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.
Casanova
Ramon
R
Advanced Neuroscience Imaging Research (ANSIR) Laboratory, Department of Radiology, Wake Forest University School of Medicine, USA. casanova@wfubmc.edu
Srikanth
Ryali
R
Baer
Aaron
A
Laurienti
Paul J
PJ
Burdette
Jonathan H
JH
Hayasaka
Satoru
S
Flowers
Lynn
L
Wood
Frank
F
Maldjian
Joseph A
JA
D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D012984:Software; D013223:Statistics as Topic