=Paper=
{{Paper
|id=Vol-3805/ICBO-2022_paper_8589
|storemode=property
|title=NIDM-Experiment: An Ontology for Annotating Neuroscientific Data
|pdfUrl=https://ceur-ws.org/Vol-3805/ICBO-2022_paper_8589.pdf
|volume=Vol-3805
|authors=Karl G. Helmer, Derek Chaplin,Nazek Queder,Satrajit Ghosh,Camille Maumet,JeanBaptiste Poline, Theo van Erp, David Keator
|dblpUrl=https://dblp.org/rec/conf/icbo/HelmerCQGMPEK22
}}
==NIDM-Experiment: An Ontology for Annotating Neuroscientific Data==
https://ceur-ws.org/Vol-3805/ICBO-2022_paper_8589.pdf
NIDM-Experiment: An Ontology for Annotating Neuroscientific
Data
Karl G. Helmer1, Derek Chaplin1, Nazek Queder2, Satrajit Ghosh3, Camille Maumet4, Jean-
Baptiste Poline6, Theo van Erp2, David Keator2
1
Massachusetts General Hospital, Boston, MA, USA
2
University of California, Irvine, Irvine, CA, USA
3
Massachusetts Institute of Technology, MA, USA
4
University of Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
5
McGill University, Montreal, Canada
Abstract
NIDM-Experiment (NIDM-E) provides a collection of general and domain-specific terms that
can be used to annotate data from neuroscientific experiments. NIDM-E reuses terms from
existing ontologies and standards (such as DICOM and the Brain Imaging Data Standard) and
adds new defined domain-specific terms. NIDM-E was created by annotating existing datasets
and provides tools such as an online schema browser and term URI-resolution pages, and a
GitHub-based workflow for users to propose new terms and edits existing ones.
Keywords 1
neuroscience, ontology, data annotation, neuroimaging
1. Introduction used to describe neuroscience experiments and
the resulting data artifacts.
Efficacious reuse of data [1] relies on the
The goal of NIDM-E is to provide semantic-
capture and availability of information describing
web and other tools, a collection of defined terms
the acquisition and processing of that data. Crucial
that can be used to annotate data to an arbitrary
to data annotation are the use of defined terms, a
level of detail. Other, often used standards, such
practice that avoids ambiguities in interpretation,
as the Brain Imaging Data Structure (BIDS) [4],
and the reuse of terms from established, active
embed metadata into fixed directory structures
vocabularies and ontologies [2]. In addition, terms
and metadata file formats, and these can be
should be resolvable through a URL so that
restrictive when dealing with complicated study
automated methods and users are able to retrieve
or experimental configurations, such as multiple
information about each term from a single
acquisition modalities, multi-site studies, and
resource that is kept up to date. It is also critical
cutting-edge acquisition methods. NIDM-E can
that any ontology allow users to have input on its
provide a framework to both annotate
development with the goal that community
complicated experiments and data, as well as
adoption will be provide a feedback loop that
accommodate terms for new modalities and
drives further development. We report here on
acquisition methods. It also provides tools to find
recent developments in the Neuroimaging Data
Model-Experiment (NIDM-E) [3], an ontology
ICBO 2022, September 22-25, 2022, Ann Arbor, MI, USA.
EMAIL: khelmer@mgh.harvard.edu (A. 1); dchaplin1@
mgh.harvard.edu (A. 2); naqueder@gmail.com (A. 3);
satra@mit.edu (A. 4); camille@inria.fr (A. 5);
jbpoline@gmail.com (A. 6); tvanerp@hs.uci.edu (A. 7);
dbkeator@hs.uci.edu (A. 8)
ORCID: 0000-0002-5113-6843 (A. 1); none (A. 2); 0000-0001-
6268-862X (A. 3); 0000-0002-5312-6729 (A. 4); 0000-0002-
6290-553X (A. 5); 0000-0002-9794-749X (A. 6); 0000-0002-
2465-2797 (A. 7); 0000-0001-5281-5576 (A. 8)
2022 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
©️
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
terms, webpages for term URL resolution, and a We show in Fig. 1 a simple example of how
framework for community involvement. NIDM-E can annotate an acquisition object,
T1.nii, with an image contrast type of “T1-
weighted” and an image usage type of
2. Methods “Anatomical”, and showing the scan session
activity it was acquired at (“Session:Visit_2”), the
protocol that was used (“MyProtocol.pdf”), and
NIDM-E began by focusing on the processing
the study participant (“ID:a8d4j3”) from which it
and analysis of neuroimaging experiment data,
was acquired and who had the role of “In-Vivo
but has since expanded to encompass other Participant”.
neuroscientific modalities as well as more general
terms that describe experiments. NIDM-E was
NIDM-E is currently used as a term source by
built by annotating of several large real-world
the PyNIDM [10] package, a set of python-based
multi-modality neuroscientific data sets.
open-source data annotation tools that is both
customizable and extensible. Recently, PyNIDM
NIDM-E reuses terms from other ontologies
has been used to augment the data dictionary
such as the Semanticscience Integrated Ontology terms for the OpenNeuro data repository [11], the
(SIO) [5], Information Artifact Ontology (IAO) publicly-accessible repository for NIH’s BRAIN
[6] and Prov-O [7]. These more general ontologies
project MRI data and other related datasets.
provide the scaffold onto which domain-specific
terms can be added. Terms that are created for
NIDM-E is accessible by cloning the
NIDM-E include formal definitions in the “X is a
repository from GitHub [3]. Web-accessible
Y that Z” format [8]. NIDM-E also includes a
infrastructure has been built so that the
wide range of datatype, object, and annotation
neuroscientific community can suggest terms to
properties.
NIDM-E vocabulary, terms have resolvable
URI’s, and the ontology can be browsed to aid in
Because NIDM-E began in support of a term discovery. We use GitHub issue templates to
project to annotate neuroimaging data, it has
allow users to suggest new terms or edits to
particularly strong coverage in that domain. It existing ones. This allows us to have a record of
contains two unique properties:
discussions regarding a particular term and its
“hadImageContrastType” and resolution. To discover terms, we have provided a
“hadImageUsageType” that are used to “Schema Browser” webpage [12] that allows
distinguish between the physical mechanism for users to view the entire graph of NIDM-E terms
the contrast in an image volume (e.g., “T1- including all of the terms imported from other
weighted”) and the eventual application for that
ontologies. For semantic web applications, we
image (e.g., “Anatomical”). These are particularly
also have created a “Terms Resolution” page in
important for the discovery of data in and across which each term has a unique URL [13] so that
repositories, where datasets with different image terms by applications have a unique reference
contrasts may be annotated by usage. For
location.
example, T1-weighted, T2-weighted, and
diffusion-weighted images all may be stored as
“Anatomical” data. To further support the 3. Summary
annotation of neuroimaging data, NIDM-E also
includes terms from two widely used standards: NIDM-E is a flexible collection of classes and
DICOM [9] and the BIDS standards, which are properties that can be used to annotate a wide
ubiquitous in the neuroimaging domain over range of neuroscientific data, with a strong current
multiple imaging modalities. We have created a focus on neuroimaging. It provides tools to help
set of datatype properties, each representing a users discover terms and provides a resolvable
specific DICOM tag, which can be used to URL for each term. It also provides a workflow
associate acquisition parameters with an for the community to suggest new terms and edits
acquisition object. We have also included BIDS to existing ones.
terms so that datasets that are organized according
to the BIDS standard can annotated using BIDS-
approved terms.
[5] M. Dumontier, C. J.O. Baker, J. Baran, A.
Callahan, L. Chepelev, J. Cruz-Toledo, N. R
Del Rio, G. Duck, L. I. Furlong, N. Keath, D.
Klassen, J. P. McCusker, N. Queralt-
Rosinach, M. Samwald, N. Villanueva-
Rosales, M. D. Wilkinson, .t Hoehndorf. The
Semanticscience Integrated Ontology (SIO)
for biomedical research and knowledge
discovery. J. Biomed. Semantics 5: 14
(2014).
[6] W. Ceusters. An information artifact
ontology perspective on data collections and
associated representational artifacts. Stud
Health Technol Inform. 180:68-72 (2012).
[7] PROV-O: The PROV Ontology. URL:
https://www.w3.org/TR/prov-o/
[8] S. Seppalla, A Ruttenberg, B. Smith.
Guidelines for writing definitions in
ontologies. Ciência da Informação 46 (1):
Figure 1: Simple example showing how NIDM-E 73-88 (2017).
can annotate an acquisition object, showing the [9] DICOM. URL:
scan session it was acquired at and from what https://www.dicomstandard.org/
study participant. [10] incf-nidash/PyNIDM. URL:
https://github.com/incf-nidash/PyNIDM.
4. Acknowledgements [11] OpenNeuro. URL: https://openneuro.org/
[12] NIDM-Experiment Schema Browser. URL:
https://incf-nidash.github.io/nidm-
We acknowledge support from NIH grant
experiment/schema_menu.html
1RF1MH120021-01 and ongoing support from
[13] NIDM Experiment Term Resolution Page.
the International Neuroinformatics Coordinating
URL: https://incf-nidash.github.io/nidm-
Facility.
experiment/
5. References
[1] J-B Poline, J.L. Breeze, S. Ghosh, K.
Gorgolewski, Y. O. Halchenko, M. Hanke,
C. Haselgrove, K. G. Helmer, D. B. Keator,
D. S. Marcus, R. A. Poldrack, Y. Schwartz,
J. Ashburner, D. N. Kennedy. " Data sharing
in neuroimaging research." Front
Neuroinform. (2012) 6: 9.
doi:10.3389/fninf.2012.00009..
[2] R. Arp, B. Smith, A. D. Spear. Building
Ontologies with Basic Formal Ontology,
MIT Press, 2015.
https://doi.org/10.7551/mitpress/978026252
7811.001.0001.
[3] incf-nidash/nidm-experiment: This
repository contains the terms used in nidm-
experiment and the code to create the term-
resolution and term-schema webpages. URL:
https://github.com/incf-nidash/nidm-
experiment.
[4] Brain Imaging Data Structure. URL:
https://bids.neuroimaging.io/