# Let's do open neuroimaging sciences

Camille Maumet

► To cite this version:

Camille Maumet. Let's do open neuroimaging sciences. GEANT 2019 - Workshop GEstion et pArtage de données en NeuroinformaTique, May 2019, Marseille, France. pp.1-54. inserm-02146290

HAL Id: inserm-02146290

https://inria.hal.science/inserm-02146290

Submitted on 3 Jun 2019

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Let's do open neuroimaging sciences

Camille Maumet

<figure><img src="image_2.png" /></figure>

@cmaumet

Univ Rennes, Inria, CNRS, Inserm
IRISA UMR 6074, Empenn ERL U-1228

<figure><img src="image_3.png" /></figure>

GEANT workshop

May 21st, 2019

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

OpenAIRE

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

<figure><img src="image_6.png" /></figure>
# Why open?
Why open?

Thoughts on
**generalizability**
<figure><img src="image_7.png" /><figcaption>happy pandas</figcaption></figure>

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

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

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

**Slide** by Tal Yarkoni (reused with permission), adapted.

Image credits: Panda 1 by N. Rupert (CC BY-SA), Panda 2 by Max Pixel (CCO), Panda 3 by D. Silaraja (Free to use), Panda 4 by N. Aishah (Free to use).
sad pandas

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

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

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

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

**Slide** by Tal Yarkoni (reused with permission), adapted.

Image credits: Panda 1 by Shizhao (CC BY-SA), Panda 2 by mozzercork (CC BY), Panda 3 by SevenLittleThings (CC BY), Panda 4 by auntie rain (CC BY-NC-SA).
Experimental manipulation

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

-

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

Emotion-related neural changes

=

<figure><img src="image_17.png" /></figure>
Greater amygdala activation in response
to affective mammalian expressions
relative to neutral mammalian expressions

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet.

Consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt
ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima
veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut
aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit
qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui
dolorem eum fugiat quo voluptas nulla pariatur?

<figure><img src="image_18.png" /></figure>
Greater amygdala activation in response
to affective mammalian expressions
relative to neutral mammalian expressions

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet.

Consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt
ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima
veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut
aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit
qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui
dolorem eum fugiat quo voluptas nulla pariatur?

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

Greater amygdala activation in 22
**UT-Austin undergraduates** when viewing
**images angsty_tree_panda.jpg** and
**panda_delight.png** than image
**pand_goes_meh.jpg**

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet.

Consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt
ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima
veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut
aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit
qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui
dolorem eum fugiat quo voluptas nulla pariatur?

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

<figure><img src="image_21.png" /></figure>
How can we get
there?
How can we get
there?

Variations on
reproducibility
Robustness to generalizability

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

Robustness → Generalizability
# Robustness to generalizability

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

**Repeat**

* Same lab
* Same experiment
* Same settings

**Reproduce**

* Different lab
* Same experiment
* Different settings

**Replicate**

* Different lab
* Same experiment
* Same settings

**Robustness**

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

**Generalizability**
Table B1. Levels of Reproducibility

This table provides an incomplete taxonomy of types of consistency of neuroimaging results. For each type of consistency (row), the variable (column) that is held constant (•, bullet) or allowed to vary (D=different) is indicated. In each instance, a bullet (•) indicates the exact same setting; for the variable "Subject" this means the very same acquired data is used, while a double bullet (**) indicates the same subjects are scanned multiple times. Examples of different Experimental Methods include fundamental changes like an event-related design vs. a block design; examples of different Experimental Code/Stimuli, include different sets of pictures used in a visual working memory experiment, or using different paradigm software; an example of different Stimuli Type would be number vs. shape vs. image stimuli in a working memory experiment. An example of different analysis methods for intrasubject (first level) fMRI data would be a confirmatory regression-based modelling vs. an exploratory data-driven method like independent components analysis; examples of different analysis code would be intrasubject fMRI fit with a regression model in two different software packages.

