Software Development
Pick Atlas
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This software provides a method for generating ROI masks based on the Talairach Daemon database [1, 2]. The atlases include Brodmann area, Lobar, Hemisphere, Anatomic Label and Tissue Type. The atlases have been extended to the vertex in MNI space (see Atlas Modifications under Technical Notes), and corrected for the precentral gyrus anomaly (see reference 3 below). Additional atlases can be added without much difficulty. The toolbox was developed in the Functional MRI Laboratory at the Wake Forest University School of Medicine. Questions can be referred to maldjian@wfubmc.edu.
Current Release: 3.0.5
Prerequisite Software:
- Matlab 2008a (7.6) or better
- SPM12 or SPM8 revision 4010 or better (or no SPM)
We are announcing a major new release of the WFU_pickatlas software. Key enhancements include:
Note: If you use the Talairach Daemon atlas within the PickAtlas software, please make sure to appropriately reference the creators of the Talairach Daemon in addition to referencing the PickAtlas. The appropriate references are listed below:
Grant Start Date: 8/1/2008
Grant End Date: 7/31/2010
Principal Investigator: Joseph A. Maldjian, MD
Investigators and Personnel: Aaron Baer, Kathy Pearson, Benjamin Wagner
Funded By: NIBIB
Grant Numbers: 1R03EB008670
Resources
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User's Guide (PDF)
Developers Guide (PDF)
WFU Biological Parametric Mapping Toolbox
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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.
Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC Funded through R01EB004673 under the Human Brain Project and NIBIB
Reference: Ramon Casanova, Ryali Srikanth, Aaron Baer, Paul J. Laurienti, Jonathan H. Burdette, Satoru Hayasaka, Lynn Flowers, Frank Wood, and Joseph A. Maldjian
Biological parametric mapping: A statistical toolbox for multimodality brain image analysis
SHORT COMMUNICATION - NeuroImage Volume 34, Issue 1, 1 January 2007, Pages 137-143
Current Release: BPM Beta Release 1.5d
Requirements:
SPM2 or SPM5
MATLAB version 6.5 or higher
SPM8 Compatibility
BPM is not compatible with SPM8. The recommended way to analyze SPM8 data is to run BPM in SPM5 with the data that has been processed in SPM8.
BPM
Integrated Tool for Biological Parametric Mapping
Grant Start Date: 8/1/2004
Grant End Date: 5/31/2008
Principal Investigator: Joseph A. Maldjian, MD
Investigators and Personnel: Ramon Casanova Luis, PhD
Funded By: NIBIB
Grant Numbers: 1R01EB004673
Resources
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