Ricerc@Sapienza

Although cerebellar-cortical interactions have been studied extensively in animal models and humans using modern neuroimaging techniques, the effects of cerebellar stroke and focal lesions on cerebral cortical processing remain unknown. In the present study, we analyzed the large-scale functional connectivity at the cortical level by combining high density electroencephalography (EEG) and source imaging techniques to evaluate and quantify the compensatory reorganization of brain networks after cerebellar damage.

We present graph-theoretic characterisations of three notions of inefficiency arising in network models: edge-weakness in flow networks, node-weakness in depletable channels, and vulnerability in traffic networks. Our characterisations lead to three polynomial algorithms that check these forms of inefficiency. Furthermore, checking vulnerability also leads to an advancement on the subgraph homeomorphism problem.

We consider three network models where information items flow from a source to a sink node: flow networks, depletable channels, and traffic networks. We start with the standard model of flow networks; we characterise graph topologies that admit non-maximum saturating flows, under some capacity-to-edge assignment. We then consider a model where routing is constrained by energy available on nodes in finite supply (like in Smartdust) and efficiency is related to energy consumption and again to maximality of saturating flows.