When Randomness Becomes Rigid: Dynamic Connectivity Entropy and Symptom-Linked Network Dysfunction in Schizophrenia.

Area of Science:

• Neuroscience
• Computational Psychiatry
• Biomarker Discovery

Background:

• High dimensionality of dynamic functional connectivity (dFNC) hinders clinical interpretation and biomarker discovery in mental illnesses.
• Existing methods struggle to capture the complex temporal dynamics of brain networks.

Purpose of the Study:

• To introduce and validate a novel analytical framework, dynamic inter-network connectivity entropy (DICE), as a potential biomarker for mental illness.
• To explore the utility of DICE features in discriminating between patients and controls and in identifying pathophysiological signatures.

Main Methods:

• Utilized resting-state fMRI data from 311 participants (160 controls, 151 schizophrenia patients).
• Identified 53 intrinsic networks, computed DICE, and derived three families of metrics: entropy level/range, distributional shape/temporal organization, and entropy-state repertoire/occupancy.
• Analyzed associations between DICE metrics and symptom dimensions using the PANSS scale.

Main Results:

• Schizophrenia (SZ) patients exhibited a multidimensional signature of altered entropy dynamics, including elevated baseline entropy with reduced fluctuation and acceleration.
• SZ patients showed reduced temporal persistence of entropy excursions, distributions closer to Gaussian, a narrowed repertoire of entropy states, and prolonged time in near-baseline configurations.
• Specific DICE metrics correlated with distinct symptom dimensions, such as general, positive, and negative symptoms, as well as depression.

Conclusions:

• DICE provides a multidimensional signature of altered brain connectivity dynamics in schizophrenia, extending beyond simple patient-control discrimination.
• DICE-based metrics reveal distinct pathophysiological signatures and differential associations with symptom dimensions, offering potential for biomarker development.
• The DICE framework may guide personalized treatment strategies for mental illnesses by elucidating specific neurobiological alterations.