In Summary

Cortical Thickness is based on ANTs software package. Please visit for a detailed description of their great work.

*If you 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 right hemispheres.

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

Cortical thickness derived from Patient’s T1 MRI are performed using BCBtoolkit (Foulon et al. 2018) that implemented the following steps. Before the calculation, in case of lesioned image, we create an enantiomorphic image (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. The estimation of the cortical thickness is then performed on the enantiomorphic image to avoid abnormal values and then the lesion is masked (indeed, the cortical thickness value inside the damaged area is irrelevant). A registration-based method (Diffeomorphic Registration based Cortical Thickness, DiReCT) was employed to estimate the cortical thickness (Das et al., 2009) from the T1-weighted imaging dataset. The first step of this method consists in creating a two voxel thick sheet, one which lies just between the grey matter and the white matter and a second lying between the grey matter and the cerebrospinal fluid. Then, the grey/white interface is expanded to the grey/cerebrospinal fluid interface using diffeomorphic deformation estimated with ANTs (Avants et al., 2007; Klein et al., 2009; Tustison & Avants, 2013). The registration produces a correspondence field that allows an estimate the distance between the grey/white and the grey/cerebrospinal fluid interfaces, and thus cortical thickness. This approach has good scan-rescan repeatability and good neurobiological validity as it can predict, with high statistical power the age and gender of the participants (Tustison et al., 2014).