Abstract
Dynamic communication and routing play important roles in the human brain in order to facilitate flexibility in task solving and thought processes. Here, we present a network perturbation methodology that allows investigating dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. We show that network pathways can have characteristic timescales and thus specific preferences for the phase lag between the regions they connect. Specifically, we identified pairs of network nodes whose connecting paths can either be (1) insensitive to the phase relationship between the node pair, (2) turned on and off via changes in the phase relationship between the node pair, or (3) switched between via changes in the phase relationship between the node pair. Regarding the latter, we found that 33% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This reveals a potential mechanistic role that phase offsets and coupling delays might play for the dynamic information routing via communication pathways in the brain.
Highlights
Over the past decades it has been shown that the brain, facing a specific task or not, exhibits well-structured functional connectivity [1,2,3,4,5]
We present an approach to investigate the routing and gating of information flow along different pathways from one region to another
Cortico-cortical coupling strengths and delays were informed by an approximation of the human connectome obtained from diffusion tensor imaging (DTI) data
Summary
Over the past decades it has been shown that the brain, facing a specific task or not, exhibits well-structured functional connectivity [1,2,3,4,5]. One mechanism that has been proposed for such dynamic re-organization is top-down suppression of distractor areas (or task-irrelevant areas) by a slow, oscillatory rhythm compared to the neural rhythm at which the signal is being processed [16,17,18] While this suppression mechanism acts on individual nodes in the network, dynamic re-organization by changes in the phase relationships could act directly on the edges of the network by modulating the effective connectivity between nodes [19,20,21]. The functional coupling pattern that arises from this process could be temporarily altered by advancing or slowing down the phase of each of the neural oscillators This way, changes in phase relationships between brain areas could serve as a mechanism for the re-organization of effective connectivity
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