Use FSL - Analysis Tools - PRE-STATS
Slice timing correction corrects each voxel's time-series for the fact that later processing assumes that all slices were acquired exactly half-way through the relevant volume's acquisition time (TR), whereas in fact each slice is taken at slightly different times. Slice timing correction works by using (Hanning-windowed) sinc interpolation to shift each time-series by an appropriate fraction of a TR relative to the middle of the TR period. It is necessary to know in what order the slices were acquired and set the appropriate option here. If slices were acquired from the bottom of the brain to the top select Regular up. If slices were acquired from the top of the brain to the bottom select Regular down. If the slices were acquired with interleaved order (0, 2, 4 ... 1, 3, 5 ...) then choose the Interleaved option. If slices were not acquired in regular order you will need to use a slice order file or a slice timings file. If a slice order file is to be used, create a text file with a single number on each line, where the first line states which slice was acquired first, the second line states which slice was acquired second, etc. The first slice is numbered 1 not 0. If a slice timings file is to be used, put one value (ie for each slice) on each line of a text file. The units are in TRs, with 0.5 corresponding to no shift. Therefore a sensible range of values will be between 0 and 1.
You will normally want to apply Motion correction; this attempts to remove the effect of subject head motion during the experiment. MCFLIRT uses FLIRT (FMRIB's Linear Registration Tool) tuned to the problem of FMRI motion correction, applying rigid-body transformations. Note that there is no "spin history" (aka "correction for movement") option with MCFLIRT. This is because this is still a poorly understood correction method which is under further investigation.
By default BET brain extraction is applied to create a brain mask from the first volume in the FMRI data. This is normally better than simple intensity-based thresholding for getting rid of unwanted voxels in FMRI data. Note that here, BET is setup to run in a quite liberal way so that there is very little danger of removing valid brain voxels. If the field-of-view of the image (in any direction) is less than 30mm then BET is turned off by default.
Spatial smoothing is carried out on each volume of the FMRI data set separately. This is intended to reduce noise without reducing valid activation; this is successful as long as the underlying activation area is larger than the extent of the smoothing. Thus if you are looking for very small activation areas then you should maybe reduce smoothing from the default of 5mm, and if you are looking for larger areas, you can increase it, maybe to 10 or even 15mm. To turn off spatial smoothing simply set FWHM to 0.
Intensity normalisation forces every FMRI volume to have the same mean intensity. For each volume it calculates the mean intensity and then scales the intensity across the whole volume so that the global mean becomes a preset constant. This step is normally discouraged - hence is turned off by default. When this step is not carried out, the whole 4D data set is still normalised by a single scaling factor ("grand mean scaling") - each volume is scaled by the same amount. This is so that higher-level analyses are valid.
Highpass temporal filtering uses a local fit of a straight line (Gaussian-weighted within the line to give a smooth response) to remove low frequency artefacts. This is preferable to sharp rolloff FIR-based filtering as it does not introduce autocorrelations into the data. Lowpass temporal filtering reduces high frequency noise by Gaussian smoothing (sigma=2.8s), but also reduces the strength of the signal of interest, particularly for single-event experiments. It is not generally considered to be helpful, so is turned off by default. By default, the temporal filtering that is applied to the data will also be applied to the model.
The MELODIC option runs the ICA (Independent Component Analysis) tool in FSL. We recommend that you run this, in order to gain insight into unexpected artefacts or activation in your data. "