Graph theory-based analysis of functional connectivity changes in brain networks underlying cognitive fatigue: An EEG study
Bibliographic record
Abstract
OBJECTIVE: This 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. METHODS: The 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. RESULTS: Statistical 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. CONCLUSION: The 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".