Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning

Detalhes bibliográficos
Autor(a) principal: Azevedo, Carolina Isabel Santos
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/58952
Resumo: Tese de Mestrado, Engenharia Biomédica e Biofísica, 2023, Universidade de Lisboa, Faculdade de Ciências
id RCAP_559381522f634abd629363145b92da56
oai_identifier_str oai:repositorio.ul.pt:10451/58952
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learningRessonância Magnética Funcional de RepousoRedes de repousoAprendizagem profundaRedes Neuronais ConvolucionaisTese de mestrado - 2023Domínio/Área Científica::Ciências Naturais::Ciências FísicasTese de Mestrado, Engenharia Biomédica e Biofísica, 2023, Universidade de Lisboa, Faculdade de CiênciasPre-surgical mapping relies on neuroimaging techniques, such as Functional Magnetic Resonance (fMRI). Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) enables the detection of brain spontaneous activity which have formed coherent Resting-State Networks (RSNs). Multiple techniques have been used to map the representation of function using rsfMRI, among these are Deep Learning (DL) methods. Based on current literature, we studied the feasibility of implementing a 3D Convolutional Neural Network for mapping RSNs. To create and evaluate our model, we used the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. An additional dataset from Hospital da Luz (HL) was used in testing stages. After data and Regions of Interest (ROIs) selection, brain maps of correlation were obtained and assigned to their respective RSNs. Subsequently, the main core architecture of our model was defined. In order to achieve better classification performance results, several trials were required. These varied according to the number of filters or neurons presented in each convolutional and fully connected layers, as well as the set of hyperparameters implemented. After performing model training, it was time to test our model. Together with accuracy results, AUC values and confusion matrices were obtained, allowing a better insight of the predictions for all classes. In addition, several model evaluation measures, such as precision, sensitivity and F1-score were acquired. All the results gathered allowed an overall comparison between the two datasets implemented at this stage. The results of this dissertation revealed that the proposed pipeline was able to classify RSNs. Overall, it demonstrated the feasibility of implementing a 3D CNN to map several RSNs, elucidating the relevance of both rs-fMRI and DL in mapping brain regions.Andrade, Alexandre da Rocha Freire deRepositório da Universidade de LisboaAzevedo, Carolina Isabel Santos2023-08-22T11:39:43Z202320232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/58952enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:07:56Zoai:repositorio.ul.pt:10451/58952Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:09:01.330164Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
title Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
spellingShingle Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
Azevedo, Carolina Isabel Santos
Ressonância Magnética Funcional de Repouso
Redes de repouso
Aprendizagem profunda
Redes Neuronais Convolucionais
Tese de mestrado - 2023
Domínio/Área Científica::Ciências Naturais::Ciências Físicas
title_short Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
title_full Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
title_fullStr Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
title_full_unstemmed Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
title_sort Mapping of resting-state networks in resting-state functional. Magnetic resonance with deep learning
author Azevedo, Carolina Isabel Santos
author_facet Azevedo, Carolina Isabel Santos
author_role author
dc.contributor.none.fl_str_mv Andrade, Alexandre da Rocha Freire de
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Azevedo, Carolina Isabel Santos
dc.subject.por.fl_str_mv Ressonância Magnética Funcional de Repouso
Redes de repouso
Aprendizagem profunda
Redes Neuronais Convolucionais
Tese de mestrado - 2023
Domínio/Área Científica::Ciências Naturais::Ciências Físicas
topic Ressonância Magnética Funcional de Repouso
Redes de repouso
Aprendizagem profunda
Redes Neuronais Convolucionais
Tese de mestrado - 2023
Domínio/Área Científica::Ciências Naturais::Ciências Físicas
description Tese de Mestrado, Engenharia Biomédica e Biofísica, 2023, Universidade de Lisboa, Faculdade de Ciências
publishDate 2023
dc.date.none.fl_str_mv 2023-08-22T11:39:43Z
2023
2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/58952
url http://hdl.handle.net/10451/58952
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799134646409101312

Registros relacionados