# The Brain Imaging Data Structure: Overview of the project and what BIDS does and allows to do

Camille Maumet

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## To cite this version:

Camille Maumet. The Brain Imaging Data Structure: Overview of the project and what BIDS does and allows to do. GliMR Hybrid Networking Event, Sep 2021, Brno (hybrid), Czech Republic., 2021. [hal-04255147](https://hal HAL Id: hal-04255147)

https://inria.hal.science/hal-04255147

Submitted on 23 Oct 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# The Brain Imaging Data Structure

Overview of the project and what BIDS does
and allows to do

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

Camille Maumet

Univ Rennes, Inria, CNRS, Inserm

<figure><img src="image_4.png" /><figcaption>@cmaumet</figcaption></figure>
# Glioma MR Imaging 2.0

* Build a pan-European and multidisciplinary network of **international experts in glioma research, patient organisations, data scientists, and MR imaging scientists**

* Use **advanced MR imaging for improved decision making** in diagnosis, patient monitoring, and assessment of treatment response in clinical trials and clinical practice.

<figure><img src="image_5.png" /><figcaption>Esther Warnert</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Radim Jancalek</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Lydiane Hirschler</figcaption></figure>

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

**WG1 - Advanced MRI biomarkers for glioma characterisation**

Working Group 1 focusses on coordinating the identification and quantification of advanced MRI biomarkers for the application in the field of glioma.

<figure><img src="image_9.png" /><figcaption>Kyrre Emblem</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Marion Smits</figcaption></figure>

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

**WG4 - Stakeholder relations**

Working Group 4 ensures representation of all relevant stakeholders within GliMR and acts as liaison from GliMR to all relevant stakeholders.

<figure><img src="image_12.png" /><figcaption>Fran Borovecki</figcaption></figure>

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

**WG2 - Multi-site data integration**

Working Group 2 coordinates multi-site data integration and enables the creation of large datasets in glioma diagnostics via the creation of common GDPR- and BIDS-compliant forms and data structures.

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

**WG5 - Dissemination**

Working Group 5 disseminates the Action's goals and results in tailored manners to all relevant stakeholders.

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

**WG3 - Clinical translation**

Working Group 3 focusses on fostering cross-border information exchange about past and current clinical trials and studies of glioma.

<figure><img src="image_16.png" /><figcaption>Patricia Clement</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Joana Pinto</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Jan Petr</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Vera Keil</figcaption></figure>

<figure><img src="image_20.png" /></figure>
# Gliomas research in Europe

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

Data preparation

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

* 50,000 new cases of gliomas per year in Europe

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

Derived data

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

* Low incidence rate

Analysis

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

>> Limited samples sizes

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# Why do we need large samples?

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

Data preparation

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

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

Derived data

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

Analysis

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

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

## Reproducibility crisis

SCIENCE

### A Waste of 1,000 Research Papers

Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?

ED YONG MAY 17, 2019

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

SEAN NEL / SHUTTERSTOCK

In 1996, a group of European researchers found that a certain gene, called *SLC6A4*, might influence a person's risk of depression.

It was a blockbuster discovery at the time. The team found that a less active version of the gene was more common among 454 people who had mood disorders than in 570 who did not. In theory, anyone who had this particular gene variant could be at higher risk for depression, and that finding, they said, might help in diagnosing such disorders, assessing suicidal behavior, or even

By Ed Yong, The Atlantic Science, May 2019, link.
# Why do we need large samples?

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

Data preparation

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

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

Derived data

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

Analysis

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

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

## Reproducibility crisis

### SCIENCE

### A Waste of 1,000 Research Papers

Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?

ED YONG MAY 17, 2019

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

SEAN NEL / SHUTTERSTOCK

In 1996, a group of European researchers found that a certain gene, called SLC6A4, might influence a person's risk of depression.

It was a blockbuster discovery at the time. The team found that a less active version of the gene was more common among 454 people who had mood disorders than in 570 who did not. In theory, anyone who had this particular gene variant could be at higher risk for depression, and that finding, they said, might help in diagnosing such disorders, assessing suicidal behavior, or even

By Ed Yong, The Atlantic Science, May 2019, link.

