Generative network models of altered structural brain connectivity in schizophrenia.

Area of Science:

• Neuroscience
• Computational Biology
• Psychiatry

Background:

• Schizophrenia is associated with altered structural brain connectomes.
• The underlying mechanisms of these alterations are not well understood.
• Generative network models offer a framework to investigate brain network formation.

Purpose of the Study:

• To evaluate generative network models for explaining structural brain network formation.
• To identify factors influencing network formation in healthy controls, schizophrenia patients, and their relatives.
• To explore the relationship between these factors, cognition, and polygenic risk for schizophrenia.

Main Methods:

• Utilized generative network models to analyze structural brain networks.
• Compared models in healthy controls (n=152), schizophrenia patients (n=66), and relatives (n=32).
• Investigated associations with cognitive performance and polygenic risk scores.

Main Results:

• A two-factor model combining spatial constraints and topological neighborhood structure best explained brain network formation.
• Schizophrenia patients and relatives showed significantly reduced spatial constraints and topological facilitation compared to controls.
• Exploratory analyses suggested links between spatial constraints, polygenic risk, and cognitive function.

Conclusions:

• Spatial constraints and local topological structure are key interrelated mechanisms in regular brain network formation.
• These mechanisms are altered in schizophrenia and in individuals at familial risk.
• Spatial constraints may be relevant for genetic risk and cognitive functioning in schizophrenia.