Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in pre-processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n=294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome; its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ϱ>0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different pre-processing choices can influence connectome organization. Highlights - We assess how diffusion preprocessing affects hubs across 1760 pipeline variations - Many preprocessing pipelines do not show a high concentration of connectivity within hubs - When present, hub location and distribution vary based on preprocessing choices - Use of probabilistic or deterministic tractography has a major impact on hub location and strength - Node strength in weighted networks can correlate highly with node size