A protocol for fMRI visual decoding

Detalhes bibliográficos
Autor(a) principal: Peres, André Salles Cunha
Data de Publicação: 2014
Outros Autores: Sato, João Ricardo, dos Santos, Antônio Carlos, Hallak, Jaime Eduardo Cecílio, Ribeiro, Sidarta Tollendal Gomes, Araújo, Dráulio Barros de
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/24027
Resumo: Introdução Functional magnetic resonance imaging (fMRI) is widely used to assess patterns of brain activity in response to specific tasks. Recent advances of signal processing tools opened the perspective of decoding information from different stimuli based on fMRI brain activity. Currently, the decoding of visual information is the most successful strategy. Typically, during the encoding phase the volunteers passively see a large number of images and a pattern of the fMRI signal is associated to each one of them. Based only on these BOLD signal patterns, statistical algorithms are used to infer what was the image seen by the subject. A common strategy used for visual cortex decoding is to separate the images into categories, with the intent of creating an average of BOLD distribution for each category. Thus, decoding refers to indicating the category to which an image belongs to. Objetivos Our purpose in this work is to evaluate the feasibility of implementing a visual cortex decoding protocol based on six categories: tree, car, house, food, person, and reptile. Métodos Two asymptomatic volunteers were invited to participate in the study. They were asked to passively watch a set of 1,440 images divided into these six categories, while fMRI data was continuously being acquired. Subjects participated in 13 sessions of 30 minutes each. fMRI analysis was based on the General Linear Model implemented in SPM8 (UCL ­ UK). A threshold was set at p < 0.05 (FWE, corrected). The BOLD distribution was compared for each pair of category, doing a subtraction between them, totaling 30 comparisons. Resultados e Conclusões We found significant differences in the BOLD distribution for all pairs analyzed, which indicate the feasibility to further perform visual cortex decoding using the protocol described above.
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spelling Peres, André Salles CunhaSato, João Ricardodos Santos, Antônio CarlosHallak, Jaime Eduardo CecílioRibeiro, Sidarta Tollendal GomesAraújo, Dráulio Barros de2017-10-10T12:00:13Z2017-10-10T12:00:13Z2014-09https://repositorio.ufrn.br/jspui/handle/123456789/24027engDecodingfMRIBOLD distributionVisual cortexA protocol for fMRI visual decodinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectIntrodução Functional magnetic resonance imaging (fMRI) is widely used to assess patterns of brain activity in response to specific tasks. Recent advances of signal processing tools opened the perspective of decoding information from different stimuli based on fMRI brain activity. Currently, the decoding of visual information is the most successful strategy. Typically, during the encoding phase the volunteers passively see a large number of images and a pattern of the fMRI signal is associated to each one of them. Based only on these BOLD signal patterns, statistical algorithms are used to infer what was the image seen by the subject. A common strategy used for visual cortex decoding is to separate the images into categories, with the intent of creating an average of BOLD distribution for each category. Thus, decoding refers to indicating the category to which an image belongs to. Objetivos Our purpose in this work is to evaluate the feasibility of implementing a visual cortex decoding protocol based on six categories: tree, car, house, food, person, and reptile. Métodos Two asymptomatic volunteers were invited to participate in the study. They were asked to passively watch a set of 1,440 images divided into these six categories, while fMRI data was continuously being acquired. Subjects participated in 13 sessions of 30 minutes each. fMRI analysis was based on the General Linear Model implemented in SPM8 (UCL ­ UK). A threshold was set at p < 0.05 (FWE, corrected). The BOLD distribution was compared for each pair of category, doing a subtraction between them, totaling 30 comparisons. Resultados e Conclusões We found significant differences in the BOLD distribution for all pairs analyzed, which indicate the feasibility to further perform visual cortex decoding using the protocol described above.