## Algorithmic injustice

The New York Times

### Many Facial-Recognition Systems Are Biased, Says U.S. Study

Algorithms falsely identified African-American and Asian faces 10 to 100 times more than Caucasian faces, researchers for the National Institute of Standards and Technology found.

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

Morning at Grand Central Terminal. Technology for facial recognition is frequently biased, a new study confirmed. Timothy A. Clary/Agence France-Presse — Getty Images

By Natasha Singer and Cade Metz, The New York Times, Dec. 2019, link.
# Why do we need large samples?

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

Data preparation

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

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

Derived data

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

Analysis

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

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

## Algorithmic injustice

The New York Times

### Many Facial-Recognition Systems Are Biased, Says U.S. Study

Algorithms falsely identified African-American and Asian faces 10 to 100 times more than Caucasian faces, researchers for the National Institute of Standards and Technology found.

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

Morning at Grand Central Terminal. Technology for facial recognition is frequently biased, a new study confirmed. Timothy A. Clary/Agence France-Presse — Getty Images

By Natasha Singer and Cade Metz, The New York Times, Dec. 2019, link.

## Reproducibility crisis

SCIENCE

### A Waste of 1,000 Research Papers

Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?

ED YONG MAY 17, 2019

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

SEAN NEL / SHUTTERSTOCK

In 1996, a group of European researchers found that a certain gene, called SLC6A4, might influence a person's risk of depression.

It was a blockbuster discovery at the time. The team found that a less active version of the gene was more common among 454 people who had mood disorders than in 570 who did not. In theory, anyone who had this particular gene variant could be at higher risk for depression, and that finding, they said, might help in diagnosing such disorders, assessing suicidal behavior, or even

By Ed Yong, The Atlantic Science, May 2019, link.

> As a community, we need to create and work on **bigger & more representative and diverse** datasets
GliMR WG2 Multi-site
data integration
# WG2 - Multi-site data integration

Working Group 2 coordinates multi-site data integration and enables the creation of large datasets in glioma diagnostics via the creation of common GDPR- and BIDS-compliant forms and data structures.

<figure><img src="image_50.png" /><figcaption>Camille Maumet</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>Cyril Pernet</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>Data Privacy</figcaption></figure>

<figure><img src="image_53.png" /><figcaption>Data infrastructure</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>Data portability</figcaption></figure>

<figure><img src="image_55.png" /></figure>
# WG2 - Multi-site data integration

Working Group 2 coordinates multi-site data integration and enables the creation of large datasets in glioma diagnostics via the creation of common GDPR- and BIDS-compliant forms and data structures.

<figure><img src="image_56.png" /><figcaption>Camille Maumet</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>Cyril Pernet</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>Data Privacy</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>Data infrastructure</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>Data portability</figcaption></figure>

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

Image credits: "Privacy lock", "Transport map", "Mini bus"
# Data portability

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How differences in acquisition parameters can
**be conveyed** with enough details to support
future post-processing?

* Extend the international BIDS standard in setting up the structures for sharing advanced MR imaging data.

**Brain Imaging Data Structure (BIDS):**

A global community of 200+ members!

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

BIDS
BRAIN IMAGING DATA STRUCTURE

<figure><img src="image_64.png" /><figcaption>Guiomar
Niso</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>Melanie
Ganz</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>Robert
Oostenveld</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Russ
Poldrack</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Kirstie
Whitaker</figcaption></figure>
# Why BIDS?

* Data sharing and exchange

* With other labs

* Within your lab

* With yourself in 6 months :)

* Automatic pipeline processing
# What is BIDS?

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my_dataset/

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participants.tsv

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sub-01/

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anat/

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sub-01_T1w.nii.gz

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func/

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sub-01_task-rest bold.nii.gz

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sub-01_task-rest bold.json

* It's a data **structure**; nothing to do with **format** per se

* It's about: - how you organize data in a folder

- how you name files

- how you document metadata

- using community standards and dictionaries to do all the above

* It cares about imaging data but also behaviour/cognition
# What is BIDS?

