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Research on brain functional network property analysis and recognition methods targeting brain fatigue.

Wei Yan1, Jiajun He2, Yaoxing Peng1

  • 1The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering, Soochow University, Suzhou, 215000, China.

Scientific Reports
|September 29, 2024
PubMed
Summary

This study used functional near-infrared spectroscopy (fNIRS) to analyze brain functional networks during mental fatigue. Combining low-order and high-order network features effectively identified multi-level fatigue with 88.095% accuracy.

Keywords:
Brain fatigue detectionBrain functional networkMultiple fatigue levelsMultitaskingfNIRS

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Current brain fatigue recognition research is limited to single tasks and simple brain region networks.
  • High-order brain functional network features and brain region mechanisms in multi-task fatigue scenarios are underexplored.
  • Complex conditions necessitate advanced fatigue recognition methods.

Purpose of the Study:

  • To investigate correlations and differences in low-order and high-order brain functional network attributes during multi-task induced mental fatigue using fNIRS.
  • To identify brain regions significantly impacting mental fatigue.
  • To develop a self-training algorithm for recognizing three levels of brain fatigue.

Main Methods:

  • Utilized functional near-infrared spectroscopy (fNIRS) to collect brain activity data.
  • Analyzed low-order and high-order brain functional network attributes across different fatigue levels.
  • Employed self-training algorithms for fatigue level identification.

Main Results:

  • Low-order network connection strength varied across frequency bands (endothelial cell metabolic, neural, myogenic, heart rate) during fatigue progression.
  • Network topology analysis revealed significant changes in clustering coefficient and characteristic path length from no to mild fatigue.
  • Severe fatigue weakened the small-world attribute in the neural frequency band, while other bands maintained it, indicating network adaptability.
  • High-order networks showed increased node degree, cluster coefficient, and efficiency in neuronal activity bands during mild fatigue.
  • Brain fatigue induced a shift from myogenic to neural dominance in high-order network frequency bands as fatigue progressed.
  • Specific brain regions (prefrontal cortex, left anterior motor, motor assist, left frontal eye movement) showed significant changes in high-order network attributes, impacting fatigue status.
  • Combined high-order and low-order fNIRS features achieved 88.095% accuracy in identifying multi-level mental fatigue.

Conclusions:

  • Combined low-order and high-order brain functional network features derived from fNIRS signals can effectively detect multi-level mental fatigue.
  • The identified brain regions play a crucial role in the status of mental fatigue.
  • This approach offers innovative strategies for developing fatigue warning systems.