Abstract
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.
Highlights
Structural Equation Models (SEM) have been among the most extensively applied statistical techniques in the scientific literature in the last 30 years
If we look at some papers (James et al, 2009; McCormick et al, 2010; Bianchi et al, 2012; Deshpande and Hu, 2012; Murray et al, 2012; Sawyer et al, 2012; Voineskos et al, 2012; Yang et al, 2012; Bringmann et al, 2013) we find that all of them account for the limits of the concept of connectivity, of its possibilities in relation to effective connectivity, and the derivatives obtained from using it from an applied perspective; but little, if anything, is said about good practices in the use of SEMs and their adaptations for the statistical estimation of brain connectivity
We aim to offer some recommendations to future users of SEM in this field in order to generate, in the near future, a good mechanism for comparing the results for functional or effective connectivity obtained in different studies
Summary
Structural Equation Models (SEM) have been among the most extensively applied statistical techniques in the scientific literature in the last 30 years. We mean to establish the effect of several variables pertaining to studies on connectivity with f MRI signal on the estimation of the R2 (Coefficient of Determination representing the proportion of explained variance) each model presents This way we intend to break through in the systematization of some of the statistical properties in their application and some of the characteristics of SEMs in the field of Computational Neuroscience when generating estimations that lead us to the consideration of a globally-analyzed functioning brain, and when generating models from complexity. We aim to offer some recommendations to future users of SEM in this field in order to generate, in the near future, a good mechanism for comparing the results for functional or effective connectivity obtained in different studies
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