Summary: Tests comparing image sets can play a critical role in PET research, providing a yes-no answer to the question "Are two image sets different?" The statistical goal is to de termine how often observed differences would occur by chance alone. We examined randomization methods to provide several omnibus test for PET images and compared these tests with two currently used methods. In the first series of analyses, normally distributed image data were simulated fulfilling the require ments of standard statistical tests. These analyses generated power estimates and compared the various test statistics under optimal conditions. Varying whether the standard deviations Tests comparing image sets can play a critical role in PET research. These tests provide a simple yes or no answer to the question "Are two image sets different?" before more qualitative or quantitative questions are asked about where the differences lie within the images. Sometimes an omnibus test provides a trivial answer to an obvious question: for instance, judging whether there is any difference in regional cerebral blood flow (rCBF) between two different cognitive task (A and B) condi tions. In this case the omnibus test might reject the null or no-difference hypothesis, which absurdly conjectures that brain metabolism does not depend on cognition, and the test may be only ceremonial. On the other hand, an omnibus test sometimes provides important information. For example, a straightforward contrast might be ger mane when comparing images from two different lan guage reading conditions in bilingual subjects. 1271were local or pooled estimates provided an assessment of a distinguishing feature between the SPM and Montreal methods. In a second series of analyses, we more closely simulated cur rent PET acquisition and analysis techniques. Finally, PET im ages from normal subjects were used as an example of ran domization. Randomization proved to be a highly flexible and powerful statistical procedure. Furthermore, the randomization test does not require extensive and unrealistic statistical as sumptions made by standard procedures currently in use. Key Words: Statistical tests-Methodology-Randomization.An omnibus test to compare position emission tomog raphy (PET) images is based on the notion of a signifi cance test The significance test has a specific and fun damental meaning in statistics: measuring the probability that an observed result could have occurred by chance. For instance, a frequently asked question is whether the differences between two sets of images suggest a signifi cant change. Task B might be a memory task using a 5-s retention interval while task A uses a 60-s retention in terval involving recognizing a word from a previously memorized list Suppose that the area with the largest difference is 10% higher during task B in the left frontal region. The question arises as to whether this observed difference could have occurred by chance. Conceivably, there could be no real activation effect at all, yet random fluctuations could...

Currently, there are many choices of software packages for the analysis of fMRI data, each offering many options. Since no one package can meet the needs of all fMRI laboratories, it is helpful to know what each package offers. Several software programs were evaluated for comparison of their documentation, ease of learning and use, referencing, data input steps required, types of statistical methods offered, and output choices. The functionality of each package was detailed and discussed. AFNI 2.01, SPM96, Stimulate 5.0, MEDIMAX 2.01, and FIT were tested. FIASCO, Yale, and MEDx 2.0 were described but not tested. A description of each package is provided.

No abstract

Currently, there are many choices of software packages for the analysis of fMRI data, each offering many options. Since no one package can meet the needs of all fMRI laboratories, it is helpful to know what each package offers. Several software programs were evaluated for comparison of their documentation, ease of learning and use, referencing, data input steps required, types of statistical methods offered, and output choices. The functionality of each package was detailed and discussed. AFNI 2.01, SPM96, Stimulate 5.0, MEDIMAX 2.01, and FIT were tested. FIASCO, Yale, and MEDx 2.0 were described but not tested. A description of each package is provided.

No abstract