<table><thead><tr><th>Type of Stability</th><th>Investigator</th><th>Experimental Method</th><th>Stimuli Type</th><th>Experimental Code/Stimuli</th><th>Subject Population</th><th>Subjects</th><th>Scanner</th><th>Acquisition Day</th><th>Data Analyst</th><th>Analysis Method</th><th>Analysis Code</th></tr></thead><tbody><tr><td><strong>Measurement Stability</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ISO Repeatability<br>e.g. 30-minute intra-scanner reliability</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>••</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>ISO Intermediate Reproducibility<br>e.g. 7-day intra-scanner reliability</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>••</td><td>•</td><td>D</td><td>•</td><td>•</td><td>•</td></tr><tr><td>ISO Reproducibility<br>e.g. 7-day inter-scanner reliability</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>••</td><td>D</td><td>D</td><td>•</td><td>•</td><td>•</td></tr><tr><td><strong>Analytical Stability</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Analysis Replicability</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Collegial Analysis Replicability</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>D</td><td>•</td><td>•</td></tr><tr><td>Peng's Reproducibility</td><td>D</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>D</td><td>•</td><td>•</td></tr><tr><td><strong>Generalisabilty over ...</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Subjects (Near Replicability)</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>D</td><td>•</td><td>D</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Labs (Intermediate Replicability)</td><td>D</td><td>•</td><td>•</td><td>D</td><td>•</td><td>D</td><td>D</td><td>D</td><td>D</td><td>•</td><td>D</td></tr><tr><td>Experimental & Analytical methods<br>(Far Replicability)</td><td>D</td><td>D</td><td>•</td><td>D</td><td>•</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td></tr><tr><td>Subject & Stimulus Populations<br>(Hypothesis Generalisability)</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td></tr></tbody></table>

[Nichols et. al, Nature Neuroscience 2017]
**Sample sizes in neuroimaging**

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

[Poldrack et. al, Nature Neuroscience 2017]

2015: 30 participants

Low diversity &
Low statistical power
# Robustness to generalizability

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

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

**Repeat**

* Same lab
* Same experiment
* Same settings

**Reproduce**

* Different lab
* Same experiment
* Different settings

**Replicate**

* Different lab
* Same experiment
* Same settings

**Re-use**

* Different lab
* Different experiment
* Different settings

**Robustness**

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

**Generalizability**
On our way...
On our way...

Tools & standards to
publish our research
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

<figure><img src="image_32.png" /></figure>
# Publishing research artefacts

<figure><img src="image_33.png" /></figure>
# Publishing research artefacts

<figure><img src="image_34.png" /></figure>
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

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

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

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

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

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

HAL, BiorXiv
Pubmed

<figure><img src="image_43.png" /></figure>
# Brain Imaging Data Structure
(BIDS)

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

my_dataset/

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

participants.tsv

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

sub-01/

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

anat/

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

sub-01_T1w.nii.gz

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

func/

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

sub-01_task-rest bold.nii.gz

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

sub-01_task-rest bold.json

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

dwi/

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

sub-01_dwi.nii.gz

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

sub-01_dwi.json

<figure><img src="image_55.png" /></figure>

sub-01_dwi.bval

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

sub-01_dwi.bvec

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

sub-02/

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

sub-03/

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

sub-04/

[Gorgolewski et. al, Sci. Data 2016]
# BIDS adoption

* Used in over 60 labs around the world

* Adopted by: FCP-INDI, Developing Human Connectome, SchizConnect and Donders Data repository.

* Anonymized data from over 20,000 participants formatted in BIDS are publicly available

* >300 members of mailing list

* Extensions: MEG, iEEG, EEG (just released)

<figure><img src="image_60.png" /><figcaption>Traffic to bids.neuroimaging.io</figcaption></figure>

~1.8K visitors per month
# BIDS-apps

<table><tr><td>BIDS-Apps/example</td><td>version 0.0.7</td></tr><tr><td>BIDS-Apps/freesurfer</td><td>version</td></tr><tr><td>BIDS-Apps/ndmg</td><td>version v0.1.0</td></tr><tr><td>BIDS-Apps/BROCCOLI</td><td>version v1.0.1</td></tr><tr><td>BIDS-Apps/FibreDensityAndCrosssection</td><td>version v0.0.1</td></tr><tr><td>BIDS-Apps/SPM</td><td>version v0.0.15</td></tr><tr><td>poldracklab/mriqc</td><td>version 0.15.0</td></tr><tr><td>BIDS-Apps/QAP</td><td>Image not found</td></tr><tr><td>BIDS-Apps/CPAC</td><td>version v1.0.1a_22</td></tr><tr><td>BIDS-Apps/hyperalignment</td><td>Image not found</td></tr><tr><td>BIDS-Apps/mindboggle</td><td>version 0.0.4-1</td></tr><tr><td>BIDS-Apps/MRtrix3_connectome</td><td>version latest</td></tr><tr><td>BIDS-Apps/rs_signal_extract</td><td>version 0.1</td></tr><tr><td>BIDS-Apps/aa</td><td>version v0.2.0</td></tr><tr><td>BIDS-Apps/niak</td><td>version latest</td></tr><tr><td>BIDS-Apps/oppni</td><td>version v0.7.0-1</td></tr><tr><td>poldracklab/fmriprep</td><td>version 1.4.1rc1</td></tr><tr><td>BIDS-Apps/brainiak-srm</td><td>version latest</td></tr><tr><td>BIDS-Apps/nippelines</td><td>version 0.3.0</td></tr><tr><td>BIDS-Apps/HCPIPipelines</td><td>version v3.17.0-18</td></tr><tr><td>BIDS-Apps/MAGETbrain</td><td>Image not found</td></tr><tr><td>BIDS-Apps/tracula</td><td>version v6.0.0-4</td></tr><tr><td>BIDS-Apps/baracus</td><td>Image not found</td></tr><tr><td>BIDS-Apps/antsCorticalThickness</td><td>version v2.2.0-1</td></tr><tr><td>BIDS-Apps/DPARSF</td><td>version v4.3.12</td></tr><tr><td>BIDS-Apps/afni_proc</td><td>Image not found</td></tr><tr><td>BIDS-Apps/rsHRF</td><td>version 1.0.0</td></tr></table>

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

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

[Gorgolewski et. al, Plos Comp. Bio. 2017]
# Publishing research artefacts

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

Preprocessing

<figure><img src="image_64.png" /></figure>

Derived data

<figure><img src="image_65.png" /></figure>

Statistical analysis

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

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

<figure><img src="image_69.png" /></figure>

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

HAL, BiorXiv
Pubmed
# Publishing research artefacts

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

Preprocessing

<figure><img src="image_72.png" /></figure>

Derived data

<figure><img src="image_73.png" /></figure>

Statistical analysis

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

OpenfMRI

<figure><img src="image_79.png" /></figure>

OpenNEURO

<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

SHANOIR

LORIS

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

HAL, BiorXiv
Pubmed
# Publishing research artefacts

<figure><img src="image_86.png" /></figure>

Preprocessing

<figure><img src="image_87.png" /></figure>

Derived data

<figure><img src="image_88.png" /></figure>

Statistical analysis

<figure><img src="image_89.png" /></figure>

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

OpenfMRI

<figure><img src="image_91.png" /></figure>

OpenNEURO

<figure><img src="image_92.png" /></figure>

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

<figure><img src="image_94.png" /></figure>

SHANOIR

<figure><img src="image_95.png" /></figure>

HAL, BiorXiv
Pubmed

<figure><img src="image_96.png" /></figure>

brainmap.org

neurosynth.org

brainspell

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

Image credits: Parcels 1 2 & 4 (CCO), Parcel 3 (CCO), Parcel 5 (CCO).
# fMRI Results

## Publication

<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /><figcaption>Figure
(selected slices)</figcaption></figure>

<figure><img src="image_100.png" /><figcaption>Thresholded
statistics</figcaption></figure>

<figure><img src="image_101.png" /><figcaption>Peak locations</figcaption></figure>

<figure><img src="image_102.png" /></figure>

- Incomplete statistical results

- Ambiguous/incomplete methods

- Metadata is not searchable
# NIDM-Results pack

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

<figure><img src="image_104.png" /><figcaption>NIDM-Results
.nidm.zip</figcaption></figure>

<figure><img src="image_105.png" /></figure>

ClusterLabels.nii.gz

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

Contrast.nii.gz

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

ContrastStandardError.nii.gz

<figure><img src="image_108.png" /></figure>

DesignMatrix.csv

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

DesignMatrix.png

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

ExcursionSet.nii.gz

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

ExcursionSet.png

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

GrandMean.nii.gz

<figure><img src="image_113.png" /></figure>

Mask.nii.gz

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

nidm.json

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

nidm.ttl

<figure><img src="image_116.png" /></figure>

ParameterEstimate_001.nii.gz

<figure><img src="image_117.png" /></figure>

ParameterEstimate_002.nii.gz

<figure><img src="image_118.png" /></figure>

ResidualMeanSquares.nii.gz

<figure><img src="image_119.png" /></figure>

SearchSpaceMask.nii.gz

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

TStatistic.nii.gz

ZStatistic.nii.gz
# NIDM-Results pack

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

<figure><img src="image_122.png" /><figcaption>NIDM-Results
.nidm.zip</figcaption></figure>

