A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia
| DOI | https://doi.org/10.1108/DTA-12-2020-0303 |
| Published date | 01 November 2021 |
| Date | 01 November 2021 |
| Pages | 358-381 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Maren Parnas Gulnes,Ahmet Soylu,Dumitru Roman |
A graph-based approach for
representing, integrating and
analysing neuroscience data:
the case of the murine basal ganglia
Maren Parnas Gulnes
SINTEF AS, Oslo, Norway
Ahmet Soylu
Oslo Metropolitan University, Oslo, Norway, and
Dumitru Roman
SINTEF AS, Oslo, Norway
Abstract
Purpose –Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and
non-standard approaches and formats and often have no connection to the related data sources. These make it
difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show
that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related
data, which is highly interconnected, evolving over time and often needed in combination.
Design/methodology/approach –The authors present an approach for organising brain-related data in a
graph model. The approach is exemplifiedin the case of a unique data set of quantitativeneuroanatomical data
about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to
movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data
from third-party repositories, published through a Web-based user interface and API, analysed from
exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.
Findings–T he evaluation of the graph model and the results of the graph data analysis and usability study of the
user interface suggest that graph-based data management in the neuroscience domain is a promising approach,
since it enables integration of various disparate data sources and improves understanding and usability of data.
Originality/value –The study provides a practical and generic approach for representing, integrating,
analysing and provisioning brain-related data and a set of software tools to support the proposed approach.
Keywords Graph databases, Neuroscience, Brain-related data, Murine basal ganglia, Data integration, Data
analytics, Data visualisation
Paper type Research paper
1. Introduction
The brain is the organ humans rely on the most but understand the least. In order to
understand the brain’s structure and function, researchers need data. In this respect,
neuroscience data, primarily representing the features of the brain and information related to
brain-related research, has increased significantly over the past decade due to the advances in
technology (Fan and Markram, 2019). The data that exist about the brain already are in large
quanta, complex and spread across repositories in multiple formats. As an example of this
complexity, brain-related data can represent a part of the human brain’s 86 billion neurons,
and for each neuron, any of the approximately 7,000 connections (synapses) (Herculano-
Houzel, 2009;Drachman, 2005). The existing neuroscience data, however, are spread across a
variety of sources, typically provisioned through ad-hoc and non-standard approaches and
formats, and often have no connection to the related data sources (Bassett et al., 2018). These
primarily hinder the reuse, integration and sharing of data (Teeters et al., 2008), and it
DTA
56,3
358
This work was partly funded by the EC H2020 DataCloud project (Grant number 101016835).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 8 December 2020
Revised 16 April 2021
3 June 2021
Accepted 21 September 2021
Data Technologies and
Applications
Vol. 56 No. 3, 2022
pp. 358-381
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-12-2020-0303
becomes increasingly difficult for researchers to combine and use relevant data. Therefore,
there is a need to examine how neuroscience data can be modelled and stored to facilitate
integration and reuse. Existing research on managing brain-related information mostly
works towards the standardisation of metadata, aiming to make it easier for researchers to
find and reuse data (Amunts et al., 2016;Gardner et al., 2012;Sivagnanam et al., 2013). This
research stream, however, mainly focuses on metadata management for data sets and little on
managing the actual data.
In thisrespect, graph data modelsand databases provideperformance, flexibilityand agility
(Fernandes andBernardino, 2018) and open up the possibility of using well-established graph
analytics solutions (Needham and Hodler, 2019); however, there is little research on graph-
based data representation as a mechanismfor integration, analysis and reuse of neuroscience
data. Therefore,in this article, we address the followingresearch questions:
(1) Can graph-based representation of brain-related data facilitate the integration of data
from a variety of neuroscience data sets?
(2) Can a graph model provide a better understanding of the data in a brain-related data
set?
(3) To what extent can a graph-based approach to neuroscience data management
improve the usability of the data?
To this end, in this article, we show that a graph-based approach offers an effective mean for
representing, analysing and accessing brain-related data by presenting an approach for
organising brain-related data in a graph model enabling integration of various disparate data
sources and improving the understanding and usability of data. The approach is exemplified
in the case of a unique data set of quantitative neuroanatomical data about the murine basal
ganglia (Bjerke et al., 2020)—a group of nuclei in the brain essential for processing
information related to movement. Specifically, the murine basal ganglia data set is modelled
as a graph, integrated with relevant data from third-party repositories, published through a
Web-based user interface and API and analysed from exploratory and confirmatory
perspectives using popular graph algorithms to extract new insights. Through exploratory
and confirmatory analysis, we managed to find interesting findings and answer specific
questions. The evaluation of the graph model and the results of the graph data analysis and
usability study of the user interface suggest that graph-based data management in the
neuroscience domain is a promising approach. The study presented in this article provides a
practical and generic approach for representing, integrating, analysing and provisioning
brain-related data and a set of software tools to support the proposed approach.
The rest of the article is structured as follows. In section 2 provides the background
information, while section 3 presents the related work. Section 4 describes the data sets, and
section 5 presents the design and implementation of the proposed solution. Section 6 presents
the evaluation; while finally, section 7 concludes the article.
2. Background
In this section, we provide some background information on neuroscience and graph-based
data representation in order to facilitate comprehensibility of rest of the article for researchers
with different backgrounds (e.g. neuroscience and computer science).
2.1 Neuroscience
The brain is a large and complex organ that, together with the spinal cord, constitutes the
central nervous system (CNS) (Kandel et al., 2000). Neuroscience typically divides the brain
Analysing
neuroscience
data
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