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
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.
Available from: Aki Nikolaidis
- "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. "
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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.
Available from: Keith M Kendrick
- "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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ABSTRACT: Many psychiatric disorders are associated with abnormal resting-state functional connectivity between pairs of brain regions, although it remains unclear whether the fault resides within the pair of regions themselves or other regions connected to them. Identifying the source of dysfunction is crucial for understanding the etiology of different disorders. Using pathway- and network-based techniques to analyze resting-state functional magnetic imaging data from a large population of patients with attention deficit hyperactivity disorder (239 patients, 251 controls), major depression (39 patients, 37 controls), and schizophrenia (69 patients, 62 controls), we show for the first time that only network-based cross-correlation identifies significant functional connectivity changes in all 3 disorders which survive correction. This demonstrates that the primary source of dysfunction resides not in the regional pairs themselves but in their external connections. Combining pathway and network-based functional-connectivity analysis, we established that, in all 3 disorders, the counterparts of pairs of regions in the opposite hemisphere contribute 60-76% to altered functional connectivity, compared with only 17-21% from the regions themselves. Thus, a transdiagnostic feature is of abnormal functional connectivity between brain regions produced via their contralateral counterparts. Our results demonstrate an important role for contralateral counterpart regions in contributing to altered regional connectivity in psychiatric disorders.
Available from: Peter J Hellyer
- "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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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
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