FUNCON PREPROCESSING: HOW DOES IT WORK?


In Summary

The preprocessing module of Funcon aims to correct the head-motion and artifacts from fMRI data using features from FSL library.


!!! IMPORTANT !!!

You need to have FSL installed if you want to run this module.

To install FSL please visit : https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation and follow instructions.

*If you choose to add lesion masks, the cortical thickness will be calculated on the enantiomorphic (Nachev et al. 2008) transformation of the T1 (We replace the lesioned area by the healthy tissue of the spared hemisphere) to avoid artifacts during the calculation and then the lesioned region is removed (because the measure of the cortical thickness inside a damaged area is not relevant). Be careful, the enantiomorphic transformation cannot be used in case of lesions in the left and the right hemispheres.



PAPER METHOD SECTION (feel free to edit or copy and paste)


Correction, Registration and seed based connectivity analyses are performed using BCBtoolkit (Foulon et al. 2018) that implemented the following steps:

fMRI images are first motion corrected using MCFLIRT (Jenkinson et al., 2002), then corrected for slice timing, smoothed with a full half width maximum equal to 1.5 times the largest voxel dimension and finally filtered for low temporal frequencies using a gaussian-weighted local fit to a straight line. These steps are available in Feat as part of FSL package (Woolrich et al., 2009).

In case of lesioned image, we create the enantiomorphic transformation (Nachev et al. 2008): Each patient lesions or signal abnormalities due to the lesion is replaced symmetrically by the healthy tissue of the contralateral hemisphere.

fMRI images are linearly registered to the (enantiomorphic in case of lesioned images) T1 images, and subsequently to the MNI152 template (2mm) using affine transformation. Confounding signals are discarded from fMRI by regressing out a confound matrix from the functional data. The confound matrix included the estimated motion parameters obtained from the previously performed motion correction, the first eigenvariate of the white matter and cerebrospinal fluid as well as their first derivative. Eigenvariates can easily be extracted using fslmeants combined with the --eig option. White matter and cerebrospinal fluid eigenvariates are extracted using masks based on the T1 derived 3-classes segmentation thresholded to a probability value of 0.9, registered to the Rs-fMRI images and binarized. Finally, the first derivative of the motion parameters, white matter and cerebrospinal fluid signal is calculated by linear convolution between their time course and a [-1 0 1] vector.

Since the resting-state fMRI signal can be heavily affected by motion, even following motion correction between temporally adjacent volumes (Van Dijk et al. 2012), we estimated the signal fluctuation associated with motion and regressed it out from the fMRI data. To this aim, we employed a recently developed and validated procedure based on data-driven Independent Component Analysis (ICA), termed ICA-Aroma (Pruim et al. 2015). This method performs an ICA decomposition of the data and estimates which components reflect motion-related noise in the fMRI signal on the basis of a robust set of spatial and temporal features. This is made possible due the distinctiveness of the motion-related components isolated by ICA on the fMRI signal (Salimi-Khorshidi et al. 2014). This approach outperforms other methods such as the regression of the motion parameter estimates, while limiting in the same time the loss in degrees of freedom (Pruim et al. 2015). Compared to spike removal methods such as scrubbing (Power et al. 2012), ICA-Aroma has the advantage of preserving the temporal structure of the fMRI signal.