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MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning

A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (...

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Autores principales: Dayan, Michael, Hurtado Rúa, Sandra M., Monohan, Elizabeth, Fujimoto, Kyoko, Pandya, Sneha, LoCastro, Eve M., Vartanian, Tim, Nguyen, Thanh D., Raj, Ashish, Gauthier, Susan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445177/
https://www.ncbi.nlm.nih.gov/pubmed/28603479
http://dx.doi.org/10.3389/fnins.2017.00284
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author Dayan, Michael
Hurtado Rúa, Sandra M.
Monohan, Elizabeth
Fujimoto, Kyoko
Pandya, Sneha
LoCastro, Eve M.
Vartanian, Tim
Nguyen, Thanh D.
Raj, Ashish
Gauthier, Susan A.
author_facet Dayan, Michael
Hurtado Rúa, Sandra M.
Monohan, Elizabeth
Fujimoto, Kyoko
Pandya, Sneha
LoCastro, Eve M.
Vartanian, Tim
Nguyen, Thanh D.
Raj, Ashish
Gauthier, Susan A.
author_sort Dayan, Michael
collection PubMed
description A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (135 and 23 patients, respectively). It was compared to an approach based on lesion masks. The gamma mixture distribution of whole brain, white matter (WM) MWF was characterized with three variables: the mode (most frequent value) m(1) of the gamma component shown to relate to lesion, the mode m(2) of the component shown to be associated with normal appearing (NA) WM, and the mixing ratio (λ) between the two distributions. The lesion-mask approach relied on the mean MWF within lesion and within NAWM. A multivariate regression analysis was carried out to find the best predictors of cortical thickness for each group and for each approach. The gamma-mixture method was shown to outperform the lesion-mask approach in terms of adjusted R(2), both for the RRMS and SPMS groups. The predictors of the final gamma-mixture models were found to be m(1) (β = 1.56, p < 0.005), λ (β = −0.30, p < 0.0005) and age (β = −0.0031, p < 0.005) for the RRMS group (adjusted R(2) = 0.16), and m(2) (β = 4.72, p < 0.0005) for the SPMS group (adjusted R(2) = 0.45). Further, a DICE coefficient analysis demonstrated that the lesion mask had more overlap to an ROI associated with m(1), than to an ROI associated with m(2) (p < 0.00001), and vice versa for the NAWM mask (p < 0.00001). These results suggest that during the relapsing phase, focal WM damage is associated with cortical thinning, yet in SPMS patients, global WM deterioration has a much stronger influence on secondary degeneration. Through these findings, we demonstrate the potential contribution of myelin loss on neuronal degeneration at different disease stages and the usefulness of our statistical reduction technique which is not affected by the typical bias associated with approaches based on lesion masks.
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spelling pubmed-54451772017-06-09 MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning Dayan, Michael Hurtado Rúa, Sandra M. Monohan, Elizabeth Fujimoto, Kyoko Pandya, Sneha LoCastro, Eve M. Vartanian, Tim Nguyen, Thanh D. Raj, Ashish Gauthier, Susan A. Front Neurosci Neuroscience A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (135 and 23 patients, respectively). It was compared to an approach based on lesion masks. The gamma mixture distribution of whole brain, white matter (WM) MWF was characterized with three variables: the mode (most frequent value) m(1) of the gamma component shown to relate to lesion, the mode m(2) of the component shown to be associated with normal appearing (NA) WM, and the mixing ratio (λ) between the two distributions. The lesion-mask approach relied on the mean MWF within lesion and within NAWM. A multivariate regression analysis was carried out to find the best predictors of cortical thickness for each group and for each approach. The gamma-mixture method was shown to outperform the lesion-mask approach in terms of adjusted R(2), both for the RRMS and SPMS groups. The predictors of the final gamma-mixture models were found to be m(1) (β = 1.56, p < 0.005), λ (β = −0.30, p < 0.0005) and age (β = −0.0031, p < 0.005) for the RRMS group (adjusted R(2) = 0.16), and m(2) (β = 4.72, p < 0.0005) for the SPMS group (adjusted R(2) = 0.45). Further, a DICE coefficient analysis demonstrated that the lesion mask had more overlap to an ROI associated with m(1), than to an ROI associated with m(2) (p < 0.00001), and vice versa for the NAWM mask (p < 0.00001). These results suggest that during the relapsing phase, focal WM damage is associated with cortical thinning, yet in SPMS patients, global WM deterioration has a much stronger influence on secondary degeneration. Through these findings, we demonstrate the potential contribution of myelin loss on neuronal degeneration at different disease stages and the usefulness of our statistical reduction technique which is not affected by the typical bias associated with approaches based on lesion masks. Frontiers Media S.A. 2017-05-26 /pmc/articles/PMC5445177/ /pubmed/28603479 http://dx.doi.org/10.3389/fnins.2017.00284 Text en Copyright © 2017 Dayan, Hurtado Rúa, Monohan, Fujimoto, Pandya, LoCastro, Vartanian, Nguyen, Raj and Gauthier. 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) or licensor 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 Neuroscience
Dayan, Michael
Hurtado Rúa, Sandra M.
Monohan, Elizabeth
Fujimoto, Kyoko
Pandya, Sneha
LoCastro, Eve M.
Vartanian, Tim
Nguyen, Thanh D.
Raj, Ashish
Gauthier, Susan A.
MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title_full MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title_fullStr MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title_full_unstemmed MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title_short MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning
title_sort mri analysis of white matter myelin water content in multiple sclerosis: a novel approach applied to finding correlates of cortical thinning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445177/
https://www.ncbi.nlm.nih.gov/pubmed/28603479
http://dx.doi.org/10.3389/fnins.2017.00284
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