Linear and nonlinear information flow based on time-delayed mutual information method and its application to...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Systems Biology

Background:

• Understanding brain-muscle communication is crucial for motor control research.
• Distinguishing linear and nonlinear interactions in corticomuscular (CM) pathways remains challenging.

Purpose of the Study:

• To develop a model-free method using time-delayed mutual information (TDMI) to identify linear and nonlinear information flow.
• To investigate the presence and contribution of nonlinear information flow in CM interactions.

Main Methods:

• Employed uni- and bi-variate surrogate tests with TDMI for model-free analysis.
• Validated the method using simulated data to assess detection of information flow direction and relationships.
• Applied the method to experimental data of CM interaction during a right wrist extension task.

Main Results:

• Simulation tests accurately detected information flow direction and relationships without prior system dynamics knowledge.
• Experimental results identified both linear and nonlinear information flow from the contralateral sensorimotor cortex to muscles.
• This is the first study to demonstrate separate linear and nonlinear information flow in CM interactions.

Conclusions:

• The proposed method reliably measures temporally varying causal interactions, differentiating linear and nonlinear information flow.
• Both linear and nonlinear information flows exist in CM interactions, potentially representing motor commands from the brain to muscles.