Structural Brain Network Disturbances in the Psychosis Spectrum

Area of Science:

• Neuroscience
• Psychiatry
• Brain Imaging

Background:

• Subclinical psychotic symptoms offer a unique model to study psychosis pathophysiology, free from confounding factors like medication or hospitalization.
• Brain network disturbances, particularly in white matter connections, are implicated in the development of psychosis.
• The structural network disconnection hypothesis suggests reduced and weaker brain connections in individuals with psychotic experiences.

Purpose of the Study:

• To investigate structural brain network differences in individuals with clinical and subclinical psychotic symptoms compared to healthy controls.
• To examine white matter microstructure and overall network organization using diffusion tensor imaging.
• To test the hypothesis of network disconnection in psychosis.

Main Methods:

• Diffusion tensor imaging (DTI) was employed on three groups: patients with psychotic disorders (n=35), subjects with subclinical psychotic symptoms (n=35), and healthy controls (n=36).
• Structural brain networks were analyzed for connection density, white matter integrity (fractional anisotropy, mean diffusivity, magnetic transfer ratio), and network organization using minimum spanning tree analysis.

Main Results:

• Both clinical and subclinical psychotic groups showed decreased fractional anisotropy and increased mean diffusivity, indicating altered white matter microstructure.
• Network topology in both psychotic groups exhibited reduced overlap with a healthy reference network.
• Decreased centrality was observed in parietal and language regions for both groups, with more significant deviations in the clinical group.

Conclusions:

• Weaker structural brain connections and reduced centrality in parietal hubs characterize the brain networks of individuals with psychotic symptoms.
• These observed network differences are more pronounced in individuals with clinical psychotic disorders compared to those with subclinical symptoms.