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Abstract
While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that 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) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies 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.
Highlights
After more than a decade of intense interest and effort (Van Horn et al, 2001, 2005; Van Horn and Gazzaniga, 2002; Van Horn and Ishai, 2007), neuroimaging data repositories are becoming commonplace as are the means for populating them using methods of electronic data capture (Van Horn and Toga, 2009a,b)
EXAMPLE NEUROIMAGING DATA SETS AND PRE-PROCESSING STEPS To illustrate its use for dynamic interaction with large neuroimaging data, we demonstrate the application of INVIZIAN for rendering an archive of T1-anatomical magnetic resonance imaging (MRI) image volumes, collected from diverse clinical neuroimaging studies contained in the image and data archive (IDA)11 based at the Laboratory of Neuro Imaging (LONI) at UCLA
APPLICATION TO REVEAL WHOLE-BRAIN SIMILARITY CLUSTERING Instead of focusing on a specific region when investigating the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we focus our exploration on the relationship between www.frontiersin.org global gray matter thickness and meta-data values
Summary
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 populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. 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.
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