We model the brain as a network of Kuramoto loosely coupled oscillators, where node frequencies are determined by physiological observations,  and the edges given by the human connectome. Global synchrony can be calculated from Kuramoto’s Order parameter, essentially the average phase over all the nodes.  We argue that structural and functional subnetworks with the global network possess frequency characteristics akin to network resonance in electric circuits, such that selective entrainment of networks could be achieved by externally driving the network close its ‘resonance’ frequency – and that such entrainment would have behavioural consequences on the tasks associated with the selected network.
This framework could be used to selectively facilitate cognitive processing by enhancing neural oscillations affected, for example, by neurodegeneration.


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