-data standard would beneficiate to all of these efforts

– NI-DM: http://nidm.nidash.org
Q & A

This work is supported by the **wellcome** trust