Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI
Structural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work publish...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-02-01
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| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fnbeh.2018.00019/full |
| _version_ | 1819050353429053440 |
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| author | Joan Guàrdia-Olmos Maribel Peró-Cebollero Esteve Gudayol-Ferré |
| author_facet | Joan Guàrdia-Olmos Maribel Peró-Cebollero Esteve Gudayol-Ferré |
| author_sort | Joan Guàrdia-Olmos |
| collection | DOAJ |
| description | Structural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity.Results: A 100 papers were found, of which 25 were rejected due to a lack of sufficient data on basic aspects of the construction of SEM. The other 75 were included and contained a total of 160 models to analyze, since most papers included more than one model. The analysis of the explained variance (R2) of each model yields an effect of the type of design used, the type of population studied, the type of study, the existence of recursive effects in the model, and the number of paths defined in the model. Along with these comments, a series of recommendations are included for the use of SEM to estimate of functional and effective connectivity models. |
| first_indexed | 2024-12-21T11:46:42Z |
| format | Article |
| id | doaj.art-64d2b3585a9f4bdc8af91c9678d2f5ec |
| institution | Directory Open Access Journal |
| issn | 1662-5153 |
| language | English |
| last_indexed | 2024-12-21T11:46:42Z |
| publishDate | 2018-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj.art-64d2b3585a9f4bdc8af91c9678d2f5ec2022-12-21T19:05:09ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532018-02-011210.3389/fnbeh.2018.00019267828Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRIJoan Guàrdia-Olmos0Maribel Peró-Cebollero1Esteve Gudayol-Ferré2Department of Social Psychology and Quantitative Psychology, School of Psychology, Institute of Neuroscience, Institute of Complexity, University of Barcelona, Barcelona, SpainDepartment of Social Psychology and Quantitative Psychology, School of Psychology, Institute of Neuroscience, Institute of Complexity, University of Barcelona, Barcelona, SpainSchool of Psychology, Universidad Michoacana de San Nicolás de Hidalgo de Morelia, Morelia, MexicoStructural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity.Results: A 100 papers were found, of which 25 were rejected due to a lack of sufficient data on basic aspects of the construction of SEM. The other 75 were included and contained a total of 160 models to analyze, since most papers included more than one model. The analysis of the explained variance (R2) of each model yields an effect of the type of design used, the type of population studied, the type of study, the existence of recursive effects in the model, and the number of paths defined in the model. Along with these comments, a series of recommendations are included for the use of SEM to estimate of functional and effective connectivity models.http://journal.frontiersin.org/article/10.3389/fnbeh.2018.00019/fullfMRIstructural equation modelsfunctional connectivityeffective connectivitycognitive neuroscience |
| spellingShingle | Joan Guàrdia-Olmos Maribel Peró-Cebollero Esteve Gudayol-Ferré Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI Frontiers in Behavioral Neuroscience fMRI structural equation models functional connectivity effective connectivity cognitive neuroscience |
| title | Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI |
| title_full | Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI |
| title_fullStr | Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI |
| title_full_unstemmed | Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI |
| title_short | Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI |
| title_sort | meta analysis of the structural equation models parameters for the estimation of brain connectivity with fmri |
| topic | fMRI structural equation models functional connectivity effective connectivity cognitive neuroscience |
| url | http://journal.frontiersin.org/article/10.3389/fnbeh.2018.00019/full |
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