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Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data
Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using inform...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555077/ https://www.ncbi.nlm.nih.gov/pubmed/36248659 http://dx.doi.org/10.3389/fnins.2022.957282 |
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author | Steiner, Aleksandra Abbas, Kausar Brzyski, Damian Pączek, Kewin Randolph, Timothy W. Goñi, Joaquín Harezlak, Jaroslaw |
author_facet | Steiner, Aleksandra Abbas, Kausar Brzyski, Damian Pączek, Kewin Randolph, Timothy W. Goñi, Joaquín Harezlak, Jaroslaw |
author_sort | Steiner, Aleksandra |
collection | PubMed |
description | Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension. |
format | Online Article Text |
id | pubmed-9555077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95550772022-10-13 Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data Steiner, Aleksandra Abbas, Kausar Brzyski, Damian Pączek, Kewin Randolph, Timothy W. Goñi, Joaquín Harezlak, Jaroslaw Front Neurosci Neuroscience Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9555077/ /pubmed/36248659 http://dx.doi.org/10.3389/fnins.2022.957282 Text en Copyright © 2022 Steiner, Abbas, Brzyski, Pączek, Randolph, Goñi and Harezlak. 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 Steiner, Aleksandra Abbas, Kausar Brzyski, Damian Pączek, Kewin Randolph, Timothy W. Goñi, Joaquín Harezlak, Jaroslaw Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title | Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title_full | Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title_fullStr | Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title_full_unstemmed | Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title_short | Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data |
title_sort | incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: application to human connectome project data |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555077/ https://www.ncbi.nlm.nih.gov/pubmed/36248659 http://dx.doi.org/10.3389/fnins.2022.957282 |
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