Intervention Models in Functional Connectivity Identification Applied to fMRI
Recent advances in neuroimaging techniques have provided precise spatial localization of brain activation applied in several neuroscience subareas. The development of functional magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular techniques related to the detectio...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/27483 |
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| author | João Ricardo Sato Daniel Yasumasa Takahashi Ellison Fernando Cardoso Maria da Graça Morais Martin Edson Amaro Júnior Pedro Alberto Morettin |
| author_facet | João Ricardo Sato Daniel Yasumasa Takahashi Ellison Fernando Cardoso Maria da Graça Morais Martin Edson Amaro Júnior Pedro Alberto Morettin |
| author_sort | João Ricardo Sato |
| collection | DOAJ |
| description | Recent advances in neuroimaging techniques have provided precise spatial localization
of brain activation applied in several neuroscience subareas. The development of functional
magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular
techniques related to the detection of neuronal activation. However, understanding the
interactions between several neuronal modules is also an important task, providing a better
comprehension about brain dynamics. Nevertheless, most connectivity studies in fMRI are based
on a simple correlation analysis, which is only an association measure and does not provide the
direction of information flow between brain areas. Other proposed methods like structural
equation modeling (SEM) seem to be attractive alternatives. However, this approach assumes
prior information about the causality direction and stationarity conditions, which may not be
satisfied in fMRI experiments. Generally, the fMRI experiments are related to an activation task;
hence, the stimulus conditions should also be included in the model. In this paper, we suggest an intervention analysis, which includes stimulus condition, allowing a nonstationary modeling. Furthermore, an illustrative application to real fMRI
dataset from a simple motor task is presented. |
| format | Article |
| id | doaj-art-a201804edd6949b9b5dda44ed502fc5a |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a201804edd6949b9b5dda44ed502fc5a2025-02-03T01:33:05ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962006-01-01200610.1155/IJBI/2006/2748327483Intervention Models in Functional Connectivity Identification Applied to fMRIJoão Ricardo Sato0Daniel Yasumasa Takahashi1Ellison Fernando Cardoso2Maria da Graça Morais Martin3Edson Amaro Júnior4Pedro Alberto Morettin5Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Sp 05508-090, BrazilDepartamento de Radiología, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, BrazilLaboratório de Neuroimagem Funcional (NIF), Lim 44, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, BrazilLaboratório de Neuroimagem Funcional (NIF), Lim 44, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, BrazilLaboratório de Neuroimagem Funcional (NIF), Lim 44, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, BrazilDepartamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Sp 05508-090, BrazilRecent advances in neuroimaging techniques have provided precise spatial localization of brain activation applied in several neuroscience subareas. The development of functional magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular techniques related to the detection of neuronal activation. However, understanding the interactions between several neuronal modules is also an important task, providing a better comprehension about brain dynamics. Nevertheless, most connectivity studies in fMRI are based on a simple correlation analysis, which is only an association measure and does not provide the direction of information flow between brain areas. Other proposed methods like structural equation modeling (SEM) seem to be attractive alternatives. However, this approach assumes prior information about the causality direction and stationarity conditions, which may not be satisfied in fMRI experiments. Generally, the fMRI experiments are related to an activation task; hence, the stimulus conditions should also be included in the model. In this paper, we suggest an intervention analysis, which includes stimulus condition, allowing a nonstationary modeling. Furthermore, an illustrative application to real fMRI dataset from a simple motor task is presented.http://dx.doi.org/10.1155/IJBI/2006/27483 |
| spellingShingle | João Ricardo Sato Daniel Yasumasa Takahashi Ellison Fernando Cardoso Maria da Graça Morais Martin Edson Amaro Júnior Pedro Alberto Morettin Intervention Models in Functional Connectivity Identification Applied to fMRI International Journal of Biomedical Imaging |
| title | Intervention Models in Functional Connectivity Identification Applied to fMRI |
| title_full | Intervention Models in Functional Connectivity Identification Applied to fMRI |
| title_fullStr | Intervention Models in Functional Connectivity Identification Applied to fMRI |
| title_full_unstemmed | Intervention Models in Functional Connectivity Identification Applied to fMRI |
| title_short | Intervention Models in Functional Connectivity Identification Applied to fMRI |
| title_sort | intervention models in functional connectivity identification applied to fmri |
| url | http://dx.doi.org/10.1155/IJBI/2006/27483 |
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