• isxavg-re-sess

Index

Name

isxavg-re-sess - computes random effects analysis for group data

Synopsis

isxavg-re-sess -analysis <analysisname> -contrast <contrastname> -group <gropuname> -space <spacename> -sf <subjectfilename>

Arguments

Positional Arguments

Required Flagged Arguments

Optional Flagged Arguments

Outputs

This will create a directory called <groupname> in the Study directory. This directory will have the same structure as an individual session and can be accessed with the –s and –sf flags as if it were an individual session. Inside this directory, you will see bold/analysisname/spacename-rfx in which there will be one volume, h-offset, in which the average EPI image is stored. Under this directory, there will be another directory called “contrastname” in which the results of the random-effects averaging are stored. Within this directory, five new volumes will be created: avg, t, sig, minsig, iminsig. The avg volume holds the averages and standard deviations that results from the application of the contrast and inter-subject averaging.

This will also create a subdirectory called "contrastname" under the bold/analysisname/spacename-rfx directory for each group. Within this directory, there will be 4 new volumes with stems "sig", "minsig", "iminsig" and "t". The variate-by-variate t-statistic is stored in the "t" volume and its significance in the "sig" volume. The variate is a time-point (or ideal hemodynamic curve). Multiple variates only apply to event-related design. In addition, the best variate (as measured by the minimum significance) at each voxel is also determined and stored as a map (after bonferroni correction) in the "minsig" volume. The index of the best variate is stored in the "iminsig" volume. In subsequent stages, you will be required to enter a map. This refers to one of these volumes.

Description

isxavg-re-sess is a program for intersubject averaging using a random-effects model (see isxavg-fe-sess for fixed effects model). It actually performs statistical analysis as well. It works by computing one number for each subject (at each voxel) based on the contrast matrix. This yields a set of numbers (at each voxel), one for each subject. The average and standard deviation of the set are computed and used to compute a t-statistic the siginficance of H_0: t=0$ is computed using N-1 degrees of freedom where N is the number of subjects. When jackknifing is turned on, a new set of N numbers is computed by successively averaging N-1 subjects while excluding a different subject each time. This reduces the effects of small sample sizes on the correctness of the siginficance levels. Jackknifing does increase the time to process data. It is highly recommended and the default in isxavg-re-sess. Note that isxavg-re can be run on any data set, not just the output of selxavg-sess.

Once the subjects have been resampled into a common space (Talairach or spherical), the averages from the individual subjects can be averaged together using a Random-Effects model (see isxavg-fe-sess for Fixed Effects averaging). You may want to combine all of your subjects into a grand average, or you may want to divide your subjects into groups and compute grand averages for each group. In any event, you will need to specify which subjects will be included in the average. This collection of subjects will be referred to as a group, and you will be required to assign each group a name (groupname) which will be needed for subsequent processing. Unlike Fixed-Effect averaging, you must also supply a contrast to test.

Examples

Example 1:Volume/Talairach

isxavg-re-sess -analysis <analysisname> -contrast <contrastname> -group <gropuname> -space tal -sf <subjectfilename>

Example 2: Spherical

isxavg-re-sess -analysis <analysisname> -contrast <contrastname> -group <gropuname> -space sph -spacedir <outdir_sphsmooth-sess> -sf <subjectfilename>

Bugs

None

See Also

paint-sess, surf-sess

Links

FreeSurfer, FsFast

Methods Description

References

References/Lastname###

Reporting Bugs

Report bugs to <analysis-bugs@nmr.mgh.harvard.edu>

Author/s

DougGreve, PhD