TY - JOUR
T1 - Biological parametric mapping
T2 - A statistical toolbox for multimodality brain image analysis
AU - Casanova, Ramon
AU - Srikanth, Ryali
AU - Baer, Aaron
AU - Laurienti, Paul J.
AU - Burdette, Jonathan H.
AU - Hayasaka, Satoru
AU - Flowers, Lynn
AU - Wood, Frank
AU - Maldjian, Joseph A
N1 - Funding Information:
This work was supported by the Human Brain Project and NIBIB through grant number EB004673, and in part by NS042568, and P01-HD-21887. We would like to thank Dr. Ann Peiffer and Ms. Christina Hugenschmidt for their extensive beta-testing of the BPM toolbox and Ms. Kathy Pearson for help with computer programming.
PY - 2007/1/1
Y1 - 2007/1/1
N2 - 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.
AB - 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.
KW - GLM
KW - Multimodal analysis
KW - SPM
UR - http://www.scopus.com/inward/record.url?scp=33751111992&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33751111992&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2006.09.011
DO - 10.1016/j.neuroimage.2006.09.011
M3 - Article
C2 - 17070709
AN - SCOPUS:33751111992
SN - 1053-8119
VL - 34
SP - 137
EP - 143
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -