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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...

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Autores principales: Steiner, Aleksandra, Abbas, Kausar, Brzyski, Damian, Pączek, Kewin, Randolph, Timothy W., Goñi, Joaquín, Harezlak, Jaroslaw
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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