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- Publisher Website: 10.3174/ajnr.A8115
- Scopus: eid_2-s2.0-85187200768
- WOS: WOS:001155686200001
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Article: Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging
Title | Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging |
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Authors | |
Issue Date | 1-Feb-2024 |
Publisher | Elsevier |
Citation | Journal of Neuroradiology, 2024, v. 45, n. 3, p. 305-311 How to Cite? |
Abstract | BACKGROUND AND PURPOSE: DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS. MATERIALS AND METHODS: Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated. RESULTS: None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively. CONCLUSIONS: The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS. |
Persistent Identifier | http://hdl.handle.net/10722/342775 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.756 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Hui-Qin | - |
dc.contributor.author | Lee, Jacky Chi-Yan | - |
dc.contributor.author | Wang, Lu | - |
dc.contributor.author | Cao, Peng | - |
dc.contributor.author | Chan, Koon-Ho | - |
dc.contributor.author | Mak, Henry Ka-Fung | - |
dc.date.accessioned | 2024-04-24T02:47:05Z | - |
dc.date.available | 2024-04-24T02:47:05Z | - |
dc.date.issued | 2024-02-01 | - |
dc.identifier.citation | Journal of Neuroradiology, 2024, v. 45, n. 3, p. 305-311 | - |
dc.identifier.issn | 0150-9861 | - |
dc.identifier.uri | http://hdl.handle.net/10722/342775 | - |
dc.description.abstract | <p><strong>BACKGROUND AND PURPOSE:</strong> DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.</p><p><strong>MATERIALS AND METHODS:</strong> Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.</p><p><strong>RESULTS:</strong> None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.</p><p><strong>CONCLUSIONS:</strong> The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.</p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Journal of Neuroradiology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging | - |
dc.type | Article | - |
dc.identifier.doi | 10.3174/ajnr.A8115 | - |
dc.identifier.scopus | eid_2-s2.0-85187200768 | - |
dc.identifier.volume | 45 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 305 | - |
dc.identifier.epage | 311 | - |
dc.identifier.eissn | 1773-0406 | - |
dc.identifier.isi | WOS:001155686200001 | - |
dc.identifier.issnl | 0150-9861 | - |