Schizophrenia MEG Network Analysis Based on Kernel Granger Causality.

Area of Science:

• Neuroscience
• Network Science
• Biophysics

Background:

• Brain network analysis is crucial for understanding neurological disorders like schizophrenia.
• Magnetoencephalography (MEG) provides valuable data for studying brain connectivity.

Purpose of the Study:

• To introduce and validate the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) method for constructing directed weighted brain networks.
• To characterize differences in brain network topology between individuals with schizophrenia and healthy controls using MEG data.

Main Methods:

• Developed and tested MKGC against existing Granger causality methods using simulated data.
• Applied MKGC to magnetoencephalography (MEG) data from schizophrenia patients (SCZs) and healthy controls (HCs).
• Quantified network features including strength, nonequilibrium, and complexity (Shannon entropy).

Main Results:

• MKGC demonstrated superior performance compared to bivariate linear and inhomogeneous polynomial kernel Granger causality methods.
• Schizophrenia patients exhibited less dense effective connectivity networks than healthy controls.
• Significant differences in in-connectivity strength (right frontal) and out-connectivity strength (left occipital) were observed.
• Schizophrenia networks showed higher nonequilibrium but lower complexity (Shannon entropy) than healthy networks.

Conclusions:

• MKGC is a reliable method for constructing and analyzing MEG-based brain networks.
• Network characteristics derived from MKGC can effectively differentiate between schizophrenia and healthy individuals.
• Findings highlight altered brain connectivity patterns in schizophrenia, with implications for understanding the pathophysiology of the disorder.