ArticleFrontiers in computational neuroscience2016
Neurocomputational Model of EEG Complexity during Mind Wandering.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Cognitive Processing and EEG Complexity.Entropy (Basel, Switzerland) · 2026Review
- Decoding the human brain during intelligence testing.Communications biology · 2025Article
- Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.Frontiers in neuroinformatics · 2025Article
- Thalamic deep brain stimulation paradigm to reduce consciousness: Cortico-striatal dynamics implicated in mechanisms of consciousness.PLoS computational biology · 2022Article
- Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease.Frontiers in computational neuroscience · 2022Article
- More than off-task: Increased freely-moving thought in ADHD.Consciousness and cognition · 2021Article
- The metastable brain associated with autistic-like traits of typically developing individuals.PLoS computational biology · 2021Article
- Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters.Human brain mapping · 2020Article
- Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis.PLoS computational biology · 2018Article
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2 authors at 1 institution in 1 country.
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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.
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