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

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, a...

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Autores principales: Anderson, Robert J., Long, Christopher M., Calabrese, Evan D., Robertson, Scott H., Johnson, G. Allan, Cofer, Gary P., O’Brien, Richard J., Badea, Alexandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8081353/
https://www.ncbi.nlm.nih.gov/pubmed/33928076
http://dx.doi.org/10.3389/fphy.2020.00088
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author Anderson, Robert J.
Long, Christopher M.
Calabrese, Evan D.
Robertson, Scott H.
Johnson, G. Allan
Cofer, Gary P.
O’Brien, Richard J.
Badea, Alexandra
author_facet Anderson, Robert J.
Long, Christopher M.
Calabrese, Evan D.
Robertson, Scott H.
Johnson, G. Allan
Cofer, Gary P.
O’Brien, Richard J.
Badea, Alexandra
author_sort Anderson, Robert J.
collection PubMed
description 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.
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spelling pubmed-80813532021-04-28 Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions Anderson, Robert J. Long, Christopher M. Calabrese, Evan D. Robertson, Scott H. Johnson, G. Allan Cofer, Gary P. O’Brien, Richard J. Badea, Alexandra Front Phys Article 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. 2020-04-21 2020-04 /pmc/articles/PMC8081353/ /pubmed/33928076 http://dx.doi.org/10.3389/fphy.2020.00088 Text en https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Article
Anderson, Robert J.
Long, Christopher M.
Calabrese, Evan D.
Robertson, Scott H.
Johnson, G. Allan
Cofer, Gary P.
O’Brien, Richard J.
Badea, Alexandra
Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title_full Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title_fullStr Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title_full_unstemmed Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title_short Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions
title_sort optimizing diffusion imaging protocols for structural connectomics in mouse models of neurological conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8081353/
https://www.ncbi.nlm.nih.gov/pubmed/33928076
http://dx.doi.org/10.3389/fphy.2020.00088
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