Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference
- Autores
- Gonzalez Gomez, Raul; Ibañez, Agustin Mariano; Moguilner, Sebastian Gabriel
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; Chile
Fil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile
Fil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile - Materia
-
CONNECTIVITY
FTD
FTD VARIANTS
MACHINE LEARNING
MULTICLASS CLASSIFICATION - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/219535
Ver los metadatos del registro completo
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Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inferenceGonzalez Gomez, RaulIbañez, Agustin MarianoMoguilner, Sebastian GabrielCONNECTIVITYFTDFTD VARIANTSMACHINE LEARNINGMULTICLASS CLASSIFICATIONhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; ChileFil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileMIT Press Journals2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/219535Gonzalez Gomez, Raul; Ibañez, Agustin Mariano; Moguilner, Sebastian Gabriel; Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference; MIT Press Journals; Network Neuroscience; 7; 1; 1-2023; 322-3502472-1751CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/7/1/322/113337/Multiclass-characterization-of-frontotemporalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:19:15Zoai:ri.conicet.gov.ar:11336/219535instacron: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:34982025-09-29 10:19:15.675CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
title |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
spellingShingle |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference Gonzalez Gomez, Raul CONNECTIVITY FTD FTD VARIANTS MACHINE LEARNING MULTICLASS CLASSIFICATION |
title_short |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
title_full |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
title_fullStr |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
title_full_unstemmed |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
title_sort |
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference |
dc.creator.none.fl_str_mv |
Gonzalez Gomez, Raul Ibañez, Agustin Mariano Moguilner, Sebastian Gabriel |
author |
Gonzalez Gomez, Raul |
author_facet |
Gonzalez Gomez, Raul Ibañez, Agustin Mariano Moguilner, Sebastian Gabriel |
author_role |
author |
author2 |
Ibañez, Agustin Mariano Moguilner, Sebastian Gabriel |
author2_role |
author author |
dc.subject.none.fl_str_mv |
CONNECTIVITY FTD FTD VARIANTS MACHINE LEARNING MULTICLASS CLASSIFICATION |
topic |
CONNECTIVITY FTD FTD VARIANTS MACHINE LEARNING MULTICLASS CLASSIFICATION |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.1 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases. Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; Chile Fil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile Fil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile |
description |
Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01 |
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/11336/219535 Gonzalez Gomez, Raul; Ibañez, Agustin Mariano; Moguilner, Sebastian Gabriel; Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference; MIT Press Journals; Network Neuroscience; 7; 1; 1-2023; 322-350 2472-1751 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/219535 |
identifier_str_mv |
Gonzalez Gomez, Raul; Ibañez, Agustin Mariano; Moguilner, Sebastian Gabriel; Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference; MIT Press Journals; Network Neuroscience; 7; 1; 1-2023; 322-350 2472-1751 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/7/1/322/113337/Multiclass-characterization-of-frontotemporal |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
MIT Press Journals |
publisher.none.fl_str_mv |
MIT Press Journals |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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