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALSBNeC 2014, Buzios RJ 11.013.pdfSBNeC 2014, Buzios RJ 11.013.pdfapplication/pdf153436https://repositorio.ufrn.br/bitstream/123456789/24027/1/SBNeC%202014%2c%20Buzios%20RJ%2011.013.pdfff72ef7d92e6f146c6469b6982c7975aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/24027/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52TEXTSBNeC 2014, Buzios RJ 11.013.pdf.txtSBNeC 2014, Buzios RJ 11.013.pdf.txtExtracted texttext/plain3145https://repositorio.ufrn.br/bitstream/123456789/24027/5/SBNeC%202014%2c%20Buzios%20RJ%2011.013.pdf.txt957dcb7785609a0774fa64570c6b2649MD55THUMBNAILSBNeC 2014, Buzios RJ 11.013.pdf.jpgSBNeC 2014, Buzios RJ 11.013.pdf.jpgIM Thumbnailimage/jpeg6337https://repositorio.ufrn.br/bitstream/123456789/24027/6/SBNeC%202014%2c%20Buzios%20RJ%2011.013.pdf.jpgc24bf2bc15deb14922178d92621a9689MD56123456789/240272021-07-10 19:38:27.031oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-07-10T22:38:27Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv A protocol for fMRI visual decoding
title A protocol for fMRI visual decoding
spellingShingle A protocol for fMRI visual decoding
Peres, André Salles Cunha
Decoding
fMRI
BOLD distribution
Visual cortex
title_short A protocol for fMRI visual decoding
title_full A protocol for fMRI visual decoding
title_fullStr A protocol for fMRI visual decoding
title_full_unstemmed A protocol for fMRI visual decoding
title_sort A protocol for fMRI visual decoding
author Peres, André Salles Cunha
author_facet Peres, André Salles Cunha
Sato, João Ricardo
dos Santos, Antônio Carlos
Hallak, Jaime Eduardo Cecílio
Ribeiro, Sidarta Tollendal Gomes
Araújo, Dráulio Barros de
author_role author
author2 Sato, João Ricardo
dos Santos, Antônio Carlos
Hallak, Jaime Eduardo Cecílio
Ribeiro, Sidarta Tollendal Gomes
Araújo, Dráulio Barros de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Peres, André Salles Cunha
Sato, João Ricardo
dos Santos, Antônio Carlos
Hallak, Jaime Eduardo Cecílio
Ribeiro, Sidarta Tollendal Gomes
Araújo, Dráulio Barros de
dc.subject.por.fl_str_mv Decoding
fMRI
BOLD distribution
Visual cortex
topic Decoding
fMRI
BOLD distribution
Visual cortex
description Introdução Functional magnetic resonance imaging (fMRI) is widely used to assess patterns of brain activity in response to specific tasks. Recent advances of signal processing tools opened the perspective of decoding information from different stimuli based on fMRI brain activity. Currently, the decoding of visual information is the most successful strategy. Typically, during the encoding phase the volunteers passively see a large number of images and a pattern of the fMRI signal is associated to each one of them. Based only on these BOLD signal patterns, statistical algorithms are used to infer what was the image seen by the subject. A common strategy used for visual cortex decoding is to separate the images into categories, with the intent of creating an average of BOLD distribution for each category. Thus, decoding refers to indicating the category to which an image belongs to. Objetivos Our purpose in this work is to evaluate the feasibility of implementing a visual cortex decoding protocol based on six categories: tree, car, house, food, person, and reptile. Métodos Two asymptomatic volunteers were invited to participate in the study. They were asked to passively watch a set of 1,440 images divided into these six categories, while fMRI data was continuously being acquired. Subjects participated in 13 sessions of 30 minutes each. fMRI analysis was based on the General Linear Model implemented in SPM8 (UCL ­ UK). A threshold was set at p < 0.05 (FWE, corrected). The BOLD distribution was compared for each pair of category, doing a subtraction between them, totaling 30 comparisons. Resultados e Conclusões We found significant differences in the BOLD distribution for all pairs analyzed, which indicate the feasibility to further perform visual cortex decoding using the protocol described above.
publishDate 2014
dc.date.issued.fl_str_mv 2014-09
dc.date.accessioned.fl_str_mv 2017-10-10T12:00:13Z
dc.date.available.fl_str_mv 2017-10-10T12:00:13Z
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