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Comprehensive decoding mental processes from Web repositories of functional brain images

Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope—from domain-specific to system-level analysis—and accuracy. Using all the functional Magnetic Resonance...

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Autores principales: Menuet, Romuald, Meudec, Raphael, Dockès, Jérôme, Varoquaux, Gael, Thirion, Bertrand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9054752/
https://www.ncbi.nlm.nih.gov/pubmed/35488032
http://dx.doi.org/10.1038/s41598-022-10710-1
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author Menuet, Romuald
Meudec, Raphael
Dockès, Jérôme
Varoquaux, Gael
Thirion, Bertrand
author_facet Menuet, Romuald
Meudec, Raphael
Dockès, Jérôme
Varoquaux, Gael
Thirion, Bertrand
author_sort Menuet, Romuald
collection PubMed
description Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope—from domain-specific to system-level analysis—and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault—an open repository of fMRI statistical maps with unconstrained annotations—and Cognitive Atlas—an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity.
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spelling pubmed-90547522022-05-01 Comprehensive decoding mental processes from Web repositories of functional brain images Menuet, Romuald Meudec, Raphael Dockès, Jérôme Varoquaux, Gael Thirion, Bertrand Sci Rep Article Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope—from domain-specific to system-level analysis—and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault—an open repository of fMRI statistical maps with unconstrained annotations—and Cognitive Atlas—an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity. Nature Publishing Group UK 2022-04-29 /pmc/articles/PMC9054752/ /pubmed/35488032 http://dx.doi.org/10.1038/s41598-022-10710-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Menuet, Romuald
Meudec, Raphael
Dockès, Jérôme
Varoquaux, Gael
Thirion, Bertrand
Comprehensive decoding mental processes from Web repositories of functional brain images
title Comprehensive decoding mental processes from Web repositories of functional brain images
title_full Comprehensive decoding mental processes from Web repositories of functional brain images
title_fullStr Comprehensive decoding mental processes from Web repositories of functional brain images
title_full_unstemmed Comprehensive decoding mental processes from Web repositories of functional brain images
title_short Comprehensive decoding mental processes from Web repositories of functional brain images
title_sort comprehensive decoding mental processes from web repositories of functional brain images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9054752/
https://www.ncbi.nlm.nih.gov/pubmed/35488032
http://dx.doi.org/10.1038/s41598-022-10710-1
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