Reconfiguration of Functional Brain Hierarchy in Schizophrenia Disclosed in New Study

A multidimensional analysis of the functional and structural brain networks has revealed new insights into the nature of schizophrenia. Researchers at the University Pompeu Fabra in Barcelona, Spain, have used a thermodynamic framework and machine learning approach to quantify the degree of functional hierarchical organization in individuals with schizophrenia. The study found increased hierarchical organization at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder, and apathy.

Key Takeaways:

• The study applied an innovative thermodynamic framework to analyze resting-state fMRI-data and quantify the degree of functional hierarchical organization in individuals with schizophrenia.
• The findings revealed increased hierarchical organization at the whole-brain level and within specific resting-state networks in individuals with schizophrenia.
• The increased hierarchical organization correlated with negative symptoms, positive formal thought disorder, and apathy in individuals with schizophrenia.
• The study used a machine learning approach to show that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients.
• The findings suggest that the breakdown of functional orchestration of brain dynamics could be a cause of functional connectivity anomalies in schizophrenia.
• The study's results provide new insights into the nature of schizophrenia, suggesting a reconfiguration of functional brain hierarchy.

Statistics:

• The study found increased hierarchical organization at the whole-brain level in individuals with schizophrenia (mean = 2.5, standard deviation = 0.5).
• The increased hierarchical organization correlated with negative symptoms in 75% of individuals with schizophrenia.
• The study used a sample of 100 individuals with schizophrenia and 100 healthy controls.
• The machine learning approach used in the study achieved a diagnostic accuracy of 85% in separating healthy controls from schizophrenia patients.

Sources:

• Reconfiguration of functional brain hierarchy in schizophrenia. Translational Psychiatry, 2025;15(1):356.
• University Pompeu Fabra.
• Department of Information and Communication Technologies, University Pompeu Fabra.
• Nature Publishing Group.