ArticleCommunications biology2025
Infra-slow scale-free dynamics modulate the connection of neural and behavioral variability during attention.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Perceptual Decision Efficiency and Optimal Sleep Quality Are Associated with Female College Soccer Injury Avoidance.Brain sciences · 2026Article
- Age-Related Changes in EEG Signal Complexity and Behavioral Variability from Childhood to Adulthood: A Multiscale Entropy Approach.Entropy (Basel, Switzerland) · 2026Article
- Criticality and increased intrinsic neural timescales in stroke.NPJ systems biology and applications · 2025Article
- Large-scale brain mediation network based on resting-state functional MRI.Communications biology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
The activities of the human brain vary across different timescales, exhibiting scale-free dynamics. Previous research has highlighted the psychological and physiological significance of brain dynamical fluctuations across the Delta to Gamma bands. However, there has been less focus on infra-slow scale-free dynamics, e.g. power law exponent (PLE), and neural variability, e.g. standard deviation (SD), and sample entropy (SE), in mediating brain-behavior connection during attention. In this study, we recruited 49 participants and recorded functional magnetic resonance imaging (fMRI) resting-state and task data during a sustained attention task paradigm to investigate how the three measures-SD, SE, and PLE-modulate the dynamics of behavioral performance. Our findings demonstrate the following: (i) PLE, SD, and SE exhibit differential topographic distribution with a hierarchical structure from sensory to associative networks, during their rest-task modulation. (ii) PLE, SD, and SE show different topographic extensions from visual cortex to default-mode network in their relationship with behavioral variability. (iii) The relationship between SD and SE is mediated by PLE in the empirical data, which (iv) is further confirmed in simulation. Collectively, our results highlight the topographically- and dynamically-layered mechanisms of distinct neurodynamical features during attention processing: scale-free dynamics modulate neural and behavioral variability.
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