Disrupted Structural Covariance in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Area of Science:

• Neuroimaging
• Psychiatry
• Computational Neuroscience

Background:

• Schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) share clinical and genetic factors, suggesting common underlying pathophysiology.
• Previous research indicates shared mechanisms, but transdiagnostic structural covariance patterns remain unclear.

Purpose of the Study:

• To investigate aberrant transdiagnostic structural covariance network (SCN) patterns across SCZ, BD, and MDD.
• To identify common and disorder-specific alterations in brain structural covariance using a multivariate approach.

Main Methods:

• Acquired structural magnetic resonance imaging (MRI) data from 704 subjects (244 controls, 119 SCZ, 159 BD, 182 MDD).
• Employed seed-based partial least squares correlation analysis to construct SCNs across six functional networks (DMN, DAN, FPCN, SMN, VAN, visual).
• Calculated individual network integrity indices and performed group comparisons to identify network-specific alterations.

Main Results:

• SCN spatial distributions mirrored functional network organization.
• Common reductions in network integrity were observed in the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN) across all three disorders.
• Bipolar disorder (BD) showed specific reductions in the somatomotor network (SMN), while both BD and major depressive disorder (MDD) exhibited reductions in the ventral attention network (VAN).
• Individual network integrity correlated significantly with clinical and cognitive manifestations.

Conclusions:

• Structural covariance network integrity shows promise as a transdiagnostic biomarker for psychiatric disorders.
• These findings underscore the potential for identifying shared neurobiological underpinnings across SCZ, BD, and MDD.