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• W2152559989 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." @default.
• W2152559989 created "2016-06-24" @default.
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• W2152559989 date "2007-01-01" @default.
• W2152559989 modified "2023-10-11" @default.
• W2152559989 title "Biological parametric mapping: A statistical toolbox for multimodality brain image analysis" @default.
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• W2152559989 doi "https://doi.org/10.1016/j.neuroimage.2006.09.011" @default.
• W2152559989 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1994117" @default.
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