Biological parametric mapping: A statistical toolbox for multimodality brain image analysis

Ramon Casanova, Ryali Srikanth, Aaron Baer, Paul J. Laurienti, Jonathan H. Burdette, Satoru Hayasaka, Lynn Flowers, Frank Wood, Joseph A Maldjian

Research output: Contribution to journalArticlepeer-review

241 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)137-143
Number of pages7
JournalNeuroImage
Volume34
Issue number1
DOIs
StatePublished - Jan 1 2007

Keywords

  • GLM
  • Multimodal analysis
  • SPM

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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