scholarly journals Coactivation of the Default Mode Network regions and Working Memory Network regions during task preparation

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hideya Koshino ◽  
Takehiro Minamoto ◽  
Ken Yaoi ◽  
Mariko Osaka ◽  
Naoyuki Osaka
Author(s):  
Yue Yuan ◽  
Xiaochuan Pan ◽  
Rubin Wang

AbstractDefault 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.


PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123354 ◽  
Author(s):  
Tommaso Piccoli ◽  
Giancarlo Valente ◽  
David E. J. Linden ◽  
Marta Re ◽  
Fabrizio Esposito ◽  
...  

2020 ◽  
Vol 41 (8) ◽  
pp. 1973-1984
Author(s):  
Qian Guo ◽  
Yang Hu ◽  
Botao Zeng ◽  
Yingying Tang ◽  
Guanjun Li ◽  
...  

2013 ◽  
Vol 150 (2-3) ◽  
pp. 555-562 ◽  
Author(s):  
Max de Leeuw ◽  
René S. Kahn ◽  
Bram B. Zandbelt ◽  
Christian G. Widschwendter ◽  
Matthijs Vink

2014 ◽  
Vol 36 (3) ◽  
pp. 1121-1137 ◽  
Author(s):  
Marcel Daamen ◽  
Josef G. Bäuml ◽  
Lukas Scheef ◽  
Christian Sorg ◽  
Barbara Busch ◽  
...  

2013 ◽  
Vol 16 (6) ◽  
pp. 1195-1204 ◽  
Author(s):  
Ayna B. Nejad ◽  
Kristoffer H. Madsen ◽  
Bjørn H. Ebdrup ◽  
Hartwig R. Siebner ◽  
Hans Rasmussen ◽  
...  

Abstract Since working memory deficits in schizophrenia have been linked to negative symptoms, we tested whether features of the one could predict the treatment outcome in the other. Specifically, we hypothesized that working memory-related functional connectivity at pre-treatment can predict improvement of negative symptoms in antipsychotic-treated patients. Fourteen antipsychotic-naive patients with first-episode schizophrenia were clinically assessed before and after 7 months of quetiapine monotherapy. At baseline, patients underwent functional magnetic resonance imaging while performing a verbal n-back task. Spatial independent component analysis identified task-modulated brain networks. A linear support vector machine was trained with these components to discriminate six patients who showed improvement in negative symptoms from eight non-improvers. Classification accuracy and significance was estimated by leave-one-out cross-validation and permutation tests, respectively. Two frontoparietal and one default mode network components predicted negative symptom improvement with a classification accuracy of 79% (p = 0.003). Discriminating features were found in the frontoparietal networks but not the default mode network. These preliminary data suggest that functional patterns at baseline can predict negative symptom treatment–response in schizophrenia. This information may be used to stratify patients into subgroups thereby facilitating personalized treatment.


2015 ◽  
Vol 11 (7S_Part_12) ◽  
pp. P552-P553 ◽  
Author(s):  
Jaroslav Rokicki ◽  
Lucia Li ◽  
Hiroshi Matsuda ◽  
Etsuko Imabayashi ◽  
Tatsuhiro Hisatsune

2019 ◽  
Vol 1248 ◽  
pp. 012005 ◽  
Author(s):  
E A Othman ◽  
A N Yusoff ◽  
M Mohamad ◽  
H Abdul Manan ◽  
A I Abd Hamid ◽  
...  

2016 ◽  
Vol 38 (1) ◽  
pp. 41-52 ◽  
Author(s):  
D. Vatansever ◽  
A.E. Manktelow ◽  
B.J. Sahakian ◽  
D.K. Menon ◽  
E.A. Stamatakis

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