Pycortex: an interactive surface visualizer for fMRI.

Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software

Use of Remote Surface Based Tools for Visualizing Integrated Brain Imaging Data

We describe a surface-based approach to 3D visualization of integrated neuroimaging data. Our web-enabled software allows researchers to use these visualization tools over the Internet. We present examples of brain imaging studies where such remote surface-based visualization techniques have proven to be quite effective

The Connectome Visualization Utility: Software for Visualization of Human Brain Networks

In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires sophisticated visualization and analysis software. The current availability of software packages to analyze the human connectome is limited. The Connectome Visualization Utility (CVU) is a new software package designed for the visualization and network analysis of human brain networks. CVU complements existing software packages by offering expanded interactive analysis and advanced visualization features, including the automated visualization of networks in three different complementary styles and features the special visualization of scalar graph theoretical properties and modular structure. By decoupling the process of network creation from network visualization and analysis, we ensure that CVU can visualize networks from any imaging modality. CVU offers a graphical user interface, interactive scripting, and represents data uses transparent neuroimaging and matrix-based file types rather than opaque application-specific file formats

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end

Experiences with Matlab and VRML in Functional Neuroimaging Visualizations

Introduction Neuroinformatics is the task of organizing, analyzing and presenting the knowledge of neuroscience. In the part of neuroinformatics that is associated with functional neuroimaging ("brain mapping") visualization plays an important role. Functional neuroimaging utilizes brain scanners to examine the inner workings of the human brain, usually by mapping the relationship between cognitive components and specialized brain regions. fMRI and PET brain scanners can produce sequential volumetric scalar data. The functional neuroimaging study sometimes incorporate a complicated design making the "time axis" multi-dimensional. This data is seldom viewed directly, but instead it is put through rigorous processing, the result being one or more scalar volumes. Center of mass points of "hot spot" areas are tabelized and included in scientific publications as so-called "Talairach-coordinates" [1]. A database exists with these coordinates: The BrainMap database [2]. The typical vis

Gaze-dependent topography in human posterior parietal cortex.

The brain must convert retinal coordinates into those required for directing an effector. One prominent theory holds that, through a combination of visual and motor/proprioceptive information, head-/body-centered representations are computed within the posterior parietal cortex (PPC). An alternative theory, supported by recent visual and saccade functional magnetic resonance imaging (fMRI) topographic mapping studies, suggests that PPC neurons provide a retinal/eye-centered coordinate system, in which the coding of a visual stimulus location and/or intended saccade endpoints should remain unaffected by changes in gaze position. To distinguish between a retinal/eye-centered and a head-/body-centered coordinate system, we measured how gaze direction affected the representation of visual space in the parietal cortex using fMRI. Subjects performed memory-guided saccades from a central starting point to locations “around the clock.” Starting points varied between left, central, and right gaze relative to the head-/body midline. We found that memory-guided saccadotopic maps throughout the PPC showed spatial reorganization with very subtle changes in starting gaze position, despite constant retinal input and eye movement metrics. Such a systematic shift is inconsistent with models arguing for a retinal/eye-centered coordinate system in the PPC, but it is consistent with head-/body-centered coordinate representations