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 brain connectivity.
• Understanding these connectivity alterations is crucial for diagnosing and treating SZ.
• Existing methods may not fully capture the heterogeneity of brain network interactions.

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 functional magnetic resonance imaging (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 the heterogeneity of connectivity levels were found between SZ patients and HC.
• SZ patients exhibited elevated ICE, indicating higher randomness in time-varying connectivity strength.
• Clustering analyses revealed distinct connectivity patterns associated with SZ and HC groups, with SZ patients showing more weak, low-scale entropy correlations.

Conclusions:

• The proposed ICE measure offers a novel framework for understanding brain connectivity in health and disease.
• Dynamic ICE analysis showed that SZ patients are less likely to exhibit focused and structured transient connectivity patterns compared to HC.
• ICE holds promise as an advanced method for the diagnosis of mental health conditions like schizophrenia.