Name

["selxavg3-sess"]

Synopsis

["selxavg3-sess"] sf <subjectfilename> df <srchdirfile> analysis <sem_assoc> [options]

Arguments

Required Arguments:

-analysis <analysisname>	name of functional analysis that you created under the analysis flag in ["mkanalysis-sess.gui"]
-sf <sessidfile>	text file list of subjects
-df <srchdirfile>	text file list of directories where subjects can be found
-d <srchdir>	use instead of df if specifying only one dir

Optional Arguments

-skip	skip session of already analyzed
-overwrite	delete analysis if session of already analyzed
-svres	saves residuals (usually not needed)
-perrun	analyze each run separately
-no-fwhm	don't compute FWHM
-float	use single precision float instead of double
-outparent dir	save output to this dir instead of in session
-version	print version and exit

Outputs

This will create a subdirectory with the same name as your analysisname under the bold directory in subjects directory. This folder has all of the results for this analysis, including:

beta.nii - regression coefficients
rvar.nii - residual error variance
mask.nii - mask (copy of bold/masks/brain.nii)
meanfunc.nii - mean functional image
fsnr.nii - functional SNR map
X.mat - design matrix (in matlab format)
dof - text file with degrees of freedom
fwhm.dat - text file with smoothness estimate (Full-Width/Half-Max)
contrast folders:
- Each contrast folder contains:
  - ces.nii - contrast effect size (contrast matrix * regression coef)
  - cesvar.nii - variance of contrast effect size
  - sig.nii - significance map (-log10(p))

Description

General Description

["selxavg3-sess"] computes the average signal intensity maps for each condition for each individual subject. This program separately analyzes the data in each session indicated in the sessid file, then computes the average signal intensity maps for each condition. This average data can be further processed on an individual basis and/or can be used for group analyses. This also compute contrasts for testing hypotheses based on a GeneralLinearModel (GLM), including t and F statistics, significances of those statistics, and contrast effects sizes (ces).This is the new version of stxgrinder, implicit intensity normalization, better whitening

Bugs

none

Links

FsFast

Methods Description

References