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.
"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   . Therefore, we used both Pearson's correlation and partial correlation to assess the relationships between these time courses. "
[Show abstract][Hide abstract] ABSTRACT: Procedural learning is the process of skill acquisition that is regulated by the basal ganglia, and this learning becomes automated over time through cortico-striatal and cortico-cortical connectivity. In the current study, we use a common machine learning regression technique to investigate how fMRI network connectivity in the subcortical and motor networks are able to predict initial performance and training-induced improvement in a skill-based cognitive training game, Space Fortress, and how these predictions interact with the strategy the trainees were given during training. To explore the reliability and validity of our findings, we use a range of regression lambda values, sizes of model complexity, and connectivity measurements.
"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]. "
[Show abstract][Hide abstract] ABSTRACT: Understanding the functional architecture of the brain in terms of networks
is becoming increasingly common. In most fMRI applications functional networks
are assumed to be stationary, resulting in a single network estimated for the
entire time course. However recent results suggest that the connectivity
between brain regions is highly non-stationary even at rest. As a result, there
is a need for new brain imaging methodologies that comprehensively account for
the dynamic (i.e., non-stationary) nature of the fMRI data. In this work we
propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm
which estimates dynamic brain networks from fMRI data. We apply the SINGLE
algorithm to functional MRI data from 24 healthy patients performing a
choice-response task to demonstrate the dynamic changes in network structure
that accompany a simple but attentionally demanding cognitive task. Using graph
theoretic measures we show that the Right Inferior Frontal Gyrus, frequently
reported as playing an important role in cognitive control, dynamically changes
with the task. Our results suggest that the Right Inferior Frontal Gyrus plays
a fundamental role in the attention and executive function during cognitively
demanding tasks and may play a key role in regulating the balance between other
"probabilistic and global fiber-tracking was performed to analyze structural connectivity. In a comparison study of different network modeling techniques (Smith et al., 2011) and in direct comparison with structural equation modeling (Marrelec et al., 2009), partial correlations performed well in reconstructing networks based on time series data. Partial correlations are commonly used to analyze direct relationships between time signals, whereas full correlations are more prone to introduce indirect connections into the model. "
[Show abstract][Hide abstract] ABSTRACT: An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional-structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxel's in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the 'default mode network' (DMN) showed the highest agreement of structure-function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional-structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.