Title
Neurocomputational Model of EEG Complexity during Mind Wandering.
Abstract
Mind wandering (MW) can be understood as a transient state in which attention drifts from an external task to internal self-generated thoughts. MW has been associated with the activation of the Default Mode Network (DMN). In addition, it has been shown that the activity of the DMN is anti-correlated with activation in brain networks related to the processing of external events (e.g., Salience network, SN). In this study, we present a mean field model based on weakly coupled Kuramoto oscillators. We simulated the oscillatory activity of the entire brain and explored the role of the interaction between the nodes from the DMN and SN in MW states. External stimulation was added to the network model in two opposite conditions. Stimuli could be presented when oscillators in the SN showed more internal coherence (synchrony) than in the DMN, or, on the contrary, when the coherence in the SN was lower than in the DMN. The resulting phases of the oscillators were analyzed and used to simulate EEG signals. Our results showed that the structural complexity from both simulated and real data was higher when the model was stimulated during periods in which DMN was more coherent than the SN. Overall, our results provided a plausible mechanistic explanation to MW as a state in which high coherence in the DMN partially suppresses the capacity of the system to process external stimuli.
Year
DOI
Venue
2016
10.3389/fncom.2016.00020
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
neural dynamics,mind wandering,Kuramoto model,synchrony,EEG complexity
Neuroscience,Default mode network,Computer science,Salience (neuroscience),Coherence (physics),Kuramoto model,Artificial intelligence,Stimulus (physiology),Mind-wandering,Electroencephalography,Network model,Machine learning
Journal
Volume
ISSN
Citations 
10
1662-5188
2
PageRank 
References 
Authors
0.39
9
2
Name
Order
Citations
PageRank
Antonio Ibañez-Molina121.74
Sergio Iglesias-Parro221.74