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

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.

A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code

“…For instance, theta rhythms may be generated in multiple sources of the brain, not just in the hippocampus, and are known to propagate through the Default Mode Network (see Hsieh and Ranganath 2014). The latter observation however does not contradict our simulations: in our model theta rhythm actually propagates from L1 to downstream layers and, likely, spreads toward other brain areas too (not included in the model), where the evoked memory patterns are utilized and manipulated (but see also Zhang et al 2020;Yuan et al 2021 for recent studies on the subject).…”

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

“…Previous research demonstrates that the abuse of MA has serious damage to various organs of the human body, including (Yuan et al, 2021). The main feature is that its functional activity is higher in the resting state than in the motion state, which is related to memory and information processing.…”

Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models

“…In addition, some published metabolomics results suggest that some metabolites related to energy metabolism are abnormal in the MDD brains, which reveals that MDD symptom is accompanied by abnormal energy consumption in the brain (Abdallah et al, 2014;Yoon et al, 2016;Zuccoli et al, 2017;MacDonald et al, 2019;Li et al, 2022). We have explored the correlation between neural energy and neural activity in a series of past studies (Zhu et al, 2019;Wang et al, 2021;Yuan et al, 2021), which provides a theoretical basis for our exploration of energy income and expenditure in MDD. In the model, we can calculate the energy consumption of electrical activity in the neural network from the membrane potential and ion channels, and synaptic currents at different moments (Wang et al, 2017;Zhu et al, 2018).…”