2021
DOI: 10.3390/brainsci11070910
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Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia

Abstract: A number of studies have focused on brain dynamics underlying mind wandering (MW) states in healthy people. However, there is limited understanding of how the oscillatory dynamics accompanying MW states and task-focused states are characterized in clinical populations. In this study, we explored EEG local synchrony of MW associated with schizophrenia, under the premise that changes in attention that arise during MW are associated with a different pattern of brain activity. To this end, we measured the power of… Show more

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Cited by 4 publications
(7 citation statements)
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“…In task-related conditions, evoked delta activity in SCZ was positively (decreased EEG activity—worse cognitive performance) correlated with visual learning, attention, speed of processing [ 88 ] and social perception [ 87 ] and was negatively (increased EEG activity—worse cognitive performance) correlated with working memory [ 89 ]. Furthermore, delta activity coherence, as measured by intertrial coherence (ITC), showed a significant positive correlation with speed of processing [ 91 ].…”
Section: Resultsmentioning
confidence: 99%
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“…In task-related conditions, evoked delta activity in SCZ was positively (decreased EEG activity—worse cognitive performance) correlated with visual learning, attention, speed of processing [ 88 ] and social perception [ 87 ] and was negatively (increased EEG activity—worse cognitive performance) correlated with working memory [ 89 ]. Furthermore, delta activity coherence, as measured by intertrial coherence (ITC), showed a significant positive correlation with speed of processing [ 91 ].…”
Section: Resultsmentioning
confidence: 99%
“…Many studies have also investigated task-related theta activity. In particular, in SCZ theta power evoked during working memory or visual tasks was correlated with working memory performance [ 89 , 98 , 99 ], attention [ 84 ], speed of processing [ 84 , 88 , 89 ], visual learning [ 88 ] and social perception [ 87 ]. Using a machine learning approach, Johannesen et al [ 99 ], found that the machine learning model, combining the evoked activity of theta, alpha, beta and gamma bands, successfully predicted working memory performance.…”
Section: Resultsmentioning
confidence: 99%
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