Articles producció científica> Medicina i Cirurgia

Multivariate Brain Functional Connectivity Through Regularized Estimators

  • Identification data

    Identifier: imarina:9139005
    Authors:
    Salvador RVerdolini NGarcia-Ruiz BJiménez ESarró SVilella EVieta ECanales-Rodríguez EJPomarol-Clotet EVoineskos AN
    Abstract:
    © 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
  • Others:

    Author, as appears in the article.: Salvador R; Verdolini N; Garcia-Ruiz B; Jiménez E; Sarró S; Vilella E; Vieta E; Canales-Rodríguez EJ; Pomarol-Clotet E; Voineskos AN
    Department: Medicina i Cirurgia
    URV's Author/s: Vilella Cuadrada, Elisabet
    Keywords: Ridge regression Random forest Global brain connectivity Gender Brain connectivity Age
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: elisabet.vilella@urv.cat
    Author identifier: 0000-0002-1887-5919
    Record's date: 2023-02-23
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.frontiersin.org/articles/10.3389/fnins.2020.569540/full
    Papper original source: Frontiers In Neuroscience. 14 (569540):
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.3389/fnins.2020.569540
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    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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