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Multivariate analytical approaches for investigating brain-behavior relationships

BACKGROUND: Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relatio...

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Autores principales: Durham, E. Leighton, Ghanem, Karam, Stier, Andrew J., Cardenas-Iniguez, Carlos, Reimann, Gabrielle E., Jeong, Hee Jung, Dupont, Randolph M., Dong, Xiaoyu, Moore, Tyler M., Berman, Marc G., Lahey, Benjamin B., Bzdok, Danilo, Kaczkurkin, Antonia N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423877/
https://www.ncbi.nlm.nih.gov/pubmed/37583413
http://dx.doi.org/10.3389/fnins.2023.1175690
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author Durham, E. Leighton
Ghanem, Karam
Stier, Andrew J.
Cardenas-Iniguez, Carlos
Reimann, Gabrielle E.
Jeong, Hee Jung
Dupont, Randolph M.
Dong, Xiaoyu
Moore, Tyler M.
Berman, Marc G.
Lahey, Benjamin B.
Bzdok, Danilo
Kaczkurkin, Antonia N.
author_facet Durham, E. Leighton
Ghanem, Karam
Stier, Andrew J.
Cardenas-Iniguez, Carlos
Reimann, Gabrielle E.
Jeong, Hee Jung
Dupont, Randolph M.
Dong, Xiaoyu
Moore, Tyler M.
Berman, Marc G.
Lahey, Benjamin B.
Bzdok, Danilo
Kaczkurkin, Antonia N.
author_sort Durham, E. Leighton
collection PubMed
description BACKGROUND: Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. METHOD: We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive Development(SM) Study (ABCD Study(®)) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. RESULTS: The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. CONCLUSION: Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method’s underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.
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spelling pubmed-104238772023-08-15 Multivariate analytical approaches for investigating brain-behavior relationships Durham, E. Leighton Ghanem, Karam Stier, Andrew J. Cardenas-Iniguez, Carlos Reimann, Gabrielle E. Jeong, Hee Jung Dupont, Randolph M. Dong, Xiaoyu Moore, Tyler M. Berman, Marc G. Lahey, Benjamin B. Bzdok, Danilo Kaczkurkin, Antonia N. Front Neurosci Neuroscience BACKGROUND: Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. METHOD: We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive Development(SM) Study (ABCD Study(®)) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. RESULTS: The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. CONCLUSION: Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method’s underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships. Frontiers Media S.A. 2023-07-31 /pmc/articles/PMC10423877/ /pubmed/37583413 http://dx.doi.org/10.3389/fnins.2023.1175690 Text en Copyright © 2023 Durham, Ghanem, Stier, Cardenas-Iniguez, Reimann, Jeong, Dupont, Dong, Moore, Berman, Lahey, Bzdok and Kaczkurkin. 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
Durham, E. Leighton
Ghanem, Karam
Stier, Andrew J.
Cardenas-Iniguez, Carlos
Reimann, Gabrielle E.
Jeong, Hee Jung
Dupont, Randolph M.
Dong, Xiaoyu
Moore, Tyler M.
Berman, Marc G.
Lahey, Benjamin B.
Bzdok, Danilo
Kaczkurkin, Antonia N.
Multivariate analytical approaches for investigating brain-behavior relationships
title Multivariate analytical approaches for investigating brain-behavior relationships
title_full Multivariate analytical approaches for investigating brain-behavior relationships
title_fullStr Multivariate analytical approaches for investigating brain-behavior relationships
title_full_unstemmed Multivariate analytical approaches for investigating brain-behavior relationships
title_short Multivariate analytical approaches for investigating brain-behavior relationships
title_sort multivariate analytical approaches for investigating brain-behavior relationships
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423877/
https://www.ncbi.nlm.nih.gov/pubmed/37583413
http://dx.doi.org/10.3389/fnins.2023.1175690
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