Regularized Functional Connectivity in Schizophrenia

Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of hea...

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Autores: Salvador R, Fuentes-Claramonte P, García-León MÁ, Ramiro N, Soler-Vidal J, Torres ML, Salgado-Pineda P, Munuera J, Voineskos A, Pomarol-Clotet E
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p21678
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=21678
Access Level:acceso abierto
Palabra clave:resting state fMRI
schizophrenia
functional connectivity
ridge regression
global brain connectivity
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spelling Regularized Functional Connectivity in SchizophreniaSalvador RFuentes-Claramonte PGarcía-León MÁRamiro NSoler-Vidal JTorres MLSalgado-Pineda PMunuera JVoineskos APomarol-Clotet Eresting state fMRIschizophreniafunctional connectivityridge regressionglobal brain connectivityRegularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia. Specifically, ridge regression is implemented with different degrees of regularization. Results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations and from the non-redundant connectivity method (NRC), a dimensionality reduction approach that applies supervised principal component regressions. Ridge regression is able to detect a larger set of abnormally connected regions than both GBC and NRC methods, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC, but it also outperforms the NRC, which is multivariate too. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity.FRONTIERS MEDIA SA2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=21678Frontiers in Human NeuroscienceISSN: 16625161reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p216782026-05-27T12:37:41Z
dc.title.none.fl_str_mv Regularized Functional Connectivity in Schizophrenia
title Regularized Functional Connectivity in Schizophrenia
spellingShingle Regularized Functional Connectivity in Schizophrenia
Salvador R
resting state fMRI
schizophrenia
functional connectivity
ridge regression
global brain connectivity
title_short Regularized Functional Connectivity in Schizophrenia
title_full Regularized Functional Connectivity in Schizophrenia
title_fullStr Regularized Functional Connectivity in Schizophrenia
title_full_unstemmed Regularized Functional Connectivity in Schizophrenia
title_sort Regularized Functional Connectivity in Schizophrenia
dc.creator.none.fl_str_mv Salvador R
Fuentes-Claramonte P
García-León MÁ
Ramiro N
Soler-Vidal J
Torres ML
Salgado-Pineda P
Munuera J
Voineskos A
Pomarol-Clotet E
author Salvador R
author_facet Salvador R
Fuentes-Claramonte P
García-León MÁ
Ramiro N
Soler-Vidal J
Torres ML
Salgado-Pineda P
Munuera J
Voineskos A
Pomarol-Clotet E
author_role author
author2 Fuentes-Claramonte P
García-León MÁ
Ramiro N
Soler-Vidal J
Torres ML
Salgado-Pineda P
Munuera J
Voineskos A
Pomarol-Clotet E
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv resting state fMRI
schizophrenia
functional connectivity
ridge regression
global brain connectivity
topic resting state fMRI
schizophrenia
functional connectivity
ridge regression
global brain connectivity
description Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia. Specifically, ridge regression is implemented with different degrees of regularization. Results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations and from the non-redundant connectivity method (NRC), a dimensionality reduction approach that applies supervised principal component regressions. Ridge regression is able to detect a larger set of abnormally connected regions than both GBC and NRC methods, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC, but it also outperforms the NRC, which is multivariate too. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=21678
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=21678
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv FRONTIERS MEDIA SA
publisher.none.fl_str_mv FRONTIERS MEDIA SA
dc.source.none.fl_str_mv Frontiers in Human Neuroscience
ISSN: 16625161
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
repository.mail.fl_str_mv
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