fScan Reference Manual, Chapter 1 (Topics): Differences
fScan differs from other fMRI analysis software in 3 important ways: 1) organization of multimodal data in coherent XML-based “workspace” descriptor files and the ability to compare
two workspaces simltaneously, 2) integration and interoperability of fScan’s large repertoire of intrinsic processing features with the ability to run programs and read data from other software packages all within a single fScan interactive session, 3) emphasis on data visualization capabilities, multimodal data integration, and a comprehensive and user-friendly graphical user interface.
1) Workspaces – A workspace is a collection of any number of related data_sets. Within a workspace, all spatial data can be registered to a common spatial coordinate system so that
any location in one data set can be directly mapped onto any other data set. Examples of spatial data can be a single 2-dimensional JPEG image, or a large 3-D, 4-D (or even 5-D or 6-D) series of MR images. It can also be 3D surface reconstruction graphics data set, DTI 3-D graphics fiber tracking data, or 3D network connectivity data generated from either DTI or functional connectivity analyses.
A workspace also binds multiple time series data sets to a common temporal coordinate system. For
example, it can link stimulus and behavioral response timing files, physiological oscillation data, eye-tracking data, and fMRI functional imaging data so that the relative timing is
accurately known across all data collected for each brain scan. All information describing a workspace can be saved in a single XML text file. fScan can load two different workspaces
simultaneously and the spatial coordinate systems of both can be registered, which then allows any data set in one workspace to be overlaid or compared to spatial data in the other.
Originally designed to facilitate comparison between a subject exam workspace and a standard brain atlas workspace, this ability can also prove invaluable for comparing and quantifying changes in brain function (or structure) across multiple scan sessions of a single subject.
2) Interoperability – fScan's processing capabilities are not limited to its own internally programmed features. It also supports in-line execution of other software packages. For
example, to use FSL's motion correction program (MCFLIRT) a user simply clicks an icon in the “FSL” taskbar to start a a shell script that automatically reformats the appropriate data file
to NIfTI format if necessary, runs the MCFLIRT program, and then returns to the waiting fScan process to automatically load the newly created data set into a new fScan window. New data sets produced in that way are automatically registered to the common workspace coordinate system.
This shell scripting option provides enormous flexibility to combine fScan's own internal features with complementary features available in other software packages. Simple graphical taskbars currently provide access to most major FSL package features and provide full integration of FreeSurfer 3-D brain reconstruction data. Additional interoperability taskbars are being added to integrate AFNI (Cox et al., 1996) software programs as well as DTI programs and data structures.
3) Visualization environment – fScan’s taskbar GUI interface is designed to be intuitively easy to use, and fScan's help features are simple and always available via direct access to a web-
based Wiki that can be easily kept up-to-date. By providing immediate access to all data components via a common workspace, a simple graphical interface to processing features in
fScan and multiple other programs, and extensive data visualization tools fScan can make functional brain mapping very accessible even to non-expert users. The result is a
powerful brain image analysis platform that will provide the foundation for thorough image data analyses. It cab also be used as a valuable teaching tool for providing students and others
with direct access to collected data and to brain mapping methods more generally.
Compared to other analysis packages, fScan provides only relatively simple statistical support. SPM, FSL, and AFNI all provide a variety of sophisticated statistical analysis tools that fScan doesn't offer. Instead, fScan relies on its interoperability features to make it possible to perform analyses using those other programs and then integrate the results into fScan workspaces for visualization and additional processing.
Another fScan weakness is that it provides only limited support for combining multiple subject data sets into higher-level statistical analyses of populations. Simple operations such as generating group mean and variance maps are suppoorted, but for anything more complicated we again rely on using other software packages as needed.