SYNOPSIS
froi is a suite of tools for ROI analysis of FS-FAST data. As well, froi provides other functions, such as mapping of ROIs to the surface, mapping of labels to ROIs, creation and viewing of selectivity maps, creation of labels from slice-based statistical contrasts, combination of labels, and creation of overlap maps for multiple ROIs.
DESCRIPTION
Freesurfer and FS-FAST (Freesurfer Functional Analysis Stream) are software suites developed at the Massachusetts General Hospital imaging center for the analysis of functional MRI (fMRI) data. FS-FAST provides for the analysis of slice-based fMRI data, including pre-processing (motion correction with AFNI tools, image normalization, spatial smoothing), design specification (including gamma and finite impulse response (FIR) models), contrast specification, and statistical map creation. Freesurfer provides for the reconstruction of 3D volume- and 2D surface-based models of a subject's brain. Data from the slices can then be mapped onto a previous-session anatomical or a 2D surface, such as an inflated or flattened representation. The surface can also be registered into a ``spherical'' coordinate system, providing for less distortions than a Talairach transformation. **insert freesurfer and fs-fast refs**
The advantages of FS-FAST and Freesurfer over other fMRI analysis packages are numerous. The tight integration of the two suites provides for relatively easy transformation of data from one representation to another. As well, the majority of the programs are command-line based, allowing for easy scriptability and transparency to the user (something that is more difficult with SPM). In addition, unlike SPM, there is no need for an individual subject's brain to be automatically smoothed and transformed into Talairach space; all analyses can be done in the subject's native slices.
However, FS-FAST is lacking one major feature, that being the analysis of regions of interest (ROIs). ROI analyses are an important part of functional MRI research. **insert old nancy refs** An ROI approach would proceed as follows:
1. Define a functionally-specific region through a localizer scan. For example, you might be interested in areas that respond more to faces than to objects, and to narrow it down further, the fusiform face area (FFA) **insert FFA refs**
2. Perform an experiment that tests for your condition of interest; in this case, you might be interested in whether or not the FFA responds different to upright faces versus inverted faces.
3. Using the ROI defined in 1., look to see if there are any differences between conditions in 2.
The advantages of an ROI approach are numerous. For one, because each subject's anatomy is different, simple anatomical ROIs are not sufficient for most analyses. Defining an independent functional ROI allows your analysis to be tailored to the ideosyncrasies of the particular subject's brain. As well, since you are only looking in a small region, you do not have the problems associated with whole brain analyses. ** are there problems? i think there are... **
To this end, I wrote a suite of programs to do functional ROI analyses in conjunction with FS-FAST and Freesurfer called froi: FS-FAST ROI. The meat of the suite is to allow one to functionally define ROIs and then extract information from the ROIs, such as mean time-course (for blocked data) and mean and percent signal change for each condition (along with the associated errors). ROIs are saved in a space-efficient format and can be edited quite easily. The results of an ROI computation can be viewed using a Matlab GUI and are saved in a Matlab structure. In addition, there is an option to create tab-delimitted text files of the results for importation into other programs like Excel or SPSS.
The functional definition of an ROI can be done in three ways: by t value, by significance (sig) value, and by false discovery rate (FDR). Defining an ROI by t or sig value is done in a simple threshold fashion; all values greater (or lower, in the case of negative values) are taken to be in the ROI. For FDR, you provide a specific false discovery rate and a hypothesis of how you think the noise is correlated and all voxels above the calculated p value are included in the ROI.
Such ROIs should not be taken verbatim into the subsequent statistical analysis, however. Many times the ROIs will include scattered voxels throughout your slices, or may contain multiple functional regions that you would like to analyze separately. After masking the image with a t, p, or FDR threshold, you need to edit the thresholded map in order to define the appropriate ROIs. Once these ROIs are created, you are ready for further statistical analysis.
Data anlysis within the ROI uses the results of the selxavg program of FS-FAST. selxavg computes the estimated deviation from baseline for each condition for each voxel in your volume. Depending on the type of analysis (either gamma or FIR), you may have only one point for each condition (gamma), or a time-course (FIR). ROI analyses proceeds by averaging these data over your ROI and computing the appropriate standard deviation and standard error. These results can then be viewed or taken to a third level of analysis, such as a group analysis.
froi provides many other ROI functions. For example, it can combine multiple ROIs (up to three) by intersection or union. As well, it can create a map of overlap for ROIs (up to three). Finally, it can create a Òselectivity mapÓ for one condition versus another, thus allowing you to see how the selectivity of voxels looks across your slices.
Slice-based ROIs provide a simple solution to the problem of converting your data to another coordinate system: there is no problem. However, there are sometimes when you might want to view an ROI in a different coordinate system, for example, to look at stability across sessions or to perform a group analysis. froi provides scripts to do these things. It can take an ROI and convert it into a Freesurfer label while also taking a label and converting it to a froi ROI. As well, it can combine labels in much the same way as you can combine ROIs; however, as of the current release, it only works for two labels and only for intersection or union.
EXAMPLE
make_roi -analysis blocked -contrast faces-objects -roi sigover4 -sf sessid -df sessdir -threshold 4
edit_roi -analysis blocked -roi ffa -sf sessid -df sessdir
compute_roi -analysis event -roianalysis blocked -roi ffa -sf sessid -df sessdir
BUGS
None known of. Send bug reports to <froi-bugs@sourceforge.net>.
AUTHOR
Nicholas Knouf, <nknouf@mit.edu>, <nknouf@mimeme.net>
ACKNOWLEDGEMENTS
The creators of FS-FAST and Freesurfer for creating a damn fine piece of software; froi of course wouldn't exist if it weren't for their work.
Doug Greve for answering questions on the martinos-tech list when I was in the early stages of this project as well as providing some of the code in this distribution.
Chris Baker, Jia Liu, Mike Mangini, and Galit Yovel for useful comments and for being (un)willing pre-alpha/alpha/beta testers.
Nancy Kanwisher for letting me work on this project and hearing me say many times, ``I just want to get the code done!''
COPYRIGHT & LICENSE
Copyright (c) 2003, Nicholas Knouf, MIT.
This program is free software and is governed by the terms of the Artistic License.
REVISION
$Id: froi,v 1.2 2003/10/06 16:03:47 nknouf Exp $
SEE ALSO
make_roi, edit_roi, compute_roi, view_roi_results, combine_roi, display_overlap, roi2label, label2roi, make_label, combine_label, view_sm, list_roi