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...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2007, Vol.34 (1), p.137-143
Main Authors: Casanova, Ramon, Srikanth, Ryali, Baer, Aaron, Laurienti, Paul J., Burdette, Jonathan H., Hayasaka, Satoru, Flowers, Lynn, Wood, Frank, Maldjian, Joseph A.
Format: Article
Language:English
Subjects:
Brain - anatomy & histology
Brain - physiology
Brain Mapping - methods
Brain research
GLM
Humans
Hypothesis testing
Methods
Multimodal analysis
Neurosciences
NMR
Nuclear magnetic resonance
Software
SPM
Statistics as Topic
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Summary: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.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2006.09.011