Understanding Schizophrenia Pathophysiology via fMRI-Based Information Theory and Multiplex Network Analysis.

Area of Science:

• Neuroscience
• Psychiatry
• Network Science

Background:

• Schizophrenia (SCZ) is associated with disruptions in brain functional connectivity.
• Understanding information transfer mechanisms in SCZ is crucial for elucidating disease pathophysiology.

Purpose of the Study:

• To investigate directed information flow and causal relationships between brain regions in SCZ patients during resting-state.
• To characterize functional interaction patterns and their impact on brain network architecture in SCZ.

Main Methods:

• Analysis of a large fMRI dataset from healthy controls and SCZ patients.
• Estimation of directed information flow using local Transfer Entropy (TE).
• Modeling functional interactions within a multiplex network framework representing four interaction rules (ActS, ActO, TfS, TfO).

Main Results:

• Identification of widespread alterations in SCZ brain networks, particularly in sensory and associative cortical pathways.
• An observed imbalance between converging (S) and diverging (O) information transfer rules, reducing network stability.
• A shift towards more randomized functional network organization in SCZ patients.

Conclusions:

• The findings provide a mechanistic link between excitation/inhibition (E/I) imbalance and mesoscopic network dysconnectivity in SCZ.
• The study offers an integrated framework for characterizing directed brain communication and psychiatric phenotypes.
• Results align with previous studies using dynamic functional connectivity and Dynamic Causal Modeling (DCM).