Biological Parametric Mapping: A Statistical Toolbox for Multimodality Brain Image Analysis

Neuroimage. 2007 Jan 1;34(1):137-43. doi: 10.1016/j.neuroimage.2006.09.011. Epub 2006 Oct 27.

Abstract

In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / anatomy & histology*
  • Brain / physiology*
  • Brain Mapping / methods*
  • Humans
  • Software*
  • Statistics as Topic