Brain structural abnormalities in six major psychiatric disorders: shared variation and network perspectives

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Comorbidities in psychiatric disorders may stem from shared brain abnormalities.
• Previous studies lacked the scope to analyze the distribution of these shared abnormalities across multiple disorders.

Purpose of the Study:

• To reanalyze existing data on brain morphometrics across six psychiatric disorders to clarify shared versus specific alterations.
• To apply network analysis to visualize patterns of brain matter correlations and identify central psychopathological markers.

Main Methods:

• Reanalysis of principal component analysis (PCA) on cortical thickness and subcortical gray matter volume data from healthy individuals and patients with six psychiatric disorders.
• Determining the optimal number of components and exploring alternative solutions.
• Applying network analysis to map shared brain matter correlations.

Main Results:

• A unidimensional PCA solution was found to be appropriate, indicating that brain alterations in autism spectrum disorder (ASD) significantly covaried with major depressive disorder (MDD), bipolar disorder (BD), schizophrenia (SCZ), and obsessive-compulsive disorder (OCD).
• Network analysis revealed SCZ had the highest network strength, BD the highest closeness, and BD and MDD the highest betweenness, highlighting distinct patterns of psychopathology.
• Different component solutions in data analysis can lead to divergent conclusions regarding shared brain alterations.

Conclusions:

• The choice of analytical method significantly impacts the interpretation of shared brain alterations in psychiatric disorders.
• Network analysis provides a complementary perspective, identifying key markers within specific psychopathological domains.
• Investigating shared variation and network structures offers a promising avenue for understanding the pathophysiology of psychiatric comorbidities.