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Software Packages that are currently available for download from the ANSIR Laboratory at Wake Forest University School of Medicine

• WFU Biological Parametric Mapping Toolbox
• WFU_PickAtlas
• WFU_DICOMtk
• Non-Stationary Cluster Extent Correction for SPM
• Adaptive Staircase Procedure for E-Prime
• MoJoe
• IDL_SPM
• IDL_Dicom_Toolkit

WFU Biological Parametric Mapping Toolbox

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

WFU_PickAtlas

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: 2.4

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:

 Non-Stationary Cluster Extent Correction for SPM

 This toolbox implements the random field theory (RFT) version of cluster size inference under non-stationarity [1].

Non-stationarity, or non-uniform smoothness, is a problem in cluster size inference of brain imaging data. This is because, under non-stationarity, clusters tend to be large in smooth areas and small in rough areas within the image data. Non-stationarity is particularly problematic in VBM (voxel-based morphometry) data, and a use of cluster p-values has been discouraged in analyses of such data [2].

A solution for this non-stationarity problem was proposed by Worsley et al [3], and has been implemented in the FMRISTAT package. Accounting for non-stationarity is important as it could lead to an erroneous outcome of a VBM analysis, as demonstrated in Moorhead et al [4]. We have ported the function for non-stationary cluster size inference from FMRISTAT to SPM, so that it can interface with the SPM output.

References

[1]. Hayasaka S, Phan K L, Liberzon I, Worsley K J & Nichols T E. Nonstationary cluster-size inference with random field and permutation methods. NeuroImage 22: 676-687 (2004)
[2]. Ashburner J & Friston K J. Voxel-based morphometry --- the methods. NeuroImage 11: 805-821 (2000)
[3]. Worsley K J, Andermann M, Koulis T, MacDonald D & Evans A C. Detecting changes in nonisotropic images. Human Brain Mapping 8: 98-101 (1999)
[4]. Moorhead T W J, Job D E, Spencer M D, Whalley H C, Johnstone E C & Lawrie S M. Empirical comparison of maximal voxel and non-isotropic adjusted cluster extent results in a voxel-based morphometry study of comorbid learning disability with schizophrenia. NeuroImage 28: 544-552 (2005)

 Adaptive Staircase Procedure for E-Prime

This package provides the framework for implementing a self-adapting staircase procedure within the E-Prime programming environment. Although staircases have been in use for decades, the few open-source examples currently available require a moderate degree of programming proficiency to integrate into a new experimental procedure.

Here, we provide an example developed within the E-Prime that can be easily copied or manipulated for almost any experimental paradigm. It is based on the simple 2-down/1-up method, with adjustable step sizes (if s\desired) and experimenter-definable parameters. See the accompanying documentation for more detailed information on its use.

Current Release: 1.1

Reference:

 Hairston WD, Burdette JH, Flowers DL, Wood FB, Wallace MT (2005) Altered temporal profile of visual-auditory multisensory interactions in dyslexia. Exp Brain Res 166: 474-480

Notes:

Developed in E-Prime 1.2. Currently not tested for E.P 2.0 and higher.

MoJoe

IDL_SPM

IDL_Dicom_Toolkit

Please read the LICENSE agreement.   You will be asked to accept its terms before you can download the software.