Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Schizophrenia (SZ) is characterized by abnormal static and dynamic functional connectivity in the brain.
• Understanding brain network alterations is crucial for diagnosing and treating SZ.
• Existing methods may not fully capture the complex, dynamic nature of brain connectivity.

Purpose of the Study:

• To introduce and validate a novel approach, inter-network connectivity entropy (ICE), for assessing brain connectivity.
• To investigate differences in static and dynamic ICE between SZ patients and healthy controls (HC).
• To explore the potential of ICE as a diagnostic tool for mental health conditions.

Main Methods:

• Analysis of fMRI data from 151 SZ patients and 160 HC.
• Calculation of static and dynamic inter-network connectivity entropy (ICE) across multiple brain networks.
• Application of C-means fuzzy clustering and k-means clustering to analyze ICE patterns.

Main Results:

• Significant differences in connectivity heterogeneity were found between SZ patients and HC across various brain networks (SC, AUD, SM, VIS, CC, DMN, CB).
• SZ patients exhibited elevated ICE, indicating higher randomness in time-varying connectivity compared to HC.
• Clustering analyses revealed distinct patterns of functional entropy correlation and occupancy rates between SZ patients and HC.

Conclusions:

• The proposed ICE measure offers a novel framework for understanding brain states in health and disease.
• Dynamic ICE analysis suggests SZ patients struggle to transition into more focused brain connectivity patterns.
• ICE holds promise as an advanced method for the diagnostics of mental health conditions.