Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

Area of Science:

• Neuroscience
• Complexity Science
• Quantitative EEG

Background:

• Neural complexity (NC) is a key indicator of brain function.
• Assessing NC using quantitative methods like q-statistics is an emerging area of research.
• Understanding the relationship between brain activity patterns and NC is crucial for neurological studies.

Purpose of the Study:

• To investigate the potential of q-statistics to assess neural complexity (NC) in humans.
• To analyze how NC, measured by the q-parameter, varies across different functional states (FSs) and brain regions.
• To explore the correlation between NC, age, and electroencephalogram (EEG) power bands.

Main Methods:

• Q-statistics were applied to EEG data from 70 adult subjects across 20 scalp channels.
• NC was estimated globally (AllCh) and locally (single channel) in various FSs.
• The q-parameter was correlated with age and EEG power in theta (4-8Hz) and gamma (15-25Hz) bands.

Main Results:

• The q-parameter was significantly higher for global (AllCh) assessments compared to local ones.
• A negative correlation was observed between NC (q-parameter) and age.
• Functional states modulated local q-values, with notable reductions in posterior sites during eye closure.
• Q-values positively correlated with theta band power and negatively with gamma band power.

Conclusions:

• Q-statistics show promise as a method for assessing human neural complexity.
• The findings suggest that q-statistics can differentiate neural states and are influenced by factors like age and specific brain activity patterns.
• The positive association between NC and theta power supports its link to higher-order cognitive processes.