Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis

Autores
Zhang, Jie; Sun, Zhe; Duan, Feng; Shi, Liang; Zhang, Yu; Solé Casals, Jordi; Caiafa, César Federico
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1–3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4–6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.
Fil: Zhang, Jie. Nankai University; China
Fil: Sun, Zhe. No especifíca;
Fil: Duan, Feng. Nankai University; China
Fil: Shi, Liang. Nankai University; China
Fil: Zhang, Yu. Lehigh University; Estados Unidos
Fil: Solé Casals, Jordi. Nankai University; China
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Materia
CORTICAL LAYERS
DIFFUSION MAGNETIC RESONANCE IMAGING
IN VIVO
LAMINAR CONNECTIONS
NONINVASIVE
WORKING MEMORY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214006
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/214006
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysisZhang, JieSun, ZheDuan, FengShi, LiangZhang, YuSolé Casals, JordiCaiafa, César FedericoCORTICAL LAYERSDIFFUSION MAGNETIC RESONANCE IMAGINGIN VIVOLAMINAR CONNECTIONSNONINVASIVEWORKING MEMORYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1–3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4–6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.Fil: Zhang, Jie. Nankai University; ChinaFil: Sun, Zhe. No especifíca;Fil: Duan, Feng. Nankai University; ChinaFil: Shi, Liang. Nankai University; ChinaFil: Zhang, Yu. Lehigh University; Estados UnidosFil: Solé Casals, Jordi. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaWiley-liss, div John Wiley & Sons Inc.2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214006Zhang, Jie; Sun, Zhe; Duan, Feng; Shi, Liang; Zhang, Yu; et al.; Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis; Wiley-liss, div John Wiley & Sons Inc.; Human Brain Mapping; 43; 17; 7-2022; 5220-52341065-9471CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1002/hbm.25998info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:56:36Zoai:ri.conicet.gov.ar:11336/214006instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:56:37.174CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
title Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
spellingShingle Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
Zhang, Jie
CORTICAL LAYERS
DIFFUSION MAGNETIC RESONANCE IMAGING
IN VIVO
LAMINAR CONNECTIONS
NONINVASIVE
WORKING MEMORY
title_short Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
title_full Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
title_fullStr Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
title_full_unstemmed Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
title_sort Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis
dc.creator.none.fl_str_mv Zhang, Jie
Sun, Zhe
Duan, Feng
Shi, Liang
Zhang, Yu
Solé Casals, Jordi
Caiafa, César Federico
author Zhang, Jie
author_facet Zhang, Jie
Sun, Zhe
Duan, Feng
Shi, Liang
Zhang, Yu
Solé Casals, Jordi
Caiafa, César Federico
author_role author
author2 Sun, Zhe
Duan, Feng
Shi, Liang
Zhang, Yu
Solé Casals, Jordi
Caiafa, César Federico
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv CORTICAL LAYERS
DIFFUSION MAGNETIC RESONANCE IMAGING
IN VIVO
LAMINAR CONNECTIONS
NONINVASIVE
WORKING MEMORY
topic CORTICAL LAYERS
DIFFUSION MAGNETIC RESONANCE IMAGING
IN VIVO
LAMINAR CONNECTIONS
NONINVASIVE
WORKING MEMORY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1–3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4–6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.
Fil: Zhang, Jie. Nankai University; China
Fil: Sun, Zhe. No especifíca;
Fil: Duan, Feng. Nankai University; China
Fil: Shi, Liang. Nankai University; China
Fil: Zhang, Yu. Lehigh University; Estados Unidos
Fil: Solé Casals, Jordi. Nankai University; China
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
description Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1–3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4–6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.
publishDate 2022
dc.date.none.fl_str_mv 2022-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/214006
Zhang, Jie; Sun, Zhe; Duan, Feng; Shi, Liang; Zhang, Yu; et al.; Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis; Wiley-liss, div John Wiley & Sons Inc.; Human Brain Mapping; 43; 17; 7-2022; 5220-5234
1065-9471
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214006
identifier_str_mv Zhang, Jie; Sun, Zhe; Duan, Feng; Shi, Liang; Zhang, Yu; et al.; Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis; Wiley-liss, div John Wiley & Sons Inc.; Human Brain Mapping; 43; 17; 7-2022; 5220-5234
1065-9471
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/hbm.25998
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley-liss, div John Wiley & Sons Inc.
publisher.none.fl_str_mv Wiley-liss, div John Wiley & Sons Inc.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.25334

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