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dc.contributor.author
Gonzalez Gomez, Raul
dc.contributor.author
Ibañez, Agustin Mariano
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dc.contributor.author
Moguilner, Sebastian Gabriel
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dc.date.available
2023-12-06T16:03:47Z
dc.date.issued
2023-01
dc.identifier.citation
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
dc.identifier.uri
http://hdl.handle.net/11336/219535
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MIT Press Journals
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CONNECTIVITY
dc.subject
FTD
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FTD VARIANTS
dc.subject
MACHINE LEARNING
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MULTICLASS CLASSIFICATION
dc.subject.classification
Neurociencias
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dc.subject.classification
Medicina Básica
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-12-05T15:03:27Z
dc.identifier.eissn
2472-1751
dc.journal.volume
7
dc.journal.number
1
dc.journal.pagination
322-350
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; Chile
dc.description.fil
Fil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile
dc.description.fil
Fil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile
dc.journal.title
Network Neuroscience
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/7/1/322/113337/Multiclass-characterization-of-frontotemporal
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