dc.contributor.author | Graña Romay, Manuel María | |
dc.contributor.author | Silva, Moisés | |
dc.date.accessioned | 2021-03-12T09:44:48Z | |
dc.date.available | 2021-03-12T09:44:48Z | |
dc.date.issued | 2021-01-20 | |
dc.identifier.citation | International Journal Of Neural Systems 31(4) : (2021) // Article ID 2150009 | es_ES |
dc.identifier.issn | 1793-6462 | |
dc.identifier.uri | http://hdl.handle.net/10810/50602 | |
dc.description.abstract | Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction, and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this paper, we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines. Specifically, we consider six brain parcellation definitions, five methods for functional connectivity matrix construction, six feature extraction/selection approaches, and nine classifier building algorithms. We report the prediction performance sensitivity to each of these choices, as well as the best results that are comparable with the state of the art. | es_ES |
dc.description.sponsorship | This work has been partially supported by theFEDER funds through MINECO project TIN2017-85827-P. This project has received funding from theEuropean Union’s Horizon 2020 research and inno-vation program under the Marie Sklodowska-Curiegrant agreement No 777720 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | World Scientific | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TIN2017-85827-P | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/777720 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | autism | es_ES |
dc.subject | brain functional connectivity | es_ES |
dc.subject | brain parcellation | es_ES |
dc.subject | feature extraction | es_ES |
dc.subject | machine learning | es_ES |
dc.title | Impact of Machine Learning Pipeline Choices in Autism Prediction from Functional Connectivity Data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This is an Open Access article published by World Scientific Publishing Company. It is distributed under the termsof the Creative Commons Attribution Non Commercial-No Derivatives 4.0 (CC BY-NC-ND) License | es_ES |
dc.rights.holder | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.relation.publisherversion | https://www.worldscientific.com/doi/10.1142/S012906572150009X | es_ES |
dc.identifier.doi | 10.1142/S012906572150009X | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |