A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia

Abstract

Purpose: Neuroscience data is spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats, and often has 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. Approach: We present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical 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: 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, since it enables integration of various disparate data sources, and improves understanding and usability of data. Originality: 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