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

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.

Pubmed ID: 17070709


  • Casanova R
  • Srikanth R
  • Baer A
  • Laurienti PJ
  • Burdette JH
  • Hayasaka S
  • Flowers L
  • Wood F
  • Maldjian JA



Publication Data

January 1, 2007

Associated Grants

  • Agency: NIBIB NIH HHS, Id: EB004673
  • Agency: NINDS NIH HHS, Id: K08 NS042568
  • Agency: NINDS NIH HHS, Id: NS042568
  • Agency: NICHD NIH HHS, Id: P01-HD-21887

Mesh Terms

  • Brain
  • Brain Mapping
  • Humans
  • Software
  • Statistics as Topic