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The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the “hub disruption index κ,” an index sensitive to graph network modifications. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to standard IQ children, not only for the whole brain graph, but also for each hemispheric graph, and for the homotopic connectivity. Moreover, two profiles of HIQ children, homogenous and heterogeneous, based on the differences between the two main IQ subscales [verbal comprehension index (VCI) and perceptual reasoning index (PRI)], were compared. Brain network changes were more pronounced in the heterogeneous than in the homogeneous HIQ subgroups. Finally, we found significant correlations between the graph networks’ changes and the full-scale IQ (FSIQ), as well as the subscales VCI and PRI. Specifically, the higher the FSIQ the greater was the brain organization modification in the whole brain, the left hemisphere, and the homotopic connectivity. These results shed new light on the relation between functional connectivity topology and high intelligence, as well as on different intelligence profiles.

Stroke, resulting in focal structural damage, induces changes in brain function at both local and global levels. Following stroke, cerebral networks present structural and functional reorganization to compensate for the dysfunctioning provoked by the lesion itself and its remote effects. As some recent studies underlined the role of the contralesional hemisphere during recovery, we studied its role %of the contralesional hemispherein the reorganization of brain function of stroke patients using resting state fMRI and graph theory. We explored this reorganization using the 'hub disruption index' (kappa), a global index sensitive to the reorganization of nodes within the graph. For a given graph metric, kappa of a subject corresponds to the slope of the linear regression model between the mean local network measures of a reference group, and the difference between that reference and the subject under study. In order to translate the use of kappa in clinical context, a prerequisite to achieve meaningful results is to investigate the reliability of this index. In a preliminary part, we studied the reliability of kappa by computing the intraclass correlation coefficient in a cohort of 100 subjects from the Human Connectome Project. Then, we measured intra-hemispheric kappa index in the contralesional hemisphere of 20 subacute stroke patients compared to 20 age-matched healthy controls. Finally, due to the small number of patients, we tested the robustness of our results repeating the experiment 1000 times by bootstrapping on the Human Connectome Project database. Statistical analysis showed a significant reduction of kappa for the contralesional hemisphere of right stroke patients compared to healthy controls. Similar results were observed for the right contralesional hemisphere of left stroke patients. We showed that kappa, is more reliable than global graph metrics and more sensitive to detect differences between groups of patients as compared to healthy controls. Using new graph metrics as kappa allows us to show that stroke induces a network-wide pattern of reorganization in the contralesional hemisphere whatever the side of the lesion. Graph modeling combined with measure of reorganization at the level of large-scale networks can become a useful tool in clinic.

CamBAfx is a workflow application designed for both researchers who use workflows to process data (consumers) and those who design them (designers). It provides a front-end (user interface) optimized for data processing designed in a way familiar to consumers. The back-end uses a pipeline model to represent workflows since this is a common and useful metaphor used by designers and is easy to manipulate compared to other representations like programming scripts. As an Eclipse Rich Client Platform application, CamBAfx's pipelines and functions can be bundled with the software or downloaded post-installation. The user interface contains all the workflow facilities expected by consumers. Using the Eclipse Extension Mechanism designers are encouraged to customize CamBAfx for their own pipelines. CamBAfx wraps a workflow facility around neuroinformatics software without modification. CamBAfx's design, licensing and Eclipse Branding Mechanism allow it to be used as the user interface for other software, facilitating exchange of innovative computational tools between originating labs.