Articles producció científica> Medicina i Cirurgia

Multivariate Brain Functional Connectivity Through Regularized Estimators

  • Datos identificativos

    Identificador: imarina:9139005
    Autores:
    Salvador RVerdolini NGarcia-Ruiz BJiménez ESarró SVilella EVieta ECanales-Rodríguez EJPomarol-Clotet EVoineskos AN
    Resumen:
    © Copyright © 2020 Salvador, Verdolini, Garcia-Ruiz, Jiménez, Sarró, Vilella, Vieta, Canales-Rodríguez, Pomarol-Clotet and Voineskos. Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic
  • Otros:

    Autor según el artículo: Salvador R; Verdolini N; Garcia-Ruiz B; Jiménez E; Sarró S; Vilella E; Vieta E; Canales-Rodríguez EJ; Pomarol-Clotet E; Voineskos AN
    Departamento: Medicina i Cirurgia
    Autor/es de la URV: Vilella Cuadrada, Elisabet
    Palabras clave: Ridge regression Random forest Global brain connectivity Gender Brain connectivity Age
    Resumen: © Copyright © 2020 Salvador, Verdolini, Garcia-Ruiz, Jiménez, Sarró, Vilella, Vieta, Canales-Rodríguez, Pomarol-Clotet and Voineskos. Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models.
    Áreas temáticas: Saúde coletiva Psicología Neurosciences Neuroscience (miscellaneous) Neuroscience (all) Medicina veterinaria Medicina iii Medicina ii Medicina i Interdisciplinar General neuroscience Filosofía Engenharias iv Educação física Ciências biológicas ii Ciências biológicas i Ciência da computação Biodiversidade Administração, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: elisabet.vilella@urv.cat
    Identificador del autor: 0000-0002-1887-5919
    Fecha de alta del registro: 2023-02-23
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.frontiersin.org/articles/10.3389/fnins.2020.569540/full
    Referencia al articulo segun fuente origial: Frontiers In Neuroscience. 14 (569540):
    Referencia de l'ítem segons les normes APA: Salvador R; Verdolini N; Garcia-Ruiz B; Jiménez E; Sarró S; Vilella E; Vieta E; Canales-Rodríguez EJ; Pomarol-Clotet E; Voineskos AN (2020). Multivariate Brain Functional Connectivity Through Regularized Estimators. Frontiers In Neuroscience, 14(569540), -. DOI: 10.3389/fnins.2020.569540
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3389/fnins.2020.569540
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Neuroscience (Miscellaneous),Neurosciences
    Ridge regression
    Random forest
    Global brain connectivity
    Gender
    Brain connectivity
    Age
    Saúde coletiva
    Psicología
    Neurosciences
    Neuroscience (miscellaneous)
    Neuroscience (all)
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Interdisciplinar
    General neuroscience
    Filosofía
    Engenharias iv
    Educação física
    Ciências biológicas ii
    Ciências biológicas i
    Ciência da computação
    Biodiversidade
    Administração, ciências contábeis e turismo
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