Elsevier

NeuroImage

Volume 144, Part B, January 2017, Pages 309-314
NeuroImage

The Brainomics/Localizer database

https://doi.org/10.1016/j.neuroimage.2015.09.052Get rights and content

Highlights

  • Neuroimaging data associated with 94 subjects are made publicly available.

  • Available data include anatomical and functional MRI images and behavioral and demographic data.

  • The database is built upon a semantic web software framework.

  • The database exposes data in original and efficient ways, both as web pages and files in standard interchange formats.

Abstract

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data.

To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways.

Introduction

The Brainomics/Localizer database is a data repository containing datasets from 94 subjects with structural MRI scans, functional MRI data, behavioral and demographic data. DNA sampling has been performed on the subjects, but we cannot publish the genetic data due to regulatory rules.

Datasets have been acquired by the in-house Localizer project which initially planned to investigate inter-subject variability (Pinel et al., 2007). We have been collecting data from volunteer research subjects taking part in different studies carried out in our lab. The investigators of these studies agreed to provide behavioral and demographic data, anatomical MRI scans and DNA sampling. They also agreed to acquire a short fMRI sequence, approximately 5 min long, after their own functional imaging session, specifically for the Localizer project. We were thus able to collect data from a considerably larger number of volunteer research subjects than a single study could afford.

We have also been working on genetic neuroimaging in the context of the Brainomics project. We felt the need for a database that could index and expose heterogeneous data including MRI images, genetic data or behavioral data. We based our software developments on the CubicWeb semantic web framework and wrote specific CubicWeb modules to describe and visualize such heterogeneous data. We decided to build a Brainomics/Localizer demonstrator based on the Localizer dataset. The resulting database is now publicly available1 as well as the source code2.

We also viewed the Brainomics/Localizer demonstrator as an opportunity to study the feasibility of opening up individual health data as support material for scientific articles. Indeed regulatory rules differ from country to country and may hamper homogeneous publication of scientific data. We do not know of other public research databases of individual health information in France – and suspect there are very few – and we were only able to spot a couple public databases created for educational purposes, NeuroPeda3 being currently active. We have found lists of neuroscience databases4 which point to sites serving individual health data mostly hosted in the United States, with a few exceptions such as MIRIAD5 hosted in the United Kingdom. Differences in regulatory rules may partly explain this discrepancy.

Section snippets

De-identification of the database

The local ethics committee had initially approved the Localizer study. Starting the Brainomics/Localizer effort to open up Localizer data, we voluntarily limited ourselves to a subset of the whole Localizer dataset. We chose to publish data related to a previous publication (Pinel et al., 2012) based on the following criteria:

  • i)

    The dataset should be seen as support material for published scientific results.

  • ii)

    If at all possible, the dataset should depend on a single initial agreement with the ethics

Purpose of the database

Our database was designed to publish data from the Localizer project (Pinel et al., 2007) and more specifically the subset of 94 subjects examined in Pinel et al. (2012), and make it available to the broader scientific community. Our intent was to set up a demonstrator for the software we have developed in the context of our Brainomics project.

We provide a static set of data. In the short term we have no plans for adding data from other subjects of the Localizer study.

Available data

Of the hundreds of

Conclusion

We opened up heterogeneous data from 94 subjects of the Localizer project, selected for the completeness of available data including anatomical MRI scans, functional MRI data, behavioral and demographic data. DNA sampling cannot be made publicly available due to regulatory rules. The 94 datasets can be downloaded under a permissive license.

The data are made available on a dedicated server hosting the Brainomics/Localizer database. The database is built upon the CubicWeb semantic web framework

Acknowledgments

This work was supported by ANR-10-BINF-04. We thank Stanislas Dehaene for his participation to the creation of the Localizer database and Bernadette Martins for helping us navigate through regulatory rules. We thank the Inria-CEA Parietal team and in particular Virgile Fritsch for the Localizer data fetcher in NiLearn.

References (12)

  • B. Fischl

    Freesurfer

    NeuroImage

    (2012)
  • A. Abraham et al.

    Machine learning for neuroimaging with scikit-learn

    Front. Neuroinform

    (2014)
  • K.L. Ashwood et al.

    European clinical network: autism spectrum disorder assessments and patient characterisation

    Eur. Child Adolesc. Psychiatry

    (2015)
  • S. Gadde et al.

    XCEDE: an extensible schema for biomedical data

    Neuroinformatics

    (2012)
  • K. Gorgolewski et al.

    NeuroVault: a web repository for sharing statistical parametric maps, poster presented at OHBM 2014

  • D.S. Marcus et al.

    The extensible neuroimaging archive toolkit

    Neuroinformatics

    (2007)
There are more references available in the full text version of this article.

Cited by (15)

  • Asynchronous neural maturation predicts transition to psychosis

    2024, Psychiatry and Clinical Neurosciences
View all citing articles on Scopus
View full text