Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

Autores
Moguilner, Sebastian; Whelan, Robert; Adams, Hieab; Valcour, Victor; Tagliazucchi, Enzo Rodolfo; Ibañez, Agustin Mariano
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. Methods: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. Findings: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. Interpretation: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. Funding: The specific funding of this article is provided in the acknowledgements section.
Fil: Moguilner, Sebastian. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile
Fil: Whelan, Robert. University of California; Estados Unidos
Fil: Adams, Hieab. Universidad Adolfo Ibañez; Chile
Fil: Valcour, Victor. University of California; Estados Unidos
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad Adolfo Ibañez; Chile
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Universidad Adolfo Ibañez; Chile
Materia
COMPUTER VISION
DEEP LEARNING
DEMENTIA
REPRODUCIBILITY
UNPROCESSED MRI
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/224957

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network_name_str CONICET Digital (CONICET)
spelling Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samplesMoguilner, SebastianWhelan, RobertAdams, HieabValcour, VictorTagliazucchi, Enzo RodolfoIbañez, Agustin MarianoCOMPUTER VISIONDEEP LEARNINGDEMENTIAREPRODUCIBILITYUNPROCESSED MRIhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Background: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. Methods: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. Findings: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. Interpretation: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. Funding: The specific funding of this article is provided in the acknowledgements section.Fil: Moguilner, Sebastian. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileFil: Whelan, Robert. University of California; Estados UnidosFil: Adams, Hieab. Universidad Adolfo Ibañez; ChileFil: Valcour, Victor. University of California; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Universidad Adolfo Ibañez; ChileElsevier2023-04info: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/11336/224957Moguilner, Sebastian; Whelan, Robert; Adams, Hieab; Valcour, Victor; Tagliazucchi, Enzo Rodolfo; et al.; Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples; Elsevier; eBioMedicine; 90; 4-2023; 1-152352-3964CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ebiom.2023.104540info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:33:12Zoai:ri.conicet.gov.ar:11336/224957instacron: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 10:33:12.356CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
title Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
spellingShingle Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
Moguilner, Sebastian
COMPUTER VISION
DEEP LEARNING
DEMENTIA
REPRODUCIBILITY
UNPROCESSED MRI
title_short Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
title_full Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
title_fullStr Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
title_full_unstemmed Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
title_sort Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples
dc.creator.none.fl_str_mv Moguilner, Sebastian
Whelan, Robert
Adams, Hieab
Valcour, Victor
Tagliazucchi, Enzo Rodolfo
Ibañez, Agustin Mariano
author Moguilner, Sebastian
author_facet Moguilner, Sebastian
Whelan, Robert
Adams, Hieab
Valcour, Victor
Tagliazucchi, Enzo Rodolfo
Ibañez, Agustin Mariano
author_role author
author2 Whelan, Robert
Adams, Hieab
Valcour, Victor
Tagliazucchi, Enzo Rodolfo
Ibañez, Agustin Mariano
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv COMPUTER VISION
DEEP LEARNING
DEMENTIA
REPRODUCIBILITY
UNPROCESSED MRI
topic COMPUTER VISION
DEEP LEARNING
DEMENTIA
REPRODUCIBILITY
UNPROCESSED MRI
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Background: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. Methods: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. Findings: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. Interpretation: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. Funding: The specific funding of this article is provided in the acknowledgements section.
Fil: Moguilner, Sebastian. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile
Fil: Whelan, Robert. University of California; Estados Unidos
Fil: Adams, Hieab. Universidad Adolfo Ibañez; Chile
Fil: Valcour, Victor. University of California; Estados Unidos
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad Adolfo Ibañez; Chile
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Universidad Adolfo Ibañez; Chile
description Background: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. Methods: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. Findings: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. Interpretation: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. Funding: The specific funding of this article is provided in the acknowledgements section.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
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/11336/224957
Moguilner, Sebastian; Whelan, Robert; Adams, Hieab; Valcour, Victor; Tagliazucchi, Enzo Rodolfo; et al.; Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples; Elsevier; eBioMedicine; 90; 4-2023; 1-15
2352-3964
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224957
identifier_str_mv Moguilner, Sebastian; Whelan, Robert; Adams, Hieab; Valcour, Victor; Tagliazucchi, Enzo Rodolfo; et al.; Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples; Elsevier; eBioMedicine; 90; 4-2023; 1-15
2352-3964
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ebiom.2023.104540
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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