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

ClusterLabels.nii.gz

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

Contrast.nii.gz

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

ContrastStandardError.nii.gz

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

DesignMatrix.csv

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

DesignMatrix.png

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

ExcursionSet.nii.gz

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

ExcursionSet.png

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

GrandMean.nii.gz

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

Mask.nii.gz

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

nidm.json

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

nidm.ttl

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

ParameterEstimate_001.nii.gz

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

ParameterEstimate_002.nii.gz

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

ResidualMeanSquares.nii.gz

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

SearchSpaceMask.nii.gz

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

TStatistic.nii.gz

ZStatistic.nii.gz

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

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

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

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

Statistical
estimation

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

Effects, standard error
and statistical maps...

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

Inference

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

Thresholded maps, list
of peaks and clusters...

<figure><img src="image_145.png" /></figure>
# NIDM in SPM, FSL & NeuroVault

## SPM

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

### 1. Export

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

$ nidmfsl fsl_ds107_group 49 -g Control

### 2. Publication on NeuroVault

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

<figure><img src="image_149.png" /></figure>
# Meta-analysis w/ NIDM-Results

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

## Coordinate-based meta-analysis

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

## Image-based meta-analysis

<figure><img src="image_152.png" /></figure>
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

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

<figure><img src="image_157.png" /></figure>

OpenfMRI

<figure><img src="image_158.png" /></figure>

OpenNEURO

<figure><img src="image_159.png" /></figure>

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

SHANOIR

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

LORIS

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

HAL, BiorXiv
Pubmed

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

brainmap.org

neurosynth.org

brainspell

<figure><img src="image_164.png" /></figure>

NEUROVAULT

<figure><img src="image_165.png" /></figure>
# Publishing research artefacts

<figure><img src="image_166.png" /></figure>

Preprocessing

<figure><img src="image_167.png" /></figure>

Derived data

<figure><img src="image_168.png" /></figure>

Statistical analysis

<figure><img src="image_169.png" /></figure>

<figure><img src="image_170.png" /></figure>

<figure><img src="image_171.png" /></figure>

<figure><img src="image_172.png" /></figure>

<figure><img src="image_173.png" /></figure>

OpenfMRI

<figure><img src="image_174.png" /></figure>

OpenNEURO

<figure><img src="image_175.png" /></figure>

SHANOIR

<figure><img src="image_176.png" /></figure>

<figure><img src="image_177.png" /></figure>

<figure><img src="image_178.png" /></figure>

NITRC

<figure><img src="image_179.png" /></figure>

brainmap.org

HAL, BiorXiv

neurosynth.org

Pubmed

brainspell

<figure><img src="image_180.png" /></figure>

NEUROVAULT
# Publishing research artefacts

<figure><img src="image_181.png" /></figure>

Preprocessing

<figure><img src="image_182.png" /></figure>

Derived data

<figure><img src="image_183.png" /></figure>

Statistical analysis

<figure><img src="image_184.png" /></figure>

<figure><img src="image_185.png" /></figure>

<figure><img src="image_186.png" /></figure>

<figure><img src="image_187.png" /></figure>

<figure><img src="image_188.png" /></figure>

<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /></figure>

OpenfMRI

<figure><img src="image_191.png" /></figure>

OpenNEURO

XNAT

<figure><img src="image_192.png" /></figure>

SHANOIR

<figure><img src="image_193.png" /></figure>

LORIS

<figure><img src="image_194.png" /></figure>

NITRC

HAL, BiorXiv
Pubmed

<figure><img src="image_195.png" /></figure>

brainmap.org

neurosynth.org

brainspell

<figure><img src="image_196.png" /></figure>

NEUROVAULT

Image credits: Parcels 1 2 & 4 (CCO), Parcel 3 (CCO), Parcel 5 (CCO).
# Publishing research artefacts

<figure><img src="image_197.png" /></figure>

Image credits: Parcels 1 2 & 4 (CCo), Parcel 3 (CCo), Parcel 5 (CCo).
# Publishing research artefacts

<figure><img src="image_198.png" /></figure>

Image credits: Parcels 1, 2 & 4 (CCO), Parcel 3 (CCO), Parcel 5 (CCO).
On our way...

