Group‐averaged anatomical connectivity mapping for improved human white matter pathway visualisation

Abstract

Anatomical connectivity mapping (ACM) is a measure of anatomical connectivity obtained by initiating streamline diffusion tractography from all parenchymal voxels and then counting the number of streamlines passing through each voxel of the brain. ACM highlights WM structures that present multiple connections to the rest of the brain but not necessarily strong microstructural orientation coherence. In this study, ACM was used to develop an atlas of the human brain. The ACM template was constructed from 3 T diffusion-weighted data from 19 healthy adults. To account for multiple diffusion directions in a voxel, a high angular resolution diffusion imaging (HARDI) technique, namely Q-ball, was used to model diffusion. To bring data from different subjects into a common space, an algorithm for rotating and averaging the principal directions was implemented, which can be generalized to any application requiring algebraic operations on principal directions derived from any HARDI method. ACM from the average dataset was computed and several white matter connections of interest were identified and highlighted. Fractional anisotropy (FA) from standard diffusion tensor modelling was also derived and FA-modulated colour coded images obtained from the mean tensor were also shown for comparison, highlighting differences and similarities. The ACM template can serve for educational purposes and as future reference for studies based on the evaluation of ACM in subjects affected by neurological and psychiatric disorders.