{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T12:42:01Z","timestamp":1793450521831,"version":"4.5.0"},"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study cohort comprised neurologically intact individuals aged 20\u201335 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: \u03b8 (4\u20138 Hz),\u03b1 (8\u201313 Hz), and \u03b2 (13\u201330 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Statistical analysis revealed significant post-fatigue elevations in global average PSD across all examined frequency bands: \u03b1 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), \u03b8 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), and \u03b2 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004). The \u03b1 band demonstrated the most pronounced effect size (Cohen\u2019s d\u2009=\u20094.23, r\u2009=\u20090.90). Topological analysis of \u03b1-band wPLI networks showed enhanced Eg (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.005), Eloc (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), and Cp (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), whereas Lp displayed significant reduction (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.005). Regional analysis revealed preferential enhancement of NE, particularly in central and anterior cortical regions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The experimental data indicated that \u03b1-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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0329212","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T17:26:51Z","timestamp":1754328411000},"page":"e0329212","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":21,"title":["Graph theory-based analysis of functional connectivity changes in brain networks underlying cognitive fatigue: An EEG 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