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Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking
A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insuffici...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501434/ https://www.ncbi.nlm.nih.gov/pubmed/32995596 http://dx.doi.org/10.1016/j.heliyon.2020.e04854 |
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author | Dørum, Erlend S. Kaufmann, Tobias Alnæs, Dag Richard, Geneviève Kolskår, Knut K. Engvig, Andreas Sanders, Anne-Marthe Ulrichsen, Kristine Ihle-Hansen, Hege Nordvik, Jan Egil Westlye, Lars T. |
author_facet | Dørum, Erlend S. Kaufmann, Tobias Alnæs, Dag Richard, Geneviève Kolskår, Knut K. Engvig, Andreas Sanders, Anne-Marthe Ulrichsen, Kristine Ihle-Hansen, Hege Nordvik, Jan Egil Westlye, Lars T. |
author_sort | Dørum, Erlend S. |
collection | PubMed |
description | A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes. We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load. MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes. |
format | Online Article Text |
id | pubmed-7501434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75014342020-09-28 Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking Dørum, Erlend S. Kaufmann, Tobias Alnæs, Dag Richard, Geneviève Kolskår, Knut K. Engvig, Andreas Sanders, Anne-Marthe Ulrichsen, Kristine Ihle-Hansen, Hege Nordvik, Jan Egil Westlye, Lars T. Heliyon Research Article A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes. We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load. MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes. Elsevier 2020-09-15 /pmc/articles/PMC7501434/ /pubmed/32995596 http://dx.doi.org/10.1016/j.heliyon.2020.e04854 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Dørum, Erlend S. Kaufmann, Tobias Alnæs, Dag Richard, Geneviève Kolskår, Knut K. Engvig, Andreas Sanders, Anne-Marthe Ulrichsen, Kristine Ihle-Hansen, Hege Nordvik, Jan Egil Westlye, Lars T. Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title | Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title_full | Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title_fullStr | Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title_full_unstemmed | Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title_short | Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
title_sort | functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501434/ https://www.ncbi.nlm.nih.gov/pubmed/32995596 http://dx.doi.org/10.1016/j.heliyon.2020.e04854 |
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