Alternating Dynamics of Segregation and Integration in Human EEG Functional Networks During Working-memory Task

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience

Background:

• Brain functional networks exhibit significant variability over short timescales.
• The underlying mechanisms of transient brain network dynamics are not well understood.
• Working memory tasks heavily rely on dynamic functional brain organization.

Purpose of the Study:

• To investigate the temporal evolution of functional brain networks during a working memory task.
• To elucidate the mechanisms governing transient dynamics in brain networks.
• To understand how network segregation and integration contribute to working memory performance.

Main Methods:

• High-density electroencephalography (EEG) was used to record brain activity in human subjects performing a working memory task.
• Analysis focused on the temporal evolution of functional segregation and integration indices.
• Network centrality and modularity measures were employed to assess oscillatory network dynamics.

Main Results:

• Functional brain networks demonstrated a biphasic temporal evolution: initial segregation followed by integration.
• Incorrect working memory trials were associated with altered segregation-integration profiles.
• Network centrality and modularity effectively captured key aspects of dynamic network behavior.

Conclusions:

• Brain functional organization during working memory tasks may follow a two-step process involving alternating segregation and integration.
• This dynamic interplay is crucial for efficient information processing in working memory.
• Disruptions in these network dynamics are linked to cognitive errors.