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Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping

Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity wit...

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Autores principales: Cummings, Jennifer A., Sipes, Benjamin, Mathalon, Daniel H., Raj, Ashish
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964629/
https://www.ncbi.nlm.nih.gov/pubmed/35368264
http://dx.doi.org/10.3389/fnins.2022.810111
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author Cummings, Jennifer A.
Sipes, Benjamin
Mathalon, Daniel H.
Raj, Ashish
author_facet Cummings, Jennifer A.
Sipes, Benjamin
Mathalon, Daniel H.
Raj, Ashish
author_sort Cummings, Jennifer A.
collection PubMed
description Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.
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spelling pubmed-89646292022-03-31 Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping Cummings, Jennifer A. Sipes, Benjamin Mathalon, Daniel H. Raj, Ashish Front Neurosci Neuroscience Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods. Frontiers Media S.A. 2022-03-15 /pmc/articles/PMC8964629/ /pubmed/35368264 http://dx.doi.org/10.3389/fnins.2022.810111 Text en Copyright © 2022 Cummings, Sipes, Mathalon and Raj. 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
Cummings, Jennifer A.
Sipes, Benjamin
Mathalon, Daniel H.
Raj, Ashish
Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title_full Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title_fullStr Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title_full_unstemmed Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title_short Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping
title_sort predicting functional connectivity from observed and latent structural connectivity via eigenvalue mapping
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964629/
https://www.ncbi.nlm.nih.gov/pubmed/35368264
http://dx.doi.org/10.3389/fnins.2022.810111
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