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Different Types of White Matter Hyperintensities in CADASIL

Objective: In CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), white matter hyperintensities (WMH) are considered to result from hypoperfusion. We hypothesized that in fact the burden of WMH results from the combination of several regional populat...

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Autores principales: Duchesnay, Edouard, Hadj Selem, Fouad, De Guio, François, Dubois, Mathieu, Mangin, Jean-François, Duering, Marco, Ropele, Stefan, Schmidt, Reinhold, Dichgans, Martin, Chabriat, Hugues, Jouvent, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048276/
https://www.ncbi.nlm.nih.gov/pubmed/30042721
http://dx.doi.org/10.3389/fneur.2018.00526
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author Duchesnay, Edouard
Hadj Selem, Fouad
De Guio, François
Dubois, Mathieu
Mangin, Jean-François
Duering, Marco
Ropele, Stefan
Schmidt, Reinhold
Dichgans, Martin
Chabriat, Hugues
Jouvent, Eric
author_facet Duchesnay, Edouard
Hadj Selem, Fouad
De Guio, François
Dubois, Mathieu
Mangin, Jean-François
Duering, Marco
Ropele, Stefan
Schmidt, Reinhold
Dichgans, Martin
Chabriat, Hugues
Jouvent, Eric
author_sort Duchesnay, Edouard
collection PubMed
description Objective: In CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), white matter hyperintensities (WMH) are considered to result from hypoperfusion. We hypothesized that in fact the burden of WMH results from the combination of several regional populations of WMH with different mechanisms and clinical consequences. Methods: To identify regional WMH populations, we used a 4-step approach. First, we used an unsupervised principal component algorithm to determine, without a priori knowledge, the main sources of variation of the global spatial pattern of WMH. Thereafter, to determine whether these sources are likely to include relevant information regarding regional populations of WMH, we tested their relationships with: (1) MRI markers of the disease; (2) the clinical severity assessed by the Mattis Dementia Rating scale (MDRS) (cognitive outcome) and the modified Rankin's score (disability outcome). Finally, through careful interpretation of all the results, we tried to identify different regional populations of WMH. Results: The unsupervised principal component algorithm identified 3 main sources of variation of the global spatial pattern of WMH, which showed significant and sometime inverse relationships with MRI markers and clinical scores. The models predicting clinical severity based on these sources outperformed those evaluating WMH by their volume (MDRS, coefficient of determination of 39.0 vs. 35.3%, p = 0.01; modified Rankin's score, 43.7 vs. 38.1%, p = 0.001). By carefully interpreting the visual aspect of these sources as well as their relationships with MRI markers and clinical severity, we found strong arguments supporting the existence of different regional populations of WMH. For instance, in multivariate analyses, larger extents of WMH in anterior temporal poles and superior frontal gyri were associated with better outcomes, while larger extents of WMH in pyramidal tracts were associated with worse outcomes, which could not be explained if WMH in these different areas shared the same mechanisms. Conclusion: The results of the present study support the hypothesis that the whole extent of WMH results from a combination of different regional populations of WMH, some of which are associated, for yet undetermined reasons, with milder forms of the disease.
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spelling pubmed-60482762018-07-24 Different Types of White Matter Hyperintensities in CADASIL Duchesnay, Edouard Hadj Selem, Fouad De Guio, François Dubois, Mathieu Mangin, Jean-François Duering, Marco Ropele, Stefan Schmidt, Reinhold Dichgans, Martin Chabriat, Hugues Jouvent, Eric Front Neurol Neurology Objective: In CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), white matter hyperintensities (WMH) are considered to result from hypoperfusion. We hypothesized that in fact the burden of WMH results from the combination of several regional populations of WMH with different mechanisms and clinical consequences. Methods: To identify regional WMH populations, we used a 4-step approach. First, we used an unsupervised principal component algorithm to determine, without a priori knowledge, the main sources of variation of the global spatial pattern of WMH. Thereafter, to determine whether these sources are likely to include relevant information regarding regional populations of WMH, we tested their relationships with: (1) MRI markers of the disease; (2) the clinical severity assessed by the Mattis Dementia Rating scale (MDRS) (cognitive outcome) and the modified Rankin's score (disability outcome). Finally, through careful interpretation of all the results, we tried to identify different regional populations of WMH. Results: The unsupervised principal component algorithm identified 3 main sources of variation of the global spatial pattern of WMH, which showed significant and sometime inverse relationships with MRI markers and clinical scores. The models predicting clinical severity based on these sources outperformed those evaluating WMH by their volume (MDRS, coefficient of determination of 39.0 vs. 35.3%, p = 0.01; modified Rankin's score, 43.7 vs. 38.1%, p = 0.001). By carefully interpreting the visual aspect of these sources as well as their relationships with MRI markers and clinical severity, we found strong arguments supporting the existence of different regional populations of WMH. For instance, in multivariate analyses, larger extents of WMH in anterior temporal poles and superior frontal gyri were associated with better outcomes, while larger extents of WMH in pyramidal tracts were associated with worse outcomes, which could not be explained if WMH in these different areas shared the same mechanisms. Conclusion: The results of the present study support the hypothesis that the whole extent of WMH results from a combination of different regional populations of WMH, some of which are associated, for yet undetermined reasons, with milder forms of the disease. Frontiers Media S.A. 2018-07-10 /pmc/articles/PMC6048276/ /pubmed/30042721 http://dx.doi.org/10.3389/fneur.2018.00526 Text en Copyright © 2018 Duchesnay, Hadj Selem, De Guio, Dubois, Mangin, Duering, Ropele, Schmidt, Dichgans, Chabriat and Jouvent. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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 Neurology
Duchesnay, Edouard
Hadj Selem, Fouad
De Guio, François
Dubois, Mathieu
Mangin, Jean-François
Duering, Marco
Ropele, Stefan
Schmidt, Reinhold
Dichgans, Martin
Chabriat, Hugues
Jouvent, Eric
Different Types of White Matter Hyperintensities in CADASIL
title Different Types of White Matter Hyperintensities in CADASIL
title_full Different Types of White Matter Hyperintensities in CADASIL
title_fullStr Different Types of White Matter Hyperintensities in CADASIL
title_full_unstemmed Different Types of White Matter Hyperintensities in CADASIL
title_short Different Types of White Matter Hyperintensities in CADASIL
title_sort different types of white matter hyperintensities in cadasil
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048276/
https://www.ncbi.nlm.nih.gov/pubmed/30042721
http://dx.doi.org/10.3389/fneur.2018.00526
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