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Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging
Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients’...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104329/ https://www.ncbi.nlm.nih.gov/pubmed/37058495 http://dx.doi.org/10.1371/journal.pone.0280892 |
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author | Bučková, Barbora Rehák Kala, David Kořenek, Jakub Matušková, Veronika Kumpošt, Vojtěch Svobodová, Lenka Otáhal, Jakub Škoch, Antonín Šulc, Vlastimil Olšerová, Anna Vyhnálek, Martin Janský, Petr Tomek, Aleš Marusič, Petr Jiruška, Přemysl Hlinka, Jaroslav |
author_facet | Bučková, Barbora Rehák Kala, David Kořenek, Jakub Matušková, Veronika Kumpošt, Vojtěch Svobodová, Lenka Otáhal, Jakub Škoch, Antonín Šulc, Vlastimil Olšerová, Anna Vyhnálek, Martin Janský, Petr Tomek, Aleš Marusič, Petr Jiruška, Přemysl Hlinka, Jaroslav |
author_sort | Bučková, Barbora Rehák |
collection | PubMed |
description | Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients’ cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling. |
format | Online Article Text |
id | pubmed-10104329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101043292023-04-15 Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging Bučková, Barbora Rehák Kala, David Kořenek, Jakub Matušková, Veronika Kumpošt, Vojtěch Svobodová, Lenka Otáhal, Jakub Škoch, Antonín Šulc, Vlastimil Olšerová, Anna Vyhnálek, Martin Janský, Petr Tomek, Aleš Marusič, Petr Jiruška, Přemysl Hlinka, Jaroslav PLoS One Research Article Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients’ cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling. Public Library of Science 2023-04-14 /pmc/articles/PMC10104329/ /pubmed/37058495 http://dx.doi.org/10.1371/journal.pone.0280892 Text en © 2023 Bučková et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bučková, Barbora Rehák Kala, David Kořenek, Jakub Matušková, Veronika Kumpošt, Vojtěch Svobodová, Lenka Otáhal, Jakub Škoch, Antonín Šulc, Vlastimil Olšerová, Anna Vyhnálek, Martin Janský, Petr Tomek, Aleš Marusič, Petr Jiruška, Přemysl Hlinka, Jaroslav Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title | Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title_full | Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title_fullStr | Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title_full_unstemmed | Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title_short | Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
title_sort | structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104329/ https://www.ncbi.nlm.nih.gov/pubmed/37058495 http://dx.doi.org/10.1371/journal.pone.0280892 |
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