Linking it all with
openAIRE-Connect
# Research Community Dashboard

A window to community's research.

Open Science as a Service
Open Science as-a-Service
for Research Communities

Facilitate Research Communities
adoption of Open Science
publishing principles by supporting
artefact publishing tools
as-a-Service

<figure><img src="image_199.png" /></figure>
# Open Science as-a-Service (OSaaS)

<figure><img src="image_200.png" /></figure>

OpenAIRE as a mediation gateway between research communities and the scientific communication landscape
# A virtual environment designed for your Community

<figure><img src="image_201.png" /></figure>

## Implement

the latest Open Science trends: FAIR data, open data

<figure><img src="image_202.png" /></figure>

## Share

all your research results: publications, data, software, methods

<figure><img src="image_203.png" /></figure>

## Link

all your research effectively

<figure><img src="image_204.png" /></figure>

## Gather

all your research results in one place

<figure><img src="image_205.png" /></figure>

## Monitor

and report your community's progress
Community dashboards

<figure><img src="image_206.png" /></figure>

SDSN
GREECE

Sustainable Development
Solutions Network -
Greece

RESEARCH COMMUNITY

The Greek United Nations (UN) Sustainable
Development Solutions Network (SDSN)

Subscribe

<figure><img src="image_207.png" /></figure>

AGINFRA

Agricultural and Food
Sciences

RESEARCH COMMUNITY

The scope of this community is to provide
access to publications, research data,
projects and software that are related to
agricultural and food sciences

Subscribe

<figure><img src="image_208.png" /></figure>

CLARIN
Common Language Resources and
Technology Infrastructure

Common Language
Resources and Technology
Infrastructure

RESEARCH INITIATIVE

CLARIN

Subscribe

<figure><img src="image_209.png" /></figure>

Fisheries and Aquaculture
Management

RESEARCH COMMUNITY

Conservation of marine resources for
sustainable development

Subscribe

<figure><img src="image_210.png" /></figure>

emso

JPI OCEANS

<figure><img src="image_211.png" /></figure>

EMBRAC

<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

European Marine Science

RESEARCH COMMUNITY

This community was initially defined to
include a very broad range of topics, with
the intention to generate a number of more
focused and sustainable dashboards for
research communities and initiatives. As
outlined in the logo of this community, we
intend to setup a community dashboard for
EuroMarine (a consortium of 56 research
and academic organisations) and monitoring
dashboards for marine research initiatives,

<figure><img src="image_214.png" /></figure>

Neuroinformatics

RESEARCH COMMUNITY

Subscribe

<figure><img src="image_215.png" /></figure>

OpenAIRE

CONNECT

<figure><img src="image_216.png" /></figure>
# Neuroinformatics: monitor

24,024

publications

5,083

research data

150

software

2,375

other research
products

8

projects

6

content providers

<figure><img src="image_217.png" /></figure>
# Neuroinformatics: link

<figure><img src="image_218.png" /></figure>

Neuroinformatics

## Exploring the Impact of Analysis Software on Task fMRI Results

PREPRINT

ENGLISH

OPEN

Nichols, Thomas; Maumet, Camille; Bowring, Alexander (2018)

**Publisher:** HAL CCSD

**Subject:** [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this 'methodological plurality' comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable. In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000-0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

SEARCH

MONITOR

SHARE

LINK

CAMILLE MAUMET

<figure><img src="image_219.png" /></figure>

Share - Bookmark

<figure><img src="image_220.png" /></figure>

Download from

<figure><img src="image_221.png" /></figure>

OpenAIRE via OpenAIRE (Preprint, 2018)

<figure><img src="image_222.png" /></figure>

Related to

Neuroinformatics

Cite this publication

select a citation style

<figure><img src="image_223.png" /></figure>

feedback

<figure><img src="image_224.png" /></figure>

Link this publication to...

<figure><img src="image_225.png" /></figure>

<figure><img src="image_226.png" /></figure>

<figure><img src="image_227.png" /></figure>

Other research results

Metrics

+

<figure><img src="image_228.png" /></figure>

OpenAIRE

CONNECT

<figure><img src="image_229.png" /></figure>
# Neuroinformatics: project view

<figure><img src="image_230.png" /></figure>
# "Gather, view and monitor all research outcomes"

<figure><img src="image_231.png" /></figure>

<figure><img src="image_232.png" /></figure>

Live demo during the poster session!
by Xavier Rolland

<figure><img src="image_233.png" /></figure>
Going further
Going further

Join the community!
# Taking part in Standards & Best practices
## INCF Special Interest Groups

### Active groups

The SIGs listed below are all active and open to new members. If you are interested in joining a SIG, please contact them on the listed address.

