Figure 1.

The connectome construction pipeline used in this study. (A) A standard Glasser atlas was established using 300 healthy individuals from the Human Connectome Project (HCP). A supervised machine learning algorithm was employed to recognise connectivity patterns for each of the 360 HCP parcels in a healthy cohort. (B) Using diffusion sequences, we applied constrained spherical deconvolution (CSD) tractography to our patient cohort. Using these images, our algorithm was applied to recognise and adjust the locations of HCP parcels in highly atypical brains. (C) After establishing maximal likely structural connectivity, we used this data to inform and constrain functional connectivity using resting-state fMRI. (D) Finally, structural and functional anomaly matrices were generated to compare network connectivity differences (i.e. language) between our patient and a normative atlas. Adopted with permission from Reference²³.