If you have a compute cluster you can send the whole command to the cluster by preceding it with something like fsl_sub -q long.q . When running fix as shown above, you will end up with a cleaned version of the 4D preprocessed FMRI data: filtered_func_data_. It is strongly recommended that you look at the ICA components yourself to check at least a few of your subjects' classifications - look in the file called something like fix4melview_Standard_thr20.txt - the final line lists the components that are considered as noise to be removed (with counting starting at 1 not 0). However, if it is very important to you that almost no good components are removed, and hence you would prefer to leave in the data a larger number of bad components, then use a low threshold (e.g., in the range 1-5). The 20 refers to the thresholding of good vs bad components sensible values are generally in the range of 5-20. For the ICA you should in general use MELODIC's automatic dimensionality estimation. If using FEAT, you need to have had ICA turned on in the Prestats. You need to feed in a full "first-level" (single-session) output directory created by the MELODIC or FEAT GUIs, with full registration run, including using a structural. usr/local/fix/fix YOURFEAT.feat /usr/local/fix/training_files/Standard.RData 20 To run: use the script "fix" in the FIX directory, e.g.: See the README file for further setup instructions Unpack FIX with tar xvfz (or tar xvf fix.tar if your browser has already uncompressed the file). MATLAB (though see above.), with official toolboxes:.While a few example training files are supplied with FIX, for major studies we would strongly recommend training FIX on your study data (see below for more details). No need to rerun feature generation from v1.05 for use in v1.06, but the old training files cannot be used with v1.06 (and any custom training files will need regenerating).įor FIX to work well, it is very important that it is run using good "training data". ![]() The other change from v1.05 is a change in the top-level meta-classifier, which gives a tiny average improvement in classification accuracy. This latest version (1.06) can now be run without Matlab, using either the supplied precompiled-matlab binaries, or with Octave. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. ![]() ![]() Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers.
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