The overarching goal of this project is to investigate and discover correlational relationships of different regions of the brain. Evidence for these relationships are gathered by PET-imaging a number of human subjects, both under baseline conditions and under the influence of a drug, in our case Ativan. The correlations in the brain activity are calculated on the basis of predefined anatomical regions-of-interest (ROIs), for now modeled as spherical regions. The correlation coefficient is then employed to quantify similarity in response, for various regions during an experimental setting. To account for inter-human anatomical variability, each test subject’s volumetric brain data is first transformed into a common anatomical coordinate system (e.g, Talairach-Tournoux). Statistical parameters that can be used to characterize various brain functions include:

  • The correlation (including the Pearson product-moment correlations, the partial correlations and the canonical correlations) matrix..
  • The ROI clusters from the cluster analysis.
  • The principal components and the factor analysis output. (The PCA and the FA are similar in their function and output, but different in their assumptions and derivations).
  • Differential relationships such as the difference of two correlation matrices (to view the change of functional relationships).
  • The times series.

The amount of statistical data can be enormous, and effective tools are essential for the brain researcher to grasp and discover functional relationships quickly from the statistical data. BrainMiner is a visualization tool that facilitates this task.



  • Nora Volkow (BNL Medical Group)
  • Klaus Mueller (Computer Science)
  • Wei Zhu (Applied Mathematics and Statistics) (Alumnus)


  • Tom Welsh and Jeffrey Meade (CS) (Alumni)
  • Juan Li, Radha Panini, and Shurou (Sue) Wu (AMS) (Alumni)


Viewing in 2D



Fig. 1: The circular ROIs are colored according to their correlation with respect to a root-ROI, marked by a red cross. The rainbow color scheme is used, where the color blue stands for highly negative correlation and the color red stands for highly positive correlation. Green and yellow stand for mildly negative and positive correlations, respectively. There are apparently no extremely strong correlations in this configuration.

The 2D approach works well as long as the axial dimension is not important. However, the decomposition of the dataset into 2D slices for visualizing 3D relationships becomes limiting when relationships are widely spread over the brain.

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