ICA-AROMA (i.e. ‘ICA-based Automatic Removal Of Motion Artifacts’) concerns a data-driven method to identify and remove motion-related independent components from fMRI data. To that end it exploits a small, but robust set of theoretically motivated features, preventing the need for classifier re-training and therefore providing direct and easy applicability. This package requires an installation of Python and FSL. Read the provided 'Manual.pdf' for a description on how to run ICA-AROMA. Make sure to first install all required python packages: pip install -r requirements.txt.

NOTE: Python 3.6 is strongly recommended.

! NOTE: Previous versions of the ICA-AROMA scripts (v0.1-beta & v0.2-beta) contained a crucial mistake at the denoising stage of the method. Unfortunately this means that the output of these scripts is incorrect! The issue is solved in version v0.3-beta onwards. It concerns the Python scripts uploaded before the 27th of April 2015.

Log report (applied changes from v0.2-beta to v0.3-beta):

1. Correct for incorrect definition of the string of indices of the components to be removed by fsl_regfilt:

   changed denIdxStr = np.char.mod('%i',denIdx) to denIdxStr = np.char.mod('%i',(denIdx+1))

2. Now take the maximum of the 'absolute' value of the correlation between the component time-course and set of realignment parameters:

   changed maxTC[i,:] = corMatrix.max(axis=1) to corMatrixAbs = np.abs(corMatrix) maxTC[i,:] = corMatrixAbs.max(axis=1)

3. Correct for the fact that the defined frequency-range, used for the high-frequency content feature, in few cases did not include the final Nyquist frequency due to limited numerical precision:

   changed step = Ny / FT.shape[0] f = np.arange(step,Ny,step) to f = Ny*(np.array(range(1,FT.shape[0]+1)))/(FT.shape[0])