Diffusion basis spectrum imaging for identifying pathologies in MS subtypes
Abstract Diffusion basis spectrum imaging (DBSI) combines discrete anisotropic diffusion tensors and the spectrum of isotropic diffusion tensors to model the underlying multiple sclerosis (MS) pathologies. We used clinical MS subtypes as a surrogate of underlying pathologies to assess DBSI as a biom...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-11-01
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| Series: | Annals of Clinical and Translational Neurology |
| Online Access: | https://doi.org/10.1002/acn3.50903 |
| _version_ | 1818607599418867712 |
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| author | Afsaneh Shirani Peng Sun Kathryn Trinkaus Dana C. Perantie Ajit George Robert T. Naismith Robert E. Schmidt Sheng‐Kwei Song Anne H. Cross |
| author_facet | Afsaneh Shirani Peng Sun Kathryn Trinkaus Dana C. Perantie Ajit George Robert T. Naismith Robert E. Schmidt Sheng‐Kwei Song Anne H. Cross |
| author_sort | Afsaneh Shirani |
| collection | DOAJ |
| description | Abstract Diffusion basis spectrum imaging (DBSI) combines discrete anisotropic diffusion tensors and the spectrum of isotropic diffusion tensors to model the underlying multiple sclerosis (MS) pathologies. We used clinical MS subtypes as a surrogate of underlying pathologies to assess DBSI as a biomarker of pathology in 55 individuals with MS. Restricted isotropic fraction (reflecting cellularity) and fiber fraction (representing apparent axonal density) were the most important DBSI metrics to classify MS using brain white matter lesions. These DBSI metrics outperformed lesion volume. When analyzing the normal‐appearing corpus callosum, the most significant DBSI metrics were fiber fraction, radial diffusivity (reflecting myelination), and nonrestricted isotropic fraction (representing edema). This study provides preliminary evidence supporting the ability of DBSI as a potential noninvasive biomarker of MS neuropathology. |
| first_indexed | 2024-12-16T14:29:19Z |
| format | Article |
| id | doaj.art-27b360271047449583364d0c60d541d6 |
| institution | Directory Open Access Journal |
| issn | 2328-9503 |
| language | English |
| last_indexed | 2024-12-16T14:29:19Z |
| publishDate | 2019-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Annals of Clinical and Translational Neurology |
| spelling | doaj.art-27b360271047449583364d0c60d541d62022-12-21T22:28:17ZengWileyAnnals of Clinical and Translational Neurology2328-95032019-11-016112323232710.1002/acn3.50903Diffusion basis spectrum imaging for identifying pathologies in MS subtypesAfsaneh Shirani0Peng Sun1Kathryn Trinkaus2Dana C. Perantie3Ajit George4Robert T. Naismith5Robert E. Schmidt6Sheng‐Kwei Song7Anne H. Cross8The John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section Department of Neurology Washington University School of Medicine St. Louis MissouriDepartment of Radiology Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MissouriBiostatistics Shared Resource and Siteman Cancer Center Washington University School of Medicine St. Louis MissouriThe John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section Department of Neurology Washington University School of Medicine St. Louis MissouriDepartment of Radiology Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MissouriThe John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section Department of Neurology Washington University School of Medicine St. Louis MissouriDepartment of Pathology and Immunology Washington University School of Medicine St. Louis MissouriDepartment of Radiology Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MissouriThe John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section Department of Neurology Washington University School of Medicine St. Louis MissouriAbstract Diffusion basis spectrum imaging (DBSI) combines discrete anisotropic diffusion tensors and the spectrum of isotropic diffusion tensors to model the underlying multiple sclerosis (MS) pathologies. We used clinical MS subtypes as a surrogate of underlying pathologies to assess DBSI as a biomarker of pathology in 55 individuals with MS. Restricted isotropic fraction (reflecting cellularity) and fiber fraction (representing apparent axonal density) were the most important DBSI metrics to classify MS using brain white matter lesions. These DBSI metrics outperformed lesion volume. When analyzing the normal‐appearing corpus callosum, the most significant DBSI metrics were fiber fraction, radial diffusivity (reflecting myelination), and nonrestricted isotropic fraction (representing edema). This study provides preliminary evidence supporting the ability of DBSI as a potential noninvasive biomarker of MS neuropathology.https://doi.org/10.1002/acn3.50903 |
| spellingShingle | Afsaneh Shirani Peng Sun Kathryn Trinkaus Dana C. Perantie Ajit George Robert T. Naismith Robert E. Schmidt Sheng‐Kwei Song Anne H. Cross Diffusion basis spectrum imaging for identifying pathologies in MS subtypes Annals of Clinical and Translational Neurology |
| title | Diffusion basis spectrum imaging for identifying pathologies in MS subtypes |
| title_full | Diffusion basis spectrum imaging for identifying pathologies in MS subtypes |
| title_fullStr | Diffusion basis spectrum imaging for identifying pathologies in MS subtypes |
| title_full_unstemmed | Diffusion basis spectrum imaging for identifying pathologies in MS subtypes |
| title_short | Diffusion basis spectrum imaging for identifying pathologies in MS subtypes |
| title_sort | diffusion basis spectrum imaging for identifying pathologies in ms subtypes |
| url | https://doi.org/10.1002/acn3.50903 |
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