A Theoretical Investigation of the Relationship between Structural Equation Modeling and Partial Correlation in Functional MRI Effective Connectivity | Zendy

Guillaume Marrelec | Zendy; Habib Benali | Zendy

Open Access

A Theoretical Investigation of the Relationship between Structural Equation Modeling and Partial Correlation in Functional MRI Effective Connectivity

Author(s) -

Guillaume Marrelec,

Habib Benali

Publication year - 2009

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2009/369341

Subject(s) - structural equation modeling , partial correlation , functional magnetic resonance imaging , blood oxygen level dependent , partial least squares regression , correlation , measure (data warehouse) , computer science , partial differential equation , statistical physics , mathematics , data mining , physics , psychology , machine learning , neuroscience , mathematical analysis , geometry

An important field of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is the investigation of effective connectivity, that is, the actions that a given set of regions exert on one another. We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the pattern of functional interaction between brain regions as represented by structural equation modeling (SEM). So far, the efficiency of this approach was mostly based on empirical evidence. In this paper, we provide theoretical fundaments explaining why and in what measure structural equation modeling and partial correlations are related. This gives better insight regarding what parts of SEM can be retrieved by partial correlation analysis and what remains inaccessible. We illustrate the different results with real data.

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