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The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging
BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960512/ https://www.ncbi.nlm.nih.gov/pubmed/35347460 http://dx.doi.org/10.1186/s13244-022-01198-4 |
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author | van der Weijden, Chris W. J. Pitombeira, Milena S. Haveman, Yudith R. A. Sanchez-Catasus, Carlos A. Campanholo, Kenia R. Kolinger, Guilherme D. Rimkus, Carolina M. Buchpiguel, Carlos A. Dierckx, Rudi A. J. O. Renken, Remco J. Meilof, Jan F. de Vries, Erik F. J. de Paula Faria, Daniele |
author_facet | van der Weijden, Chris W. J. Pitombeira, Milena S. Haveman, Yudith R. A. Sanchez-Catasus, Carlos A. Campanholo, Kenia R. Kolinger, Guilherme D. Rimkus, Carolina M. Buchpiguel, Carlos A. Dierckx, Rudi A. J. O. Renken, Remco J. Meilof, Jan F. de Vries, Erik F. J. de Paula Faria, Daniele |
author_sort | van der Weijden, Chris W. J. |
collection | PubMed |
description | BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. METHODS: The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. RESULTS: Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. CONCLUSION: Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-022-01198-4. |
format | Online Article Text |
id | pubmed-8960512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-89605122022-04-12 The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging van der Weijden, Chris W. J. Pitombeira, Milena S. Haveman, Yudith R. A. Sanchez-Catasus, Carlos A. Campanholo, Kenia R. Kolinger, Guilherme D. Rimkus, Carolina M. Buchpiguel, Carlos A. Dierckx, Rudi A. J. O. Renken, Remco J. Meilof, Jan F. de Vries, Erik F. J. de Paula Faria, Daniele Insights Imaging Original Article BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. METHODS: The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. RESULTS: Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. CONCLUSION: Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-022-01198-4. Springer Vienna 2022-03-28 /pmc/articles/PMC8960512/ /pubmed/35347460 http://dx.doi.org/10.1186/s13244-022-01198-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article van der Weijden, Chris W. J. Pitombeira, Milena S. Haveman, Yudith R. A. Sanchez-Catasus, Carlos A. Campanholo, Kenia R. Kolinger, Guilherme D. Rimkus, Carolina M. Buchpiguel, Carlos A. Dierckx, Rudi A. J. O. Renken, Remco J. Meilof, Jan F. de Vries, Erik F. J. de Paula Faria, Daniele The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title | The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title_full | The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title_fullStr | The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title_full_unstemmed | The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title_short | The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
title_sort | effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960512/ https://www.ncbi.nlm.nih.gov/pubmed/35347460 http://dx.doi.org/10.1186/s13244-022-01198-4 |
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