BrainVoyager

• Extremely fast and highly optimized 2D and 3D image analysis and visualization routines
• Easy to install and configure: built-in support for major data formats
• Standard and advanced volume-based statistical analysis methods including conjunction and random effects analysis for single and group analysis
• Analysis of block and event-related designs
• Easy selection of regions-of-interest and display of time courses
• Integration of volume and surface rendering with powerful tools for creation of high-quality figures and movies
• Advanced methods for automatic brain segmentation, surface reconstruction, cortex inflation and flattening
• Cortex-based statistical data analysis (cbGLM) and inter-subject alignment based on gyral / sulcal pattern
• Cortex-based Independent Component Analysis (cbICA)
• Creation and visualization of EEG / MEG multiple dipole models (fMRI "seeding" with link to BESA 2000)
• Multi-processor support, for ultimate performance
• Open architecture via COM interface, including scripting and automation

BrainVoyager is designed for high performance, ease of use and flexible data processing. BrainVoyager offers a comprehensive set of analysis and visualization tools which start its operation on raw data (2D structural and functional matrices) and produce beautiful visualizations of the obtained results. All software features are available via an intuitive Windows interface.

Data analysis includes preprocessing (motion correction, Gaussian spatial and temporal data smoothing, linear trend removal, filtering in the frequency domain), correlation analysis, determination of Talairach coordinates, volume rendering, surface rendering and cortex flattening.

Parametric and non-parametric statistical maps may be computed and superimposed both on the original functional scans as well as onto T1-weighted 2D or 3D anatomical reference scans. Time courses of selected regions-of-interest (ROIs) are available both in 2D and 3D representations. Statistical maps may be computed either in the 2D or 3D representation since structural as well as functional 4D data (space x time) are transformed into Talairach space. This allows you to compare activated brain regions across different experiments and across different subjects.

Talairach transformation is performed in two steps. The first step consists of rotating the 3D data set for each subject to be aligned with the stereotaxic axes. For this step the location of the anterior commissure (AC) and the posterior commissure (PC) as well as two rotation parameters for midsagittal alignment has to be specified interactively. In the second step the extreme points of the cerebrum are specified. These points together with the AC and PC coordinates are then used to scale the 3D data sets into the dimensions of the standard brain of the Talairach and Tournaux atlas.

Segmentation of tissue (e.g., isolating the brain, differentiating gray and white matter) is performed using region-growing methods, filter operations as well as the application of 3D templates. Using the mouse it is very easy to explore a 3D volume with superimposed pseudocolor-coded statistical maps in a four-window representation showing a sagittal, coronal, transversal and oblique section. Based on a (segmented) 3D data set a three-dimensional reconstruction of the subjects' head and brain can be calculated and displayed from any specified viewpoint using volume or surface rendering.

Volume rendering is performed with a very fast ray casting algorithm; lightning calculations are based on Phong-shading. Surface rendering of reconstructed surfaces is performed using OpenGL. Using texture mapping, a reconstructed surface (e.g., head or brain) may be sliced in real time, showing both surface and volume data at the same time. Initial polygon meshes serve as the basis for surface finding, cortex inflation and cortex flattening computations.

The surface reconstruction procedure starts with a sphere (recursively tesselated icosahedron) or a rectangle, which slowly wraps around a (segmented) volume data set. In order to avoid topological defects and to let the surface smoothly grow into deep sulci, a dynamic mesh algorithm was developed which automatically invents new polygons on the fly at places where they are needed. A reconstructed cortical surface may be inflated, cut interactively and slowly unfolded minimizing areal distortions. Statistical 3D maps may be superimposed on reconstructed, inflated or flattened cortex. Signal time courses may be invoked by simply pointing to any region of a visualized surface.

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