Article

Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI

Inserm, u678, CHU Pitié-Salpêtrière, Paris F-75013, France.
Human Brain Mapping (Impact Factor: 5.97). 03/2009; 30(3):941-50. DOI: 10.1002/hbm.20555
Source: PubMed

ABSTRACT

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.

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    • "To extract and process the network connectivity information, we calculated averaged timecourses for each node within each subject's motor and subcortical networks. Previous literature has suggested that correlation and partial correlation represent different aspects of network construction [17] [18] [19]. Therefore, we used both Pearson's correlation and partial correlation to assess the relationships between these time courses. "
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    • "There is also growing interest in establishing transdiagnostic approaches aiming to identify common molecular, neural, and behavioral phenotypes in mental disorders (Buckholtz and Meyer-Lindenberg 2012; Robbins et al. 2012; Consortium C-DGotPG 2013). Functional connectivity is primarily measured by temporal correlation of activities in pairs of brain regions and analyzed using either cross-correlation (Pearson) (Biswal et al. 1995; Friston et al. 1996) or partial correlation (Marrelec et al. 2006, 2009; Tao et al. 2013) techniques. Despite the extensive research carried out on functional connectivity analysis in mental disorders, it is still unclear whether the cause of the reported changes resides within the region pairs themselves, or in other external regions connected to them in the brain network. "
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    • "In this case, the correlations between neural units is inferred once the effects of all other units have been removed. Partial correlations are typically preferred to Pearson's correlation coefficient as they have been shown to be better suited to detecting changes in connectivity structure [Smith et al., 2011, Marrelec et al., 2009]. "
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