Neurocognitive characterization of behaviour and mental illness through time-varying brain network analysis

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Human cognition involves dynamic interactions within brain networks.
• Traditional neuroimaging analysis often overlooks the temporal dynamics of brain activity.
• Understanding time-varying functional network connectivity (FNC) is crucial for a comprehensive model of cognition.

Purpose of the Study:

• To characterize FNC fluctuations across resting-state and task conditions.
• To investigate the behavioral relevance of dynamic FNC in relation to task performance and psychopathology.
• To compare the explanatory power of time-varying FNC versus static FNC.

Main Methods:

• Analysis of functional network connectivity (FNC) dynamics during resting-state and reinforcement-related tasks using fMRI.
• Identification of consistent whole-brain FNC states and task-specific regional FNC.
• Correlation analysis of FNC measures with task performance, psychopathology symptoms, depression, and alcohol use disorder in independent samples.

Main Results:

• Consistent whole-brain FNC states were observed across resting and task states, sensitive to prominent task stimuli.
• Task-specific conditions were distinguished by pair-wise, regional FNC.
• Time-varying FNC significantly accounted for higher variance in psychopathology and clinical measures compared to static FNC.

Conclusions:

• Cognitive processing dynamically modulates regional and whole-brain FNC, impacting brain network architecture.
• Time-varying FNC analysis provides a more comprehensive representation of brain interactions.
• Dynamic FNC serves as a more precise neural correlate for quantifying individual risk for psychopathology.