Interactive exploration of neuroanatomical meta-spaces

Abstract

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.

Keywords: 3D visualization; meta-analysis; neuroanatomical data mining; visual data mining.

Figure1

A schematic of the data mining workflows exposed through the LONI pipeline (Rex et al., 2003). The workflow is divided into three parts, i) Processing, ii) Feature Extraction – extracting anatomical features such as cortical surfaces, sub-cortical structures etc. and having 3D mesh representations for each feature, and iii) Feature Analysis – calculating the local curvature, shape index, cortical complexity, and encoding each surface mesh with these attributes. Each stage is implemented via pipeline without user intervention.

Figure 2

Surface rendering of segmented sub-cortical structures labeled according to regions. (A) Examples of image slices along the axial view. (B–D) Parcellated cortical and sub-cortical regions along three views.

Figure 3

An illustration of distributions of brain surfaces in an atlas meta-space. The atlas can be treated as the origin. The locations of the brain surfaces are derived using MDS applied to the distance matrix of discriminative features. A radial coordinate system is shown for convenience, in practice any other informative reference frame can be used.

Figure 4

(A) Visualization of the pair-wise distance matrix for N = 400 subjects of the ADNI dataset. (B) MDS projection of the dissimilarity matrix into 3D coordinates, each projection colored according to subject status, NC = blue, AD = red, MCI = yellow.

Figure 5

A snapshot of the 3D Visualization environment for the neuro- meta-space displaying a group of brain surfaces from the ADNI 400 dataset. (A) A zoomed-in view of the 3D interface. (B) A close-up of an individual brain belonging to the AD category. (C) Alternate view of the interface with the meta-data (green text) displayed, as a result of a right-click action on one of the brains.