ArticlePloS one2025
Graph theory-based analysis of functional connectivity changes in brain networks underlying cognitive fatigue: An EEG study.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Tailor-made notched music training-induced residual inhibition in subjective tinnitus: resting-state EEG spectral power ratio evidence.Frontiers in neuroscience · 2026Trial
- Closed-Loop Neuromodulation for Brain Fatigue: From Real-Time Biomarkers to Adaptive Intervention.International journal of molecular sciences · 2026Review
- Dynamic Global and Local Enhancement of Theta-Band Functional Networks as Neural Markers of Spinal Cord Stimulation Efficacy in Disorders of Consciousness.CNS neuroscience & therapeutics · 2026Article
- Heartfulness Meditation-induced State and Trait Effects on Long-term, Short-term and Non-meditators Using Connectivity Analysis: An EEG Study on Prefrontal Cortex.Annals of neurosciences · 2026Article
- Dual-Task Training Improves Shoulder Function and is Associated with Changes in Sensorimotor Network Connectivity After Arthroscopic Rotator Cuff Repair: A Randomized Controlled Trial.Sports medicine - open · 2026Article
- Mapping the Human Performance Envelope Through Multivariate Information Transfer.Brain sciences · 2026Article
- Lower resting-state EEG mean eigenvector centrality is associated with higher math anxiety.Frontiers in human neuroscience · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
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Abstract
objectiveThis investigation was designed to analyze alterations in functional connectivity across brain networks associated with cognitive fatigue through electroencephalogram (EEG) data analysis. Through the application of both global and local graph-theoretical metrics to characterize the topology of brain networks, this study establishes a conceptual framework supporting enhanced detection of cognitive fatigue manifestations while facilitating examination of its neurophysiological substrates.
methodsThe study cohort comprised neurologically intact individuals aged 20-35 years, recruited from Beijing Rehabilitation Hospital, Capital Medical University between February 6 and September 30, 2024 for participation in a cognitive fatigue induction task. Following acquisition of written informed consent, data before and after the task were obtained, including both subjective fatigue assessments using the Visual analog scale for fatigue (VAS-F) scores and EEG data. The preprocessed EEG signals were segmented into three frequency bands: θ (4-8 Hz),α (8-13 Hz), and β (13-30 Hz). To determine the frequency band exhibiting maximal sensitivity to cognitive fatigue, cross-band comparative power spectral density (PSD) was implemented. The selected frequency band subsequently served as the basis for weighted Phase Lag Index (wPLI) computation, yielding a functional connectivity matrix derived from wPLI measurements. Network topology was evaluated through application of five global graph theory metrics (global efficiency [Eg], local efficiency [Eloc], clustering coefficient [Cp], shortest path length [Lp], and small-world property [Sigma]) complemented by two local graph theory metrics (nodal efficiency [NE] and degree centrality [DC]). This analytical framework enabled systematic comparison of connectivity patterns and topological characteristics between before and after cognitive fatigue states.
resultsStatistical analysis revealed significant post-fatigue elevations in global average PSD across all examined frequency bands: α (p < 0.001), θ (p < 0.001), and β (p = 0.004). The α band demonstrated the most pronounced effect size (Cohen's d = 4.23, r = 0.90). Topological analysis of α-band wPLI networks showed enhanced Eg (p = 0.005), Eloc (p < 0.001), and Cp (p < 0.001), whereas Lp displayed significant reduction (p = 0.005). Regional analysis revealed preferential enhancement of NE, particularly in central and anterior cortical regions.
conclusionThe experimental data indicated that α-band activity exhibited the highest sensitivity to cognitive fatigue induced by the sustained Stroop task, establishing a framework for accurate identification of fatigue states. Cognitive fatigue compensatory mechanisms manifested as concurrent improvements in both local and global neural information processing efficiency. Although such adaptive reorganization may compromise overall network efficiency, these findings implied an inherent balance between adaptive network reconfiguration and system efficiency. These results elucidated novel neurophysiological mechanisms underlying cognitive fatigue, substantially advancing our understanding of brain network dynamics during prolonged cognitive demand.
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