ArticleCognitive, affective & behavioral neuroscience2024
Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.
Article in Cognitive, affective & behavioral neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
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- st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.Human brain mapping · 2025Article
- Infraslow Dynamic Patterns in Human Cortical Networks Track a Spectrum of External to Internal Attention.Human brain mapping · 2025Article
- Article
- QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity.bioRxiv : the preprint server for biology · 2025Article
- Brain neural mechanisms underlying VR enhanced aerobic exercise for mood enhancement in depressed adolescents.Frontiers in human neuroscience · 2025Article
- Brain endurance training as a strategy for reducing mental fatigue.Frontiers in psychology · 2025Article
- Sensitivity to and Control of Distraction: Distractor-Entrained Oscillation and Frontoparietal EEG Gamma Synchronization.Brain sciences · 2024Article
- Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States.bioRxiv : the preprint server for biology · 2024Article
- Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention.bioRxiv : the preprint server for biology · 2024Article
- Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.Frontiers in systems neuroscience · 2024Article
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7 authors at 1 institution in 1 country.
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Abstract
The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.
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