Abstract
Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.
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Acknowledgements
We are particularly grateful to Dr. Michael Lustig at University of California, Berkley, for his toolbox for compressed sensing reconstruction. This work was supported by the NIH/NIBIB National Biomedical Technology Resource Center (P41 EB015897 to GA Johnson), NIH 1S10OD010683-01 (to GA Johnson), 1R01NS096720-01A1 (to GA Johnson) and NIA (AG041211 to A Badea). The authors thank James Cook and Lucy Upchurch for significant technical support. The authors thank Sally Zimney and Tatiana Johnson for editorial comments on the manuscript.
Funding
NIH P41 EB015897, 1R01NS096720-01A1, 1S10OD010683-01, 5K01 AG041211.
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Wang, N., Anderson, R.J., Badea, A. et al. Whole mouse brain structural connectomics using magnetic resonance histology. Brain Struct Funct 223, 4323–4335 (2018). https://doi.org/10.1007/s00429-018-1750-x
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DOI: https://doi.org/10.1007/s00429-018-1750-x