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Accuracy and reliability of diffusion imaging models

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain’s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled indiv...

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Autores principales: Seider, Nicole A., Adeyemo, Babatunde, Miller, Ryland, Newbold, Dillan J., Hampton, Jacqueline M., Scheidter, Kristen M., Rutlin, Jerrel, Laumann, Timothy O., Roland, Jarod L., Montez, David F., Van, Andrew N., Zheng, Annie, Marek, Scott, Kay, Benjamin P., Bretthorst, G. Larry, Schlaggar, Bradley L., Greene, Deanna J., Wang, Yong, Petersen, Steven E., Barch, Deanna M., Gordon, Evan M., Snyder, Abraham Z., Shimony, Joshua S., Dosenbach, Nico U.F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841915/
https://www.ncbi.nlm.nih.gov/pubmed/35339687
http://dx.doi.org/10.1016/j.neuroimage.2022.119138
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author Seider, Nicole A.
Adeyemo, Babatunde
Miller, Ryland
Newbold, Dillan J.
Hampton, Jacqueline M.
Scheidter, Kristen M.
Rutlin, Jerrel
Laumann, Timothy O.
Roland, Jarod L.
Montez, David F.
Van, Andrew N.
Zheng, Annie
Marek, Scott
Kay, Benjamin P.
Bretthorst, G. Larry
Schlaggar, Bradley L.
Greene, Deanna J.
Wang, Yong
Petersen, Steven E.
Barch, Deanna M.
Gordon, Evan M.
Snyder, Abraham Z.
Shimony, Joshua S.
Dosenbach, Nico U.F.
author_facet Seider, Nicole A.
Adeyemo, Babatunde
Miller, Ryland
Newbold, Dillan J.
Hampton, Jacqueline M.
Scheidter, Kristen M.
Rutlin, Jerrel
Laumann, Timothy O.
Roland, Jarod L.
Montez, David F.
Van, Andrew N.
Zheng, Annie
Marek, Scott
Kay, Benjamin P.
Bretthorst, G. Larry
Schlaggar, Bradley L.
Greene, Deanna J.
Wang, Yong
Petersen, Steven E.
Barch, Deanna M.
Gordon, Evan M.
Snyder, Abraham Z.
Shimony, Joshua S.
Dosenbach, Nico U.F.
author_sort Seider, Nicole A.
collection PubMed
description Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain’s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927–1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL’s BedpostX [BPX], DSI Studio’s Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3’s Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI
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spelling pubmed-98419152023-01-16 Accuracy and reliability of diffusion imaging models Seider, Nicole A. Adeyemo, Babatunde Miller, Ryland Newbold, Dillan J. Hampton, Jacqueline M. Scheidter, Kristen M. Rutlin, Jerrel Laumann, Timothy O. Roland, Jarod L. Montez, David F. Van, Andrew N. Zheng, Annie Marek, Scott Kay, Benjamin P. Bretthorst, G. Larry Schlaggar, Bradley L. Greene, Deanna J. Wang, Yong Petersen, Steven E. Barch, Deanna M. Gordon, Evan M. Snyder, Abraham Z. Shimony, Joshua S. Dosenbach, Nico U.F. Neuroimage Article Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain’s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927–1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL’s BedpostX [BPX], DSI Studio’s Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3’s Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI 2022-07-01 2022-03-23 /pmc/articles/PMC9841915/ /pubmed/35339687 http://dx.doi.org/10.1016/j.neuroimage.2022.119138 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) )
spellingShingle Article
Seider, Nicole A.
Adeyemo, Babatunde
Miller, Ryland
Newbold, Dillan J.
Hampton, Jacqueline M.
Scheidter, Kristen M.
Rutlin, Jerrel
Laumann, Timothy O.
Roland, Jarod L.
Montez, David F.
Van, Andrew N.
Zheng, Annie
Marek, Scott
Kay, Benjamin P.
Bretthorst, G. Larry
Schlaggar, Bradley L.
Greene, Deanna J.
Wang, Yong
Petersen, Steven E.
Barch, Deanna M.
Gordon, Evan M.
Snyder, Abraham Z.
Shimony, Joshua S.
Dosenbach, Nico U.F.
Accuracy and reliability of diffusion imaging models
title Accuracy and reliability of diffusion imaging models
title_full Accuracy and reliability of diffusion imaging models
title_fullStr Accuracy and reliability of diffusion imaging models
title_full_unstemmed Accuracy and reliability of diffusion imaging models
title_short Accuracy and reliability of diffusion imaging models
title_sort accuracy and reliability of diffusion imaging models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841915/
https://www.ncbi.nlm.nih.gov/pubmed/35339687
http://dx.doi.org/10.1016/j.neuroimage.2022.119138
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