Front Neuroinformatics. 2012, 6:11. doi: 10.3389/fninf.2012.00011

Visual systems for interactive exploration and mining of large-scale neuroimaging data archives.

Bowman, I, Joshi SH, Van Horn JD
Laboratory of Neuro Imaging, CA, USA.


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.


Front Neuroinformatics. 2009;3:38. Epub 2009 Nov 6.

Interactive exploration of neuroanatomical meta-spaces.

Joshi SH, Van Horn JD, Toga AW.
Laboratory of Neuro Imaging, Department of Neurology, CA, USA.


Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain’s surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data.

PMID: 19915734 PMCID: PMC2776489


ISBI 2011


Shantanu H. Joshi, Ian Bowman, Arthur W. Toga, and John D. Van Horn

We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions
are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled
by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly
avoids pairwise registrations between brains, but instead focuses on modeling and discriminating between the cortical
structural patterns. We demonstrate our approach on 120 subject brains from an Alzheimer’s dataset, and present applications
to clustering, classification, and dimension reduction.

Index Terms— cortical distributions, Jensen-Shannon divergence, clustering, dimension reduction


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