Carlo Nicolini

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The modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman's Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined(More)
Activity recognition can be seen as a local task aimed at identifying an ongoing activity performed at a certain time, or a global one identifying time segments in which a certain activity is being performed. We combine these tasks by a hierarchical approach which locally predicts ongoing activities by a Support Vector Machine and globally refines them by a(More)
Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a(More)
Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular(More)
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