Dynamic process of information transmission complexity in human brains

Area of Science:

• Neuroscience
• Complexity Science
• Signal Processing

Background:

• Mutual information transmission quantifies information flow between brain regions.
• Previous complexity analysis of EEG information transmission laid the groundwork for this study.
• Addressing preprocessing limitations like coarse graining is crucial for accurate complexity measures.

Purpose of the Study:

• To investigate dynamic changes in the complexity of mutual information transmission in the human brain.
• To explore how sudden changes in brain states affect information processing complexity.
• To assess the utility of novel complexity measures for analyzing brain activity.

Main Methods:

• Utilized a complexity analysis of mutual information transmission in electroencephalography (EEG) data.
• Employed new complexity measures to mitigate issues associated with coarse-graining preprocessing.
• Examined EEG data from human subjects during various cognitive and neurological states.

Main Results:

• A significant drop in information transmission complexity was observed in most brain areas before and after generalized seizures.
• Complexity temporarily decreased when subjects shifted their attention.
• Mental arithmetic tasks led to increased information exchange between the left temporal lobe and other brain regions.

Conclusions:

• Sudden changes in brain state, such as seizures or attention shifts, are associated with a transient decrease in information transmission complexity.
• The developed methods show potential for observing rapid dynamic processes within the living brain.
• Complexity analysis of EEG offers a novel approach to understanding brain function and dysfunction.