Skip to main content

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

Publication ,  Journal Article
Anderson, RJ; Long, CM; Calabrese, ED; Robertson, SH; Johnson, GA; Cofer, GP; O'Brien, RJ; Badea, A
Published in: Front Phys
April 2020

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 <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Front Phys

DOI

ISSN

2296-424X

Publication Date

April 2020

Volume

8

Location

Switzerland

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Anderson, R. J., Long, C. M., Calabrese, E. D., Robertson, S. H., Johnson, G. A., Cofer, G. P., … Badea, A. (2020). Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. Front Phys, 8. https://doi.org/10.3389/fphy.2020.00088
Anderson, Robert J., Christopher M. Long, Evan D. Calabrese, Scott H. Robertson, G Allan Johnson, Gary P. Cofer, Richard J. O’Brien, and Alexandra Badea. “Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions.Front Phys 8 (April 2020). https://doi.org/10.3389/fphy.2020.00088.
Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, et al. Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. Front Phys. 2020 Apr;8.
Anderson, Robert J., et al. “Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions.Front Phys, vol. 8, Apr. 2020. Pubmed, doi:10.3389/fphy.2020.00088.
Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, O’Brien RJ, Badea A. Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. Front Phys. 2020 Apr;8.

Published In

Front Phys

DOI

ISSN

2296-424X

Publication Date

April 2020

Volume

8

Location

Switzerland

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences