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

• Published in: Harvard data science review 2023-12, Vol.7 (4), p.1326-1350
• Main Authors: Gajwani, Mehul, Oldham, Stuart, Pang, James C, Arnatkevičiūtė, Aurina, Tiego, Jeggan, Bellgrove, Mark A, Fornito, Alex
• Format: Article
• Language: English
• Subjects: Algorithms; Decision making; Hubs; Magnetic resonance imaging; Substantia alba; Young adults
• ISSN: 2472-1751; 2472-1751; 2644-2353
• DOI: 10.1162/netn_a_00324

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population ( = 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 ( > 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 processing choices can influence connectome organization.