AutomaticAnalysisRecipes - MRC CBU Imaging Wiki

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Automatic Analysis Recipes

These apply to the development version, soon to be released (Sep 2006).

Choosing a recipe

This first section describes available recipes. The next section discusses issues that might help you choose the appropriate one for you.

Issues

Order of slice timing and motion correction

Slice timing correction involves resampling through time but not moving images, while motion correction involves moving images relative to each other. There is no simple answer to which should be done first. If slice timing is done first, then if there are abrupt movements between scans, the interpolation across time will be blurring across voxels from different parts of the brain in its correction. If motion correction is done first, then the slices in the image no longer always correspond to order of acquisition of the slices.

If you use an interleaved slice order during acqusition (no longer the default at the CBU - see [http://imaging.mrc-cbu.cam.ac.uk/imaging/TipsForDataAcquisition#head-050cc11391e60b6442119a9e05cb0936e1659bf1 discussion of slice order] then you will probably want to apply slice timing before motion correction. Because all of the odd-numbered slices are acquired first, and the all the even ones, adjacent slices are acquired apart in time (0.5 * TR = around a second). A shift in the image by motion correction of 1 slice (4 mm) will cause a substantial shift in acqusition time for a particular slice, and slice timing will not be at all appropriately applied.

If you use a sequential slice order during acquisition, many people prefer to apply motion correction before slice timing. I do not know if this has been evaluated in any thorough way.

Slice timing or not?

Some people prefer to not use slice timing, and instead to put the temporal derivative at the modelling stage. Rik Henson (ref.?) has shown the effectiveness of this method, for TR of 2s.

Realign and unwarp or just realign?

Jesper Anderson wrote a tool that models how the brain changes in shape with small motions due to distortion in the image. If there are substantial distortions in your images this may be useful. However, unless you are testing patients with clips, or have some other reason to expect distortions, this tool is probably not vital.

Normalisation using segement

By default, normalisation is now performed using the semgent tool.

AA modelling?

The newer releases of aa allow scripted first level modelling, first level contrasts, and second level modelling. The first level modelling requires you to do a little coding, to specify your design. Unless you are the one neuroimager on the planet that gets the design of the analysis of their experiment exactly right first time, I would recommend this - you'll find it a little effort now for a big gain later.