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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/224957
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/224957 |
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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) |
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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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13.069144 |