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4488 Neural Network of the Cognitive Model of Reading

OBJECTIVES/GOALS: A particularly debilitating consequence of stroke is alexia, an acquired impairment in reading. Cognitive models aim to characterize how information is processed based on behavioral data. If we can concurrently characterize how neural networks process that information, we can enhan...

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Autores principales: Posner, Joseph, Dickens, Vivian, DeMarco, Andrew, Snider, Sarah, Turkeltaub, Peter, Friedman, Rhonda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823351/
http://dx.doi.org/10.1017/cts.2020.415
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author Posner, Joseph
Dickens, Vivian
DeMarco, Andrew
Snider, Sarah
Turkeltaub, Peter
Friedman, Rhonda
author_facet Posner, Joseph
Dickens, Vivian
DeMarco, Andrew
Snider, Sarah
Turkeltaub, Peter
Friedman, Rhonda
author_sort Posner, Joseph
collection PubMed
description OBJECTIVES/GOALS: A particularly debilitating consequence of stroke is alexia, an acquired impairment in reading. Cognitive models aim to characterize how information is processed based on behavioral data. If we can concurrently characterize how neural networks process that information, we can enhance the models to reflect the neuronal interactions that drive them. METHODS/STUDY POPULATION: There will be 10 unimpaired adult readers. Two functional localizer tasks, deigned to consistently activate robust language areas, identify the regions of interest that process the cognitive reading functions (orthography, phonology, semantics). Another task, designed for this experiment, analyses the reading-related functional-connectivity between these areas by presenting words classified along the attributes of frequency, concreteness, and regularity, which utilize specific cognitive routes, and a visual control. Connectivity is analyzed during word reading overall vs. a control condition to determine overall reading-related connectivity, and while reading words that have high vs. low attribute values, to determine if cognitive processing routes bias the neural reading network connectivity. RESULTS/ANTICIPATED RESULTS: The localizer analysis is expected to result in the activation of canonical reading areas. The degree of functional connectivity observed between these regions is expected to depend on the degree to which each cognitive route is utilized to read a given word. After orthographic, phonologic, and semantic areas have been identified, the connectivity analysis should show that there is high correlation between all three types of areas during reading compared to the control condition. Then the frequency, regularity, and concreteness of the words being read should alter the reliance on the pathways between these area types. This would support the hypothesized pattern of connectivity as predicted by the cognitive reading routes. Otherwise, it will show how the neural reading network differs from the cognitive model. DISCUSSION/SIGNIFICANCE OF IMPACT: The results will determine the relationship between the cognitive reading model and the neural reading network. Cognitive models show what processes occur in the brain, but neural networks show how these processes occur. By relating these components, we obtain a more complete view of reading in the brain, which can inform future alexia treatments.
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spelling pubmed-88233512022-02-18 4488 Neural Network of the Cognitive Model of Reading Posner, Joseph Dickens, Vivian DeMarco, Andrew Snider, Sarah Turkeltaub, Peter Friedman, Rhonda J Clin Transl Sci Translational Science, Policy, & Health Outcomes Science OBJECTIVES/GOALS: A particularly debilitating consequence of stroke is alexia, an acquired impairment in reading. Cognitive models aim to characterize how information is processed based on behavioral data. If we can concurrently characterize how neural networks process that information, we can enhance the models to reflect the neuronal interactions that drive them. METHODS/STUDY POPULATION: There will be 10 unimpaired adult readers. Two functional localizer tasks, deigned to consistently activate robust language areas, identify the regions of interest that process the cognitive reading functions (orthography, phonology, semantics). Another task, designed for this experiment, analyses the reading-related functional-connectivity between these areas by presenting words classified along the attributes of frequency, concreteness, and regularity, which utilize specific cognitive routes, and a visual control. Connectivity is analyzed during word reading overall vs. a control condition to determine overall reading-related connectivity, and while reading words that have high vs. low attribute values, to determine if cognitive processing routes bias the neural reading network connectivity. RESULTS/ANTICIPATED RESULTS: The localizer analysis is expected to result in the activation of canonical reading areas. The degree of functional connectivity observed between these regions is expected to depend on the degree to which each cognitive route is utilized to read a given word. After orthographic, phonologic, and semantic areas have been identified, the connectivity analysis should show that there is high correlation between all three types of areas during reading compared to the control condition. Then the frequency, regularity, and concreteness of the words being read should alter the reliance on the pathways between these area types. This would support the hypothesized pattern of connectivity as predicted by the cognitive reading routes. Otherwise, it will show how the neural reading network differs from the cognitive model. DISCUSSION/SIGNIFICANCE OF IMPACT: The results will determine the relationship between the cognitive reading model and the neural reading network. Cognitive models show what processes occur in the brain, but neural networks show how these processes occur. By relating these components, we obtain a more complete view of reading in the brain, which can inform future alexia treatments. Cambridge University Press 2020-07-29 /pmc/articles/PMC8823351/ http://dx.doi.org/10.1017/cts.2020.415 Text en © The Association for Clinical and Translational Science 2020 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Translational Science, Policy, & Health Outcomes Science
Posner, Joseph
Dickens, Vivian
DeMarco, Andrew
Snider, Sarah
Turkeltaub, Peter
Friedman, Rhonda
4488 Neural Network of the Cognitive Model of Reading
title 4488 Neural Network of the Cognitive Model of Reading
title_full 4488 Neural Network of the Cognitive Model of Reading
title_fullStr 4488 Neural Network of the Cognitive Model of Reading
title_full_unstemmed 4488 Neural Network of the Cognitive Model of Reading
title_short 4488 Neural Network of the Cognitive Model of Reading
title_sort 4488 neural network of the cognitive model of reading
topic Translational Science, Policy, & Health Outcomes Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823351/
http://dx.doi.org/10.1017/cts.2020.415
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