Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the population of large-scale neuroimaging databases. As they do, a particular challenge lies in examining and...

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Bibliographic Details
Main Authors: Ian eBowman, Shantanu H Joshi, John eVan Horn
Format: Article
Language:English
Published: Frontiers Media S.A. 2012-04-01
Series:Frontiers in Neuroinformatics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2012.00011/full
Description
Summary:While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the population of large-scale neuroimaging databases. As they do, a particular challenge lies in examining and interacting with the information these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the Informatics Visualization for Neuroimaging (INVIZIAN) program for the graphical rendering of and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, describe its development, and demonstrate its use to examine a collection of over 900 T1-anatomical MRI image volumes from across a diverse set of clinical neuroimaging studies and drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining.
ISSN:1662-5196