Electronic Data Capture, Representation, and Applications for Neuroimaging

Abstract

With growing emphasis on data sharing across neuroimaging studies in humans, both within and across institutions and studies, there is a growing awareness of the need for clear connections between the images and the other data about the subject, and between the images and the behavioral or physiological data that is relevant for analysis (Van Horn and Toga, 2009a). Means for electronic data capture, storage, organization, and levels of interactivity are required for the large and rich datasets which modern neuroimaging studies now generate and seek to analyze (Helmer et al., 2011). Beyond brain imaging volumes, researchers are including detailed meta-data on scan data types, cognitive protocols, phenomics, and graphical renderings as part of shareable dataset. Capturing these data using automated and semi-automated means represents a particular challenge for subsequent examination, mining, and visualization (Figure ​(Figure1).1). The aim of this Frontiers in Neuroinformatics special topic was developed to sample from the current state of the art in automated and semi-automated data collection methods, data management, and their practical applications in neuroimaging research and related studies. Figure 1 Electronic data capture and efficient representation of neuroimaging enables comprehensive data mining and compelling visualization which, in turn, contributes to greater data sharing and openness in the brain sciences. These articles, from experts in data capture, management, and visualization, provide an overview of the ways different research teams have addressed similar issues across the various stages of data capture in large-scale neuroimaging studies. Articles focus on how databases are populated, how to make sure that the information so gathered is accurate, and how to manage it so that it can be successfully communicated to others. Specifically, these articles address: Automated and semi-automated data capture Two of the papers deal directly with specific methods for data collection during the study. Voyvodic et al. (this issue) present a software package that has been applied in several studies both locally and across multiple sites. The CIGAL software makes a point of capturing behavioral and physiological data during experimental tasks in a way that is interpretable both during the experiment and afterward, and usable in single and multi-site neuroimaging or other behavioral studies. The timing of the various events during the experiment can be extracted from the resulting text files for storage in a database, translation into XML, or use in analysis pipelines. Both the behavioral/physiological data capture of CIGAL and the clinical assessments capture of CARAT (Turner et al., 2010), make a point of representing the data in a way that is research-friendly and works with arbitrary data management systems. The CARAT package for collecting clinical measurements allows the user to connect to various databases to store the demographic and other data automatically, without need for transferring data from paper records.