NeAT: a nonlinear analysis toolbox for neuroimaging

Casamitjana Díaz, Adrià|||0000-0002-0539-3638; Vilaplana Besler, Verónica|||0000-0001-6924-9961; Puch Giner, Santi; Aduriz Saiz, Asier; Operto, Grégory; Cacciaglia, Raffaele; Falcón, Carlos; Molinuevo, José Luis; Gispert, Juan Domingo; López Molina, Carlos Alejandro

NeAT: a nonlinear analysis toolbox for neuroimaging

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear met...

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Detalhes bibliográficos
Autores:	Casamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638, Vilaplana Besler, Verónica\|\|\|0000-0001-6924-9961, Puch Giner, Santi, Aduriz Saiz, Asier, Operto, Grégory, Cacciaglia, Raffaele, Falcón, Carlos, Molinuevo, José Luis, Gispert, Juan Domingo, López Molina, Carlos Alejandro
Formato:	artículo
Fecha de publicación:	2020
País:	España
Recursos:	Universitat Politècnica de Catalunya (UPC)
Repositorio:	UPCommons. Portal del coneixement obert de la UPC
Idioma:	inglés
OAI Identifier:	oai:upcommons.upc.edu:2117/192478
Acesso em linha:	https://hdl.handle.net/2117/192478 https://dx.doi.org/10.1007/s12021-020-09456-w
Access Level:	acceso abierto
Palavra-chave:	Machine learning Neurology Nonlinear Neuroimaging GLM GAM SVR Alzheimer's disease Inference APOE Aprenentatge automàtic Neurologia Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia

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spelling	NeAT: a nonlinear analysis toolbox for neuroimagingCasamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638Vilaplana Besler, Verónica\|\|\|0000-0001-6924-9961Puch Giner, SantiAduriz Saiz, AsierOperto, GrégoryCacciaglia, RaffaeleFalcón, CarlosMolinuevo, José LuisGispert, Juan DomingoLópez Molina, Carlos AlejandroMachine learningNeurologyNonlinearNeuroimagingGLMGAMSVRAlzheimer's diseaseInferenceAPOEAprenentatge automàticNeurologiaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialÀrees temàtiques de la UPC::Ciències de la salut::Medicina::NeurologiaNeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-e4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). Adrià Casamitjana is supported by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU Research Fellowship. Juan D. Gispert holds a “‘Ramón y Cajal’” fellowship (RYC-2013-13054). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf.Peer Reviewed20202020-03-2520202020-07-06journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/192478https://dx.doi.org/10.1007/s12021-020-09456-wreponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1924782026-05-27T15:37:01Z
dc.title.none.fl_str_mv	NeAT: a nonlinear analysis toolbox for neuroimaging
title	NeAT: a nonlinear analysis toolbox for neuroimaging
spellingShingle	NeAT: a nonlinear analysis toolbox for neuroimaging Casamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638 Machine learning Neurology Nonlinear Neuroimaging GLM GAM SVR Alzheimer's disease Inference APOE Aprenentatge automàtic Neurologia Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia
title_short	NeAT: a nonlinear analysis toolbox for neuroimaging
title_full	NeAT: a nonlinear analysis toolbox for neuroimaging
title_fullStr	NeAT: a nonlinear analysis toolbox for neuroimaging
title_full_unstemmed	NeAT: a nonlinear analysis toolbox for neuroimaging
title_sort	NeAT: a nonlinear analysis toolbox for neuroimaging
dc.creator.none.fl_str_mv	Casamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638 Vilaplana Besler, Verónica\|\|\|0000-0001-6924-9961 Puch Giner, Santi Aduriz Saiz, Asier Operto, Grégory Cacciaglia, Raffaele Falcón, Carlos Molinuevo, José Luis Gispert, Juan Domingo López Molina, Carlos Alejandro
author	Casamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638
author_facet	Casamitjana Díaz, Adrià\|\|\|0000-0002-0539-3638 Vilaplana Besler, Verónica\|\|\|0000-0001-6924-9961 Puch Giner, Santi Aduriz Saiz, Asier Operto, Grégory Cacciaglia, Raffaele Falcón, Carlos Molinuevo, José Luis Gispert, Juan Domingo López Molina, Carlos Alejandro
author_role	author
author2	Vilaplana Besler, Verónica\|\|\|0000-0001-6924-9961 Puch Giner, Santi Aduriz Saiz, Asier Operto, Grégory Cacciaglia, Raffaele Falcón, Carlos Molinuevo, José Luis Gispert, Juan Domingo López Molina, Carlos Alejandro
author2_role	author author author author author author author author author
dc.subject.none.fl_str_mv	Machine learning Neurology Nonlinear Neuroimaging GLM GAM SVR Alzheimer's disease Inference APOE Aprenentatge automàtic Neurologia Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia
topic	Machine learning Neurology Nonlinear Neuroimaging GLM GAM SVR Alzheimer's disease Inference APOE Aprenentatge automàtic Neurologia Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia
description	NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-e4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.
publishDate	2020
dc.date.none.fl_str_mv	2020 2020-03-25 2020 2020-07-06
dc.type.none.fl_str_mv	journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv	info:eu-repo/semantics/article
format	article
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2117/192478 https://dx.doi.org/10.1007/s12021-020-09456-w
url	https://hdl.handle.net/2117/192478 https://dx.doi.org/10.1007/s12021-020-09456-w
dc.language.none.fl_str_mv	Inglés eng
language_invalid_str_mv	Inglés
language	eng
dc.rights.none.fl_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.source.none.fl_str_mv	reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC)
instname_str	Universitat Politècnica de Catalunya (UPC)
reponame_str	UPCommons. Portal del coneixement obert de la UPC
collection	UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score	15,301629

NeAT: a nonlinear analysis toolbox for neuroimaging

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