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Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke

Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to rep...

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Autores principales: Beh, Anthony, McGraw, Paul V., Webb, Ben S., Schluppeck, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766758/
https://www.ncbi.nlm.nih.gov/pubmed/35069094
http://dx.doi.org/10.3389/fnins.2021.737215
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author Beh, Anthony
McGraw, Paul V.
Webb, Ben S.
Schluppeck, Denis
author_facet Beh, Anthony
McGraw, Paul V.
Webb, Ben S.
Schluppeck, Denis
author_sort Beh, Anthony
collection PubMed
description Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities—visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data—provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity—in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation.
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spelling pubmed-87667582022-01-20 Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke Beh, Anthony McGraw, Paul V. Webb, Ben S. Schluppeck, Denis Front Neurosci Neuroscience Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities—visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data—provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity—in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation. Frontiers Media S.A. 2022-01-05 /pmc/articles/PMC8766758/ /pubmed/35069094 http://dx.doi.org/10.3389/fnins.2021.737215 Text en Copyright © 2022 Beh, McGraw, Webb and Schluppeck. https://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 Neuroscience
Beh, Anthony
McGraw, Paul V.
Webb, Ben S.
Schluppeck, Denis
Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title_full Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title_fullStr Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title_full_unstemmed Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title_short Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke
title_sort linking multi-modal mri to clinical measures of visual field loss after stroke
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766758/
https://www.ncbi.nlm.nih.gov/pubmed/35069094
http://dx.doi.org/10.3389/fnins.2021.737215
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