* Biophysically based Computational Models of Glial - Neuron Coupling

* Neuroinformatics for Aging

* Neuroinformatics for Cell Types

* Neuroshapes: Open SHACL schemas for FAIR neuroscience data

* Reproducibility & Best Practices in Human Brain Neuroimaging

* Reproducible Research and Open Neuroscience

* Standardised Representations of Network Structures

* Neuroimaging Quality Control (niQC)

<figure><img src="image_234.png" /></figure>

BIDS, NIDM & more!

<figure><img src="image_235.png" /></figure>

Survey open now!

http://tinyurl.com/WeNeedNiQC

### Create your own SIG!
# Getting started & spreading open science

<figure><img src="image_236.png" /></figure>

ReproNim: A Center for Reproducible Neuroimaging Computation

<figure><img src="image_237.png" /></figure>

<figure><img src="image_238.png" /></figure>

Mozilla Open Leaders

Work Open, Lead Open
# Researching collaboratively
Hackathons

<figure><img src="image_239.png" /><figcaption>## BRAINHACK OPEN GENEVA 2019</figcaption></figure>

BRAIN
HACK

<figure><img src="image_240.png" /></figure>

OPEN GENEVA 2019

EVENTS 2019

Brainhack Open Geneva will take place from March 22-23, 2019

<figure><img src="image_241.png" /><figcaption>## BRAINHACK IN PADOVA: EVOLUTION</figcaption></figure>

<figure><img src="image_242.png" /></figure>

BrainHack: Evolution of the Mind and the Brain

<figure><img src="image_243.png" /></figure>

EVENTS 2018

Brainhack in Padova will take place from December 5-7, 2018

<figure><img src="image_244.png" /><figcaption>## LA BRAINHACK 2018</figcaption></figure>

<figure><img src="image_245.png" /></figure>

LA BRAINHACK 2018

EVENTS 2018

The LA Brainhack 2018 will take place November 18 - 19 at the Information Sciences Institute, Marina del Rey.

<figure><img src="image_246.png" /><figcaption>## BRAINHACK ZURICH 2018</figcaption></figure>

<figure><img src="image_247.png" /></figure>

BRAIN
HACK

Oct
ZURICH
2018

EVENTS 2018

Brainhack Zurich 2018 will take place from October 24-26 at ETH Zurich & University Hospital Zurich.

<figure><img src="image_248.png" /><figcaption>## 2018 OHBM HACKATHON</figcaption></figure>

OHBM Brainhack 2018

Singapore
June 14-16, 2018

EVENTS 2018

The 2018 OHBM Hackathon will take place June 14 - 16 at the National University of Singapore, U-Town campus, Singapore.

<figure><img src="image_249.png" /><figcaption>## BRAINHACK NETWORKS</figcaption></figure>

<figure><img src="image_250.png" /></figure>

EVENTS 2018

9 & 10 of June 2018, Brain and Spine Institute, Paris

<figure><img src="image_251.png" /></figure>
# Acknowledgements

Thank you!

**NIDASH working group**

Tibor Auer, Alexander Bowring, Samir Das, Fariba Fana, Guillaume Flandin, Satra Ghosh, Tristan Glatard, Chris Gorgolewski, Yaroslaw Halchenko, Michael Hanke, Karl Helmer, Dorota Jarecka, David Keator, Tom Maullin-Sapey, Nolan Nichols, Tom Nichols, Smruti Padhy, Jean-Baptiste Poline, Vanessa Sochat, Jason Steffener, Jessica Turner & all NIDM and BIDS contributors!

**Neuroimaging software teams**

Gang Chen, Richard Reynolds, Robert Cox (AFNI), Mark Jenkinson, Matthew Webster, Paul McCarthy, Eugene Duff, Steve Smith (FSL), Guillaume Flandin (SPM).

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

**OpenAIRE neuroinformatics**

Christian Barillot, Axel Bonnet, Xavier Rolland, Sorina Caramasu Pop.
<figure><img src="image_252.png" /></figure>

Thank you!

Any questions?

Camille Maumet

Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228

http://camillemaumet.com

<figure><img src="image_253.png" /></figure>

cmaumet