DISCONNECTOME MAPS: HOW DOES IT WORK?


In Summary

This approach uses a set of 10 healthy controls (Thiebaut de Schotten et al., 2017) diffusion weighted imaging datasets to track fibers passing through each lesion. For each participant tractography was estimated as indicated in (Thiebaut de Schotten et al., 2011). You can increase this dataset up to 35 participants downloading preprocessed data package available in the Opendata section (see boost my disconnectome)













Figure 1: Disconnectome maps step by step

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


Disconnectome maps were calculated using BCBtoolkit (Foulon et al. 2018).This approach uses a set of 10 healthy controls (Rojkova et al., BSF 2015) diffusion weighted imaging datasets to track fibers passing through each lesion. For each participant tractography was estimated as indicated in (Thiebaut de Schotten et al., 2011). Patients' lesions in the MNI152 space are registered to each control native space using affine and diffeomorphic deformations (Klein et al., 2009; Avants et al., 2011) and subsequently used as seed for the tractography in Trackvis (Wang et al., 2007). Tractographies from the lesions were transformed in visitation maps (Thiebaut de Schotten et al., 2011), binarised and brought to the MNI152 using the inverse of precedent deformations. Finally, we produce a percentage overlap map by summing at each point in MNI space the normalized visitation map of each healthy subject. Hence, in the resulting disconnectome map, the value in each voxel take into account the interindividual variability of tract reconstructions in controls, and indicate a probability of disconnection from 0 to 100% for a given lesion (Thiebaut de Schotten et al., 2015).