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The relationship between fMRI and MEG signals between different cortical regions (functional connectivity, FC) has been extensively analyzed in the resting state (De Pasquale et al 2010; Brookes et al 2011; Hipp et al 2012). Much less is known about FC modulations from rest to task states, and how they appear respectively in these two imaging modalities. Previously we have shown task-specific alterations of FC in fMRI during a visuospatial attention task (Spadone et al., PNAS, 2015). Specifically, decrements of resting correlation in visual areas were coupled with increments of correlation between visual and dorsal attention regions. Here, we compared fMRI with band-limited power (BLP) correlation obtained with MEG on the same group of subjects. Aim (i) is to measure frequency specific task-related FC modulations in MEG. Aim (ii) is to compare fMRI- and MEG-FC modulations (task-rest).

In this paper we study cluster synchronization in a network of Kuramoto oscillators, where groups of oscillators evolve cohesively and at different frequencies from the neighboring oscillators. Synchronization is critical in a variety of systems, where it enables complex functionalities and behaviors. Synchronization over networks depends on the oscillators’ dynamics, the interaction topology, and coupling strengths, and the relationship between these different factors can be quite intricate. In this work we formally show that three network properties enable the emergence of cluster synchronization. Specifically, weak inter-cluster connections, strong intra-cluster connections, and sufficiently diverse natural frequencies among oscillators belonging to different groups. Our approach relies on system-theoretic tools, and is validated with numerical studies.

The problem of emergent synchronization pat-terns in a complex network of coupled oscillators has caughtscientists’ interest in a lot of different disciplines. In particular,from a biological point of view, considerable attention has beenrecently devoted to the study of the human brain as a networkof different cortical regions that show coherent activity duringresting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease.In this context, the Kuramoto model, a classical model apt todescribe oscillators’ dynamics, has been extended to capture thespatial displacement and the communication conditions in suchbrain network. Starting from a previous work in this ?eld ,we analyze this modi?ed model and compare it with otherexisting large-scale models. In doing so, our aim is to promotea set of mathematical tools useful to better understand realexperimental data in neuroscience and estimate brain dynamics.