Example GUI view

Introduction

For other information on FEAT and updated journal references, see the FEAT web page. If you use FEAT in your research, please quote the journal references listed there.

This is the user guide for FEAT version 5. We recommend switching to this from FEAT4, unless you want to finish analysing a dataset which you have already part-analysed with FEAT4. To use FEAT5 either press the FEAT button in the FSL mini-GUI or type Feat at the command line. To use FEAT4 type Feat4 at the command line. Note that one of the major new features in FEAT5 is advanced group stats - there is no problem in feeding existing first-level FEAT4 analyses into FEAT5 group stats.

FEAT is a software tool for high quality model-based FMRI data analysis, with an easy-to-use graphical user interface (GUI). FEAT is part of FSL (FMRIB's Software Library). FEAT automates as many of the analysis decisions as possible, and allows easy (though still robust, efficient and valid) analysis of simple experiments whilst giving enough flexibility to also allow sophisticated analysis of the most complex experiments.

Analysis for a simple experiment can be set up in less than 1 minute, whilst a highly complex experiment need take no longer than 5 minutes to set up. The FEAT programs then typically take 10-30 minutes to run (per first-level session), producing a web page analysis report, including colour activation images and time-course plots of data vs model.

The data modelling which FEAT uses is based on general linear modelling (GLM), otherwise known as multiple regression. It allows you to describe the experimental design; then a model is created that should fit the data, telling you where the brain has activated in response to the stimuli. In FEAT, the GLM method used on first-level (time-series) data is known as FILM (FMRIB's Improved Linear Model). FILM uses a robust and accurate nonparametric estimation of time series autocorrelation to prewhiten each voxel's time series; this gives improved estimation efficiency compared with methods that do not pre-whiten.

FEAT saves many images to file - various filtered data, statistical output and colour rendered output images - into a separate FEAT directory for each session. If you want to re-run the final stages of analysis ("contrasts, thresholding and rendering"), you can do so without re-running any of the computationally intensive parts of FEAT, by telling FEAT to look in a FEAT directory for all of the raw statistical images which it needs to do this. The results of this re-run of the final stages either overwrite the old ones, or optionally are put in a new FEAT directory, named similarly to the original FEAT directory, but with an extra "+" included in the name.

FEAT can also carry out the registration of the low resolution functional images to a high resolution scan, and registration of the high resolution scan to a standard (e.g. Talairached) image. Registration is carried out using FLIRT.

For higher-level analysis (e.g. analysis across sessions or across subjects) FEAT uses FLAME (FMRIB's Local Analysis of Mixed Effects). FLAME uses very sophisticated methods for modelling and estimating the random-effects component of the measured inter-session mixed-effects variance, using MCMC sampling to get an accurate estimation of the true random-effects variance and degrees of freedom at each voxel.

There is a brief overview of GLM analysis in Appendix A and an overview of how the design matrix is setup in FEAT in Appendix B.

Example colour rendered output     Example time series plot