Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions

• Published in: Frontiers in physics 2020-04, Vol.8
• Main Authors: Anderson, Robert J, Long, Christopher M, Calabrese, Evan D, Robertson, Scott H, Johnson, G Allan, Cofer, Gary P, O'Brien, Richard J, Badea, Alexandra
• Format: Article
• Language: English
• Subjects: brain; connectivity (graph theory); diffusion imaging; mouse model; MRI; neurodegenerative diseases
• ISSN: 2296-424X; 2296-424X
• DOI: 10.3389/fphy.2020.00088

Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with