## CREDITS

## SCIENTIFIC DATA

OPEN

SUBJECT CATEGORIES

* Data publication and archiving

* Research data

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Received: 18 December 2015

Accepted: 19 May 2016

Published: 21 June 2016

Krzysztof J. Gorgolewski¹, Tibor Auer², Vince D. Calhoun³,⁴, R. Cameron Craddock⁵,⁶, Samir Das⁷, Eugene P. Duff⁸, Guillaume Flandin⁹, Satrajit S. Ghosh¹⁰,¹¹, Tristan Glatard⁷,¹², Yaroslav O. Halchenko¹³, Daniel A. Handwerker¹⁴, Michael Hanke¹⁵,¹⁶, David Keator¹⁷, Xiangrui Li¹⁸, Zachary Michael¹⁹, Camille Maumet²⁰, B. Nolan Nichols²¹,²², Thomas E. Nichols²⁰,²³, John Pellman⁶, Jean-Baptiste Poline²⁴, Ariel Rokem²⁵, Gunnar Schaefer¹,²⁶, Vanessa Sochat²⁷, William Triplett¹, Jessica A. Turner³,²⁸, Gaël Varoquaux²⁹ & Russell A. Poldrack¹

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

Data Sharing Task Force

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

The Poldrack Lab @ Stanford
# What is BIDS?

## Evolution of BIDS

1. Kickoff meeting at Stanford in Spring 2015

2. Meeting at OHBM 2015 (June)

3. Introduced to neuroinformatics community at INCF Congress 2015 (August)

4. First release candidate and public call for comments (September)

5. Version 1.0.0 published along the introductory paper

→ Initially covered structural MRI and fMRI, now all sort of MRI, PET, EEG, MEG, iEEG, extensions for animal, connectivity, imaging genomics ..
# What is BIDS?

## Essence of BIDS

* Directory hierarchy and file naming system

* Conveys essential meta-data in filenames alone

* Remaining meta-data in “sidecar” files

### dicomdir/

1208200617178_22/

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1208200617178_23/

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

### my_dataset/

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

participants.tsv

### sub-01/

#### anat/

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

sub-01_T1w.nii.gz

#### func/

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

sub-01_task-rest bold.nii.gz

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

sub-01_task-rest bold.json

### dwi/

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

sub-01_dwi.nii.gz

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

sub-01_dwi.json

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

sub-01_dwi.bval

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

sub-01_dwi.bvec

### sub-02/
# What is BIDS?

## Metadata as text files (tsv, json) with standard dictionary

<figure><img src="image_99.png" /></figure>

```
participant_id age sex
sub-002       34   M
sub-003       12   F
sub-004       33   F
```

Subjects info are shared at the root - easy to figure populations, age, and other basic demographics
# What is BIDS? A community!

<figure><img src="image_100.png" /></figure>

**Technical challenges:** Standards, Databases, Good practices (version control, etc.)

**A culture change: towards community-driven research**

* Making resources available to the scientific community

* Reusing existing resources (datasets, tools) that were created by others

* Developing tools collaboratively with a community of contributors

https://github.com/bids-standard
# Extending BIDS: the BEPs

## BIDS Extension Proposals

<figure><img src="image_101.png" /></figure>

## BIDS Extension Proposals: A Guide

### Why contribute to BIDS?

* You can make something that is good even better without building it from scratch.

* You can interact with and get to know other experts in the field.

* A more substantial extension can lead to standalone publications (see for instance BIDS MEG: https://www.nature.com/articles/sdata2018110)

### Overarching principles in the design of BIDS

* Folder structure must be not only machine-readable but also easy to interpret by humans. In other words, readability is as important as the ability to programmatically traverse the folder structure.

* When it comes to file names within folders, redundancy trumps concision -- the files should be identifiable by their names even if removed from the folder structure.

### When and how to start a BIDS Extension Proposal?

* Small contributions (typos, rephrasing of a description, adding a single new metadata field) can be just added as a Pull Request on GitHub.

* Larger contributions that are expected to involve longer and more involved discussions
<figure><img src="image_102.png" /></figure>

# The Brain Imaging Data Structure

Overview of the project and what BIDS does and allows to do

Camille Maumet

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

Thank you!