Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral...

Area of Science:

• Neuroscience
• Systems Neuroscience
• Computational Neuroscience

Background:

• Brain network analysis is key to understanding post-stroke function.
• Limited research exists on cortical network dynamics related to muscle activity post-stroke.
• Understanding these dynamics is crucial for motor control system alterations.

Purpose of the Study:

• To introduce and validate the time-delayed maximal information spectral coefficient (TDMISC) method.
• To assess local frequency band characteristics of functional corticomuscular coupling (FCMC).
• To analyze cortico-cortical network parameters in post-stroke individuals.

Main Methods:

• Developed the time-delayed maximal information spectral coefficient (TDMISC) method.
• Validated TDMISC using unidirectionally coupled Hénon maps and a neural mass model.
• Applied TDMISC to analyze FCMC and network parameters during a grip task in stroke patients.

Main Results:

• TDMISC accurately characterized signal frequency and directionality.
• Strong ascending FCMC in the gamma band and weak FCMC in the beta band were observed on the affected side.
• The affected side showed reduced clustering coefficients and longer shortest path lengths in all frequency bands.
• The unaffected motor cortex inhibited affected motor cortex and associated areas.

Conclusions:

• The TDMISC method effectively analyzes brain network dynamics post-stroke.
• Altered FCMC and network topology are significant findings in stroke motor control.
• Results provide insights into the neural mechanisms of post-stroke motor dysfunction.