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White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex

Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the loc...

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Autores principales: Grotheer, Mareike, Yeatman, Jason, Grill-Spector, Kalanit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416179/
https://www.ncbi.nlm.nih.gov/pubmed/33359351
http://dx.doi.org/10.1016/j.neuroimage.2020.117669
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author Grotheer, Mareike
Yeatman, Jason
Grill-Spector, Kalanit
author_facet Grotheer, Mareike
Yeatman, Jason
Grill-Spector, Kalanit
author_sort Grotheer, Mareike
collection PubMed
description Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brain’s fascicles and to map their endpoint densities in lateral VTC; qMRI was used to measure proton relaxation time (T(1)), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T(1) and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint densities of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T(1) of lateral VTC further improves the predictions of the fascicle model. In the remaining participants (N=20, validation set), we show that a linear model that includes T(1), AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individual’s reading-related responses in lateral VTC.
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spelling pubmed-84161792021-09-03 White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex Grotheer, Mareike Yeatman, Jason Grill-Spector, Kalanit Neuroimage Article Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brain’s fascicles and to map their endpoint densities in lateral VTC; qMRI was used to measure proton relaxation time (T(1)), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T(1) and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint densities of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T(1) of lateral VTC further improves the predictions of the fascicle model. In the remaining participants (N=20, validation set), we show that a linear model that includes T(1), AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individual’s reading-related responses in lateral VTC. 2020-12-24 2021-02-15 /pmc/articles/PMC8416179/ /pubmed/33359351 http://dx.doi.org/10.1016/j.neuroimage.2020.117669 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Grotheer, Mareike
Yeatman, Jason
Grill-Spector, Kalanit
White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title_full White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title_fullStr White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title_full_unstemmed White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title_short White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
title_sort white matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416179/
https://www.ncbi.nlm.nih.gov/pubmed/33359351
http://dx.doi.org/10.1016/j.neuroimage.2020.117669
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