Biophysical mechanism of the interaction between default mode network and working memory network

A theoretical model coupling the default mode network (DMN) and working memory network (WMN) simulates neural dynamics during encoding, maintenance, and retrieval phases. AMPA channels produce synchronous oscillations that shift oscillation patterns in both networks. Different NMDA conductance between networks generates multiple neural activity modes, potentially switching network states across memory phases. The number of sequentially memorized stimuli relates to energy consumption determined by internal parameters, with the DMN contributing to more stable working memory. Different memory phases correspond to different functional connections between DMN and WMN, with coupling strengths differing in phase synchronization. Phase synchronization characteristics of contained energy match observed negative and positive correlations between networks from fMRI experiments.

Study at a glance

Abstract

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.