FEAT - A Complete Tool for Model-Based FMRI Analysis

FEAT is an advanced GLM-based FMRI analysis tool with a straightforward but powerful GUI (graphical user interface), carrying out data preprocessing (including slice timing correction and MCFLIRT motion correction - see below); first-level GLM time-series analysis with prewhitening (FILM); registration to subject-specific structural images and standard space (FLIRT); and fully generalised mixed-effects group analysis using Bayesian estimation techniques (FLAME).

FEAT has been developed with two goals in mind; to use the most sophisticated image processing/statistical methodologies available (FILM, FLAME, FLIRT etc.), whilst at the same time making the user interface as intuitive and simple as possible, though allowing fully general/flexible analysis design. Complete analysis for a single simple FMRI experiment can often be set up in less than 1 minute, whilst a highly complex experiment typically 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 activation overlay images, activation cluster tables, time-course plots of data vs model, registration overlay images and an ``Analysis Methods'' paragraph describing the exact analysis carried out, including references). Multiple experiments having the same design can be analysed with a single setup. A complete setup can be saved to file, for easy reloading, amendment and re-running later, or to be used in script-based analysis of multiple experiments.

Figure 2: Example FEAT GUI snapshots. Top left: main GUI, with preprocessing section exposed. Bottom left: simple model wizard for regular block or single-event designs. Centre: model setup GUI for more complex designs enabling the selection of any number of covariates, various stimulus timing options, a variety of HRF convolution options (including basis functions), covariate orthogonalisation and any number of contrasts and f-tests. Right: design matrix and contrasts display showing covariates as the main columns with contrasts below.

In [31] 33 supposedly identical FMRI sessions from the same subject were analysed, partly to investigate the nature of session variability (as a follow-up to the original investigation using this data [28]), and partly to compare the different preprocessing, registration and time series statistical options in FEAT and SPM. The general conclusion (regarding the latter issue) was that both with respect to preprocessing/registration and time series modelling, less ``extra'' apparent session variability was induced by the FEAT processing modules, suggesting higher accuracy. Similar results were reported in [9].